From ad54b34b0791122cf5db93aee94b794cbb9061fc Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Mon, 12 Jun 2023 13:30:25 -0400 Subject: [PATCH] fixes plus tfjs upgrade for new release --- CHANGELOG.md | 7 +- demo/typescript/index.js | 4 +- demo/typescript/index.js.map | 6 +- demo/typescript/index.ts | 6 +- dist/human.esm-nobundle.js | 22 +- dist/human.esm.js | 26297 ++++++++-------- dist/human.esm.js.map | 6 +- dist/human.js | 1535 +- dist/human.node-gpu.js | 22 +- dist/human.node-wasm.js | 22 +- dist/human.node.js | 22 +- dist/tfjs.esm.js | 1301 +- dist/tfjs.version.js | 2 +- src/draw/primitives.ts | 2 +- src/face/facemesh.ts | 2 + src/hand/handpose.ts | 35 +- src/hand/handposepipeline.ts | 3 +- src/models.ts | 6 +- src/util/webcam.ts | 17 +- test/build.log | 102 +- test/test.log | 1810 +- typedoc/classes/Env.html | 8 +- typedoc/classes/GraphModel.html | 50 +- typedoc/classes/Human.html | 8 +- typedoc/classes/Tensor-1.html | 612 +- typedoc/classes/WebCam.html | 18 +- typedoc/classes/models.Models.html | 14 +- typedoc/enums/Rank.html | 24 +- typedoc/functions/draw.all.html | 8 +- typedoc/functions/draw.body.html | 8 +- typedoc/functions/draw.canvas.html | 8 +- typedoc/functions/draw.face.html | 8 +- typedoc/functions/draw.gesture.html | 8 +- typedoc/functions/draw.hand.html | 8 +- typedoc/functions/draw.init.html | 8 +- typedoc/functions/draw.object.html | 8 +- typedoc/functions/draw.person.html | 8 +- typedoc/functions/empty.html | 8 +- typedoc/functions/match.distance.html | 8 +- typedoc/functions/match.find.html | 8 +- typedoc/functions/match.similarity.html | 8 +- typedoc/functions/models.validateModel.html | 8 +- typedoc/index.html | 8 +- typedoc/interfaces/BodyConfig.html | 8 +- typedoc/interfaces/BodyKeypoint.html | 8 +- typedoc/interfaces/BodyResult.html | 8 +- typedoc/interfaces/Config.html | 8 +- typedoc/interfaces/DrawOptions.html | 8 +- typedoc/interfaces/FaceAntiSpoofConfig.html | 8 +- typedoc/interfaces/FaceAttentionConfig.html | 8 +- typedoc/interfaces/FaceConfig.html | 8 +- typedoc/interfaces/FaceDescriptionConfig.html | 8 +- typedoc/interfaces/FaceDetectorConfig.html | 8 +- typedoc/interfaces/FaceEmotionConfig.html | 8 +- typedoc/interfaces/FaceGearConfig.html | 8 +- typedoc/interfaces/FaceIrisConfig.html | 8 +- typedoc/interfaces/FaceLivenessConfig.html | 8 +- typedoc/interfaces/FaceMeshConfig.html | 8 +- typedoc/interfaces/FaceResult.html | 8 +- typedoc/interfaces/FilterConfig.html | 8 +- typedoc/interfaces/GenericConfig.html | 8 +- typedoc/interfaces/GestureConfig.html | 8 +- typedoc/interfaces/HandConfig.html | 8 +- typedoc/interfaces/HandResult.html | 8 +- typedoc/interfaces/ModelInfo.html | 8 +- typedoc/interfaces/ObjectConfig.html | 8 +- typedoc/interfaces/ObjectResult.html | 8 +- typedoc/interfaces/PersonResult.html | 8 +- typedoc/interfaces/Result.html | 8 +- typedoc/interfaces/SegmentationConfig.html | 8 +- typedoc/interfaces/WebCamConfig.html | 8 +- typedoc/interfaces/models.KernelOps.html | 8 +- typedoc/interfaces/models.ModelStats.html | 8 +- typedoc/modules/Tensor.html | 286 +- typedoc/modules/draw.html | 8 +- typedoc/modules/match.html | 8 +- typedoc/modules/models.html | 8 +- typedoc/types/AnyCanvas.html | 8 +- typedoc/types/AnyImage.html | 8 +- typedoc/types/AnyVideo.html | 8 +- typedoc/types/BackendEnum.html | 8 +- typedoc/types/BodyAnnotation.html | 8 +- typedoc/types/BodyAnnotationBlazePose.html | 8 +- .../types/BodyAnnotationEfficientPose.html | 8 +- typedoc/types/BodyGesture.html | 8 +- typedoc/types/BodyLandmark.html | 8 +- typedoc/types/BodyLandmarkBlazePose.html | 8 +- typedoc/types/BodyLandmarkEfficientNet.html | 8 +- typedoc/types/BodyLandmarkMoveNet.html | 8 +- typedoc/types/BodyLandmarkPoseNet.html | 8 +- typedoc/types/Box.html | 8 +- typedoc/types/Emotion.html | 8 +- typedoc/types/Events.html | 8 +- typedoc/types/ExternalCanvas.html | 8 +- typedoc/types/FaceGesture.html | 8 +- typedoc/types/FaceLandmark.html | 8 +- typedoc/types/Finger.html | 8 +- typedoc/types/FingerCurl.html | 8 +- typedoc/types/FingerDirection.html | 8 +- typedoc/types/Gender.html | 8 +- typedoc/types/GestureResult.html | 8 +- typedoc/types/HandGesture.html | 8 +- typedoc/types/HandType.html | 8 +- typedoc/types/ImageObjects.html | 8 +- typedoc/types/Input.html | 8 +- typedoc/types/IrisGesture.html | 8 +- typedoc/types/ObjectType.html | 8 +- typedoc/types/Point.html | 8 +- typedoc/types/Race.html | 8 +- typedoc/types/SegmentationEnum.html | 8 +- typedoc/types/Tensor1D.html | 10 +- typedoc/types/Tensor2D.html | 10 +- typedoc/types/Tensor3D.html | 10 +- typedoc/types/Tensor4D.html | 10 +- typedoc/types/TensorLike.html | 10 +- typedoc/types/WarmupEnum.html | 8 +- typedoc/types/match.Descriptor.html | 8 +- typedoc/types/match.MatchOptions.html | 8 +- typedoc/variables/defaults.html | 8 +- typedoc/variables/draw.options.html | 8 +- typedoc/variables/env-1.html | 8 +- types/human.d.ts | 73 +- types/tfjs-core.d.ts | 83 +- types/tfjs.esm.d.ts | 36 +- 124 files changed, 16782 insertions(+), 16405 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 19f2df9b..5dea7433 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/human - Version: **3.0.6** + Version: **3.0.7** Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition** Author: **Vladimir Mandic ** @@ -9,11 +9,12 @@ ## Changelog -### **HEAD -> main** 2023/05/08 mandic00@live.com +### **3.0.7** 2023/06/12 mandic00@live.com -### **origin/main** 2023/05/03 76097937+dalvine@users.noreply.github.com +### **origin/main** 2023/05/08 mandic00@live.com +- fix memory leak in histogramequalization - initial work on tracker ### **3.0.6** 2023/03/21 mandic00@live.com diff --git a/demo/typescript/index.js b/demo/typescript/index.js index dd24c449..0aded5ab 100644 --- a/demo/typescript/index.js +++ b/demo/typescript/index.js @@ -4,6 +4,6 @@ author: ' */ -import*as m from"../../dist/human.esm.js";var v=1920,b={debug:!0,backend:"webgl",modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;e.draw.options.drawPoints=!0;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+` -`,console.log(...t)},r=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function w(){var d,i,c;if(!a.video.paused){let l=e.next(e.result),p=await e.image(a.video);e.draw.canvas(p.canvas,a.canvas);let u={bodyLabels:`person confidence [score] and ${(c=(i=(d=e.result)==null?void 0:d.body)==null?void 0:i[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,u),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,r(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(w,30)}async function y(){let d=(await e.webcam.enumerate())[0].deviceId;await e.webcam.start({element:a.video,crop:!1,width:v,id:d}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function h(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),r("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),r("initializing..."),await e.warmup(),await y(),await f(),await w()}window.onload=h; +import*as m from"../../dist/human.esm.js";var v=1920,b={debug:!0,backend:"webgl",modelBasePath:"https://vladmandic.github.io/human-models/models/",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;e.draw.options.drawPoints=!0;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+` +`,console.log(...t)},i=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function u(){var d,r,c;if(!a.video.paused){let l=e.next(e.result),w=await e.image(a.video);e.draw.canvas(w.canvas,a.canvas);let p={bodyLabels:`person confidence [score] and ${(c=(r=(d=e.result)==null?void 0:d.body)==null?void 0:r[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,p),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,i(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function h(){let d=(await e.webcam.enumerate())[0].deviceId,r=await e.webcam.start({element:a.video,crop:!1,width:v,id:d});o(r),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function y(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),i("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),i("initializing..."),await e.warmup(),await h(),await f(),await u()}window.onload=y; //# sourceMappingURL=index.js.map diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map index 0df28d25..66c7c38d 100644 --- a/demo/typescript/index.js.map +++ b/demo/typescript/index.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["index.ts"], - "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: false, flip: false },\n face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\nhuman.draw.options.drawPoints = true; // draw points on face mesh\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = human.next(human.result); // smoothen result using last-known results\n const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n const opt: Partial = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n const devices = await human.webcam.enumerate();\n const id = devices[0].deviceId; // use first available video source\n await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n dom.canvas.width = human.webcam.width;\n dom.canvas.height = human.webcam.height;\n dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n if (human.webcam.paused) await human.webcam.play();\n else human.webcam.pause();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); 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// equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n // modelBasePath: '../../models',\n modelBasePath: 'https://vladmandic.github.io/human-models/models/',\n filter: { enabled: true, equalization: false, flip: false },\n face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\nhuman.draw.options.drawPoints = true; // draw points on face mesh\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = human.next(human.result); // smoothen result using last-known results\n const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n const opt: Partial = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n const devices = await human.webcam.enumerate();\n const id = devices[0].deviceId; // use first available video source\n const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n log(webcamStatus);\n dom.canvas.width = human.webcam.width;\n dom.canvas.height = human.webcam.height;\n dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n if (human.webcam.paused) await human.webcam.play();\n else human.webcam.pause();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models stats:', human.models.stats());\n log('models loaded:', human.models.loaded());\n log('environment', human.env);\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await webCam(); // start webcam\n await detectionLoop(); // start detection loop\n await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"], + "mappings": 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+ "names": ["H", "width", "humanConfig", "human", "dom", "timestamp", "fps", "log", "msg", "status", "perf", "detectionLoop", "tensors", "drawLoop", "_a", "_b", "_c", "interpolated", "processed", "opt", "now", "webCam", "id", "webcamStatus", "main"] } diff --git a/demo/typescript/index.ts b/demo/typescript/index.ts index 9632df58..591c3aef 100644 --- a/demo/typescript/index.ts +++ b/demo/typescript/index.ts @@ -17,7 +17,8 @@ const humanConfig: Partial = { // user configuration for human, used t // cacheSensitivity: 0, // cacheModels: false, // warmup: 'none', - modelBasePath: '../../models', + // modelBasePath: '../../models', + modelBasePath: 'https://vladmandic.github.io/human-models/models/', filter: { enabled: true, equalization: false, flip: false }, face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } }, body: { enabled: false }, @@ -89,7 +90,8 @@ async function drawLoop() { // main screen refresh loop async function webCam() { const devices = await human.webcam.enumerate(); const id = devices[0].deviceId; // use first available video source - await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element + const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element + log(webcamStatus); dom.canvas.width = human.webcam.width; dom.canvas.height = human.webcam.height; dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js index d4ca4539..5d5a96ba 100644 --- a/dist/human.esm-nobundle.js +++ b/dist/human.esm-nobundle.js @@ -4,7 +4,7 @@ author: ' */ -var 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o=t.shape[1],s=t.shape[2],A=[e.startPoint[1]/o,e.startPoint[0]/s,e.endPoint[1]/o,e.endPoint[0]/s],a=r.image.cropAndResize(t,[A],[0],n),i=r.div(a,O.tf255);return r.dispose(a),i},N2=(e,t)=>{let n=S2(e),o=Fe(e),s=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-s[0],n[1]-s[1]],endPoint:[n[0]+s[0],n[1]+s[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},L2=e=>{let t=S2(e),n=Fe(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},z1=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},Ot=[[1,0,0],[0,1,0],[0,0,1]],is=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),ls=(e,t)=>is(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var k1=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ee=(e,t)=>{let n=0;for(let o=0;o{let n=[];for(let o=0;o{let n=[],o=e.length;for(let s=0;s{let n=Math.cos(e),o=Math.sin(e),s=[[n,-o,0],[o,n,0],[0,0,1]],A=k1(t[0],t[1]),a=w1(A,s),i=k1(-t[0],-t[1]);return w1(a,i)},ds=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Ee(t[0],n),-Ee(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},xs=(e,t)=>[Ee(e,t[0]),Ee(e,t[1])];function j1(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[A[0]/s*(x[0]-s/2),A[1]/s*(x[1]-s/2),x[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,c=i?S1(n,[0,0]):Ot,d=i?a.map(x=>[...xs(x,c),x[2]]):a,y=i?ds(o):Ot,l=S2(t),m=[Ee(l,y[0]),Ee(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2]||0)])}function N1(e,t,n,o){let s=t.landmarks.length>=pt.count?pt.symmetryLine:ve.symmetryLine,A=0,a=Ot,i;if(e&&P.kernels.includes("rotatewithoffset"))if(A=ls(t.landmarks[s[0]],t.landmarks[s[1]]),A&&A!==0&&Math.abs(A)>.2){let d=S2(t),y=[d[0]/n.shape[2],d[1]/n.shape[1]],l=r.image.rotateWithOffset(n,A,0,[y[0],y[1]]);a=S1(-A,d),i=Lt(t,l,[o,o]),r.dispose(l)}else i=Lt(t,n,[o,o]);else i=Lt(t,n,[o,o]);return[A,a,i]}var ys=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},L1=(e,t)=>{let n=ys(e),o=Fe(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var O1=6,fs=1.4,G0,O2=null,le=0,Be=null,C1=()=>le;async function W1(e){var t;return P.initial&&(G0=null),G0?e.debug&&b("cached model:",G0.modelUrl):G0=await L((t=e.face.detector)==null?void 0:t.modelPath),le=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=r.scalar(le,"int32"),O2=r.tensor2d(j1(le)),G0}function ms(e){if(!O2||!Be)return r.zeros([0,0]);let t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,O2),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,Be),t.centersNormalized=r.div(t.centers,Be),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,Be),t.endNormalized=r.mul(t.ends,Be);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function D1(e,t){var i,c,d,y,l,m;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[le,le]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let x=o.sort((p,f)=>p.size-f.size);n.concat384=r.concat([x[0],x[2]],2),n.concat512=r.concat([x[1],x[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=r.squeeze(o[0]):n.batch=r.squeeze(o);r.dispose(o),n.boxes=ms(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let x=0;x(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let f={};f.bbox=r.slice(n.boxes,[s[x],0],[1,-1]),f.slice=r.slice(n.batch,[s[x],O1-1],[1,-1]),f.squeeze=r.squeeze(f.slice),f.landmarks=r.reshape(f.squeeze,[O1,-1]);let h=await f.bbox.data(),v={startPoint:[h[0],h[1]],endPoint:[h[2],h[3]],landmarks:await f.landmarks.array(),confidence:p};f.anchor=r.slice(O2,[s[x],0],[1,2]);let T=await f.anchor.data(),u=E1(v,[(e.shape[2]||0)/le,(e.shape[1]||0)/le],T),g=N2(u,t.face.scale||fs),w=L2(g);w.size[0]>(((l=t.face.detector)==null?void 0:l.minSize)||0)&&w.size[1]>(((m=t.face.detector)==null?void 0:m.minSize)||0)&&A.push(w),Object.keys(f).forEach(k=>r.dispose(f[k]))}}return Object.keys(n).forEach(x=>r.dispose(n[x])),A}var z0,ce=0,ps=2.3,Wt=F0.leftEyeLower0,Dt=F0.rightEyeLower0,He={leftBounds:[Wt[0],Wt[Wt.length-1]],rightBounds:[Dt[0],Dt[Dt.length-1]]},Ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function V1(e){var t,n;return P.initial&&(z0=null),z0?e.debug&&b("cached model:",z0.modelUrl):z0=await L((t=e.face.iris)==null?void 0:t.modelPath),ce=z0!=null&&z0.executor&&((n=z0.inputs)!=null&&n[0].shape)?z0.inputs[0].shape[2]:0,ce===-1&&(ce=64),z0}function C2(e,t,n,o){for(let s=0;s{let t=e[He.leftBounds[0]][2],n=e[He.rightBounds[0]][2];return t-n},B1=(e,t,n,o,s,A=!1)=>{let a=L2(N2(z1([e[n],e[o]]),ps)),i=Fe(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[ce,ce]);if(A&&P.kernels.includes("flipleftright")){let d=r.image.flipLeftRight(c);r.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},H1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A{let o=e[F0[`${n}EyeUpper0`][Ge.upperCenter]][2],s=e[F0[`${n}EyeLower0`][Ge.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function Z1(e,t,n){if(!(z0!=null&&z0.executor))return e;let{box:o,boxSize:s,crop:A}=B1(e,t,He.leftBounds[0],He.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=B1(e,t,He.rightBounds[0],He.rightBounds[1],n,!0),d=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=z0.execute(d);r.dispose(d);let l=await y.data();r.dispose(y);let m=l.slice(0,Ge.numCoordinates*3),{rawCoords:x,iris:p}=H1(m,o,s,!0),f=l.slice(Ge.numCoordinates*3),{rawCoords:h,iris:v}=H1(f,a,i,!1),T=us(e);Math.abs(T)<30?(C2(e,x,"left",null),C2(e,h,"right",null)):T<1?C2(e,x,"left",["EyeUpper0","EyeLower0"]):C2(e,h,"right",["EyeUpper0","EyeLower0"]);let u=G1(e,p,"left"),g=G1(e,v,"right");return e.concat(u).concat(g)}async function q1(e,t){var A,a,i,c,d,y,l,m,x,p;let n={lips:await((a=(A=t.filter(f=>f.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(f=>f.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(f=>f.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((m=(l=t.filter(f=>f.size===10))==null?void 0:l[1])==null?void 0:m.data()),eyeR:await((p=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:p.data())};for(let f of Object.values(n))if(!f)return e;let o=Me.reduce((f,h)=>f+=e[h][2],0)/Me.length;for(let f=0;ff+=e[h][2],0)/Pe.length;for(let f=0;fR()-K0.timestamp,o=K0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||K0.boxes.length===0?(K0.boxes=await D1(e,t),K0.timestamp=R(),K0.skipped=0):K0.skipped++;let s=[],A=[],a=0,i=o2;for(let T=0;T[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/i]);for(let N of Object.keys(ve))k.annotations[N]=[k.mesh[ve[N]]]}else if(!K)t.debug&&b("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!P.kernels.includes("atan2"))return t.face.attention.enabled=!1,r.dispose(k.tensor),s;let N=K.execute(k.tensor),V=await N.find(D=>D.shape[D.shape.length-1]===1).data();if(k.faceScore=Math.round(100*V[0])/100,k.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(u.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=j2(u,e),k.boxRaw=I2(u,e),k.score=k.boxScore,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(D=>[D[0]/(e.shape[2]||1),D[1]/(e.shape[1]||1),(D[2]||0)/i]);for(let D of Object.keys(ve))k.annotations[D]=[k.mesh[ve[D]]]}}else{let D=N.find(q=>q.shape[q.shape.length-1]===1404),Z=r.reshape(D,[-1,3]),J=await Z.array();r.dispose(Z),(f=t.face.attention)!=null&&f.enabled?J=await q1(J,N):(h=t.face.iris)!=null&&h.enabled&&(J=await Z1(J,k.tensor,o2)),k.mesh=I1(J,u,g,w,o2),k.meshRaw=k.mesh.map(q=>[q[0]/(e.shape[2]||0),q[1]/(e.shape[1]||0),(q[2]||0)/i]);for(let q of Object.keys(F0))k.annotations[q]=F0[q].map(m0=>k.mesh[m0]);k.score=k.faceScore;let U={...L1(k.mesh,u),confidence:u.confidence,landmarks:u.landmarks,size:u.size};k.box=j2(U,e),k.boxRaw=I2(U,e),A.push(U)}r.dispose(N)}k.score>(((v=t.face.detector)==null?void 0:v.minConfidence)||1)?s.push(k):r.dispose(k.tensor)}return K0.boxes=A,s}async function Y1(e){var t,n,o,s,A,a;return P.initial&&(K=null),(t=e.face.attention)!=null&&t.enabled&&(K!=null&&K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&b("cached model:",K.modelUrl):(o=e.face.attention)!=null&&o.enabled?K=await L(e.face.attention.modelPath):K=await L((s=e.face.mesh)==null?void 0:s.modelPath),o2=K.executor&&((A=K==null?void 0:K.inputs)!=null&&A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var K1=Re,J1=t2;var Ht=[],d0,W2=[],Q1=0,_1=0,Bt=Number.MAX_SAFE_INTEGER,Gt=!1;async function $1(e){var t,n,o;return P.initial&&(d0=null),d0?e.debug&&b("cached model:",d0.modelUrl):(d0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Gt=((o=(n=d0==null?void 0:d0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,Gt?Ht=["angry","disgust","fear","happy","neutral","sad","surprise"]:Ht=["angry","disgust","fear","happy","sad","surprise","neutral"]),d0}async function Vt(e,t,n,o){var a,i;if(!d0)return[];let s=Bt<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>R()-_1;return t.skipAllowed&&A&&s&&Q1===o&&W2[n]&&W2[n].length>0?(Bt++,W2[n]):(Bt=0,new Promise(async c=>{var y,l,m;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let x={},p=d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0;if(((l=t.face.emotion)==null?void 0:l.crop)>0){let h=(m=t.face.emotion)==null?void 0:m.crop,v=[[h,h,1-h,1-h]];x.resize=r.image.cropAndResize(e,v,[0],[p,p])}else x.resize=r.image.resizeBilinear(e,[p,p],!1);Gt?(x.mul=r.mul(x.resize,255),x.normalize=r.sub(x.mul,[103.939,116.779,123.68]),x.emotion=d0==null?void 0:d0.execute(x.normalize)):(x.channels=r.mul(x.resize,O.rgb),x.grayscale=r.sum(x.channels,3,!0),x.grayscaleSub=r.sub(x.grayscale,O.tf05),x.grayscaleMul=r.mul(x.grayscaleSub,O.tf2),x.emotion=d0==null?void 0:d0.execute(x.grayscaleMul)),_1=R();let f=await x.emotion.data();for(let h=0;h(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*f[h])/100),emotion:Ht[h]});d.sort((h,v)=>v.score-h.score),Object.keys(x).forEach(h=>r.dispose(x[h]))}W2[n]=d,Q1=o,c(d)}))}var x0,de=[],t3=0,n3=0,Zt=Number.MAX_SAFE_INTEGER;async function o3(e){var t;return P.initial&&(x0=null),x0?e.debug&&b("cached model:",x0.modelUrl):x0=await L((t=e.face.description)==null?void 0:t.modelPath),x0}function bs(e,t){var A,a;let n=e.image||e.tensor||e;if(!(x0!=null&&x0.inputs[0].shape))return n;let o;if(((A=t.face.description)==null?void 0:A.crop)>0){let i=(a=t.face.description)==null?void 0:a.crop,c=[[i,i,1-i,1-i]];o=r.image.cropAndResize(n,c,[0],[x0.inputs[0].shape[2],x0.inputs[0].shape[1]])}else o=r.image.resizeBilinear(n,[x0.inputs[0].shape[2],x0.inputs[0].shape[1]],!1);let s=r.mul(o,O.tf255);return r.dispose(o),s}async function Xt(e,t,n,o){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(x0!=null&&x0.executor))return s;let A=Zt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>R()-t3;return t.skipAllowed&&A&&a&&n3===o&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((y=de==null?void 0:de[n])==null?void 0:y.genderScore)>0?(Zt++,de[n]):(Zt=0,new Promise(async l=>{var m;if((m=t.face.description)!=null&&m.enabled){let x=bs(e,t),p=x0==null?void 0:x0.execute(x);t3=R(),r.dispose(x);let h=await p.find(W=>W.shape[1]===1).data(),v=Math.trunc(200*Math.abs(h[0]-.5))/100;v>(t.face.description.minConfidence||0)&&(s.gender=h[0]<=.5?"female":"male",s.genderScore=Math.min(.99,v));let T=r.argMax(p.find(W=>W.shape[1]===100),1),u=(await T.data())[0];r.dispose(T);let w=await p.find(W=>W.shape[1]===100).data();s.age=Math.round(w[u-1]>w[u+1]?10*u-100*w[u-1]:10*u+100*w[u+1])/10,(Number.isNaN(h[0])||Number.isNaN(w[0]))&&b("faceres error:",{model:x0,result:p});let k=p.find(W=>W.shape[1]===1024),N=k?await k.data():[];s.descriptor=Array.from(N),p.forEach(W=>r.dispose(W))}de[n]=s,n3=o,l(s)}))}var Ve=.1,qt=.5;function gs(e,t,n){let o=!1,s=n.length-1;for(let A=0;At!=n[s].y>t&&e<(n[s].x-n[A].x)*(t-n[A].y)/(n[s].y-n[A].y)+n[A].x&&(o=!o);return o}async function s3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),s=[];for(let a of F0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ve&&Ve>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ve:a.x-Ve,y:a.y>.5?a.y+Ve:a.y-Ve})));for(let a=0;aR()-a3,A=Ut<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&A3===o&&D2[n]?(Ut++,D2[n]):(Ut=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[2]:0,y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[1]:0],!1),y=y0==null?void 0:y0.execute(d),l=(await y.data())[0];D2[n]=Math.round(100*l)/100,A3=o,a3=R(),r.dispose([d,y]),c(D2[n])}))}var f0,F2=[],Kt=Number.MAX_SAFE_INTEGER,c3=0,d3=0;async function x3(e){var t;return P.initial&&(f0=null),f0?e.debug&&b("cached model:",f0.modelUrl):f0=await L((t=e.face.liveness)==null?void 0:t.modelPath),f0}async function Jt(e,t,n,o){var a,i;if(!(f0!=null&&f0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>R()-d3,A=Kt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&c3===o&&F2[n]?(Kt++,F2[n]):(Kt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[2]:0,f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[1]:0],!1),y=f0==null?void 0:f0.execute(d),l=(await y.data())[0];F2[n]=Math.round(100*l)/100,c3=o,d3=R(),r.dispose([d,y]),c(F2[n])}))}var B0,Qt=[],vs=["white","black","asian","indian","other"],Rs=[15,23,28,35.5,45.5,55.5,65],f3=0,m3=0,_t=Number.MAX_SAFE_INTEGER;async function p3(e){var t;return P.initial&&(B0=null),B0?e.debug&&b("cached model:",B0.modelUrl):B0=await L((t=e.face.gear)==null?void 0:t.modelPath),B0}async function $t(e,t,n,o){var a,i;if(!B0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=_t<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>R()-m3;return t.skipAllowed&&A&&s&&f3===o&&Qt[n]?(_t++,Qt[n]):(_t=0,new Promise(async c=>{var v,T,u,g;if(!(B0!=null&&B0.inputs[0].shape))return;let d={},y=[[0,.1,.9,.9]];if(((v=t.face.gear)==null?void 0:v.crop)>0){let w=(T=t.face.gear)==null?void 0:T.crop;y=[[w,w,1-w,1-w]]}d.resize=r.image.cropAndResize(e,y,[0],[B0.inputs[0].shape[2],B0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(u=t.face.gear)!=null&&u.enabled&&([d.age,d.gender,d.race]=B0.execute(d.resize,["age_output","gender_output","race_output"]));let m=await d.gender.data();l.gender=m[0]>m[1]?"male":"female",l.genderScore=Math.round(100*(m[0]>m[1]?m[0]:m[1]))/100;let x=await d.race.data();for(let w=0;w(((g=t.face.gear)==null?void 0:g.minConfidence)||.2)&&l.race.push({score:Math.round(100*x[w])/100,race:vs[w]});l.race.sort((w,k)=>k.score-w.score);let f=Array.from(await d.age.data()).map((w,k)=>[Rs[k],w]).sort((w,k)=>k[1]-w[1]),h=f[0][0];for(let w=1;wr.dispose(d[w])),Qt[n]=l,f3=o,m3=R(),c(l)}))}var R0,B2=[],h3=0,b3=0,e5=Number.MAX_SAFE_INTEGER;async function g3(e){return P.initial&&(R0=null),R0?e.debug&&b("cached model:",R0.modelUrl):R0=await L(e.face.ssrnet.modelPathAge),R0}async function t5(e,t,n,o){var a,i,c,d;if(!R0)return{age:0};let s=e5<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>R()-b3;return t.skipAllowed&&s&&A&&h3===o&&((c=B2[n])!=null&&c.age)&&((d=B2[n])==null?void 0:d.age)>0?(e5++,B2[n]):(e5=0,new Promise(async y=>{var x,p,f;if(!(R0!=null&&R0.inputs)||!R0.inputs[0]||!R0.inputs[0].shape)return;let l={};if(((x=t.face.ssrnet)==null?void 0:x.crop)>0){let h=(p=t.face.ssrnet)==null?void 0:p.crop,v=[[h,h,1-h,1-h]];l.resize=r.image.cropAndResize(e,v,[0],[R0.inputs[0].shape[2],R0.inputs[0].shape[1]])}else l.resize=r.image.resizeBilinear(e,[R0.inputs[0].shape[2],R0.inputs[0].shape[1]],!1);l.enhance=r.mul(l.resize,O.tf255);let m={age:0};if((f=t.face.ssrnet)!=null&&f.enabled&&(l.age=R0.execute(l.enhance)),l.age){let h=await l.age.data();m.age=Math.trunc(10*h[0])/10}Object.keys(l).forEach(h=>r.dispose(l[h])),B2[n]=m,h3=o,b3=R(),y(m)}))}var u0,H2=[],v3=0,R3=0,n5=Number.MAX_SAFE_INTEGER,o5=[.2989,.587,.114];async function M3(e){var t;return P.initial&&(u0=null),u0?e.debug&&b("cached model:",u0.modelUrl):u0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),u0}async function r5(e,t,n,o){var a,i,c,d;if(!u0)return{gender:"unknown",genderScore:0};let s=n5<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>R()-R3;return t.skipAllowed&&s&&A&&v3===o&&((c=H2[n])!=null&&c.gender)&&((d=H2[n])==null?void 0:d.genderScore)>0?(n5++,H2[n]):(n5=0,new Promise(async y=>{var p,f,h;if(!(u0!=null&&u0.inputs[0].shape))return;let l={};if(((p=t.face.ssrnet)==null?void 0:p.crop)>0){let v=(f=t.face.ssrnet)==null?void 0:f.crop,T=[[v,v,1-v,1-v]];l.resize=r.image.cropAndResize(e,T,[0],[u0.inputs[0].shape[2],u0.inputs[0].shape[1]])}else l.resize=r.image.resizeBilinear(e,[u0.inputs[0].shape[2],u0.inputs[0].shape[1]],!1);l.enhance=r.tidy(()=>{var T,u;let v;if(((u=(T=u0==null?void 0:u0.inputs)==null?void 0:T[0].shape)==null?void 0:u[3])===1){let[g,w,k]=r.split(l.resize,3,3),N=r.mul(g,o5[0]),W=r.mul(w,o5[1]),V=r.mul(k,o5[2]),D=r.addN([N,W,V]);v=r.mul(r.sub(D,O.tf05),2)}else v=r.mul(r.sub(l.resize,O.tf05),2);return v});let m={gender:"unknown",genderScore:0};(h=t.face.ssrnet)!=null&&h.enabled&&(l.gender=u0.execute(l.enhance));let x=await l.gender.data();m.gender=x[0]>x[1]?"female":"male",m.genderScore=x[0]>x[1]?Math.trunc(100*x[0])/100:Math.trunc(100*x[1])/100,Object.keys(l).forEach(v=>r.dispose(l[v])),H2[n]=m,v3=o,R3=R(),y(m)}))}var S0,s5=[],k3=0,w3=0,E3=Number.MAX_SAFE_INTEGER;async function z3(e){var t;return P.initial&&(S0=null),S0?e.debug&&b("cached model:",S0.modelUrl):S0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),S0}async function A5(e,t,n,o){var a,i;if(!(S0!=null&&S0.executor))return[];let s=E3<(((a=t.face.mobilefacenet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.mobilefacenet)==null?void 0:i.skipTime)||0)>R()-w3;return t.skipAllowed&&A&&s&&k3===o&&s5[n]?(E3++,s5[n]):new Promise(async c=>{var y;let d=[];if((y=t.face.mobilefacenet)!=null&&y.enabled&&(S0!=null&&S0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[S0.inputs[0].shape[2],S0.inputs[0].shape[1]],!1),l.data=S0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}s5[n]=d,k3=o,w3=R(),c(d)})}var j0,a5=[],j3=0,I3=0,N3=Number.MAX_SAFE_INTEGER;async function L3(e){return P.initial&&(j0=null),j0?e.debug&&b("cached model:",j0.modelUrl):j0=await L(e.face.insightface.modelPath),j0}async function i5(e,t,n,o){var a,i;if(!(j0!=null&&j0.executor))return[];let s=N3<(((a=t.face.insightface)==null?void 0:a.skipFrames)||0),A=(((i=t.face.insightface)==null?void 0:i.skipTime)||0)>R()-I3;return t.skipAllowed&&A&&s&&j3===o&&a5[n]?(N3++,a5[n]):new Promise(async c=>{var y;let d=[];if((y=t.face.insightface)!=null&&y.enabled&&(j0!=null&&j0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),l.data=j0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}a5[n]=d,j3=o,I3=R(),c(d)})}var Ms=e=>{let t=(l,m)=>Math.atan2(l[1]-m[1],l[0]-m[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,s=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=s?e.mesh[473]:e.mesh[468],a=s?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=s?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],c=[(a[0]-A[0])/i[0]-n[0],o*(A[1]-a[1])/i[1]-n[1]],d=Math.sqrt(c[0]*c[0]+c[1]*c[1]);return d=Math.min(d,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],c)+Math.PI/2)%Math.PI,strength:d}},C3=(e,t)=>{let n=f=>{let h=Math.sqrt(f[0]*f[0]+f[1]*f[1]+f[2]*f[2]);return f[0]/=h,f[1]/=h,f[2]/=h,f},o=(f,h)=>{let v=f[0]-h[0],T=f[1]-h[1],u=f[2]-h[2];return[v,T,u]},s=(f,h)=>{let v=f[1]*h[2]-f[2]*h[1],T=f[2]*h[0]-f[0]*h[2],u=f[0]*h[1]-f[1]*h[0];return[v,T,u]},A=f=>{let[h,v,T,u,g,w,k,N,W]=f,V,D,Z;return u<1?u>-1?(Z=Math.asin(u),D=Math.atan2(-k,h),V=Math.atan2(-w,g)):(Z=-Math.PI/2,D=-Math.atan2(N,W),V=0):(Z=Math.PI/2,D=Math.atan2(N,W),V=0),Number.isNaN(V)&&(V=0),Number.isNaN(D)&&(D=0),Number.isNaN(Z)&&(Z=0),{pitch:2*-V,yaw:2*-D,roll:2*-Z}},a=e.meshRaw;if(!a||a.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[a[10],a[152],a[234],a[454]].map(f=>[f[0]*t[0]/i,f[1]*t[1]/i,f[2]]),d=n(o(c[1],c[0])),y=n(o(c[3],c[2])),l=n(s(y,d));y=s(d,l);let m=[y[0],y[1],y[2],d[0],d[1],d[2],l[0],l[1],l[2]],x=A(m),p=a.length===478?Ms(e):{bearing:0,strength:0};return{angle:x,matrix:m,gaze:p}};function W3(e,t){let n=e==null?void 0:e.annotations;if(!(n!=null&&n.leftEyeIris)||!(n!=null&&n.rightEyeIris))return 0;let o=Math.max(Math.abs(n.leftEyeIris[3][0]-n.leftEyeIris[1][0]),Math.abs(n.rightEyeIris[3][0]-n.rightEyeIris[1][0]))/t;return Math.round(1.17/o)/100}var l5=async(e,t)=>{var p,f,h,v,T,u,g,w,k,N,W,V,D,Z,J,U,q,m0,M,i0,g0,e0,G;let n=R(),o,s,A,a,i,c,d,y,l,m=[];e.state="run:face";let x=await U1(t,e.config);if(e.performance.face=P.perfadd?(e.performance.face||0)+Math.trunc(R()-n):Math.trunc(R()-n),!t.shape||t.shape.length!==4)return[];if(!x)return[];for(let S=0;S200?C3(x[S],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?a=(f=e.config.face.emotion)!=null&&f.enabled?Vt(x[S].tensor||r.tensor([]),e.config,S,x.length):[]:(e.state="run:emotion",n=R(),a=(h=e.config.face.emotion)!=null&&h.enabled?await Vt(x[S].tensor||r.tensor([]),e.config,S,x.length):[],e.performance.emotion=P.perfadd?(e.performance.emotion||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?d=(v=e.config.face.antispoof)!=null&&v.enabled?Yt(x[S].tensor||r.tensor([]),e.config,S,x.length):0:(e.state="run:antispoof",n=R(),d=(T=e.config.face.antispoof)!=null&&T.enabled?await Yt(x[S].tensor||r.tensor([]),e.config,S,x.length):0,e.performance.antispoof=P.perfadd?(e.performance.antispoof||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?y=(u=e.config.face.liveness)!=null&&u.enabled?Jt(x[S].tensor||r.tensor([]),e.config,S,x.length):0:(e.state="run:liveness",n=R(),y=(g=e.config.face.liveness)!=null&&g.enabled?await Jt(x[S].tensor||r.tensor([]),e.config,S,x.length):0,e.performance.liveness=P.perfadd?(e.performance.antispoof||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?s=(w=e.config.face.gear)!=null&&w.enabled?$t(x[S].tensor||r.tensor([]),e.config,S,x.length):null:(e.state="run:gear",n=R(),s=(k=e.config.face.gear)!=null&&k.enabled?await $t(x[S].tensor||r.tensor([]),e.config,S,x.length):null,e.performance.gear=Math.trunc(R()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(o=(N=e.config.face.ssrnet)!=null&&N.enabled?t5(x[S].tensor||r.tensor([]),e.config,S,x.length):null,A=(W=e.config.face.ssrnet)!=null&&W.enabled?r5(x[S].tensor||r.tensor([]),e.config,S,x.length):null):(e.state="run:ssrnet",n=R(),o=(V=e.config.face.ssrnet)!=null&&V.enabled?await t5(x[S].tensor||r.tensor([]),e.config,S,x.length):null,A=(D=e.config.face.ssrnet)!=null&&D.enabled?await r5(x[S].tensor||r.tensor([]),e.config,S,x.length):null,e.performance.ssrnet=Math.trunc(R()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(Z=e.config.face.mobilefacenet)!=null&&Z.enabled?A5(x[S].tensor||r.tensor([]),e.config,S,x.length):null:(e.state="run:mobilefacenet",n=R(),i=(J=e.config.face.mobilefacenet)!=null&&J.enabled?await A5(x[S].tensor||r.tensor([]),e.config,S,x.length):null,e.performance.mobilefacenet=Math.trunc(R()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start InsightFace:"),e.config.async?c=(U=e.config.face.insightface)!=null&&U.enabled?i5(x[S].tensor||r.tensor([]),e.config,S,x.length):null:(e.state="run:mobilefacenet",n=R(),c=(q=e.config.face.insightface)!=null&&q.enabled?await i5(x[S].tensor||r.tensor([]),e.config,S,x.length):null,e.performance.mobilefacenet=Math.trunc(R()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?l=Xt(x[S].tensor||r.tensor([]),e.config,S,x.length):(e.state="run:description",n=R(),l=await Xt(x[S].tensor||r.tensor([]),e.config,S,x.length),e.performance.description=P.perfadd?(e.performance.description||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End Description:"),e.config.async&&([o,A,a,i,c,l,s,d,y]=await Promise.all([o,A,a,i,c,l,s,d,y])),e.analyze("Finish Face:"),(m0=e.config.face.ssrnet)!=null&&m0.enabled&&o&&A&&(l={...l,age:o.age,gender:A.gender,genderScore:A.genderScore}),(M=e.config.face.gear)!=null&&M.enabled&&s&&(l={...l,age:s.age,gender:s.gender,genderScore:s.genderScore,race:s.race}),(i0=e.config.face.mobilefacenet)!=null&&i0.enabled&&i&&(l.descriptor=i),(g0=e.config.face.insightface)!=null&&g0.enabled&&c&&(l.descriptor=c);let F=(e0=e.config.face.iris)!=null&&e0.enabled?W3(x[S],t.shape[2]):0,w0=(G=e.config.face.detector)!=null&&G.return?r.squeeze(x[S].tensor):null;r.dispose(x[S].tensor),x[S].tensor&&delete x[S].tensor;let t0={...x[S],id:S};l.age&&(t0.age=l.age),l.gender&&(t0.gender=l.gender),l.genderScore&&(t0.genderScore=l.genderScore),l.descriptor&&(t0.embedding=l.descriptor),l.race&&(t0.race=l.race),a&&(t0.emotion=a),d&&(t0.real=d),y&&(t0.live=y),F>0&&(t0.distance=F),C&&(t0.rotation=C),w0&&(t0.tensor=w0),m.push(t0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),m};var 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this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c=n.hand.minConfidence/4){let w=r.reshape(u,[-1,3]),k=await w.array();r.dispose(u),r.dispose(w);let N=this.transformRawCoords(k,f,y,p),W=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...W,confidence:g};let V={landmarks:N,confidence:g,boxConfidence:d.confidence,fingerConfidence:g,box:{topLeft:W.startPoint,bottomRight:W.endPoint}};i.push(V)}else this.storedBoxes[c]=null;r.dispose(u)}else{let y=Z2(X2(d),rn),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var an={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Ie,Ne,ln;async function f5(e,t){let n=await ln.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function cn(e){var n,o;P.initial&&(Ie=null,Ne=null),!Ie||!Ne?[Ie,Ne]=await Promise.all([e.hand.enabled?L((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?L((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&b("cached model:",Ie.modelUrl),e.debug&&b("cached model:",Ne.modelUrl));let t=Ie?new q2(Ie):void 0;return t&&Ne&&(ln=new U2(t,Ne)),[Ie,Ne]}var r0=[null,null],Gs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],Vs=["hand","fist","pinch","point","face","tip","pinchtip"],xn=4,yn=1.6,Zs=512,Xs=1.4,Y2=Number.MAX_SAFE_INTEGER,m5=0,ee=[0,0],o0={boxes:[],hands:[]},fn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function mn(e){var t;if(P.initial&&(r0[0]=null),r0[0])e.debug&&b("cached model:",r0[0].modelUrl);else{h2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function pn(e){var t;if(P.initial&&(r0[1]=null),r0[1])e.debug&&b("cached model:",r0[1].modelUrl);else{r0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function qs(e,t){let n=[];if(!e||!r0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Zs),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await r0[0].executeAsync(o.cast,Gs),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[xn]),i.splice(xn,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let m of Array.from(d)){let x=r.slice(o.boxes,m,1),p=await x.data();r.dispose(x);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],h=k2(f,Xs),v=[Math.trunc(f[0]*ee[0]),Math.trunc(f[1]*ee[1]),Math.trunc(f[2]*ee[0]),Math.trunc(f[3]*ee[1])],T=y[m],u=Vs[l[m]],g={id:c++,score:T,box:v,boxRaw:h,label:u};n.push(g)}return Object.keys(o).forEach(m=>r.dispose(o[m])),n.sort((m,x)=>x.score-m.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function p5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[ee[0]*(l[0]+t.boxRaw[0]),ee[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=G2(o.keypoints);for(let l of Object.keys(fn))o.annotations[l]=fn[l].map(m=>o.landmarks&&o.keypoints[m]?o.keypoints[m]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function u5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];ee=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>R()-m5,o=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>R()-m5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(y=>p5(e,y,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(y=>p5(e,y,t))):(o0.boxes=await qs(e,t),m5=R(),o0.hands=await Promise.all(o0.boxes.map(y=>p5(e,y,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[y].fingerScore&&o0.hands[y].fingerScore>(t.hand.minConfidence||0)){let m=k2(l.box,yn),x=k2(l.boxRaw,yn);o0.boxes.push({...d[y],box:m,boxRaw:x})}}for(let y=0;y({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var s2={};oe(s2,{connected:()=>J2,horizontal:()=>h5,kpt:()=>K2,relative:()=>g5,vertical:()=>b5});var K2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],h5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],b5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],g5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var j=te(),T5=0;function hn(e,t){var a,i,c,d,y,l,m,x,p,f,h,v,T,u,g,w,k,N,W,V,D,Z,J,U,q,m0;let n=R();if(!e)return te();let o=Date.now()-e.timestamp,s=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((s-1)*j.body[M].box[F]+C)/s),g0=e.body[M].boxRaw.map((C,F)=>((s-1)*j.body[M].boxRaw[F]+C)/s),e0=e.body[M].keypoints.map((C,F)=>{var w0,t0,ne,Ke,Oe,F5,B5,H5,G5;return{score:C.score,part:C.part,position:[j.body[M].keypoints[F]?((s-1)*(j.body[M].keypoints[F].position[0]||0)+(C.position[0]||0))/s:C.position[0],j.body[M].keypoints[F]?((s-1)*(j.body[M].keypoints[F].position[1]||0)+(C.position[1]||0))/s:C.position[1],j.body[M].keypoints[F]?((s-1)*(j.body[M].keypoints[F].position[2]||0)+(C.position[2]||0))/s:C.position[2]],positionRaw:[j.body[M].keypoints[F]?((s-1)*(j.body[M].keypoints[F].positionRaw[0]||0)+(C.positionRaw[0]||0))/s:C.positionRaw[0],j.body[M].keypoints[F]?((s-1)*(j.body[M].keypoints[F].positionRaw[1]||0)+(C.positionRaw[1]||0))/s:C.positionRaw[1],j.body[M].keypoints[F]?((s-1)*(j.body[M].keypoints[F].positionRaw[2]||0)+(C.positionRaw[2]||0))/s:C.positionRaw[2]],distance:[j.body[M].keypoints[F]?((s-1)*(((w0=j.body[M].keypoints[F].distance)==null?void 0:w0[0])||0)+(((t0=C.distance)==null?void 0:t0[0])||0))/s:(ne=C.distance)==null?void 0:ne[0],j.body[M].keypoints[F]?((s-1)*(((Ke=j.body[M].keypoints[F].distance)==null?void 0:Ke[1])||0)+(((Oe=C.distance)==null?void 0:Oe[1])||0))/s:(F5=C.distance)==null?void 0:F5[1],j.body[M].keypoints[F]?((s-1)*(((B5=j.body[M].keypoints[F].distance)==null?void 0:B5[2])||0)+(((H5=C.distance)==null?void 0:H5[2])||0))/s:(G5=C.distance)==null?void 0:G5[2]]}}),G={},S={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?S=z2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?S=M2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(S=s2);for(let[C,F]of Object.entries(S.connected)){let w0=[];for(let t0=0;t0Oe.part===F[t0]),Ke=e0.find(Oe=>Oe.part===F[t0+1]);ne&&Ke&&w0.push([ne.position,Ke.position])}G[C]=w0}j.body[M]={...e.body[M],box:i0,boxRaw:g0,keypoints:e0,annotations:G}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let M=0;M((s-1)*j.hand[M].box[C]+S)/s),g0=e.hand[M].boxRaw.map((S,C)=>((s-1)*j.hand[M].boxRaw[C]+S)/s);j.hand[M].keypoints.length!==e.hand[M].keypoints.length&&(j.hand[M].keypoints=e.hand[M].keypoints);let e0=e.hand[M].keypoints&&e.hand[M].keypoints.length>0?e.hand[M].keypoints.map((S,C)=>S.map((F,w0)=>((s-1)*(j.hand[M].keypoints[C][w0]||1)+(F||0))/s)):[],G={};if(Object.keys(j.hand[M].annotations).length!==Object.keys(e.hand[M].annotations).length)j.hand[M].annotations=e.hand[M].annotations,G=j.hand[M].annotations;else if(e.hand[M].annotations)for(let S of Object.keys(e.hand[M].annotations))G[S]=(l=(y=(d=e.hand[M])==null?void 0:d.annotations)==null?void 0:y[S])!=null&&l[0]?e.hand[M].annotations[S].map((C,F)=>C.map((w0,t0)=>((s-1)*j.hand[M].annotations[S][F][t0]+w0)/s)):null;j.hand[M]={...e.hand[M],box:i0,boxRaw:g0,keypoints:e0,annotations:G}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let M=0;M((s-1)*j.face[M].box[S]+G)/s),g0=e.face[M].boxRaw.map((G,S)=>((s-1)*j.face[M].boxRaw[S]+G)/s),e0=e.face[M].annotations;if(Object.keys(j.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)j.face[M].annotations=e.face[M].annotations,e0=j.face[M].annotations;else if(e.face[M].annotations)for(let G of Object.keys(e.face[M].annotations))e0[G]=(p=(x=(m=e.face[M])==null?void 0:m.annotations)==null?void 0:x[G])!=null&&p[0]?e.face[M].annotations[G].map((S,C)=>S.map((F,w0)=>((s-1)*j.face[M].annotations[G][C][w0]+F)/s)):null;if(e.face[M].rotation){let G={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};G.matrix=(f=e.face[M].rotation)==null?void 0:f.matrix,G.angle={roll:((s-1)*(((v=(h=j.face[M].rotation)==null?void 0:h.angle)==null?void 0:v.roll)||0)+(((u=(T=e.face[M].rotation)==null?void 0:T.angle)==null?void 0:u.roll)||0))/s,yaw:((s-1)*(((w=(g=j.face[M].rotation)==null?void 0:g.angle)==null?void 0:w.yaw)||0)+(((N=(k=e.face[M].rotation)==null?void 0:k.angle)==null?void 0:N.yaw)||0))/s,pitch:((s-1)*(((V=(W=j.face[M].rotation)==null?void 0:W.angle)==null?void 0:V.pitch)||0)+(((Z=(D=e.face[M].rotation)==null?void 0:D.angle)==null?void 0:Z.pitch)||0))/s},G.gaze={bearing:((s-1)*(((J=j.face[M].rotation)==null?void 0:J.gaze.bearing)||0)+(((U=e.face[M].rotation)==null?void 0:U.gaze.bearing)||0))/s,strength:((s-1)*(((q=j.face[M].rotation)==null?void 0:q.gaze.strength)||0)+(((m0=e.face[M].rotation)==null?void 0:m0.gaze.strength)||0))/s},j.face[M]={...e.face[M],rotation:G,box:i0,boxRaw:g0,annotations:e0}}else j.face[M]={...e.face[M],box:i0,boxRaw:g0,annotations:e0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let M=0;M((s-1)*j.object[M].box[G]+e0)/s),g0=e.object[M].boxRaw.map((e0,G)=>((s-1)*j.object[M].boxRaw[G]+e0)/s);j.object[M]={...e.object[M],box:i0,boxRaw:g0}}if(e.persons){let M=e.persons;if(!j.persons||M.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(M));else for(let i0=0;i0((s-1)*j.persons[i0].box[e0]+g0)/s)}e.gesture&&(j.gesture=e.gesture),j.width=e.width,j.height=e.height;let A=R();return T5=P.perfadd?T5+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:T5}),j}var h0;async function v5(e){return!h0||P.initial?h0=await L(e.segmentation.modelPath):e.debug&&b("cached model:",h0.modelUrl),h0}async function bn(e,t){var s;if(h0||(h0=await v5(t)),!(h0!=null&&h0.executor)||!((s=h0==null?void 0:h0.inputs)!=null&&s[0].shape))return null;let n={};n.resize=r.image.resizeBilinear(e,[h0.inputs[0].shape?h0.inputs[0].shape[1]:0,h0.inputs[0].shape?h0.inputs[0].shape[2]:0],!1),n.norm=r.div(n.resize,O.tf255),n.res=h0.execute(n.norm),n.squeeze=r.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=r.unstack(n.squeeze,2),n.fg=r.softmax(n.fgRaw),n.mul=r.mul(n.fg,O.tf255),n.expand=r.expandDims(n.mul,2),n.output=r.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=r.squeeze(e),n.concat=r.concat([n.input,n.output],-1),o=r.cast(n.concat,"int32");break;case"alpha":o=r.cast(n.output,"int32");break;default:o=r.tensor(0)}return Object.keys(n).forEach(A=>r.dispose(n[A])),o}var M5={};oe(M5,{distance:()=>R5,find:()=>Ks,similarity:()=>Ys});function R5(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let s=0;s{if(e===0)return 1;let A=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(A,1),0)};function 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i=e.keypoints[o]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[o].position[0])]:[0,0],c=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[s].position[0])]:[0,0];if(i[0]>i[1]||c[0]>c[1]){let d=e.keypoints[o];e.keypoints[o]=e.keypoints[s],e.keypoints[s]=d}}}function Rn(e){for(let t=0;te.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=r.pad(e,I0.padding),n.resize=r.image.resizeBilinear(n.pad,[t,t]);let o=r.cast(n.resize,"int32");return Object.keys(n).forEach(a=>r.dispose(n[a])),o}function Pn(e,t){e.keypoints=e.keypoints.filter(o=>o==null?void 0:o.position);for(let o of 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0:y.box),v((l=p.hands.left)==null?void 0:l.box),v((m=p.hands.right)==null?void 0:m.box);let T=Math.min(...f),u=Math.min(...h);p.box=[T,u,Math.max(...f)-T,Math.max(...h)-u],s!=null&&s[1]&&(s!=null&&s[2])&&(p.boxRaw=[p.box[0]/s[2],p.box[1]/s[1],p.box[2]/s[2],p.box[3]/s[1]]),a.push(p)}return a}var rt=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var bt=0;function Jr(e,t,n){let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=o.font;for(let A=0;ARt,kpt:()=>vt});var 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W0,ke=224,d1,$r=5,P2=[8,16,32,32,32];function es(){let e=[],t=0;for(;t<$r;){let n=0,o=t;for(;on.x)),y:r.tensor1d(e.map(n=>n.y))}}async function x1(e){if(M.initial&&(W0=null),!W0&&e.body.detector&&e.body.detector.modelPath){W0=await L(e.body.detector.modelPath);let t=W0!=null&&W0.executor?Object.values(W0.modelSignature.inputs):void 0;ke=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&W0&&b("cached model:",W0.modelUrl);return es(),W0}var c1=[5,5];function ts(e,t){return r.tidy(()=>{let n=r.split(e,12,1),o=r.squeeze(n[0]),s=r.squeeze(n[1]),A=r.squeeze(n[2]),a=r.squeeze(n[3]);o=r.add(r.div(o,ke),t.x),s=r.add(r.div(s,ke),t.y),A=r.mul(r.div(A,ke),c1[0]),a=r.mul(r.div(a,ke),c1[1]);let i=r.sub(o,r.div(A,2)),c=r.sub(s,r.div(a,2)),d=r.add(i,A),y=r.add(c,a);return r.stack([i,c,d,y],1)})}async function ns(e,t,n,o){var d,y;let s=[],A={};A.boxes=ts(e,d1),A.scores=r.sigmoid(t),A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((d=n.body.detector)==null?void 0:d.minConfidence)||.1,((y=n.body.detector)==null?void 0:y.iouThreshold)||.1);let a=await A.nms.data(),i=await A.scores.data(),c=await A.boxes.array();for(let l of Array.from(a)){let m=i[l],x=c[l],p=[Math.round(x[0]*o[0]),Math.round(x[1]*o[1]),Math.round(x[2]*o[0]),Math.round(x[3]*o[1])],f={score:m,boxRaw:x,box:p};s.push(f)}return Object.keys(A).forEach(l=>r.dispose(A[l])),s}async function y1(e,t,n){let o={};o.res=W0==null?void 0:W0.execute(e,["Identity"]),o.logitsRaw=r.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=r.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=r.squeeze(o.logitsRaw),o.boxes=r.squeeze(o.boxesRaw);let s=await ns(o.boxes,o.logits,t,n);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}function ae(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],s[0]-o[0],s[1]-o[1]],a=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:a}}function f1(e,t=[1,1]){let n=[e.map(d=>d[0]),e.map(d=>d[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+s[0])/2,(o[1]+s[1])/2],a=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+s[0],-A[1]+s[1]),i=[Math.trunc(A[0]-a),Math.trunc(A[1]-a),Math.trunc(2*a),Math.trunc(2*a)],c=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:c}}function k2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var P0,Pt=256,Mt=Number.MAX_SAFE_INTEGER,os={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},E2=[],ie=[[0,0],[0,0],[0,0],[0,0]],m1=0,p1=e=>1-1/(1+Math.exp(e)),h1=e=>x1(e);async function b1(e){if(M.initial&&(P0=null),P0)e.debug&&b("cached model:",P0.modelUrl);else{P0=await L(e.body.modelPath);let t=P0!=null&&P0.executor?Object.values(P0.modelSignature.inputs):void 0;Pt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return P0}function u1(e,t,n){var A,a;let o={};if(!((A=e==null?void 0:e.shape)!=null&&A[1])||!((a=e==null?void 0:e.shape)!=null&&a[2]))return e;let s;if(n&&(o.cropped=r.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let i=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],c=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ie=[[0,0],i,c,[0,0]],o.pad=r.pad(o.cropped||e,ie),o.resize=r.image.resizeBilinear(o.pad,[t,t]),s=r.div(o.resize,O.tf255)}else e.shape[1]!==t?(o.resize=r.image.resizeBilinear(o.cropped||e,[t,t]),s=r.div(o.resize,O.tf255)):s=r.div(o.cropped||e,O.tf255);return Object.keys(o).forEach(i=>r.dispose(o[i])),s}function rs(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ie[2][0]+ie[2][1])/t[0]-ie[2][0]),Math.trunc(o.position[1]*(t[1]+ie[1][0]+ie[1][1])/t[1]-ie[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],s=n[3]-n[1];for(let A of e)A.positionRaw=[A.positionRaw[0]/s+n[1],A.positionRaw[1]/o+n[0],A.positionRaw[2]],A.position=[Math.trunc(A.positionRaw[0]*t[0]),Math.trunc(A.positionRaw[1]*t[1]),A.positionRaw[2]]}return e}function ss(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),o=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),A=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");s.position[2]=((A.position[2]||0)+(a.position[2]||0))/2}async function As(e,t,n){if(!(P0!=null&&P0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=P0==null?void 0:P0.execute(e,os.landmarks);let s=(await o.poseflag.data())[0],A=await o.ld.data(),a=await o.world.data();Object.keys(o).forEach(p=>r.dispose(o[p]));let i=[],c=5;for(let p=0;pp.position),l=ae(y,[n[0],n[1]]),m={};for(let[p,f]of Object.entries(Rt)){let h=[];for(let v=0;vg.part===f[v]),u=d.find(g=>g.part===f[v+1]);T&&u&&h.push([T.position,u.position])}m[p]=h}return{id:0,score:Math.trunc(100*s)/100,box:l.box,boxRaw:l.boxRaw,keypoints:d,annotations:m}}async function kt(e,t){var A,a,i;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>R()-m1,s=Mt<(t.body.skipFrames||0);if(t.skipAllowed&&o&&s&&E2!==null)Mt++;else{let c=[];if((a=(A=t.body)==null?void 0:A.detector)!=null&&a.enabled){let d=u1(e,224);c=await y1(d,t,n),r.dispose(d)}else c=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let d=0;dr.dispose(o[d])),s}async function zt(e,t){if(!(k0!=null&&k0.executor))return[];let n=(t.object.skipTime||0)>R()-T1,o=Et<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&wt.length>0?(Et++,wt):(Et=0,new Promise(async s=>{let A=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[we,we]),i=t.object.enabled?k0==null?void 0:k0.execute(a,["tower_0/detections"]):null;T1=R(),r.dispose(a);let c=await as(i,A,t);wt=c,s(c)}))}var z2={};oe(z2,{connected:()=>jt,kpt:()=>St});var St=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var _,M1=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},It=Number.MAX_SAFE_INTEGER;async function P1(e){return M.initial&&(_=null),_?e.debug&&b("cached model:",_.modelUrl):_=await L(e.body.modelPath),_}async function 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x=0;x(t.body.minConfidence||0)&&v0.keypoints.push({score:Math.round(100*h)/100,part:St[x],positionRaw:[p/_.inputs[0].shape[2],f/_.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/_.inputs[0].shape[2]),Math.round(e.shape[1]*f/_.inputs[0].shape[1])]})}m.forEach(x=>r.dispose(x))}v0.score=v0.keypoints.reduce((l,m)=>m.score>l?m.score:l,0);let i=v0.keypoints.map(l=>l.position[0]),c=v0.keypoints.map(l=>l.position[1]);v0.box=[Math.min(...i),Math.min(...c),Math.max(...i)-Math.min(...i),Math.max(...c)-Math.min(...c)];let d=v0.keypoints.map(l=>l.positionRaw[0]),y=v0.keypoints.map(l=>l.positionRaw[1]);v0.boxRaw=[Math.min(...d),Math.min(...y),Math.max(...d)-Math.min(...d),Math.max(...y)-Math.min(...y)];for(let[l,m]of Object.entries(jt)){let x=[];for(let p=0;pv.part===m[p]),h=v0.keypoints.find(v=>v.part===m[p+1]);f&&h&&f.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&x.push([f.position,h.position])}v0.annotations[l]=x}s([v0])}))}var 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o=t.shape[1],s=t.shape[2],A=[e.startPoint[1]/o,e.startPoint[0]/s,e.endPoint[1]/o,e.endPoint[0]/s],a=r.image.cropAndResize(t,[A],[0],n),i=r.div(a,O.tf255);return r.dispose(a),i},N2=(e,t)=>{let n=S2(e),o=Fe(e),s=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-s[0],n[1]-s[1]],endPoint:[n[0]+s[0],n[1]+s[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},L2=e=>{let t=S2(e),n=Fe(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},S1=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},Ot=[[1,0,0],[0,1,0],[0,0,1]],ls=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),cs=(e,t)=>ls(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var w1=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ee=(e,t)=>{let n=0;for(let o=0;o{let n=[];for(let o=0;o{let n=[],o=e.length;for(let s=0;s{let n=Math.cos(e),o=Math.sin(e),s=[[n,-o,0],[o,n,0],[0,0,1]],A=w1(t[0],t[1]),a=E1(A,s),i=w1(-t[0],-t[1]);return E1(a,i)},xs=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Ee(t[0],n),-Ee(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},ys=(e,t)=>[Ee(e,t[0]),Ee(e,t[1])];function I1(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[A[0]/s*(x[0]-s/2),A[1]/s*(x[1]-s/2),x[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,c=i?j1(n,[0,0]):Ot,d=i?a.map(x=>[...ys(x,c),x[2]]):a,y=i?xs(o):Ot,l=S2(t),m=[Ee(l,y[0]),Ee(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2]||0)])}function L1(e,t,n,o){let s=t.landmarks.length>=pt.count?pt.symmetryLine:ve.symmetryLine,A=0,a=Ot,i;if(e&&M.kernels.includes("rotatewithoffset"))if(A=cs(t.landmarks[s[0]],t.landmarks[s[1]]),A&&A!==0&&Math.abs(A)>.2){let d=S2(t),y=[d[0]/n.shape[2],d[1]/n.shape[1]],l=r.image.rotateWithOffset(n,A,0,[y[0],y[1]]);a=j1(-A,d),i=Lt(t,l,[o,o]),r.dispose(l)}else i=Lt(t,n,[o,o]);else i=Lt(t,n,[o,o]);return[A,a,i]}var fs=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},O1=(e,t)=>{let n=fs(e),o=Fe(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var C1=6,ms=1.4,G0,O2=null,le=0,Be=null,W1=()=>le;async function D1(e){var t;return M.initial&&(G0=null),G0?e.debug&&b("cached model:",G0.modelUrl):G0=await L((t=e.face.detector)==null?void 0:t.modelPath),le=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=r.scalar(le,"int32"),O2=r.tensor2d(I1(le)),G0}function ps(e){if(!O2||!Be)return r.zeros([0,0]);let t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,O2),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,Be),t.centersNormalized=r.div(t.centers,Be),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,Be),t.endNormalized=r.mul(t.ends,Be);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function F1(e,t){var i,c,d,y,l,m;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[le,le]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let x=o.sort((p,f)=>p.size-f.size);n.concat384=r.concat([x[0],x[2]],2),n.concat512=r.concat([x[1],x[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=r.squeeze(o[0]):n.batch=r.squeeze(o);r.dispose(o),n.boxes=ps(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let x=0;x(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let f={};f.bbox=r.slice(n.boxes,[s[x],0],[1,-1]),f.slice=r.slice(n.batch,[s[x],C1-1],[1,-1]),f.squeeze=r.squeeze(f.slice),f.landmarks=r.reshape(f.squeeze,[C1,-1]);let h=await f.bbox.data(),v={startPoint:[h[0],h[1]],endPoint:[h[2],h[3]],landmarks:await f.landmarks.array(),confidence:p};f.anchor=r.slice(O2,[s[x],0],[1,2]);let T=await f.anchor.data(),u=z1(v,[(e.shape[2]||0)/le,(e.shape[1]||0)/le],T),g=N2(u,t.face.scale||ms),w=L2(g);w.size[0]>(((l=t.face.detector)==null?void 0:l.minSize)||0)&&w.size[1]>(((m=t.face.detector)==null?void 0:m.minSize)||0)&&A.push(w),Object.keys(f).forEach(k=>r.dispose(f[k]))}}return Object.keys(n).forEach(x=>r.dispose(n[x])),A}var z0,ce=0,us=2.3,Wt=F0.leftEyeLower0,Dt=F0.rightEyeLower0,He={leftBounds:[Wt[0],Wt[Wt.length-1]],rightBounds:[Dt[0],Dt[Dt.length-1]]},Ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Z1(e){var t,n;return M.initial&&(z0=null),z0?e.debug&&b("cached model:",z0.modelUrl):z0=await L((t=e.face.iris)==null?void 0:t.modelPath),ce=z0!=null&&z0.executor&&((n=z0.inputs)!=null&&n[0].shape)?z0.inputs[0].shape[2]:0,ce===-1&&(ce=64),z0}function C2(e,t,n,o){for(let s=0;s{let t=e[He.leftBounds[0]][2],n=e[He.rightBounds[0]][2];return t-n},H1=(e,t,n,o,s,A=!1)=>{let a=L2(N2(S1([e[n],e[o]]),us)),i=Fe(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[ce,ce]);if(A&&M.kernels.includes("flipleftright")){let d=r.image.flipLeftRight(c);r.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},G1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A{let o=e[F0[`${n}EyeUpper0`][Ge.upperCenter]][2],s=e[F0[`${n}EyeLower0`][Ge.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function X1(e,t,n){if(!(z0!=null&&z0.executor))return e;let{box:o,boxSize:s,crop:A}=H1(e,t,He.leftBounds[0],He.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=H1(e,t,He.rightBounds[0],He.rightBounds[1],n,!0),d=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=z0.execute(d);r.dispose(d);let l=await y.data();r.dispose(y);let m=l.slice(0,Ge.numCoordinates*3),{rawCoords:x,iris:p}=G1(m,o,s,!0),f=l.slice(Ge.numCoordinates*3),{rawCoords:h,iris:v}=G1(f,a,i,!1),T=hs(e);Math.abs(T)<30?(C2(e,x,"left",null),C2(e,h,"right",null)):T<1?C2(e,x,"left",["EyeUpper0","EyeLower0"]):C2(e,h,"right",["EyeUpper0","EyeLower0"]);let u=V1(e,p,"left"),g=V1(e,v,"right");return e.concat(u).concat(g)}async function U1(e,t){var A,a,i,c,d,y,l,m,x,p;let n={lips:await((a=(A=t.filter(f=>f.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(f=>f.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(f=>f.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((m=(l=t.filter(f=>f.size===10))==null?void 0:l[1])==null?void 0:m.data()),eyeR:await((p=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:p.data())};for(let f of Object.values(n))if(!f)return e;let o=Me.reduce((f,h)=>f+=e[h][2],0)/Me.length;for(let f=0;ff+=e[h][2],0)/Pe.length;for(let f=0;fR()-K0.timestamp,o=K0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||K0.boxes.length===0?(K0.boxes=await F1(e,t),K0.timestamp=R(),K0.skipped=0):K0.skipped++;let s=[],A=[],a=0,i=o2;for(let T=0;T[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/i]);for(let N of Object.keys(ve))k.annotations[N]=[k.mesh[ve[N]]]}else if(!K)t.debug&&b("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!M.kernels.includes("atan2"))return t.face.attention.enabled=!1,r.dispose(k.tensor),s;let N=K.execute(k.tensor),V=await N.find(D=>D.shape[D.shape.length-1]===1).data();if(k.faceScore=Math.round(100*V[0])/100,k.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(u.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=j2(u,e),k.boxRaw=I2(u,e),k.size=u.size,k.score=k.boxScore,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(D=>[D[0]/(e.shape[2]||1),D[1]/(e.shape[1]||1),(D[2]||0)/i]);for(let D of Object.keys(ve))k.annotations[D]=[k.mesh[ve[D]]]}}else{let D=N.find(U=>U.shape[U.shape.length-1]===1404),Z=r.reshape(D,[-1,3]),J=await Z.array();r.dispose(Z),(f=t.face.attention)!=null&&f.enabled?J=await U1(J,N):(h=t.face.iris)!=null&&h.enabled&&(J=await X1(J,k.tensor,o2)),k.mesh=N1(J,u,g,w,o2),k.meshRaw=k.mesh.map(U=>[U[0]/(e.shape[2]||0),U[1]/(e.shape[1]||0),(U[2]||0)/i]);for(let U of Object.keys(F0))k.annotations[U]=F0[U].map(m0=>k.mesh[m0]);k.score=k.faceScore;let q={...O1(k.mesh,u),confidence:u.confidence,landmarks:u.landmarks,size:u.size};k.box=j2(q,e),k.boxRaw=I2(q,e),k.size=q.size,A.push(q)}r.dispose(N)}k.score>(((v=t.face.detector)==null?void 0:v.minConfidence)||1)?s.push(k):r.dispose(k.tensor)}return K0.boxes=A,s}async function K1(e){var t,n,o,s,A,a;return M.initial&&(K=null),(t=e.face.attention)!=null&&t.enabled&&(K!=null&&K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&b("cached model:",K.modelUrl):(o=e.face.attention)!=null&&o.enabled?K=await L(e.face.attention.modelPath):K=await L((s=e.face.mesh)==null?void 0:s.modelPath),o2=K.executor&&((A=K==null?void 0:K.inputs)!=null&&A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var J1=Re,Q1=t2;var Ht=[],d0,W2=[],_1=0,$1=0,Bt=Number.MAX_SAFE_INTEGER,Gt=!1;async function e3(e){var t,n,o;return M.initial&&(d0=null),d0?e.debug&&b("cached model:",d0.modelUrl):(d0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Gt=((o=(n=d0==null?void 0:d0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,Gt?Ht=["angry","disgust","fear","happy","neutral","sad","surprise"]:Ht=["angry","disgust","fear","happy","sad","surprise","neutral"]),d0}async function Vt(e,t,n,o){var a,i;if(!d0)return[];let s=Bt<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>R()-$1;return t.skipAllowed&&A&&s&&_1===o&&W2[n]&&W2[n].length>0?(Bt++,W2[n]):(Bt=0,new Promise(async c=>{var y,l,m;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let x={},p=d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0;if(((l=t.face.emotion)==null?void 0:l.crop)>0){let h=(m=t.face.emotion)==null?void 0:m.crop,v=[[h,h,1-h,1-h]];x.resize=r.image.cropAndResize(e,v,[0],[p,p])}else x.resize=r.image.resizeBilinear(e,[p,p],!1);Gt?(x.mul=r.mul(x.resize,255),x.normalize=r.sub(x.mul,[103.939,116.779,123.68]),x.emotion=d0==null?void 0:d0.execute(x.normalize)):(x.channels=r.mul(x.resize,O.rgb),x.grayscale=r.sum(x.channels,3,!0),x.grayscaleSub=r.sub(x.grayscale,O.tf05),x.grayscaleMul=r.mul(x.grayscaleSub,O.tf2),x.emotion=d0==null?void 0:d0.execute(x.grayscaleMul)),$1=R();let f=await x.emotion.data();for(let h=0;h(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*f[h])/100),emotion:Ht[h]});d.sort((h,v)=>v.score-h.score),Object.keys(x).forEach(h=>r.dispose(x[h]))}W2[n]=d,_1=o,c(d)}))}var x0,de=[],n3=0,o3=0,Zt=Number.MAX_SAFE_INTEGER;async function r3(e){var t;return M.initial&&(x0=null),x0?e.debug&&b("cached model:",x0.modelUrl):x0=await L((t=e.face.description)==null?void 0:t.modelPath),x0}function gs(e,t){var A,a;let n=e.image||e.tensor||e;if(!(x0!=null&&x0.inputs[0].shape))return n;let o;if(((A=t.face.description)==null?void 0:A.crop)>0){let i=(a=t.face.description)==null?void 0:a.crop,c=[[i,i,1-i,1-i]];o=r.image.cropAndResize(n,c,[0],[x0.inputs[0].shape[2],x0.inputs[0].shape[1]])}else o=r.image.resizeBilinear(n,[x0.inputs[0].shape[2],x0.inputs[0].shape[1]],!1);let s=r.mul(o,O.tf255);return r.dispose(o),s}async function Xt(e,t,n,o){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(x0!=null&&x0.executor))return s;let A=Zt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>R()-n3;return t.skipAllowed&&A&&a&&o3===o&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((y=de==null?void 0:de[n])==null?void 0:y.genderScore)>0?(Zt++,de[n]):(Zt=0,new 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t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),s=[];for(let a of F0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ve&&Ve>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ve:a.x-Ve,y:a.y>.5?a.y+Ve:a.y-Ve})));for(let a=0;aR()-i3,A=Ut<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&a3===o&&D2[n]?(Ut++,D2[n]):(Ut=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[2]:0,y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[1]:0],!1),y=y0==null?void 0:y0.execute(d),l=(await y.data())[0];D2[n]=Math.round(100*l)/100,a3=o,i3=R(),r.dispose([d,y]),c(D2[n])}))}var f0,F2=[],Kt=Number.MAX_SAFE_INTEGER,d3=0,x3=0;async function y3(e){var t;return M.initial&&(f0=null),f0?e.debug&&b("cached model:",f0.modelUrl):f0=await L((t=e.face.liveness)==null?void 0:t.modelPath),f0}async function Jt(e,t,n,o){var a,i;if(!(f0!=null&&f0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>R()-x3,A=Kt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&d3===o&&F2[n]?(Kt++,F2[n]):(Kt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[2]:0,f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[1]:0],!1),y=f0==null?void 0:f0.execute(d),l=(await y.data())[0];F2[n]=Math.round(100*l)/100,d3=o,x3=R(),r.dispose([d,y]),c(F2[n])}))}var B0,Qt=[],Rs=["white","black","asian","indian","other"],Ms=[15,23,28,35.5,45.5,55.5,65],m3=0,p3=0,_t=Number.MAX_SAFE_INTEGER;async function u3(e){var t;return M.initial&&(B0=null),B0?e.debug&&b("cached model:",B0.modelUrl):B0=await L((t=e.face.gear)==null?void 0:t.modelPath),B0}async function $t(e,t,n,o){var a,i;if(!B0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=_t<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>R()-p3;return t.skipAllowed&&A&&s&&m3===o&&Qt[n]?(_t++,Qt[n]):(_t=0,new Promise(async c=>{var v,T,u,g;if(!(B0!=null&&B0.inputs[0].shape))return;let d={},y=[[0,.1,.9,.9]];if(((v=t.face.gear)==null?void 0:v.crop)>0){let w=(T=t.face.gear)==null?void 0:T.crop;y=[[w,w,1-w,1-w]]}d.resize=r.image.cropAndResize(e,y,[0],[B0.inputs[0].shape[2],B0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(u=t.face.gear)!=null&&u.enabled&&([d.age,d.gender,d.race]=B0.execute(d.resize,["age_output","gender_output","race_output"]));let m=await d.gender.data();l.gender=m[0]>m[1]?"male":"female",l.genderScore=Math.round(100*(m[0]>m[1]?m[0]:m[1]))/100;let x=await d.race.data();for(let w=0;w(((g=t.face.gear)==null?void 0:g.minConfidence)||.2)&&l.race.push({score:Math.round(100*x[w])/100,race:Rs[w]});l.race.sort((w,k)=>k.score-w.score);let f=Array.from(await d.age.data()).map((w,k)=>[Ms[k],w]).sort((w,k)=>k[1]-w[1]),h=f[0][0];for(let w=1;wr.dispose(d[w])),Qt[n]=l,m3=o,p3=R(),c(l)}))}var R0,B2=[],b3=0,g3=0,e5=Number.MAX_SAFE_INTEGER;async function T3(e){return M.initial&&(R0=null),R0?e.debug&&b("cached model:",R0.modelUrl):R0=await L(e.face.ssrnet.modelPathAge),R0}async function t5(e,t,n,o){var a,i,c,d;if(!R0)return{age:0};let s=e5<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>R()-g3;return t.skipAllowed&&s&&A&&b3===o&&((c=B2[n])!=null&&c.age)&&((d=B2[n])==null?void 0:d.age)>0?(e5++,B2[n]):(e5=0,new Promise(async y=>{var x,p,f;if(!(R0!=null&&R0.inputs)||!R0.inputs[0]||!R0.inputs[0].shape)return;let l={};if(((x=t.face.ssrnet)==null?void 0:x.crop)>0){let h=(p=t.face.ssrnet)==null?void 0:p.crop,v=[[h,h,1-h,1-h]];l.resize=r.image.cropAndResize(e,v,[0],[R0.inputs[0].shape[2],R0.inputs[0].shape[1]])}else l.resize=r.image.resizeBilinear(e,[R0.inputs[0].shape[2],R0.inputs[0].shape[1]],!1);l.enhance=r.mul(l.resize,O.tf255);let m={age:0};if((f=t.face.ssrnet)!=null&&f.enabled&&(l.age=R0.execute(l.enhance)),l.age){let h=await l.age.data();m.age=Math.trunc(10*h[0])/10}Object.keys(l).forEach(h=>r.dispose(l[h])),B2[n]=m,b3=o,g3=R(),y(m)}))}var u0,H2=[],R3=0,M3=0,n5=Number.MAX_SAFE_INTEGER,o5=[.2989,.587,.114];async function P3(e){var t;return M.initial&&(u0=null),u0?e.debug&&b("cached model:",u0.modelUrl):u0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),u0}async function r5(e,t,n,o){var a,i,c,d;if(!u0)return{gender:"unknown",genderScore:0};let s=n5<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>R()-M3;return t.skipAllowed&&s&&A&&R3===o&&((c=H2[n])!=null&&c.gender)&&((d=H2[n])==null?void 0:d.genderScore)>0?(n5++,H2[n]):(n5=0,new Promise(async y=>{var p,f,h;if(!(u0!=null&&u0.inputs[0].shape))return;let l={};if(((p=t.face.ssrnet)==null?void 0:p.crop)>0){let v=(f=t.face.ssrnet)==null?void 0:f.crop,T=[[v,v,1-v,1-v]];l.resize=r.image.cropAndResize(e,T,[0],[u0.inputs[0].shape[2],u0.inputs[0].shape[1]])}else l.resize=r.image.resizeBilinear(e,[u0.inputs[0].shape[2],u0.inputs[0].shape[1]],!1);l.enhance=r.tidy(()=>{var T,u;let v;if(((u=(T=u0==null?void 0:u0.inputs)==null?void 0:T[0].shape)==null?void 0:u[3])===1){let[g,w,k]=r.split(l.resize,3,3),N=r.mul(g,o5[0]),W=r.mul(w,o5[1]),V=r.mul(k,o5[2]),D=r.addN([N,W,V]);v=r.mul(r.sub(D,O.tf05),2)}else v=r.mul(r.sub(l.resize,O.tf05),2);return v});let m={gender:"unknown",genderScore:0};(h=t.face.ssrnet)!=null&&h.enabled&&(l.gender=u0.execute(l.enhance));let x=await l.gender.data();m.gender=x[0]>x[1]?"female":"male",m.genderScore=x[0]>x[1]?Math.trunc(100*x[0])/100:Math.trunc(100*x[1])/100,Object.keys(l).forEach(v=>r.dispose(l[v])),H2[n]=m,R3=o,M3=R(),y(m)}))}var S0,s5=[],w3=0,E3=0,z3=Number.MAX_SAFE_INTEGER;async function S3(e){var t;return M.initial&&(S0=null),S0?e.debug&&b("cached model:",S0.modelUrl):S0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),S0}async function A5(e,t,n,o){var a,i;if(!(S0!=null&&S0.executor))return[];let s=z3<(((a=t.face.mobilefacenet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.mobilefacenet)==null?void 0:i.skipTime)||0)>R()-E3;return t.skipAllowed&&A&&s&&w3===o&&s5[n]?(z3++,s5[n]):new Promise(async c=>{var y;let d=[];if((y=t.face.mobilefacenet)!=null&&y.enabled&&(S0!=null&&S0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[S0.inputs[0].shape[2],S0.inputs[0].shape[1]],!1),l.data=S0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}s5[n]=d,w3=o,E3=R(),c(d)})}var j0,a5=[],I3=0,N3=0,L3=Number.MAX_SAFE_INTEGER;async function O3(e){return M.initial&&(j0=null),j0?e.debug&&b("cached model:",j0.modelUrl):j0=await L(e.face.insightface.modelPath),j0}async function i5(e,t,n,o){var a,i;if(!(j0!=null&&j0.executor))return[];let s=L3<(((a=t.face.insightface)==null?void 0:a.skipFrames)||0),A=(((i=t.face.insightface)==null?void 0:i.skipTime)||0)>R()-N3;return t.skipAllowed&&A&&s&&I3===o&&a5[n]?(L3++,a5[n]):new Promise(async c=>{var y;let d=[];if((y=t.face.insightface)!=null&&y.enabled&&(j0!=null&&j0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),l.data=j0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}a5[n]=d,I3=o,N3=R(),c(d)})}var Ps=e=>{let t=(l,m)=>Math.atan2(l[1]-m[1],l[0]-m[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,s=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=s?e.mesh[473]:e.mesh[468],a=s?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=s?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],c=[(a[0]-A[0])/i[0]-n[0],o*(A[1]-a[1])/i[1]-n[1]],d=Math.sqrt(c[0]*c[0]+c[1]*c[1]);return 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m=[y[0],y[1],y[2],d[0],d[1],d[2],l[0],l[1],l[2]],x=A(m),p=a.length===478?Ps(e):{bearing:0,strength:0};return{angle:x,matrix:m,gaze:p}};function D3(e,t){let n=e==null?void 0:e.annotations;if(!(n!=null&&n.leftEyeIris)||!(n!=null&&n.rightEyeIris))return 0;let o=Math.max(Math.abs(n.leftEyeIris[3][0]-n.leftEyeIris[1][0]),Math.abs(n.rightEyeIris[3][0]-n.rightEyeIris[1][0]))/t;return Math.round(1.17/o)/100}var l5=async(e,t)=>{var p,f,h,v,T,u,g,w,k,N,W,V,D,Z,J,q,U,m0,P,i0,g0,e0,G;let n=R(),o,s,A,a,i,c,d,y,l,m=[];e.state="run:face";let x=await Y1(t,e.config);if(e.performance.face=M.perfadd?(e.performance.face||0)+Math.trunc(R()-n):Math.trunc(R()-n),!t.shape||t.shape.length!==4)return[];if(!x)return[];for(let S=0;S200?W3(x[S],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?a=(f=e.config.face.emotion)!=null&&f.enabled?Vt(x[S].tensor||r.tensor([]),e.config,S,x.length):[]:(e.state="run:emotion",n=R(),a=(h=e.config.face.emotion)!=null&&h.enabled?await 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n=t.map(a=>a[0]),o=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...o)],A=[Math.max(...n),Math.max(...o)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let o=t.map(A=>y5([...A,1],n)),s=this.calculateLandmarksBoundingBox(o);return Z2(X2(s),Fs)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Z2(X2(n),sn);o.palmLandmarks=[];for(let s=0;s[a[0]*(x[0]-this.inputSize/2),a[1]*(x[1]-this.inputSize/2),a[2]*x[2]]),c=x5(o,[0,0]),d=i.map(x=>[...y5(x,c),x[2]]),y=nn(s),l=[...r2(n),1],m=[ue(l,y[0]),ue(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,s,A=(n.hand.skipTime||0)>R()-an,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a?this.skipped++:(s=await this.handDetector.predict(t,n),this.skipped=0),s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c=n.hand.minConfidence/4){let w=r.reshape(u,[-1,3]),k=await w.array();r.dispose(u),r.dispose(w);let N=this.transformRawCoords(k,f,y,p),W=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...W,confidence:g};let V={landmarks:N,confidence:g,boxConfidence:d.confidence,fingerConfidence:g,box:{topLeft:W.startPoint,bottomRight:W.endPoint}};i.push(V)}else this.storedBoxes[c]=null;r.dispose(u)}else{let y=Z2(X2(d),sn),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var ln={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Ie,Ne,f5;function Vs(){let e=Ie?new q2(Ie):void 0;e&&Ne&&(f5=new U2(e,Ne))}async function m5(e,t){f5||Vs();let n=await f5.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function cn(e){var t;return M.initial&&(Ie=null),Ie?e.debug&&b("cached model:",Ie.modelUrl):Ie=await L((t=e.hand.detector)==null?void 0:t.modelPath),Ie}async function dn(e){var t;return M.initial&&(Ne=null),Ne?e.debug&&b("cached model:",Ne.modelUrl):Ne=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),Ne}var r0=[null,null],Zs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],Xs=["hand","fist","pinch","point","face","tip","pinchtip"],yn=4,fn=1.6,qs=512,Us=1.4,Y2=Number.MAX_SAFE_INTEGER,p5=0,ee=[0,0],o0={boxes:[],hands:[]},mn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function pn(e){var t;if(M.initial&&(r0[0]=null),r0[0])e.debug&&b("cached model:",r0[0].modelUrl);else{h2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function un(e){var t;if(M.initial&&(r0[1]=null),r0[1])e.debug&&b("cached model:",r0[1].modelUrl);else{r0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function Ys(e,t){let n=[];if(!e||!r0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,qs),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await r0[0].executeAsync(o.cast,Zs),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[yn]),i.splice(yn,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let m of Array.from(d)){let x=r.slice(o.boxes,m,1),p=await x.data();r.dispose(x);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],h=k2(f,Us),v=[Math.trunc(f[0]*ee[0]),Math.trunc(f[1]*ee[1]),Math.trunc(f[2]*ee[0]),Math.trunc(f[3]*ee[1])],T=y[m],u=Xs[l[m]],g={id:c++,score:T,box:v,boxRaw:h,label:u};n.push(g)}return Object.keys(o).forEach(m=>r.dispose(o[m])),n.sort((m,x)=>x.score-m.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function u5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[ee[0]*(l[0]+t.boxRaw[0]),ee[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=G2(o.keypoints);for(let l of Object.keys(mn))o.annotations[l]=mn[l].map(m=>o.landmarks&&o.keypoints[m]?o.keypoints[m]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function h5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];ee=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>R()-p5,o=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>R()-p5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))):(o0.boxes=await Ys(e,t),p5=R(),o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[y].fingerScore&&o0.hands[y].fingerScore>(t.hand.minConfidence||0)){let m=k2(l.box,fn),x=k2(l.boxRaw,fn);o0.boxes.push({...d[y],box:m,boxRaw:x})}}for(let y=0;y({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var s2={};oe(s2,{connected:()=>J2,horizontal:()=>b5,kpt:()=>K2,relative:()=>T5,vertical:()=>g5});var K2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],b5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],g5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],T5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var j=te(),v5=0;function bn(e,t){var a,i,c,d,y,l,m,x,p,f,h,v,T,u,g,w,k,N,W,V,D,Z,J,q,U,m0;let n=R();if(!e)return te();let o=Date.now()-e.timestamp,s=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P((s-1)*j.body[P].box[F]+C)/s),g0=e.body[P].boxRaw.map((C,F)=>((s-1)*j.body[P].boxRaw[F]+C)/s),e0=e.body[P].keypoints.map((C,F)=>{var w0,t0,ne,Ke,Oe,B5,H5,G5,V5;return{score:C.score,part:C.part,position:[j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].position[0]||0)+(C.position[0]||0))/s:C.position[0],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].position[1]||0)+(C.position[1]||0))/s:C.position[1],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].position[2]||0)+(C.position[2]||0))/s:C.position[2]],positionRaw:[j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].positionRaw[0]||0)+(C.positionRaw[0]||0))/s:C.positionRaw[0],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].positionRaw[1]||0)+(C.positionRaw[1]||0))/s:C.positionRaw[1],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].positionRaw[2]||0)+(C.positionRaw[2]||0))/s:C.positionRaw[2]],distance:[j.body[P].keypoints[F]?((s-1)*(((w0=j.body[P].keypoints[F].distance)==null?void 0:w0[0])||0)+(((t0=C.distance)==null?void 0:t0[0])||0))/s:(ne=C.distance)==null?void 0:ne[0],j.body[P].keypoints[F]?((s-1)*(((Ke=j.body[P].keypoints[F].distance)==null?void 0:Ke[1])||0)+(((Oe=C.distance)==null?void 0:Oe[1])||0))/s:(B5=C.distance)==null?void 0:B5[1],j.body[P].keypoints[F]?((s-1)*(((H5=j.body[P].keypoints[F].distance)==null?void 0:H5[2])||0)+(((G5=C.distance)==null?void 0:G5[2])||0))/s:(V5=C.distance)==null?void 0:V5[2]]}}),G={},S={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?S=z2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?S=M2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(S=s2);for(let[C,F]of Object.entries(S.connected)){let w0=[];for(let t0=0;t0Oe.part===F[t0]),Ke=e0.find(Oe=>Oe.part===F[t0+1]);ne&&Ke&&w0.push([ne.position,Ke.position])}G[C]=w0}j.body[P]={...e.body[P],box:i0,boxRaw:g0,keypoints:e0,annotations:G}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P((s-1)*j.hand[P].box[C]+S)/s),g0=e.hand[P].boxRaw.map((S,C)=>((s-1)*j.hand[P].boxRaw[C]+S)/s);j.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(j.hand[P].keypoints=e.hand[P].keypoints);let e0=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((S,C)=>S.map((F,w0)=>((s-1)*(j.hand[P].keypoints[C][w0]||1)+(F||0))/s)):[],G={};if(Object.keys(j.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)j.hand[P].annotations=e.hand[P].annotations,G=j.hand[P].annotations;else if(e.hand[P].annotations)for(let S of Object.keys(e.hand[P].annotations))G[S]=(l=(y=(d=e.hand[P])==null?void 0:d.annotations)==null?void 0:y[S])!=null&&l[0]?e.hand[P].annotations[S].map((C,F)=>C.map((w0,t0)=>((s-1)*j.hand[P].annotations[S][F][t0]+w0)/s)):null;j.hand[P]={...e.hand[P],box:i0,boxRaw:g0,keypoints:e0,annotations:G}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P((s-1)*j.face[P].box[S]+G)/s),g0=e.face[P].boxRaw.map((G,S)=>((s-1)*j.face[P].boxRaw[S]+G)/s),e0=e.face[P].annotations;if(Object.keys(j.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)j.face[P].annotations=e.face[P].annotations,e0=j.face[P].annotations;else if(e.face[P].annotations)for(let G of Object.keys(e.face[P].annotations))e0[G]=(p=(x=(m=e.face[P])==null?void 0:m.annotations)==null?void 0:x[G])!=null&&p[0]?e.face[P].annotations[G].map((S,C)=>S.map((F,w0)=>((s-1)*j.face[P].annotations[G][C][w0]+F)/s)):null;if(e.face[P].rotation){let G={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};G.matrix=(f=e.face[P].rotation)==null?void 0:f.matrix,G.angle={roll:((s-1)*(((v=(h=j.face[P].rotation)==null?void 0:h.angle)==null?void 0:v.roll)||0)+(((u=(T=e.face[P].rotation)==null?void 0:T.angle)==null?void 0:u.roll)||0))/s,yaw:((s-1)*(((w=(g=j.face[P].rotation)==null?void 0:g.angle)==null?void 0:w.yaw)||0)+(((N=(k=e.face[P].rotation)==null?void 0:k.angle)==null?void 0:N.yaw)||0))/s,pitch:((s-1)*(((V=(W=j.face[P].rotation)==null?void 0:W.angle)==null?void 0:V.pitch)||0)+(((Z=(D=e.face[P].rotation)==null?void 0:D.angle)==null?void 0:Z.pitch)||0))/s},G.gaze={bearing:((s-1)*(((J=j.face[P].rotation)==null?void 0:J.gaze.bearing)||0)+(((q=e.face[P].rotation)==null?void 0:q.gaze.bearing)||0))/s,strength:((s-1)*(((U=j.face[P].rotation)==null?void 0:U.gaze.strength)||0)+(((m0=e.face[P].rotation)==null?void 0:m0.gaze.strength)||0))/s},j.face[P]={...e.face[P],rotation:G,box:i0,boxRaw:g0,annotations:e0}}else j.face[P]={...e.face[P],box:i0,boxRaw:g0,annotations:e0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let P=0;P((s-1)*j.object[P].box[G]+e0)/s),g0=e.object[P].boxRaw.map((e0,G)=>((s-1)*j.object[P].boxRaw[G]+e0)/s);j.object[P]={...e.object[P],box:i0,boxRaw:g0}}if(e.persons){let P=e.persons;if(!j.persons||P.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(P));else for(let i0=0;i0((s-1)*j.persons[i0].box[e0]+g0)/s)}e.gesture&&(j.gesture=e.gesture),j.width=e.width,j.height=e.height;let A=R();return v5=M.perfadd?v5+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:v5}),j}var h0;async function R5(e){return!h0||M.initial?h0=await L(e.segmentation.modelPath):e.debug&&b("cached model:",h0.modelUrl),h0}async function gn(e,t){var s;if(h0||(h0=await R5(t)),!(h0!=null&&h0.executor)||!((s=h0==null?void 0:h0.inputs)!=null&&s[0].shape))return null;let 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0:i[0])!=null&&c.shape)?[...m.inputs[0].shape]:[1,64,64,3],p=m!=null&&m.modelSignature&&((y=(d=m==null?void 0:m.inputs)==null?void 0:d[0])!=null&&y.dtype)?m.inputs[0].dtype:"float32";for(let h=0;hr.dispose(v)):r.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",l)}r.dispose(f)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:s,kernels:A.length}),r.env().set("ENGINE_COMPILE_ONLY",!1);let a=r.engine().state.numTensors;a-o>0&&b("tensor leak:",a-o)}async function Un(e,t){await e2(e,!1);let n=R();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?te():new Promise(async o=>{await e.models.load(),await hA(e);let s=await uA(e),A=R();e.config.debug&&b("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(s)})}var Ye,l2,c2,At,be,Yn=class{constructor(t){z(this,"version");z(this,"config");z(this,"result");z(this,"state");z(this,"process");z(this,"tf");z(this,"env",P);z(this,"draw",Tt);z(this,"match",M5);z(this,"models");z(this,"events");z(this,"faceTriangulation");z(this,"faceUVMap");z(this,"performance");q0(this,Ye,void 0);q0(this,l2,void 0);q0(this,c2,void 0);z(this,"analyze",(...t)=>{if(!E0(this,l2))return;let n=this.tf.engine().state.numTensors,o=E0(this,Ye);Q0(this,Ye,n);let s=n-o;s!==0&&b(...t,s)});q0(this,At,t=>{if(!E0(this,c2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});z(this,"webcam",new u2);z(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Je.tfjs||r.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=P.browser?"../models/":"file://models/",this.version=yt,Object.defineProperty(this,"version",{value:yt}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),n1(this.config),this.tf=r,this.state="idle",Q0(this,Ye,0),Q0(this,l2,!1),Q0(this,c2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new i2(this),gt(),this.result=te(),this.process={tensor:null,canvas:null},this.faceTriangulation=K1,this.faceUVMap=J1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ce)),this.config.backend=t,dt(),P.initial=!0}validate(t){let n=it(Ce,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return R()}image(t,n=!1){return f2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let o=await f2(t,this.config);if(!o.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Hn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await bn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Vn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return t1(this.config,t,n)}async init(){await e2(this,!0),await this.tf.ready(),dt()}async load(t){this.state="load";let n=R(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await e2(this,!1)||b("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(R()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return hn(t,this.config)}async warmup(t){let n=R(),o=await Un(this,t),s=R();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var h,v,T,u,g,w,k,N,W,V,D,Z,J,U,q,m0,M,i0,g0,e0,G;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=E0(this,At).call(this,t);A&&(b(A,t),this.emit("error"),o(te(A)));let a=R();await this.load(),s=R(),this.state="image";let i=await f2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),o(te("could not convert input to tensor"));return}this.emit("image"),s=R(),this.config.skipAllowed=await e1(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?l5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=R(),c=this.config.face.enabled?await l5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let m=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?d=this.config.body.enabled?L5(i.tensor,m):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?d=this.config.body.enabled?kt(i.tensor,m):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?d=this.config.body.enabled?Nt(i.tensor,m):[]:(u=this.config.body.modelPath)!=null&&u.includes("movenet")&&(d=this.config.body.enabled?w5(i.tensor,m):[]),this.performance.body&&delete this.performance.body):(s=R(),(g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?await L5(i.tensor,m):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?d=this.config.body.enabled?await kt(i.tensor,m):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await Nt(i.tensor,m):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(d=this.config.body.enabled?await w5(i.tensor,m):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((V=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&V.includes("handdetect")?y=this.config.hand.enabled?f5(i.tensor,x):[]:(Z=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&Z.includes("handtrack")&&(y=this.config.hand.enabled?u5(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(s=R(),(U=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&U.includes("handdetect")?y=this.config.hand.enabled?await f5(i.tensor,x):[]:(m0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&m0.includes("handtrack")&&(y=this.config.hand.enabled?await u5(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?l=this.config.object.enabled?z5(i.tensor,this.config):[]:(i0=this.config.object.modelPath)!=null&&i0.includes("centernet")&&(l=this.config.object.enabled?zt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=R(),(g0=this.config.object.modelPath)!=null&&g0.includes("nanodet")?l=this.config.object.enabled?await z5(i.tensor,this.config):[]:(e0=this.config.object.modelPath)!=null&&e0.includes("centernet")&&(l=this.config.object.enabled?await zt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(s=R(),p=[...U3(c),...q3(d),...K3(y),...Y3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(R()-a):Math.trunc(R()-a);let f=((G=this.process.tensor)==null?void 0:G.shape)||[0,0,0,0];this.result={face:c,body:d,hand:y,gesture:p,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return qn(c,d,y,p,f)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(E0(this,be)[t.id]||(this.config.debug&&b("video start",t.id),E0(this,be)[t.id]=!0),!t.paused&&E0(this,be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),E0(this,be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&b("video stop",t.id),E0(this,be)[t.id]=!1)}};Ye=new WeakMap,l2=new WeakMap,c2=new WeakMap,At=new WeakMap,be=new WeakMap;export{p2 as Env,Yn as Human,Yn as default,Ce as defaults,Tt as draw,te as empty,P as env,M5 as match,Xn as models}; +2Q==`;async function pA(e){let t=(s,A="application/octet-stream")=>fetch(`data:${A};base64,${s}`).then(a=>a.blob()),n,o;switch(e.config.warmup){case"face":n=await t(rt);break;case"body":case"full":n=await t(st);break;default:n=null}if(n){let s=await createImageBitmap(n);o=await e.detect(s,e.config),s.close()}return o}async function uA(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+rt;break;case"full":case"body":n="data:image/jpeg;base64,"+st;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(M.Image)o=new M.Image;else{t(void 0);return}o.onload=async()=>{let s=D0(o.naturalWidth,o.naturalHeight);if(!s)b("Warmup: Canvas not found"),t(void 0);else{let A=s.getContext("2d");A&&A.drawImage(o,0,0);let a=await e.image(s,!0),i=a.tensor?await e.detect(a.tensor,e.config):void 0;t(i)}},n?o.src=n:t(void 0)})}async function hA(e){let t=s=>Buffer.from(s,"base64"),n;e.config.warmup==="face"?n=t(rt):n=t(st);let o;if("node"in r&&r.getBackend()==="tensorflow"){let s=r.node.decodeJpeg(n),A=r.expandDims(s,0);e.tf.dispose(s),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&b("Warmup tfjs-node not loaded");return o}async function bA(e){let t;return typeof createImageBitmap=="function"?t=await pA(e):typeof Image!="undefined"||M.Canvas!==void 0?t=await uA(e):t=await hA(e),t}async function gA(e){var i,c,d,y;if(!r.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=r.getBackend(),n=r.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;r.env().set("ENGINE_COMPILE_ONLY",!0);let o=r.engine().state.numTensors,s=[];for(let[l,m]of Object.entries(e.models.models)){if(!m)continue;let x=m!=null&&m.modelSignature&&((c=(i=m==null?void 0:m.inputs)==null?void 0:i[0])!=null&&c.shape)?[...m.inputs[0].shape]:[1,64,64,3],p=m!=null&&m.modelSignature&&((y=(d=m==null?void 0:m.inputs)==null?void 0:d[0])!=null&&y.dtype)?m.inputs[0].dtype:"float32";for(let h=0;hr.dispose(v)):r.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",l)}r.dispose(f)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:s,kernels:A.length}),r.env().set("ENGINE_COMPILE_ONLY",!1);let a=r.engine().state.numTensors;a-o>0&&b("tensor leak:",a-o)}async function Yn(e,t){await e2(e,!1);let n=R();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?te():new Promise(async o=>{await e.models.load(),await gA(e);let s=await bA(e),A=R();e.config.debug&&b("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(s)})}var Ye,l2,c2,At,be,Kn=class{constructor(t){z(this,"version");z(this,"config");z(this,"result");z(this,"state");z(this,"process");z(this,"tf");z(this,"env",M);z(this,"draw",Tt);z(this,"match",P5);z(this,"models");z(this,"events");z(this,"faceTriangulation");z(this,"faceUVMap");z(this,"performance");q0(this,Ye,void 0);q0(this,l2,void 0);q0(this,c2,void 0);z(this,"analyze",(...t)=>{if(!E0(this,l2))return;let n=this.tf.engine().state.numTensors,o=E0(this,Ye);Q0(this,Ye,n);let s=n-o;s!==0&&b(...t,s)});q0(this,At,t=>{if(!E0(this,c2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});z(this,"webcam",new u2);z(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Je.tfjs||r.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=M.browser?"../models/":"file://models/",this.version=yt,Object.defineProperty(this,"version",{value:yt}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),o1(this.config),this.tf=r,this.state="idle",Q0(this,Ye,0),Q0(this,l2,!1),Q0(this,c2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new i2(this),gt(),this.result=te(),this.process={tensor:null,canvas:null},this.faceTriangulation=J1,this.faceUVMap=Q1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ce)),this.config.backend=t,dt(),M.initial=!0}validate(t){let n=it(Ce,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return R()}image(t,n=!1){return f2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let o=await f2(t,this.config);if(!o.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Gn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await gn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Zn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return n1(this.config,t,n)}async init(){await e2(this,!0),await this.tf.ready(),dt()}async load(t){this.state="load";let n=R(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await e2(this,!1)||b("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(R()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return bn(t,this.config)}async warmup(t){let n=R(),o=await Yn(this,t),s=R();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var h,v,T,u,g,w,k,N,W,V,D,Z,J,q,U,m0,P,i0,g0,e0,G;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=E0(this,At).call(this,t);A&&(b(A,t),this.emit("error"),o(te(A)));let a=R();await this.load(),s=R(),this.state="image";let i=await f2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),o(te("could not convert input to tensor"));return}this.emit("image"),s=R(),this.config.skipAllowed=await t1(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?l5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=R(),c=this.config.face.enabled?await l5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let m=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?d=this.config.body.enabled?O5(i.tensor,m):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?d=this.config.body.enabled?kt(i.tensor,m):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?d=this.config.body.enabled?Nt(i.tensor,m):[]:(u=this.config.body.modelPath)!=null&&u.includes("movenet")&&(d=this.config.body.enabled?E5(i.tensor,m):[]),this.performance.body&&delete this.performance.body):(s=R(),(g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?await O5(i.tensor,m):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?d=this.config.body.enabled?await kt(i.tensor,m):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await Nt(i.tensor,m):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(d=this.config.body.enabled?await E5(i.tensor,m):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((V=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&V.includes("handdetect")?y=this.config.hand.enabled?m5(i.tensor,x):[]:(Z=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&Z.includes("handtrack")&&(y=this.config.hand.enabled?h5(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(s=R(),(q=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&q.includes("handdetect")?y=this.config.hand.enabled?await m5(i.tensor,x):[]:(m0=(U=this.config.hand.detector)==null?void 0:U.modelPath)!=null&&m0.includes("handtrack")&&(y=this.config.hand.enabled?await h5(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?l=this.config.object.enabled?S5(i.tensor,this.config):[]:(i0=this.config.object.modelPath)!=null&&i0.includes("centernet")&&(l=this.config.object.enabled?zt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=R(),(g0=this.config.object.modelPath)!=null&&g0.includes("nanodet")?l=this.config.object.enabled?await S5(i.tensor,this.config):[]:(e0=this.config.object.modelPath)!=null&&e0.includes("centernet")&&(l=this.config.object.enabled?await zt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(s=R(),p=[...Y3(c),...U3(d),...J3(y),...K3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(R()-a):Math.trunc(R()-a);let f=((G=this.process.tensor)==null?void 0:G.shape)||[0,0,0,0];this.result={face:c,body:d,hand:y,gesture:p,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return Un(c,d,y,p,f)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(E0(this,be)[t.id]||(this.config.debug&&b("video start",t.id),E0(this,be)[t.id]=!0),!t.paused&&E0(this,be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),E0(this,be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&b("video stop",t.id),E0(this,be)[t.id]=!1)}};Ye=new WeakMap,l2=new WeakMap,c2=new WeakMap,At=new WeakMap,be=new WeakMap;export{p2 as Env,Kn as Human,Kn as default,Ce as defaults,Tt as draw,te as empty,M as env,P5 as match,qn as models}; diff --git a/dist/human.esm.js b/dist/human.esm.js index 022c61ed..e1ae6000 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -36,613 +36,614 @@ var __privateSet = (obj, member, value, setter) => { // dist/tfjs.esm.js var tfjs_esm_exports = {}; __export(tfjs_esm_exports, { - Abs: () => Gs, - Acos: () => zo, - Acosh: () => Vo, - AdadeltaOptimizer: () => Yu, - AdagradOptimizer: () => Qu, - AdamOptimizer: () => Zu, - AdamaxOptimizer: () => Ju, - Add: () => no, - AddN: () => Wo, - All: () => Uo, - Any: () => Go, - ArgMax: () => Hs, - ArgMin: () => Ks, - Asin: () => Ho, - Asinh: () => Ko, - Atan: () => qo, - Atan2: () => Xo, - Atanh: () => jo, - AvgPool: () => Yo, - AvgPool3D: () => qs, - AvgPool3DGrad: () => Ni, - AvgPoolGrad: () => Gp, + Abs: () => js, + Acos: () => Vo, + Acosh: () => Wo, + AdadeltaOptimizer: () => ep, + AdagradOptimizer: () => tp, + AdamOptimizer: () => rp, + AdamaxOptimizer: () => op, + Add: () => io, + AddN: () => Uo, + All: () => Go, + Any: () => Ho, + ArgMax: () => Xs, + ArgMin: () => Ys, + Asin: () => Ko, + Asinh: () => qo, + Atan: () => jo, + Atan2: () => Yo, + Atanh: () => Xo, + AvgPool: () => Qo, + AvgPool3D: () => Qs, + AvgPool3DGrad: () => Ei, + AvgPoolGrad: () => $i, BackendWasm: () => am, - BatchMatMul: () => Qo, - BatchToSpaceND: () => js, - Bincount: () => Zo, - BitwiseAnd: () => ml, - BroadcastArgs: () => Xs, - BroadcastTo: () => Kpe, - Cast: () => ho, - Ceil: () => Jo, - ClipByValue: () => go, - Complex: () => Ti, - ComplexAbs: () => _i, - Concat: () => Ys, - Conv2D: () => en, - Conv2DBackpropFilter: () => $i, - Conv2DBackpropInput: () => tn, - Conv3D: () => rn, - Conv3DBackpropFilterV2: () => za, - Conv3DBackpropInputV2: () => on, - Cos: () => nn, - Cosh: () => sn, - CropAndResize: () => pn, - Cumprod: () => an, - Cumsum: () => un, - DataStorage: () => Lo, - DenseBincount: () => Qs, - DepthToSpace: () => cn, - DepthwiseConv2dNative: () => ln, - DepthwiseConv2dNativeBackpropFilter: () => Ei, - DepthwiseConv2dNativeBackpropInput: () => Ri, - Diag: () => Zs, - Dilation2D: () => mn, - Dilation2DBackpropFilter: () => Ai, - Dilation2DBackpropInput: () => Di, - ENV: () => WC, - Einsum: () => Fi, - Elu: () => fn, - EluGrad: () => Va, - Environment: () => cl, - Equal: () => hn, - Erf: () => Wa, - Exp: () => gn, - ExpandDims: () => Js, - Expm1: () => xn, - FFT: () => Pi, - Fill: () => ea, - FlipLeftRight: () => yn, - Floor: () => bn, - FloorDiv: () => Cn, - FromPixels: () => $u, - FusedBatchNorm: () => wn, - FusedConv2D: () => Co, - FusedDepthwiseConv2D: () => wo, - GPGPUContext: () => xp, - GatherNd: () => Sn, - GatherV2: () => ta, + BatchMatMul: () => Zo, + BatchToSpaceND: () => Zs, + Bincount: () => Jo, + BitwiseAnd: () => Ha, + BroadcastArgs: () => Js, + BroadcastTo: () => _ce, + Cast: () => yo, + Ceil: () => en, + ClipByValue: () => bo, + Complex: () => Ri, + ComplexAbs: () => Di, + Concat: () => ea, + Conv2D: () => tn, + Conv2DBackpropFilter: () => Ai, + Conv2DBackpropInput: () => rn, + Conv3D: () => on, + Conv3DBackpropFilterV2: () => Ka, + Conv3DBackpropInputV2: () => nn, + Cos: () => sn, + Cosh: () => an, + CropAndResize: () => cn, + Cumprod: () => un, + Cumsum: () => pn, + DataStorage: () => Bo, + DenseBincount: () => ta, + DepthToSpace: () => ln, + DepthwiseConv2dNative: () => mn, + DepthwiseConv2dNativeBackpropFilter: () => Fi, + DepthwiseConv2dNativeBackpropInput: () => Pi, + Diag: () => ra, + Dilation2D: () => dn, + Dilation2DBackpropFilter: () => Mi, + Dilation2DBackpropInput: () => Oi, + Draw: () => qm, + ENV: () => QC, + Einsum: () => Li, + Elu: () => hn, + EluGrad: () => qa, + Environment: () => ll, + Equal: () => xn, + Erf: () => gn, + Exp: () => yn, + ExpandDims: () => oa, + Expm1: () => bn, + FFT: () => Bi, + Fill: () => na, + FlipLeftRight: () => Cn, + Floor: () => wn, + FloorDiv: () => Sn, + FromPixels: () => Au, + FusedBatchNorm: () => In, + FusedConv2D: () => Io, + FusedDepthwiseConv2D: () => vo, + GPGPUContext: () => wp, + GatherNd: () => vn, + GatherV2: () => sa, GraphModel: () => Ol, - Greater: () => In, - GreaterEqual: () => vn, - IFFT: () => Oi, - Identity: () => xo, - Imag: () => Mi, - IsFinite: () => kn, - IsInf: () => Nn, - IsNan: () => Tn, - KernelBackend: () => ro, - LRN: () => Mn, - LRNGrad: () => Ua, - LeakyRelu: () => _n, - Less: () => $n, - LessEqual: () => En, - LinSpace: () => Rn, - Log: () => Dn, - Log1p: () => An, - LogSoftmax: () => qpe, - LogicalAnd: () => Fn, - LogicalNot: () => Pn, - LogicalOr: () => On, - LogicalXor: () => m0, - LowerBound: () => jpe, - MathBackendCPU: () => lu, - MathBackendWebGL: () => hu, - MatrixBandPart: () => Xpe, - Max: () => Ln, - MaxPool: () => zn, - MaxPool3D: () => ra, - MaxPool3DGrad: () => Li, - MaxPoolGrad: () => Hp, - MaxPoolWithArgmax: () => Bi, - Maximum: () => Bn, - Mean: () => Vn, - Min: () => Wn, - Minimum: () => Un, - MirrorPad: () => Gn, - Mod: () => Ga, - MomentumOptimizer: () => ep, - Multinomial: () => Hn, - Multiply: () => Kn, - Neg: () => oa, - NonMaxSuppressionV3: () => jn, - NonMaxSuppressionV4: () => Ha, - NonMaxSuppressionV5: () => Xn, - NotEqual: () => qn, - OP_SCOPE_SUFFIX: () => pw, - OneHot: () => Yn, - OnesLike: () => na, - Optimizer: () => kr, + Greater: () => kn, + GreaterEqual: () => Nn, + IFFT: () => zi, + Identity: () => Co, + Imag: () => Vi, + IsFinite: () => Tn, + IsInf: () => _n, + IsNan: () => $n, + KernelBackend: () => so, + LRN: () => Bn, + LRNGrad: () => ja, + LeakyRelu: () => En, + Less: () => Rn, + LessEqual: () => Dn, + LinSpace: () => An, + Log: () => Fn, + Log1p: () => Pn, + LogSoftmax: () => $ce, + LogicalAnd: () => On, + LogicalNot: () => Mn, + LogicalOr: () => Ln, + LogicalXor: () => w0, + LowerBound: () => Ece, + MathBackendCPU: () => hu, + MathBackendWebGL: () => bu, + MatrixBandPart: () => Rce, + Max: () => zn, + MaxPool: () => Wn, + MaxPool3D: () => aa, + MaxPool3DGrad: () => Ui, + MaxPoolGrad: () => Wi, + MaxPoolWithArgmax: () => ia, + Maximum: () => Vn, + Mean: () => Un, + Min: () => Gn, + Minimum: () => Hn, + MirrorPad: () => Kn, + Mod: () => qn, + MomentumOptimizer: () => np, + Multinomial: () => jn, + Multiply: () => Xn, + Neg: () => ua, + NonMaxSuppressionV3: () => Qn, + NonMaxSuppressionV4: () => Xa, + NonMaxSuppressionV5: () => Zn, + NotEqual: () => Yn, + OP_SCOPE_SUFFIX: () => yw, + OneHot: () => Jn, + OnesLike: () => pa, + Optimizer: () => Nr, OptimizerConstructors: () => Dl, - Pack: () => sa, - PadV2: () => Qn, - Pool: () => Ype, - Pow: () => Zn, - Prelu: () => Jn, - Prod: () => es, - RMSPropOptimizer: () => tp, - RaggedGather: () => Kp, - RaggedRange: () => qp, - RaggedTensorToTensor: () => jp, - Range: () => aa, - Rank: () => JC, - Real: () => zi, - RealDiv: () => dn, - Reciprocal: () => ts, + Pack: () => ca, + PadV2: () => es, + Pool: () => Dce, + Pow: () => ts, + Prelu: () => rs, + Prod: () => os, + RMSPropOptimizer: () => sp, + RaggedGather: () => jp, + RaggedRange: () => Xp, + RaggedTensorToTensor: () => Yp, + Range: () => la, + Rank: () => uw, + Real: () => Gi, + RealDiv: () => fn, + Reciprocal: () => ns, Reduction: () => Et, - Relu: () => rs, - Relu6: () => ss, - Reshape: () => ia, - ResizeBilinear: () => ns, - ResizeBilinearGrad: () => qa, - ResizeNearestNeighbor: () => os, - ResizeNearestNeighborGrad: () => Ka, - Reverse: () => as, - RotateWithOffset: () => _s, - Round: () => is, - Rsqrt: () => us, - SGDOptimizer: () => ii, - ScatterNd: () => ps, - SearchSorted: () => ls, - Select: () => ua, - Selu: () => ms, - Sigmoid: () => hs, - Sign: () => fs, - Sin: () => ds, - Sinh: () => ja, - Slice: () => pa, - Softmax: () => bs, - Softplus: () => gs, - SpaceToBatchND: () => ca, - SparseFillEmptyRows: () => Vi, - SparseReshape: () => Xa, - SparseSegmentMean: () => Wi, - SparseSegmentSum: () => Ui, - SparseToDense: () => Cs, - SplitV: () => la, - Sqrt: () => xs, - Square: () => Gi, - SquaredDifference: () => ws, - StaticRegexReplace: () => _u, - Step: () => yo, - StridedSlice: () => Ss, - StringNGrams: () => ma, - StringSplit: () => Hi, - StringToHashBucketFast: () => Ki, - Sub: () => Is, - Sum: () => ys, - Tan: () => vs, - Tanh: () => ks, - Tensor: () => pt, + Relu: () => ss, + Relu6: () => us, + Reshape: () => ma, + ResizeBilinear: () => is, + ResizeBilinearGrad: () => Qa, + ResizeNearestNeighbor: () => as, + ResizeNearestNeighborGrad: () => Ya, + Reverse: () => ps, + RotateWithOffset: () => Ds, + Round: () => cs, + Rsqrt: () => ls, + SGDOptimizer: () => ci, + ScatterNd: () => ms, + SearchSorted: () => fs, + Select: () => da, + Selu: () => hs, + Sigmoid: () => bs, + Sign: () => ys, + Sin: () => gs, + Sinh: () => xs, + Slice: () => fa, + Softmax: () => Is, + Softplus: () => Cs, + SpaceToBatchND: () => ha, + SparseFillEmptyRows: () => Hi, + SparseReshape: () => Za, + SparseSegmentMean: () => Ki, + SparseSegmentSum: () => qi, + SparseToDense: () => vs, + SplitV: () => ga, + Sqrt: () => ws, + Square: () => ji, + SquaredDifference: () => ks, + StaticRegexReplace: () => Du, + Step: () => wo, + StridedSlice: () => Ns, + StringNGrams: () => xa, + StringSplit: () => Xi, + StringToHashBucketFast: () => Yi, + Sub: () => Ts, + Sum: () => Ss, + Tan: () => _s, + Tanh: () => $s, + Tensor: () => ut, TensorBuffer: () => tt, - TensorScatterUpdate: () => cs, - Tile: () => so, - TopK: () => Ns, - Transform: () => Ts, - Transpose: () => ao, - Unique: () => qi, - Unpack: () => da, - UnsortedSegmentSum: () => ji, - UpperBound: () => Qpe, - Variable: () => Qa, - WebGPUBackend: () => Cu, - ZerosLike: () => fa, - _FusedMatMul: () => bo, - abs: () => Zt, - acos: () => ik, - acosh: () => uk, - add: () => be, - addN: () => pk, - all: () => ck, - any: () => lk, - argMax: () => mk, - argMin: () => dk, - asin: () => fk, - asinh: () => hk, - atan: () => gk, - atan2: () => xk, - atanh: () => yk, - avgPool: () => cd, - avgPool3d: () => wk, - backend: () => Tme, - backend_util: () => C, - basicLSTMCell: () => Sk, - batchNorm: () => tu, - batchNorm2d: () => vk, - batchNorm3d: () => kk, - batchNorm4d: () => Nk, - batchToSpaceND: () => ld, - bincount: () => md, - bitwiseAnd: () => Tk, - booleanMaskAsync: () => qq, - broadcastArgs: () => _k, - broadcastTo: () => ru, - broadcast_util: () => Sr, - browser: () => MN, + TensorScatterUpdate: () => ds, + Tile: () => uo, + TopK: () => Es, + Transform: () => Rs, + Transpose: () => po, + Unique: () => Qi, + Unpack: () => ya, + UnsortedSegmentSum: () => Zi, + UpperBound: () => Ace, + Variable: () => ei, + WebGPUBackend: () => vu, + ZerosLike: () => ba, + _FusedMatMul: () => So, + abs: () => Jt, + acos: () => xk, + acosh: () => yk, + add: () => Ce, + addN: () => bk, + all: () => Ck, + any: () => wk, + argMax: () => Sk, + argMin: () => Ik, + asin: () => vk, + asinh: () => kk, + atan: () => Nk, + atan2: () => Tk, + atanh: () => _k, + avgPool: () => md, + avgPool3d: () => Rk, + backend: () => dde, + backend_util: () => w, + basicLSTMCell: () => Dk, + batchNorm: () => su, + batchNorm2d: () => Fk, + batchNorm3d: () => Pk, + batchNorm4d: () => Ok, + batchToSpaceND: () => dd, + bincount: () => fd, + bitwiseAnd: () => Mk, + booleanMaskAsync: () => b6, + broadcastArgs: () => Lk, + broadcastTo: () => au, + broadcast_util: () => Ir, + browser: () => XN, buffer: () => me, - cast: () => Ye, - ceil: () => $k, - clipByValue: () => Ek, - clone: () => Vr, - complex: () => $r, - concat: () => yt, - concat1d: () => Rk, - concat2d: () => Dk, - concat3d: () => Ak, - concat4d: () => Fk, - conv1d: () => Pk, - conv2d: () => ou, - conv2dTranspose: () => Ok, - conv3d: () => Mk, - conv3dTranspose: () => Bk, - copyRegisteredKernels: () => sce, - cos: () => zk, - cosh: () => Vk, + cast: () => qe, + ceil: () => Bk, + clipByValue: () => zk, + clone: () => Ur, + complex: () => Er, + concat: () => bt, + concat1d: () => Vk, + concat2d: () => Wk, + concat3d: () => Uk, + concat4d: () => Gk, + conv1d: () => Hk, + conv2d: () => iu, + conv2dTranspose: () => Kk, + conv3d: () => qk, + conv3dTranspose: () => Xk, + copyRegisteredKernels: () => Vce, + cos: () => Yk, + cosh: () => Qk, cosineWindow: () => _l, - cumprod: () => Wk, - cumsum: () => Uk, - customGrad: () => Ir, - denseBincount: () => Gk, - deprecationWarn: () => bw, - depthToSpace: () => Hk, - depthwiseConv2d: () => ac, - deregisterOp: () => KX, - device_util: () => Zi, - diag: () => Kk, - dilation2d: () => qk, - disableDeprecationWarnings: () => gme, + cumprod: () => Zk, + cumsum: () => Jk, + customGrad: () => vr, + denseBincount: () => e2, + deprecationWarn: () => _w, + depthToSpace: () => t2, + depthwiseConv2d: () => ic, + deregisterOp: () => b5, + device_util: () => ru, + diag: () => r2, + dilation2d: () => o2, + disableDeprecationWarnings: () => rde, dispose: () => Ot, - disposeVariables: () => xme, - div: () => Ke, - divNoNan: () => Xk, - dot: () => Yk, - dropout: () => s6, - einsum: () => Qk, - elu: () => gd, - enableDebugMode: () => hme, - enableProdMode: () => fme, - enclosingPowerOfTwo: () => Pw, + disposeVariables: () => ode, + div: () => je, + divNoNan: () => s2, + dot: () => a2, + dropout: () => R6, + einsum: () => i2, + elu: () => yd, + enableDebugMode: () => tde, + enableProdMode: () => ede, + enclosingPowerOfTwo: () => Gw, engine: () => ur, - ensureShape: () => Zk, - env: () => P, - equal: () => hd, - erf: () => Jk, - euclideanNorm: () => r2, - exp: () => ko, - expandDims: () => oi, - expm1: () => o2, - eye: () => xd, - fft: () => pc, - fill: () => Sa, - findBackend: () => kme, - findBackendFactory: () => Nme, - floor: () => yd, - floorDiv: () => pd, - forceHalfFloat: () => hD, - fused: () => Ow, - gather: () => bd, - gatherND: () => o6, - gather_util: () => of, - getBackend: () => Ime, - getGradient: () => HC, + ensureShape: () => u2, + env: () => A, + equal: () => xd, + erf: () => p2, + euclideanNorm: () => m2, + exp: () => _o, + expandDims: () => ai, + expm1: () => d2, + eye: () => bd, + fft: () => cc, + fill: () => Ta, + findBackend: () => lde, + findBackendFactory: () => mde, + floor: () => Cd, + floorDiv: () => ld, + forceHalfFloat: () => $D, + fused: () => Hw, + gather: () => wd, + gatherND: () => $6, + gather_util: () => nf, + getBackend: () => pde, + getGradient: () => ew, getKernel: () => fl, - getKernelsForBackend: () => Km, - getThreadsCount: () => ese, - gpgpu_util: () => qI, - grad: () => YH, - grads: () => QH, - greater: () => Bu, - greaterEqual: () => Cd, - ifft: () => Hu, - imag: () => su, - image: () => uj, - inTopKAsync: () => i6, - io: () => pi, - irfft: () => Wd, - isFinite: () => n2, - isInf: () => s2, - isNaN: () => a2, - keep: () => Er, + getKernelsForBackend: () => jm, + getThreadsCount: () => Lse, + gpgpu_util: () => rv, + grad: () => SK, + grads: () => IK, + greater: () => Uu, + greaterEqual: () => Sd, + ifft: () => Xu, + imag: () => pu, + image: () => Fj, + inTopKAsync: () => A6, + io: () => mi, + irfft: () => Gd, + isFinite: () => f2, + isInf: () => h2, + isNaN: () => g2, + keep: () => Rr, kernel_impls: () => Wt, - leakyRelu: () => wd, + leakyRelu: () => Id, less: () => kl, - lessEqual: () => ic, - linalg: () => pj, - linspace: () => i2, - loadGraphModel: () => W5, - loadGraphModelSync: () => U5, - localResponseNormalization: () => u2, - log: () => ni, - log1p: () => Sd, - logSigmoid: () => p2, - logSoftmax: () => c2, - logSumExp: () => kd, - logicalAnd: () => zu, - logicalNot: () => Nd, - logicalOr: () => Td, - logicalXor: () => l2, - losses: () => cj, - lowerBound: () => m2, - matMul: () => Qe, - math: () => PN, - max: () => Ia, - maxPool: () => $d, - maxPool3d: () => d2, - maxPoolWithArgmax: () => f2, - maximum: () => Ed, - mean: () => Vu, - memory: () => yme, - meshgrid: () => h2, + lessEqual: () => uc, + linalg: () => Pj, + linspace: () => x2, + loadGraphModel: () => h8, + loadGraphModelSync: () => g8, + localResponseNormalization: () => y2, + log: () => ii, + log1p: () => vd, + logSigmoid: () => b2, + logSoftmax: () => C2, + logSumExp: () => Td, + logicalAnd: () => Gu, + logicalNot: () => _d, + logicalOr: () => $d, + logicalXor: () => w2, + losses: () => Oj, + lowerBound: () => S2, + matMul: () => Ze, + math: () => HN, + max: () => _a, + maxPool: () => Rd, + maxPool3d: () => I2, + maxPoolWithArgmax: () => v2, + maximum: () => Dd, + mean: () => Hu, + memory: () => nde, + meshgrid: () => k2, min: () => vl, - minimum: () => Wu, - mirrorPad: () => g2, - mod: () => x2, - moments: () => y2, - movingAverage: () => Yq, + minimum: () => Ku, + mirrorPad: () => N2, + mod: () => T2, + moments: () => _2, + movingAverage: () => S6, mul: () => se, - multiRNNCell: () => b2, - multinomial: () => C2, + multiRNNCell: () => $2, + multinomial: () => E2, neg: () => pr, - nextFrame: () => Kw, - norm: () => Lu, - notEqual: () => Rd, + nextFrame: () => tS, + norm: () => Wu, + notEqual: () => Ad, oneHot: () => Tl, - ones: () => va, - onesLike: () => w2, + ones: () => $a, + onesLike: () => R2, op: () => N, - outerProduct: () => S2, - pad: () => ka, - pad1d: () => I2, - pad2d: () => v2, - pad3d: () => k2, - pad4d: () => N2, - pool: () => T2, - pow: () => ri, - prelu: () => Ad, - print: () => ud, - prod: () => _2, - profile: () => bme, - raggedGather: () => $2, - raggedRange: () => E2, - raggedTensorToTensor: () => R2, - rand: () => D2, - randomGamma: () => J2, - randomNormal: () => Bd, - randomStandardNormal: () => e1, - randomUniform: () => uc, - randomUniformInt: () => t1, - range: () => au, - ready: () => Sme, - real: () => si, - reciprocal: () => r1, - registerBackend: () => eu, - registerGradient: () => rce, - registerKernel: () => Ya, - registerOp: () => HX, - relu: () => iu, - relu6: () => zd, - removeBackend: () => vme, + outerProduct: () => D2, + pad: () => Ea, + pad1d: () => A2, + pad2d: () => F2, + pad3d: () => P2, + pad4d: () => O2, + pool: () => M2, + pow: () => si, + prelu: () => Pd, + print: () => cd, + prod: () => L2, + profile: () => sde, + raggedGather: () => B2, + raggedRange: () => z2, + raggedTensorToTensor: () => V2, + rand: () => W2, + randomGamma: () => p1, + randomNormal: () => Vd, + randomStandardNormal: () => c1, + randomUniform: () => pc, + randomUniformInt: () => l1, + range: () => cu, + ready: () => ude, + real: () => ui, + reciprocal: () => m1, + registerBackend: () => nu, + registerGradient: () => Lce, + registerKernel: () => Ja, + registerOp: () => y5, + relu: () => lu, + relu6: () => Wd, + removeBackend: () => cde, reshape: () => W, - reverse: () => uo, - reverse1d: () => o1, - reverse2d: () => n1, - reverse3d: () => s1, - reverse4d: () => a1, - rfft: () => cc, - round: () => Vd, - rsqrt: () => i1, + reverse: () => lo, + reverse1d: () => d1, + reverse2d: () => f1, + reverse3d: () => h1, + reverse4d: () => g1, + rfft: () => lc, + round: () => Ud, + rsqrt: () => x1, scalar: () => ke, - scatterND: () => Zq, - scatter_util: () => pu, + scatterND: () => v6, + scatter_util: () => du, searchSorted: () => Nl, - selu: () => u1, - separableConv2d: () => p1, - serialization: () => vN, - setBackend: () => wme, - setPlatform: () => _me, - setThreadsCount: () => Jne, - setWasmPath: () => Qne, - setWasmPaths: () => Zne, - setWebGLContext: () => iI, - setdiff1dAsync: () => c1, - shared: () => Sc, - sigmoid: () => wa, - sign: () => l1, - signal: () => ij, - sin: () => m1, - sinh: () => d1, - slice: () => qe, - slice1d: () => f1, - slice2d: () => h1, - slice3d: () => g1, - slice4d: () => x1, + selu: () => y1, + separableConv2d: () => b1, + serialization: () => FN, + setBackend: () => ide, + setPlatform: () => fde, + setThreadsCount: () => Mse, + setWasmPath: () => Pse, + setWasmPaths: () => Ose, + setWebGLContext: () => gI, + setdiff1dAsync: () => C1, + shared: () => Ic, + sigmoid: () => Na, + sign: () => w1, + signal: () => Aj, + sin: () => S1, + sinh: () => I1, + slice: () => Xe, + slice1d: () => v1, + slice2d: () => k1, + slice3d: () => N1, + slice4d: () => T1, slice_util: () => ct, - softmax: () => y1, - softplus: () => vd, - spaceToBatchND: () => Dd, - sparse: () => lj, - sparseToDense: () => t6, - spectral: () => aj, - split: () => ai, - sqrt: () => Rr, - square: () => Jt, - squaredDifference: () => Ud, - squeeze: () => lc, - stack: () => vr, - step: () => Gd, - stridedSlice: () => b1, - string: () => mj, + softmax: () => _1, + softplus: () => Nd, + spaceToBatchND: () => Fd, + sparse: () => Mj, + sparseToDense: () => T6, + spectral: () => Dj, + split: () => pi, + sqrt: () => Dr, + square: () => er, + squaredDifference: () => Hd, + squeeze: () => mc, + stack: () => kr, + step: () => Kd, + stridedSlice: () => $1, + string: () => Lj, sub: () => Te, sum: () => ot, - sumOutType: () => Za, - tan: () => C1, + sumOutType: () => ti, + tan: () => E1, tanh: () => Il, tensor: () => ir, tensor1d: () => xr, - tensor2d: () => uu, - tensor3d: () => Hd, - tensor4d: () => w1, - tensor5d: () => S1, - tensor6d: () => I1, - tensorScatterUpdate: () => k1, - tensor_util: () => M0, - test_util: () => Z2, + tensor2d: () => mu, + tensor3d: () => qd, + tensor4d: () => R1, + tensor5d: () => D1, + tensor6d: () => A1, + tensorScatterUpdate: () => P1, + tensor_util: () => K0, + test_util: () => u1, tidy: () => De, - tile: () => nu, - time: () => Cme, - topk: () => N1, - train: () => CUe, - transpose: () => dc, - truncatedNormal: () => T1, - unique: () => _1, - unregisterGradient: () => nce, - unregisterKernel: () => oce, - unsortedSegmentSum: () => $1, - unstack: () => po, + tile: () => uu, + time: () => ade, + topk: () => O1, + train: () => uGe, + transpose: () => fc, + truncatedNormal: () => M1, + unique: () => L1, + unregisterGradient: () => zce, + unregisterKernel: () => Bce, + unsortedSegmentSum: () => B1, + unstack: () => mo, upcastType: () => dt, - upperBound: () => E1, + upperBound: () => z1, util: () => y, - valueAndGrad: () => ZH, - valueAndGrads: () => JH, - variable: () => R1, - variableGrads: () => vw, - version: () => zpe, - version_converter: () => H5, - version_core: () => Vj, - version_cpu: () => I8, - version_wasm: () => tse, - version_webgl: () => xZ, - webgl: () => cst, - webgl_util: () => _c, - webgpu_util: () => Fv, - where: () => io, - whereAsync: () => qd, - zeros: () => Wr, + valueAndGrad: () => vK, + valueAndGrads: () => kK, + variable: () => V1, + variableGrads: () => Aw, + version: () => Sce, + version_converter: () => y8, + version_core: () => fX, + version_cpu: () => Y8, + version_wasm: () => Bse, + version_webgl: () => HZ, + webgl: () => tat, + webgl_util: () => $c, + webgpu_util: () => Wv, + where: () => co, + whereAsync: () => Xd, + zeros: () => Gr, zerosLike: () => Ht }); -var BU = Object.create; -var PC = Object.defineProperty; -var zU = Object.getOwnPropertyDescriptor; -var VU = Object.getOwnPropertyNames; -var WU = Object.getPrototypeOf; -var UU = Object.prototype.hasOwnProperty; +var cG = Object.create; +var GC = Object.defineProperty; +var lG = Object.getOwnPropertyDescriptor; +var mG = Object.getOwnPropertyNames; +var dG = Object.getPrototypeOf; +var fG = Object.prototype.hasOwnProperty; var qt = (r, e) => () => (e || r((e = { exports: {} }).exports, e), e.exports); -var He = (r, e) => { - for (var t10 in e) - PC(r, t10, { get: e[t10], enumerable: true }); +var Ke = (r, e) => { + for (var t8 in e) + GC(r, t8, { get: e[t8], enumerable: true }); }; -var GU = (r, e, t10, o) => { +var hG = (r, e, t8, o) => { if (e && typeof e == "object" || typeof e == "function") - for (let n of VU(e)) - !UU.call(r, n) && n !== t10 && PC(r, n, { get: () => e[n], enumerable: !(o = zU(e, n)) || o.enumerable }); + for (let n of mG(e)) + !fG.call(r, n) && n !== t8 && GC(r, n, { get: () => e[n], enumerable: !(o = lG(e, n)) || o.enumerable }); return r; }; -var Bp = (r, e, t10) => (t10 = r != null ? BU(WU(r)) : {}, GU(e || !r || !r.__esModule ? PC(t10, "default", { value: r, enumerable: true }) : t10, r)); -var v0 = qt((uce, I0) => { - I0.exports = vt; - var So = null; +var Up = (r, e, t8) => (t8 = r != null ? cG(dG(r)) : {}, hG(e || !r || !r.__esModule ? GC(t8, "default", { value: r, enumerable: true }) : t8, r)); +var A0 = qt((Gce, D0) => { + D0.exports = kt; + var ko = null; try { - So = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports; + ko = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports; } catch (r) { } - function vt(r, e, t10) { - this.low = r | 0, this.high = e | 0, this.unsigned = !!t10; + function kt(r, e, t8) { + this.low = r | 0, this.high = e | 0, this.unsigned = !!t8; } - vt.prototype.__isLong__; - Object.defineProperty(vt.prototype, "__isLong__", { value: true }); - function zr(r) { + kt.prototype.__isLong__; + Object.defineProperty(kt.prototype, "__isLong__", { value: true }); + function Wr(r) { return (r && r.__isLong__) === true; } - vt.isLong = zr; - var f0 = {}, h0 = {}; - function Ru(r, e) { - var t10, o, n; - return e ? (r >>>= 0, (n = 0 <= r && r < 256) && (o = h0[r], o) ? o : (t10 = kt(r, (r | 0) < 0 ? -1 : 0, true), n && (h0[r] = t10), t10)) : (r |= 0, (n = -128 <= r && r < 128) && (o = f0[r], o) ? o : (t10 = kt(r, r < 0 ? -1 : 0, false), n && (f0[r] = t10), t10)); + kt.isLong = Wr; + var I0 = {}, v0 = {}; + function Pu(r, e) { + var t8, o, n; + return e ? (r >>>= 0, (n = 0 <= r && r < 256) && (o = v0[r], o) ? o : (t8 = Nt(r, (r | 0) < 0 ? -1 : 0, true), n && (v0[r] = t8), t8)) : (r |= 0, (n = -128 <= r && r < 128) && (o = I0[r], o) ? o : (t8 = Nt(r, r < 0 ? -1 : 0, false), n && (I0[r] = t8), t8)); } - vt.fromInt = Ru; - function Io(r, e) { + kt.fromInt = Pu; + function No(r, e) { if (isNaN(r)) - return e ? Eu : vo; + return e ? Fu : To; if (e) { if (r < 0) - return Eu; - if (r >= b0) - return S0; + return Fu; + if (r >= _0) + return R0; } else { - if (r <= -x0) - return Br; - if (r + 1 >= x0) - return w0; + if (r <= -N0) + return Vr; + if (r + 1 >= N0) + return E0; } - return r < 0 ? Io(-r, e).neg() : kt(r % Qp | 0, r / Qp | 0, e); + return r < 0 ? No(-r, e).neg() : Nt(r % Jp | 0, r / Jp | 0, e); } - vt.fromNumber = Io; - function kt(r, e, t10) { - return new vt(r, e, t10); + kt.fromNumber = No; + function Nt(r, e, t8) { + return new kt(r, e, t8); } - vt.fromBits = kt; - var jm = Math.pow; - function jC(r, e, t10) { + kt.fromBits = Nt; + var Ym = Math.pow; + function ow(r, e, t8) { if (r.length === 0) throw Error("empty string"); if (r === "NaN" || r === "Infinity" || r === "+Infinity" || r === "-Infinity") - return vo; - if (typeof e == "number" ? (t10 = e, e = false) : e = !!e, t10 = t10 || 10, t10 < 2 || 36 < t10) + return To; + if (typeof e == "number" ? (t8 = e, e = false) : e = !!e, t8 = t8 || 10, t8 < 2 || 36 < t8) throw RangeError("radix"); var o; if ((o = r.indexOf("-")) > 0) throw Error("interior hyphen"); if (o === 0) - return jC(r.substring(1), e, t10).neg(); - for (var n = Io(jm(t10, 8)), s = vo, a = 0; a < r.length; a += 8) { - var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), t10); + return ow(r.substring(1), e, t8).neg(); + for (var n = No(Ym(t8, 8)), s = To, a = 0; a < r.length; a += 8) { + var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), t8); if (i < 8) { - var u = Io(jm(t10, i)); - s = s.mul(u).add(Io(p)); + var u = No(Ym(t8, i)); + s = s.mul(u).add(No(p)); } else - s = s.mul(n), s = s.add(Io(p)); + s = s.mul(n), s = s.add(No(p)); } return s.unsigned = e, s; } - vt.fromString = jC; - function $s(r, e) { - return typeof r == "number" ? Io(r, e) : typeof r == "string" ? jC(r, e) : kt(r.low, r.high, typeof e == "boolean" ? e : r.unsigned); + kt.fromString = ow; + function As(r, e) { + return typeof r == "number" ? No(r, e) : typeof r == "string" ? ow(r, e) : Nt(r.low, r.high, typeof e == "boolean" ? e : r.unsigned); } - vt.fromValue = $s; - var g0 = 1 << 16, dG = 1 << 24, Qp = g0 * g0, b0 = Qp * Qp, x0 = b0 / 2, y0 = Ru(dG), vo = Ru(0); - vt.ZERO = vo; - var Eu = Ru(0, true); - vt.UZERO = Eu; - var Yp = Ru(1); - vt.ONE = Yp; - var C0 = Ru(1, true); - vt.UONE = C0; - var qC = Ru(-1); - vt.NEG_ONE = qC; - var w0 = kt(-1, 2147483647, false); - vt.MAX_VALUE = w0; - var S0 = kt(-1, -1, true); - vt.MAX_UNSIGNED_VALUE = S0; - var Br = kt(0, -2147483648, false); - vt.MIN_VALUE = Br; - var de = vt.prototype; + kt.fromValue = As; + var k0 = 65536, LG = 1 << 24, Jp = k0 * k0, _0 = Jp * Jp, N0 = _0 / 2, T0 = Pu(LG), To = Pu(0); + kt.ZERO = To; + var Fu = Pu(0, true); + kt.UZERO = Fu; + var Zp = Pu(1); + kt.ONE = Zp; + var $0 = Pu(1, true); + kt.UONE = $0; + var rw = Pu(-1); + kt.NEG_ONE = rw; + var E0 = Nt(-1, 2147483647, false); + kt.MAX_VALUE = E0; + var R0 = Nt(-1, -1, true); + kt.MAX_UNSIGNED_VALUE = R0; + var Vr = Nt(0, -2147483648, false); + kt.MIN_VALUE = Vr; + var de = kt.prototype; de.toInt = function() { return this.unsigned ? this.low >>> 0 : this.low; }; de.toNumber = function() { - return this.unsigned ? (this.high >>> 0) * Qp + (this.low >>> 0) : this.high * Qp + (this.low >>> 0); + return this.unsigned ? (this.high >>> 0) * Jp + (this.low >>> 0) : this.high * Jp + (this.low >>> 0); }; de.toString = function(e) { if (e = e || 10, e < 2 || 36 < e) @@ -650,12 +651,12 @@ var v0 = qt((uce, I0) => { if (this.isZero()) return "0"; if (this.isNegative()) - if (this.eq(Br)) { - var t10 = Io(e), o = this.div(t10), n = o.mul(t10).sub(this); + if (this.eq(Vr)) { + var t8 = No(e), o = this.div(t8), n = o.mul(t8).sub(this); return o.toString(e) + n.toInt().toString(e); } else return "-" + this.neg().toString(e); - for (var s = Io(jm(e, 6), this.unsigned), a = this, i = ""; ; ) { + for (var s = No(Ym(e, 6), this.unsigned), a = this, i = ""; ; ) { var p = a.div(s), u = a.sub(p.mul(s)).toInt() >>> 0, c = u.toString(e); if (a = p, a.isZero()) return c + i; @@ -678,10 +679,10 @@ var v0 = qt((uce, I0) => { }; de.getNumBitsAbs = function() { if (this.isNegative()) - return this.eq(Br) ? 64 : this.neg().getNumBitsAbs(); - for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && !(e & 1 << t10); t10--) + return this.eq(Vr) ? 64 : this.neg().getNumBitsAbs(); + for (var e = this.high != 0 ? this.high : this.low, t8 = 31; t8 > 0 && !(e & 1 << t8); t8--) ; - return this.high != 0 ? t10 + 33 : t10 + 1; + return this.high != 0 ? t8 + 33 : t8 + 1; }; de.isZero = function() { return this.high === 0 && this.low === 0; @@ -700,7 +701,7 @@ var v0 = qt((uce, I0) => { return (this.low & 1) === 0; }; de.equals = function(e) { - return zr(e) || (e = $s(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low; + return Wr(e) || (e = As(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low; }; de.eq = de.equals; de.notEquals = function(e) { @@ -727,165 +728,165 @@ var v0 = qt((uce, I0) => { de.gte = de.greaterThanOrEqual; de.ge = de.greaterThanOrEqual; de.compare = function(e) { - if (zr(e) || (e = $s(e)), this.eq(e)) + if (Wr(e) || (e = As(e)), this.eq(e)) return 0; - var t10 = this.isNegative(), o = e.isNegative(); - return t10 && !o ? -1 : !t10 && o ? 1 : this.unsigned ? e.high >>> 0 > this.high >>> 0 || e.high === this.high && e.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(e).isNegative() ? -1 : 1; + var t8 = this.isNegative(), o = e.isNegative(); + return t8 && !o ? -1 : !t8 && o ? 1 : this.unsigned ? e.high >>> 0 > this.high >>> 0 || e.high === this.high && e.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(e).isNegative() ? -1 : 1; }; de.comp = de.compare; de.negate = function() { - return !this.unsigned && this.eq(Br) ? Br : this.not().add(Yp); + return !this.unsigned && this.eq(Vr) ? Vr : this.not().add(Zp); }; de.neg = de.negate; de.add = function(e) { - zr(e) || (e = $s(e)); - var t10 = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = e.high >>> 16, i = e.high & 65535, p = e.low >>> 16, u = e.low & 65535, c = 0, l = 0, m = 0, d = 0; - return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t10 + a, c &= 65535, kt(m << 16 | d, c << 16 | l, this.unsigned); + Wr(e) || (e = As(e)); + var t8 = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = e.high >>> 16, i = e.high & 65535, p = e.low >>> 16, u = e.low & 65535, c = 0, l = 0, m = 0, d = 0; + return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t8 + a, c &= 65535, Nt(m << 16 | d, c << 16 | l, this.unsigned); }; de.subtract = function(e) { - return zr(e) || (e = $s(e)), this.add(e.neg()); + return Wr(e) || (e = As(e)), this.add(e.neg()); }; de.sub = de.subtract; de.multiply = function(e) { if (this.isZero()) - return vo; - if (zr(e) || (e = $s(e)), So) { - var t10 = So.mul(this.low, this.high, e.low, e.high); - return kt(t10, So.get_high(), this.unsigned); + return To; + if (Wr(e) || (e = As(e)), ko) { + var t8 = ko.mul(this.low, this.high, e.low, e.high); + return Nt(t8, ko.get_high(), this.unsigned); } if (e.isZero()) - return vo; - if (this.eq(Br)) - return e.isOdd() ? Br : vo; - if (e.eq(Br)) - return this.isOdd() ? Br : vo; + return To; + if (this.eq(Vr)) + return e.isOdd() ? Vr : To; + if (e.eq(Vr)) + return this.isOdd() ? Vr : To; if (this.isNegative()) return e.isNegative() ? this.neg().mul(e.neg()) : this.neg().mul(e).neg(); if (e.isNegative()) return this.mul(e.neg()).neg(); - if (this.lt(y0) && e.lt(y0)) - return Io(this.toNumber() * e.toNumber(), this.unsigned); + if (this.lt(T0) && e.lt(T0)) + return No(this.toNumber() * e.toNumber(), this.unsigned); var o = this.high >>> 16, n = this.high & 65535, s = this.low >>> 16, a = this.low & 65535, i = e.high >>> 16, p = e.high & 65535, u = e.low >>> 16, c = e.low & 65535, l = 0, m = 0, d = 0, f = 0; - return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, kt(d << 16 | f, l << 16 | m, this.unsigned); + return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, Nt(d << 16 | f, l << 16 | m, this.unsigned); }; de.mul = de.multiply; de.divide = function(e) { - if (zr(e) || (e = $s(e)), e.isZero()) + if (Wr(e) || (e = As(e)), e.isZero()) throw Error("division by zero"); - if (So) { + if (ko) { if (!this.unsigned && this.high === -2147483648 && e.low === -1 && e.high === -1) return this; - var t10 = (this.unsigned ? So.div_u : So.div_s)(this.low, this.high, e.low, e.high); - return kt(t10, So.get_high(), this.unsigned); + var t8 = (this.unsigned ? ko.div_u : ko.div_s)(this.low, this.high, e.low, e.high); + return Nt(t8, ko.get_high(), this.unsigned); } if (this.isZero()) - return this.unsigned ? Eu : vo; + return this.unsigned ? Fu : To; var o, n, s; if (this.unsigned) { if (e.unsigned || (e = e.toUnsigned()), e.gt(this)) - return Eu; + return Fu; if (e.gt(this.shru(1))) - return C0; - s = Eu; + return $0; + s = Fu; } else { - if (this.eq(Br)) { - if (e.eq(Yp) || e.eq(qC)) - return Br; - if (e.eq(Br)) - return Yp; + if (this.eq(Vr)) { + if (e.eq(Zp) || e.eq(rw)) + return Vr; + if (e.eq(Vr)) + return Zp; var a = this.shr(1); - return o = a.div(e).shl(1), o.eq(vo) ? e.isNegative() ? Yp : qC : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s); - } else if (e.eq(Br)) - return this.unsigned ? Eu : vo; + return o = a.div(e).shl(1), o.eq(To) ? e.isNegative() ? Zp : rw : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s); + } else if (e.eq(Vr)) + return this.unsigned ? Fu : To; if (this.isNegative()) return e.isNegative() ? this.neg().div(e.neg()) : this.neg().div(e).neg(); if (e.isNegative()) return this.div(e.neg()).neg(); - s = vo; + s = To; } for (n = this; n.gte(e); ) { o = Math.max(1, Math.floor(n.toNumber() / e.toNumber())); - for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : jm(2, i - 48), u = Io(o), c = u.mul(e); c.isNegative() || c.gt(n); ) - o -= p, u = Io(o, this.unsigned), c = u.mul(e); - u.isZero() && (u = Yp), s = s.add(u), n = n.sub(c); + for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : Ym(2, i - 48), u = No(o), c = u.mul(e); c.isNegative() || c.gt(n); ) + o -= p, u = No(o, this.unsigned), c = u.mul(e); + u.isZero() && (u = Zp), s = s.add(u), n = n.sub(c); } return s; }; de.div = de.divide; de.modulo = function(e) { - if (zr(e) || (e = $s(e)), So) { - var t10 = (this.unsigned ? So.rem_u : So.rem_s)(this.low, this.high, e.low, e.high); - return kt(t10, So.get_high(), this.unsigned); + if (Wr(e) || (e = As(e)), ko) { + var t8 = (this.unsigned ? ko.rem_u : ko.rem_s)(this.low, this.high, e.low, e.high); + return Nt(t8, ko.get_high(), this.unsigned); } return this.sub(this.div(e).mul(e)); }; de.mod = de.modulo; de.rem = de.modulo; de.not = function() { - return kt(~this.low, ~this.high, this.unsigned); + return Nt(~this.low, ~this.high, this.unsigned); }; de.and = function(e) { - return zr(e) || (e = $s(e)), kt(this.low & e.low, this.high & e.high, this.unsigned); + return Wr(e) || (e = As(e)), Nt(this.low & e.low, this.high & e.high, this.unsigned); }; de.or = function(e) { - return zr(e) || (e = $s(e)), kt(this.low | e.low, this.high | e.high, this.unsigned); + return Wr(e) || (e = As(e)), Nt(this.low | e.low, this.high | e.high, this.unsigned); }; de.xor = function(e) { - return zr(e) || (e = $s(e)), kt(this.low ^ e.low, this.high ^ e.high, this.unsigned); + return Wr(e) || (e = As(e)), Nt(this.low ^ e.low, this.high ^ e.high, this.unsigned); }; de.shiftLeft = function(e) { - return zr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? kt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : kt(0, this.low << e - 32, this.unsigned); + return Wr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? Nt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : Nt(0, this.low << e - 32, this.unsigned); }; de.shl = de.shiftLeft; de.shiftRight = function(e) { - return zr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? kt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : kt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned); + return Wr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? Nt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : Nt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned); }; de.shr = de.shiftRight; de.shiftRightUnsigned = function(e) { - if (zr(e) && (e = e.toInt()), e &= 63, e === 0) + if (Wr(e) && (e = e.toInt()), e &= 63, e === 0) return this; - var t10 = this.high; + var t8 = this.high; if (e < 32) { var o = this.low; - return kt(o >>> e | t10 << 32 - e, t10 >>> e, this.unsigned); + return Nt(o >>> e | t8 << 32 - e, t8 >>> e, this.unsigned); } else - return e === 32 ? kt(t10, 0, this.unsigned) : kt(t10 >>> e - 32, 0, this.unsigned); + return e === 32 ? Nt(t8, 0, this.unsigned) : Nt(t8 >>> e - 32, 0, this.unsigned); }; de.shru = de.shiftRightUnsigned; de.shr_u = de.shiftRightUnsigned; de.toSigned = function() { - return this.unsigned ? kt(this.low, this.high, false) : this; + return this.unsigned ? Nt(this.low, this.high, false) : this; }; de.toUnsigned = function() { - return this.unsigned ? this : kt(this.low, this.high, true); + return this.unsigned ? this : Nt(this.low, this.high, true); }; de.toBytes = function(e) { return e ? this.toBytesLE() : this.toBytesBE(); }; de.toBytesLE = function() { - var e = this.high, t10 = this.low; - return [t10 & 255, t10 >>> 8 & 255, t10 >>> 16 & 255, t10 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24]; + var e = this.high, t8 = this.low; + return [t8 & 255, t8 >>> 8 & 255, t8 >>> 16 & 255, t8 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24]; }; de.toBytesBE = function() { - var e = this.high, t10 = this.low; - return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t10 >>> 24, t10 >>> 16 & 255, t10 >>> 8 & 255, t10 & 255]; + var e = this.high, t8 = this.low; + return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t8 >>> 24, t8 >>> 16 & 255, t8 >>> 8 & 255, t8 & 255]; }; - vt.fromBytes = function(e, t10, o) { - return o ? vt.fromBytesLE(e, t10) : vt.fromBytesBE(e, t10); + kt.fromBytes = function(e, t8, o) { + return o ? kt.fromBytesLE(e, t8) : kt.fromBytesBE(e, t8); }; - vt.fromBytesLE = function(e, t10) { - return new vt(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t10); + kt.fromBytesLE = function(e, t8) { + return new kt(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t8); }; - vt.fromBytesBE = function(e, t10) { - return new vt(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t10); + kt.fromBytesBE = function(e, t8) { + return new kt(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t8); }; }); -var sk = qt(() => { +var hk = qt(() => { }); -var ak = qt(() => { +var gk = qt(() => { }); -var F2 = qt((A2, kw) => { - (function(r, e, t10) { +var G2 = qt((U2, Fw) => { + (function(r, e, t8) { function o(i) { var p = this, u = a(); p.next = function() { @@ -918,13 +919,13 @@ var F2 = qt((A2, kw) => { }; return p; } - e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { + e && e.exports ? e.exports = s : t8 && t8.amd ? t8(function() { return s; }) : this.alea = s; - })(A2, typeof kw == "object" && kw, typeof define == "function" && define); + })(U2, typeof Fw == "object" && Fw, typeof define == "function" && define); }); -var O2 = qt((P2, Nw) => { - (function(r, e, t10) { +var K2 = qt((H2, Pw) => { + (function(r, e, t8) { function o(a) { var i = this, p = ""; i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.next = function() { @@ -950,13 +951,13 @@ var O2 = qt((P2, Nw) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { + e && e.exports ? e.exports = s : t8 && t8.amd ? t8(function() { return s; }) : this.xor128 = s; - })(P2, typeof Nw == "object" && Nw, typeof define == "function" && define); + })(H2, typeof Pw == "object" && Pw, typeof define == "function" && define); }); -var L2 = qt((M2, Tw) => { - (function(r, e, t10) { +var j2 = qt((q2, Ow) => { + (function(r, e, t8) { function o(a) { var i = this, p = ""; i.next = function() { @@ -982,13 +983,13 @@ var L2 = qt((M2, Tw) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { + e && e.exports ? e.exports = s : t8 && t8.amd ? t8(function() { return s; }) : this.xorwow = s; - })(M2, typeof Tw == "object" && Tw, typeof define == "function" && define); + })(q2, typeof Ow == "object" && Ow, typeof define == "function" && define); }); -var z2 = qt((B2, _w) => { - (function(r, e, t10) { +var Y2 = qt((X2, Mw) => { + (function(r, e, t8) { function o(a) { var i = this; i.next = function() { @@ -1028,13 +1029,13 @@ var z2 = qt((B2, _w) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { + e && e.exports ? e.exports = s : t8 && t8.amd ? t8(function() { return s; }) : this.xorshift7 = s; - })(B2, typeof _w == "object" && _w, typeof define == "function" && define); + })(X2, typeof Mw == "object" && Mw, typeof define == "function" && define); }); -var W2 = qt((V2, $w) => { - (function(r, e, t10) { +var Z2 = qt((Q2, Lw) => { + (function(r, e, t8) { function o(a) { var i = this; i.next = function() { @@ -1068,13 +1069,13 @@ var W2 = qt((V2, $w) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { + e && e.exports ? e.exports = s : t8 && t8.amd ? t8(function() { return s; }) : this.xor4096 = s; - })(V2, typeof $w == "object" && $w, typeof define == "function" && define); + })(Q2, typeof Lw == "object" && Lw, typeof define == "function" && define); }); -var G2 = qt((U2, Ew) => { - (function(r, e, t10) { +var e1 = qt((J2, Bw) => { + (function(r, e, t8) { function o(a) { var i = this, p = ""; i.next = function() { @@ -1100,252 +1101,252 @@ var G2 = qt((U2, Ew) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { + e && e.exports ? e.exports = s : t8 && t8.amd ? t8(function() { return s; }) : this.tychei = s; - })(U2, typeof Ew == "object" && Ew, typeof define == "function" && define); + })(J2, typeof Bw == "object" && Bw, typeof define == "function" && define); }); -var H2 = qt(() => { +var t1 = qt(() => { }); -var q2 = qt((K2, Fd) => { - (function(r, e, t10) { - var o = 256, n = 6, s = 52, a = "random", i = t10.pow(o, n), p = t10.pow(2, s), u = p * 2, c = o - 1, l; - function m(w, S, k) { +var o1 = qt((r1, Od) => { + (function(r, e, t8) { + var o = 256, n = 6, s = 52, a = "random", i = t8.pow(o, n), p = t8.pow(2, s), u = p * 2, c = o - 1, l; + function m(C, S, k) { var _ = []; S = S == true ? { entropy: true } : S || {}; - var E = g(h(S.entropy ? [w, b(e)] : w == null ? x() : w, 3), _), R = new d(_), D = function() { - for (var F = R.g(n), O = i, M = 0; F < p; ) - F = (F + M) * o, O *= o, M = R.g(1); - for (; F >= u; ) - F /= 2, O /= 2, M >>>= 1; - return (F + M) / O; + var E = g(h(S.entropy ? [C, b(e)] : C == null ? x() : C, 3), _), R = new d(_), D = function() { + for (var P = R.g(n), O = i, M = 0; P < p; ) + P = (P + M) * o, O *= o, M = R.g(1); + for (; P >= u; ) + P /= 2, O /= 2, M >>>= 1; + return (P + M) / O; }; return D.int32 = function() { return R.g(4) | 0; }, D.quick = function() { return R.g(4) / 4294967296; - }, D.double = D, g(b(R.S), e), (S.pass || k || function(F, O, M, L) { - return L && (L.S && f(L, R), F.state = function() { + }, D.double = D, g(b(R.S), e), (S.pass || k || function(P, O, M, L) { + return L && (L.S && f(L, R), P.state = function() { return f(R, {}); - }), M ? (t10[a] = F, O) : F; - })(D, E, "global" in S ? S.global : this == t10, S.state); + }), M ? (t8[a] = P, O) : P; + })(D, E, "global" in S ? S.global : this == t8, S.state); } - function d(w) { - var S, k = w.length, _ = this, E = 0, R = _.i = _.j = 0, D = _.S = []; - for (k || (w = [k++]); E < o; ) + function d(C) { + var S, k = C.length, _ = this, E = 0, R = _.i = _.j = 0, D = _.S = []; + for (k || (C = [k++]); E < o; ) D[E] = E++; for (E = 0; E < o; E++) - D[E] = D[R = c & R + w[E % k] + (S = D[E])], D[R] = S; - (_.g = function(F) { - for (var O, M = 0, L = _.i, B = _.j, z = _.S; F--; ) + D[E] = D[R = c & R + C[E % k] + (S = D[E])], D[R] = S; + (_.g = function(P) { + for (var O, M = 0, L = _.i, B = _.j, z = _.S; P--; ) O = z[L = c & L + 1], M = M * o + z[c & (z[L] = z[B = c & B + O]) + (z[B] = O)]; return _.i = L, _.j = B, M; })(o); } - function f(w, S) { - return S.i = w.i, S.j = w.j, S.S = w.S.slice(), S; + function f(C, S) { + return S.i = C.i, S.j = C.j, S.S = C.S.slice(), S; } - function h(w, S) { - var k = [], _ = typeof w, E; + function h(C, S) { + var k = [], _ = typeof C, E; if (S && _ == "object") - for (E in w) + for (E in C) try { - k.push(h(w[E], S - 1)); + k.push(h(C[E], S - 1)); } catch (R) { } - return k.length ? k : _ == "string" ? w : w + "\0"; + return k.length ? k : _ == "string" ? C : C + "\0"; } - function g(w, S) { - for (var k = w + "", _, E = 0; E < k.length; ) + function g(C, S) { + for (var k = C + "", _, E = 0; E < k.length; ) S[c & E] = c & (_ ^= S[c & E] * 19) + k.charCodeAt(E++); return b(S); } function x() { try { - var w; - return l && (w = l.randomBytes) ? w = w(o) : (w = new Uint8Array(o), (r.crypto || r.msCrypto).getRandomValues(w)), b(w); + var C; + return l && (C = l.randomBytes) ? C = C(o) : (C = new Uint8Array(o), (r.crypto || r.msCrypto).getRandomValues(C)), b(C); } catch (_) { var S = r.navigator, k = S && S.plugins; return [+/* @__PURE__ */ new Date(), r, k, r.screen, b(e)]; } } - function b(w) { - return String.fromCharCode.apply(0, w); + function b(C) { + return String.fromCharCode.apply(0, C); } - if (g(t10.random(), e), typeof Fd == "object" && Fd.exports) { - Fd.exports = m; + if (g(t8.random(), e), typeof Od == "object" && Od.exports) { + Od.exports = m; try { - l = H2(); - } catch (w) { + l = t1(); + } catch (C) { } } else typeof define == "function" && define.amd ? define(function() { return m; - }) : t10["seed" + a] = m; - })(typeof self != "undefined" ? self : K2, [], Math); + }) : t8["seed" + a] = m; + })(typeof self != "undefined" ? self : r1, [], Math); }); -var Rw = qt((Jke, j2) => { - var VK = F2(), WK = O2(), UK = L2(), GK = z2(), HK = W2(), KK = G2(), Uu = q2(); - Uu.alea = VK; - Uu.xor128 = WK; - Uu.xorwow = UK; - Uu.xorshift7 = GK; - Uu.xor4096 = HK; - Uu.tychei = KK; - j2.exports = Uu; +var zw = qt((B2e, n1) => { + var dq = G2(), fq = K2(), hq = j2(), gq = Y2(), xq = Z2(), yq = e1(), qu = o1(); + qu.alea = dq; + qu.xor128 = fq; + qu.xorwow = hq; + qu.xorshift7 = gq; + qu.xor4096 = xq; + qu.tychei = yq; + n1.exports = qu; }); -var Sv = qt(() => { +var Rv = qt(() => { }); -var Iv = qt(() => { +var Dv = qt(() => { }); -var ZL = qt(() => { +var IB = qt(() => { }); -var JL = qt(() => { +var vB = qt(() => { }); -var eB = qt(() => { +var kB = qt(() => { }); -var tB = qt((Bg, kv) => { - var vv = (() => { +var NB = qt((zg, Fv) => { + var Av = (() => { var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; return typeof __filename != "undefined" && (r = r || __filename), function(e) { e = e || {}; - function t10() { - return oe.buffer != We && Tt(oe.buffer), mt; + function t8() { + return oe.buffer != Ue && Tt(oe.buffer), mt; } function o() { - return oe.buffer != We && Tt(oe.buffer), it; + return oe.buffer != Ue && Tt(oe.buffer), it; } function n() { - return oe.buffer != We && Tt(oe.buffer), ht; + return oe.buffer != Ue && Tt(oe.buffer), gt; } function s() { - return oe.buffer != We && Tt(oe.buffer), Or; + return oe.buffer != Ue && Tt(oe.buffer), Lr; } function a() { - return oe.buffer != We && Tt(oe.buffer), Mt; + return oe.buffer != Ue && Tt(oe.buffer), Mt; } function i() { - return oe.buffer != We && Tt(oe.buffer), Qr; + return oe.buffer != Ue && Tt(oe.buffer), eo; } function p() { - return oe.buffer != We && Tt(oe.buffer), or; + return oe.buffer != Ue && Tt(oe.buffer), or; } var u = typeof e != "undefined" ? e : {}, c, l; - u.ready = new Promise(function(A, V) { - c = A, l = V; + u.ready = new Promise(function(F, V) { + c = F, l = V; }); var m; typeof process != "undefined" && process.listeners && (m = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }); - var d = Object.assign({}, u), f = [], h = "./this.program", g = (A, V) => { + var d = Object.assign({}, u), f = [], h = "./this.program", g = (F, V) => { throw V; - }, x = typeof window == "object", b = typeof importScripts == "function", w = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", S = u.ENVIRONMENT_IS_PTHREAD || false, k = ""; - function _(A) { - return u.locateFile ? u.locateFile(A, k) : k + A; + }, x = typeof window == "object", b = typeof importScripts == "function", C = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", S = u.ENVIRONMENT_IS_PTHREAD || false, k = ""; + function _(F) { + return u.locateFile ? u.locateFile(F, k) : k + F; } - var E, R, D, F; - function O(A) { - if (A instanceof Iu) + var E, R, D, P; + function O(F) { + if (F instanceof Tu) return; - j("exiting due to exception: " + A); + j("exiting due to exception: " + F); } - if (w) { - var M = Sv(), L = Iv(); - b ? k = L.dirname(k) + "/" : k = __dirname + "/", E = (V, ue) => (V = Ap(V) ? new URL(V) : L.normalize(V), M.readFileSync(V, ue ? void 0 : "utf8")), D = (V) => { + if (C) { + var M = Rv(), L = Dv(); + b ? k = L.dirname(k) + "/" : k = __dirname + "/", E = (V, ue) => (V = Mp(V) ? new URL(V) : L.normalize(V), M.readFileSync(V, ue ? void 0 : "utf8")), D = (V) => { var ue = E(V, true); return ue.buffer || (ue = new Uint8Array(ue)), ue; }, R = (V, ue, Ee) => { - V = Ap(V) ? new URL(V) : L.normalize(V), M.readFile(V, function(Be, Le) { + V = Mp(V) ? new URL(V) : L.normalize(V), M.readFile(V, function(Be, Le) { Be ? Ee(Be) : ue(Le.buffer); }); }, process.argv.length > 1 && (h = process.argv[1].replace(/\\/g, "/")), f = process.argv.slice(2), process.on("uncaughtException", function(V) { - if (!(V instanceof Iu)) + if (!(V instanceof Tu)) throw V; }), process.on("unhandledRejection", function(V) { throw V; }), g = (V, ue) => { - if (Mo()) + if (Lo()) throw process.exitCode = V, ue; O(ue), process.exit(V); }, u.inspect = function() { return "[Emscripten Module object]"; }; - let A; + let F; try { - A = ZL(); + F = IB(); } catch (V) { throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), V; } - global.Worker = A.Worker; + global.Worker = F.Worker; } else - (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", w || (E = (A) => { + (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || (E = (F) => { var V = new XMLHttpRequest(); - return V.open("GET", A, false), V.send(null), V.responseText; - }, b && (D = (A) => { + return V.open("GET", F, false), V.send(null), V.responseText; + }, b && (D = (F) => { var V = new XMLHttpRequest(); - return V.open("GET", A, false), V.responseType = "arraybuffer", V.send(null), new Uint8Array(V.response); - }), R = (A, V, ue) => { + return V.open("GET", F, false), V.responseType = "arraybuffer", V.send(null), new Uint8Array(V.response); + }), R = (F, V, ue) => { var Ee = new XMLHttpRequest(); - Ee.open("GET", A, true), Ee.responseType = "arraybuffer", Ee.onload = () => { + Ee.open("GET", F, true), Ee.responseType = "arraybuffer", Ee.onload = () => { if (Ee.status == 200 || Ee.status == 0 && Ee.response) { V(Ee.response); return; } ue(); }, Ee.onerror = ue, Ee.send(null); - }), F = (A) => document.title = A); - w && typeof performance == "undefined" && (global.performance = JL().performance); + }), P = (F) => document.title = F); + C && typeof performance == "undefined" && (global.performance = vB().performance); var B = console.log.bind(console), z = console.warn.bind(console); - w && (B = (A) => M.writeSync(1, A + ` -`), z = (A) => M.writeSync(2, A + ` + C && (B = (F) => M.writeSync(1, F + ` +`), z = (F) => M.writeSync(2, F + ` `)); var U = u.print || B, j = u.printErr || z; Object.assign(u, d), d = null, u.arguments && (f = u.arguments), u.thisProgram && (h = u.thisProgram), u.quit && (g = u.quit); - var H = 4, X = Atomics.load, J = Atomics.store, re = Atomics.compareExchange, ne; + var q = 4, Y = Atomics.load, J = Atomics.store, re = Atomics.compareExchange, ne; u.wasmBinary && (ne = u.wasmBinary); var ee = u.noExitRuntime || true; - typeof WebAssembly != "object" && Su("no native wasm support detected"); - var oe, ie, le = false, ye; - function _e(A, V) { - A || Su(V); + typeof WebAssembly != "object" && Nu("no native wasm support detected"); + var oe, ie, le = false, be; + function _e(F, V) { + F || Nu(V); } var ve = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; - function Fe(A, V, ue) { + function Fe(F, V, ue) { V >>>= 0; - for (var Ee = V + ue, Be = V; A[Be] && !(Be >= Ee); ) + for (var Ee = V + ue, Be = V; F[Be] && !(Be >= Ee); ) ++Be; - if (Be - V > 16 && A.buffer && ve) - return ve.decode(A.buffer instanceof SharedArrayBuffer ? A.slice(V, Be) : A.subarray(V, Be)); + if (Be - V > 16 && F.buffer && ve) + return ve.decode(F.buffer instanceof SharedArrayBuffer ? F.slice(V, Be) : F.subarray(V, Be)); for (var Le = ""; V < Be; ) { - var he = A[V++]; - if (!(he & 128)) { - Le += String.fromCharCode(he); + var ge = F[V++]; + if (!(ge & 128)) { + Le += String.fromCharCode(ge); continue; } - var Ne = A[V++] & 63; - if ((he & 224) == 192) { - Le += String.fromCharCode((he & 31) << 6 | Ne); + var Ne = F[V++] & 63; + if ((ge & 224) == 192) { + Le += String.fromCharCode((ge & 31) << 6 | Ne); continue; } - var Ft = A[V++] & 63; - if ((he & 240) == 224 ? he = (he & 15) << 12 | Ne << 6 | Ft : he = (he & 7) << 18 | Ne << 12 | Ft << 6 | A[V++] & 63, he < 65536) - Le += String.fromCharCode(he); + var Ft = F[V++] & 63; + if ((ge & 240) == 224 ? ge = (ge & 15) << 12 | Ne << 6 | Ft : ge = (ge & 7) << 18 | Ne << 12 | Ft << 6 | F[V++] & 63, ge < 65536) + Le += String.fromCharCode(ge); else { - var to = he - 65536; - Le += String.fromCharCode(55296 | to >> 10, 56320 | to & 1023); + var no = ge - 65536; + Le += String.fromCharCode(55296 | no >> 10, 56320 | no & 1023); } } return Le; } - function Pe(A, V) { - return A >>>= 0, A ? Fe(o(), A, V) : ""; + function Pe(F, V) { + return F >>>= 0, F ? Fe(o(), F, V) : ""; } - function st(A, V, ue, Ee) { + function st(F, V, ue, Ee) { if (ue >>>= 0, !(Ee > 0)) return 0; - for (var Be = ue, Le = ue + Ee - 1, he = 0; he < A.length; ++he) { - var Ne = A.charCodeAt(he); + for (var Be = ue, Le = ue + Ee - 1, ge = 0; ge < F.length; ++ge) { + var Ne = F.charCodeAt(ge); if (Ne >= 55296 && Ne <= 57343) { - var Ft = A.charCodeAt(++he); + var Ft = F.charCodeAt(++ge); Ne = 65536 + ((Ne & 1023) << 10) | Ft & 1023; } if (Ne <= 127) { @@ -1368,137 +1369,137 @@ var tB = qt((Bg, kv) => { } return V[ue >>> 0] = 0, ue - Be; } - function lt(A, V, ue) { - return st(A, o(), V, ue); + function lt(F, V, ue) { + return st(F, o(), V, ue); } - var We, mt, it, ht, gt, Or, Mt, Qr, or; - S && (We = u.buffer); - function Tt(A) { - We = A, u.HEAP8 = mt = new Int8Array(A), u.HEAP16 = ht = new Int16Array(A), u.HEAP32 = Or = new Int32Array(A), u.HEAPU8 = it = new Uint8Array(A), u.HEAPU16 = gt = new Uint16Array(A), u.HEAPU32 = Mt = new Uint32Array(A), u.HEAPF32 = Qr = new Float32Array(A), u.HEAPF64 = or = new Float64Array(A); + var Ue, mt, it, gt, xt, Lr, Mt, eo, or; + S && (Ue = u.buffer); + function Tt(F) { + Ue = F, u.HEAP8 = mt = new Int8Array(F), u.HEAP16 = gt = new Int16Array(F), u.HEAP32 = Lr = new Int32Array(F), u.HEAPU8 = it = new Uint8Array(F), u.HEAPU16 = xt = new Uint16Array(F), u.HEAPU32 = Mt = new Uint32Array(F), u.HEAPF32 = eo = new Float32Array(F), u.HEAPF64 = or = new Float64Array(F); } var nr = u.INITIAL_MEMORY || 16777216; if (S) - oe = u.wasmMemory, We = u.buffer; + oe = u.wasmMemory, Ue = u.buffer; else if (u.wasmMemory) oe = u.wasmMemory; else if (oe = new WebAssembly.Memory({ initial: nr / 65536, maximum: 65536, shared: true }), !(oe.buffer instanceof SharedArrayBuffer)) - throw j("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), w && j("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"), Error("bad memory"); - oe && (We = oe.buffer), nr = We.byteLength, Tt(We); - var sr, Zr = [], Jr = [], fr = [], Fa = false; - function Mo() { + throw j("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), C && j("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"), Error("bad memory"); + oe && (Ue = oe.buffer), nr = Ue.byteLength, Tt(Ue); + var sr, to = [], ro = [], fr = [], Ba = false; + function Lo() { return ee; } - function Vs() { + function Hs() { if (u.preRun) for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; ) - el(u.preRun.shift()); - ol(Zr); + tl(u.preRun.shift()); + nl(to); } - function Xt() { - Fa = true, !S && ol(Jr); + function Yt() { + Ba = true, !S && nl(ro); } - function Pa() { + function za() { if (!S) { if (u.postRun) for (typeof u.postRun == "function" && (u.postRun = [u.postRun]); u.postRun.length; ) - jv(u.postRun.shift()); - ol(fr); + o0(u.postRun.shift()); + nl(fr); } } - function el(A) { - Zr.unshift(A); + function tl(F) { + to.unshift(F); } - function tl(A) { - Jr.unshift(A); + function rl(F) { + ro.unshift(F); } - function jv(A) { - fr.unshift(A); + function o0(F) { + fr.unshift(F); } - var wi = 0, Dp = null, Oa = null; - function ly(A) { - wi++, u.monitorRunDependencies && u.monitorRunDependencies(wi); + var vi = 0, Op = null, Va = null; + function dy(F) { + vi++, u.monitorRunDependencies && u.monitorRunDependencies(vi); } - function xm(A) { - if (wi--, u.monitorRunDependencies && u.monitorRunDependencies(wi), wi == 0 && (Dp !== null && (clearInterval(Dp), Dp = null), Oa)) { - var V = Oa; - Oa = null, V(); + function ym(F) { + if (vi--, u.monitorRunDependencies && u.monitorRunDependencies(vi), vi == 0 && (Op !== null && (clearInterval(Op), Op = null), Va)) { + var V = Va; + Va = null, V(); } } - function Su(A) { - u.onAbort && u.onAbort(A), A = "Aborted(" + A + ")", j(A), le = true, ye = 1, A += ". Build with -sASSERTIONS for more info."; - var V = new WebAssembly.RuntimeError(A); + function Nu(F) { + u.onAbort && u.onAbort(F), F = "Aborted(" + F + ")", j(F), le = true, be = 1, F += ". Build with -sASSERTIONS for more info."; + var V = new WebAssembly.RuntimeError(F); throw l(V), V; } - var my = "data:application/octet-stream;base64,"; - function ym(A) { - return A.startsWith(my); + var fy = "data:application/octet-stream;base64,"; + function bm(F) { + return F.startsWith(fy); } - function Ap(A) { - return A.startsWith("file://"); + function Mp(F) { + return F.startsWith("file://"); } var hr; - hr = "tfjs-backend-wasm-threaded-simd.wasm", ym(hr) || (hr = _(hr)); - function bm(A) { + hr = "tfjs-backend-wasm-threaded-simd.wasm", bm(hr) || (hr = _(hr)); + function Cm(F) { try { - if (A == hr && ne) + if (F == hr && ne) return new Uint8Array(ne); if (D) - return D(A); + return D(F); throw "both async and sync fetching of the wasm failed"; } catch (V) { - Su(V); + Nu(V); } } - function dy() { + function hy() { if (!ne && (x || b)) { - if (typeof fetch == "function" && !Ap(hr)) - return fetch(hr, { credentials: "same-origin" }).then(function(A) { - if (!A.ok) + if (typeof fetch == "function" && !Mp(hr)) + return fetch(hr, { credentials: "same-origin" }).then(function(F) { + if (!F.ok) throw "failed to load wasm binary file at '" + hr + "'"; - return A.arrayBuffer(); + return F.arrayBuffer(); }).catch(function() { - return bm(hr); + return Cm(hr); }); if (R) - return new Promise(function(A, V) { + return new Promise(function(F, V) { R(hr, function(ue) { - A(new Uint8Array(ue)); + F(new Uint8Array(ue)); }, V); }); } return Promise.resolve().then(function() { - return bm(hr); + return Cm(hr); }); } - function fy() { - var A = { env: Dm, wasi_snapshot_preview1: Dm }; - function V(he, Ne) { - var Ft = he.exports; - if (u.asm = Ft, Iy(u.asm._emscripten_tls_init), sr = u.asm.__indirect_function_table, tl(u.asm.__wasm_call_ctors), ie = Ne, !S) { - var to = Me.unusedWorkers.length; - Me.unusedWorkers.forEach(function(La) { - Me.loadWasmModuleToWorker(La, function() { - --to || xm("wasm-instantiate"); + function gy() { + var F = { env: Am, wasi_snapshot_preview1: Am }; + function V(ge, Ne) { + var Ft = ge.exports; + if (u.asm = Ft, ky(u.asm._emscripten_tls_init), sr = u.asm.__indirect_function_table, rl(u.asm.__wasm_call_ctors), ie = Ne, !S) { + var no = Me.unusedWorkers.length; + Me.unusedWorkers.forEach(function(Ua) { + Me.loadWasmModuleToWorker(Ua, function() { + --no || ym("wasm-instantiate"); }); }); } } - S || ly("wasm-instantiate"); - function ue(he) { - V(he.instance, he.module); + S || dy("wasm-instantiate"); + function ue(ge) { + V(ge.instance, ge.module); } - function Ee(he) { - return dy().then(function(Ne) { - return WebAssembly.instantiate(Ne, A); + function Ee(ge) { + return hy().then(function(Ne) { + return WebAssembly.instantiate(Ne, F); }).then(function(Ne) { return Ne; - }).then(he, function(Ne) { - j("failed to asynchronously prepare wasm: " + Ne), Su(Ne); + }).then(ge, function(Ne) { + j("failed to asynchronously prepare wasm: " + Ne), Nu(Ne); }); } function Be() { - return !ne && typeof WebAssembly.instantiateStreaming == "function" && !ym(hr) && !Ap(hr) && !w && typeof fetch == "function" ? fetch(hr, { credentials: "same-origin" }).then(function(he) { - var Ne = WebAssembly.instantiateStreaming(he, A); + return !ne && typeof WebAssembly.instantiateStreaming == "function" && !bm(hr) && !Mp(hr) && !C && typeof fetch == "function" ? fetch(hr, { credentials: "same-origin" }).then(function(ge) { + var Ne = WebAssembly.instantiateStreaming(ge, F); return Ne.then(ue, function(Ft) { return j("wasm streaming compile failed: " + Ft), j("falling back to ArrayBuffer instantiation"), Ee(ue); }); @@ -1506,662 +1507,676 @@ var tB = qt((Bg, kv) => { } if (u.instantiateWasm) try { - var Le = u.instantiateWasm(A, V); + var Le = u.instantiateWasm(F, V); return Le; - } catch (he) { - j("Module.instantiateWasm callback failed with error: " + he), l(he); + } catch (ge) { + j("Module.instantiateWasm callback failed with error: " + ge), l(ge); } return Be().catch(l), {}; } - var Xv, Yv, Cm = {}; - function Iu(A) { - this.name = "ExitStatus", this.message = "Program terminated with exit(" + A + ")", this.status = A; + var n0, s0, wm = {}; + function Tu(F) { + this.name = "ExitStatus", this.message = "Program terminated with exit(" + F + ")", this.status = F; } - function hy(A) { - var V = Me.pthreads[A]; - delete Me.pthreads[A], V.terminate(), DC(A), Me.runningWorkers.splice(Me.runningWorkers.indexOf(V), 1), V.pthread_ptr = 0; + function xy(F) { + var V = Me.pthreads[F]; + delete Me.pthreads[F], V.terminate(), VC(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(V), 1), V.pthread_ptr = 0; } - function gy(A) { - var V = Me.pthreads[A]; + function yy(F) { + var V = Me.pthreads[F]; V.postMessage({ cmd: "cancel" }); } - function rl(A) { - var V = Me.pthreads[A]; + function ol(F) { + var V = Me.pthreads[F]; _e(V), Me.returnWorkerToPool(V); } - function xy(A) { + function by(F) { var V = Me.getNewWorker(); if (!V) return 6; - Me.runningWorkers.push(V), Me.pthreads[A.pthread_ptr] = V, V.pthread_ptr = A.pthread_ptr; - var ue = { cmd: "run", start_routine: A.startRoutine, arg: A.arg, pthread_ptr: A.pthread_ptr }; + Me.runningWorkers.push(V), Me.pthreads[F.pthread_ptr] = V, V.pthread_ptr = F.pthread_ptr; + var ue = { cmd: "run", start_routine: F.startRoutine, arg: F.arg, pthread_ptr: F.pthread_ptr }; return V.runPthread = () => { - w && V.ref(), V.postMessage(ue, A.transferList), delete V.runPthread; + C && V.ref(), V.postMessage(ue, F.transferList), delete V.runPthread; }, V.loaded && V.runPthread(), 0; } - var wm = { varargs: void 0, get: function() { - wm.varargs += 4; - var A = s()[wm.varargs - 4 >>> 2]; - return A; - }, getStr: function(A) { - var V = Pe(A); + var Sm = { varargs: void 0, get: function() { + Sm.varargs += 4; + var F = s()[Sm.varargs - 4 >>> 2]; + return F; + }, getStr: function(F) { + var V = Pe(F); return V; } }; - function Sm(A) { + function Im(F) { if (S) - return Si(1, 1, A); - ye = A, Mo() || (Me.terminateAllThreads(), u.onExit && u.onExit(A), le = true), g(A, new Iu(A)); + return ki(1, 1, F); + be = F, Lo() || (Me.terminateAllThreads(), u.onExit && u.onExit(F), le = true), g(F, new Tu(F)); } - function yy(A, V) { - if (ye = A, !V && S) - throw vm(A), "unwind"; - Sm(A); + function Cy(F, V) { + if (be = F, !V && S) + throw km(F), "unwind"; + Im(F); } - var Im = yy; - function by(A) { - if (A instanceof Iu || A == "unwind") - return ye; - g(1, A); + var vm = Cy; + function wy(F) { + if (F instanceof Tu || F == "unwind") + return be; + g(1, F); } var Me = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() { S ? Me.initWorker() : Me.initMainThread(); }, initMainThread: function() { - for (var A = 8; A--; ) + for (var F = 8; F--; ) Me.allocateUnusedWorker(); }, initWorker: function() { ee = false; - }, setExitStatus: function(A) { - ye = A; + }, setExitStatus: function(F) { + be = F; }, terminateAllThreads: function() { - for (var A of Object.values(Me.pthreads)) - Me.returnWorkerToPool(A); - for (var A of Me.unusedWorkers) - A.terminate(); + for (var F of Object.values(Me.pthreads)) + Me.returnWorkerToPool(F); + for (var F of Me.unusedWorkers) + F.terminate(); Me.unusedWorkers = []; - }, returnWorkerToPool: function(A) { - var V = A.pthread_ptr; - delete Me.pthreads[V], Me.unusedWorkers.push(A), Me.runningWorkers.splice(Me.runningWorkers.indexOf(A), 1), A.pthread_ptr = 0, w && A.unref(), DC(V); - }, receiveObjectTransfer: function(A) { + }, returnWorkerToPool: function(F) { + var V = F.pthread_ptr; + delete Me.pthreads[V], Me.unusedWorkers.push(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, C && F.unref(), VC(V); + }, receiveObjectTransfer: function(F) { }, threadInitTLS: function() { - Me.tlsInitFunctions.forEach((A) => A()); - }, loadWasmModuleToWorker: function(A, V) { - A.onmessage = (Le) => { - var he = Le.data, Ne = he.cmd; - if (A.pthread_ptr && (Me.currentProxiedOperationCallerThread = A.pthread_ptr), he.targetThread && he.targetThread != Lm()) { - var Ft = Me.pthreads[he.targetThread]; - Ft ? Ft.postMessage(he, he.transferList) : j('Internal error! Worker sent a message "' + Ne + '" to target pthread ' + he.targetThread + ", but that thread no longer exists!"), Me.currentProxiedOperationCallerThread = void 0; + Me.tlsInitFunctions.forEach((F) => F()); + }, loadWasmModuleToWorker: function(F, V) { + F.onmessage = (Le) => { + var ge = Le.data, Ne = ge.cmd; + if (F.pthread_ptr && (Me.currentProxiedOperationCallerThread = F.pthread_ptr), ge.targetThread && ge.targetThread != Bm()) { + var Ft = Me.pthreads[ge.targetThread]; + Ft ? Ft.postMessage(ge, ge.transferList) : j('Internal error! Worker sent a message "' + Ne + '" to target pthread ' + ge.targetThread + ", but that thread no longer exists!"), Me.currentProxiedOperationCallerThread = void 0; return; } - Ne === "processProxyingQueue" ? nl(he.queue) : Ne === "spawnThread" ? xy(he) : Ne === "cleanupThread" ? rl(he.thread) : Ne === "killThread" ? hy(he.thread) : Ne === "cancelThread" ? gy(he.thread) : Ne === "loaded" ? (A.loaded = true, w && A.unref(), V && V(A), A.runPthread && A.runPthread()) : Ne === "print" ? U("Thread " + he.threadId + ": " + he.text) : Ne === "printErr" ? j("Thread " + he.threadId + ": " + he.text) : Ne === "alert" ? alert("Thread " + he.threadId + ": " + he.text) : he.target === "setimmediate" ? A.postMessage(he) : Ne === "callHandler" ? u[he.handler](...he.args) : Ne && j("worker sent an unknown command " + Ne), Me.currentProxiedOperationCallerThread = void 0; - }, A.onerror = (Le) => { - var he = "worker sent an error!"; - throw j(he + " " + Le.filename + ":" + Le.lineno + ": " + Le.message), Le; - }, w && (A.on("message", function(Le) { - A.onmessage({ data: Le }); - }), A.on("error", function(Le) { - A.onerror(Le); - }), A.on("detachedExit", function() { + Ne === "processProxyingQueue" ? sl(ge.queue) : Ne === "spawnThread" ? by(ge) : Ne === "cleanupThread" ? ol(ge.thread) : Ne === "killThread" ? xy(ge.thread) : Ne === "cancelThread" ? yy(ge.thread) : Ne === "loaded" ? (F.loaded = true, C && F.unref(), V && V(F), F.runPthread && F.runPthread()) : Ne === "print" ? U("Thread " + ge.threadId + ": " + ge.text) : Ne === "printErr" ? j("Thread " + ge.threadId + ": " + ge.text) : Ne === "alert" ? alert("Thread " + ge.threadId + ": " + ge.text) : ge.target === "setimmediate" ? F.postMessage(ge) : Ne === "callHandler" ? u[ge.handler](...ge.args) : Ne && j("worker sent an unknown command " + Ne), Me.currentProxiedOperationCallerThread = void 0; + }, F.onerror = (Le) => { + var ge = "worker sent an error!"; + throw j(ge + " " + Le.filename + ":" + Le.lineno + ": " + Le.message), Le; + }, C && (F.on("message", function(Le) { + F.onmessage({ data: Le }); + }), F.on("error", function(Le) { + F.onerror(Le); + }), F.on("detachedExit", function() { })); var ue = [], Ee = ["onExit", "onAbort", "print", "printErr"]; for (var Be of Ee) u.hasOwnProperty(Be) && ue.push(Be); - A.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: oe, wasmModule: ie }); + F.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: oe, wasmModule: ie }); }, allocateUnusedWorker: function() { - var A, V = _("tfjs-backend-wasm-threaded-simd.worker.js"); - A = new Worker(V), Me.unusedWorkers.push(A); + var F, V = _("tfjs-backend-wasm-threaded-simd.worker.js"); + F = new Worker(V), Me.unusedWorkers.push(F); }, getNewWorker: function() { return Me.unusedWorkers.length == 0 && (Me.allocateUnusedWorker(), Me.loadWasmModuleToWorker(Me.unusedWorkers[0])), Me.unusedWorkers.pop(); } }; u.PThread = Me; - function ol(A) { - for (; A.length > 0; ) - A.shift()(u); + function nl(F) { + for (; F.length > 0; ) + F.shift()(u); } - function Cy() { - var A = Lm(), V = s()[A + 52 >>> 2], ue = s()[A + 56 >>> 2], Ee = V - ue; - r0(V, Ee), Bm(V); + function Sy() { + var F = Bm(), V = s()[F + 52 >>> 2], ue = s()[F + 56 >>> 2], Ee = V - ue; + l0(V, Ee), zm(V); } - u.establishStackSpace = Cy; - function vm(A) { + u.establishStackSpace = Sy; + function km(F) { if (S) - return Si(2, 0, A); + return ki(2, 0, F); try { - Im(A); + vm(F); } catch (V) { - by(V); + wy(V); } } - var Fp = []; - function wy(A) { - var V = Fp[A]; - return V || (A >= Fp.length && (Fp.length = A + 1), Fp[A] = V = sr.get(A)), V; + var Lp = []; + function Iy(F) { + var V = Lp[F]; + return V || (F >= Lp.length && (Lp.length = F + 1), Lp[F] = V = sr.get(F)), V; } - function Sy(A, V) { - var ue = wy(A)(V); - Mo() ? Me.setExitStatus(ue) : t0(ue); + function vy(F, V) { + var ue = Iy(F)(V); + Lo() ? Me.setExitStatus(ue) : c0(ue); } - u.invokeEntryPoint = Sy; - function Iy(A) { - Me.tlsInitFunctions.push(A); + u.invokeEntryPoint = vy; + function ky(F) { + Me.tlsInitFunctions.push(F); } - function vy(A) { - Zv(A, !b, 1, !x), Me.threadInitTLS(); + function Ny(F) { + i0(F, !b, 1, !x), Me.threadInitTLS(); } - function ky(A) { - S ? postMessage({ cmd: "cleanupThread", thread: A }) : rl(A); + function Ty(F) { + S ? postMessage({ cmd: "cleanupThread", thread: F }) : ol(F); } - function km(A, V, ue, Ee) { - return S ? Si(3, 1, A, V, ue, Ee) : Nm(A, V, ue, Ee); + function Nm(F, V, ue, Ee) { + return S ? ki(3, 1, F, V, ue, Ee) : Tm(F, V, ue, Ee); } - function Nm(A, V, ue, Ee) { + function Tm(F, V, ue, Ee) { if (typeof SharedArrayBuffer == "undefined") return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6; var Be = [], Le = 0; if (S && (Be.length === 0 || Le)) - return km(A, V, ue, Ee); + return Nm(F, V, ue, Ee); if (Le) return Le; - var he = { startRoutine: ue, pthread_ptr: A, arg: Ee, transferList: Be }; - return S ? (he.cmd = "spawnThread", postMessage(he, Be), 0) : xy(he); + var ge = { startRoutine: ue, pthread_ptr: F, arg: Ee, transferList: Be }; + return S ? (ge.cmd = "spawnThread", postMessage(ge, Be), 0) : by(ge); } - function Ny() { + function _y() { return 65536; } - var Ty = true; - function _y() { - return Ty; + var $y = true; + function Ey() { + return $y; } - function nl(A) { - Atomics.store(s(), A >> 2, 1), Lm() && e0(A), Atomics.compareExchange(s(), A >> 2, 1, 0); + function sl(F) { + Atomics.store(s(), F >> 2, 1), Bm() && p0(F), Atomics.compareExchange(s(), F >> 2, 1, 0); } - u.executeNotifiedProxyingQueue = nl; - function $y(A, V, ue, Ee) { - if (A == V) - setTimeout(() => nl(Ee)); + u.executeNotifiedProxyingQueue = sl; + function Ry(F, V, ue, Ee) { + if (F == V) + setTimeout(() => sl(Ee)); else if (S) - postMessage({ targetThread: A, cmd: "processProxyingQueue", queue: Ee }); + postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: Ee }); else { - var Be = Me.pthreads[A]; + var Be = Me.pthreads[F]; if (!Be) return; Be.postMessage({ cmd: "processProxyingQueue", queue: Ee }); } return 1; } - function Ey(A, V, ue) { + function Dy(F, V, ue) { return -1; } - function Ry() { - Su(""); - } - function vu(A) { - vu.shown || (vu.shown = {}), vu.shown[A] || (vu.shown[A] = 1, w && (A = "warning: " + A), j(A)); - } - function Dy() { - w || b || vu("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); - } function Ay() { - return Date.now(); + Nu(""); } - function Tm() { - return 4294901760; + function _u(F) { + _u.shown || (_u.shown = {}), _u.shown[F] || (_u.shown[F] = 1, C && (F = "warning: " + F), j(F)); } function Fy() { - return Tm(); + C || b || _u("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); } - var sl; - w ? sl = () => { - var A = process.hrtime(); - return A[0] * 1e3 + A[1] / 1e6; - } : sl = () => performance.timeOrigin + performance.now(); - function Py(A, V, ue) { - o().copyWithin(A >>> 0, V >>> 0, V + ue >>> 0); + function Py() { + return Date.now(); + } + function _m() { + return 4294901760; } function Oy() { - return w ? eB().cpus().length : navigator.hardwareConcurrency; + return _m(); } - function My(A) { - var V = AC(), ue = A(); - return Bm(V), ue; + var al; + C ? al = () => { + var F = process.hrtime(); + return F[0] * 1e3 + F[1] / 1e6; + } : al = () => performance.timeOrigin + performance.now(); + function My(F, V, ue) { + o().copyWithin(F >>> 0, V >>> 0, V + ue >>> 0); } - function Si(A, V) { + function Ly() { + return C ? kB().cpus().length : navigator.hardwareConcurrency; + } + function By(F) { + var V = WC(), ue = F(); + return zm(V), ue; + } + function ki(F, V) { var ue = arguments.length - 2, Ee = arguments; - return My(() => { - for (var Be = ue, Le = zm(Be * 8), he = Le >> 3, Ne = 0; Ne < ue; Ne++) { + return By(() => { + for (var Be = ue, Le = Vm(Be * 8), ge = Le >> 3, Ne = 0; Ne < ue; Ne++) { var Ft = Ee[2 + Ne]; - p()[he + Ne >>> 0] = Ft; + p()[ge + Ne >>> 0] = Ft; } - return Jv(A, Be, Le, V); + return u0(F, Be, Le, V); }); } - var al = []; - function Ly(A, V, ue) { - al.length = V; + var il = []; + function zy(F, V, ue) { + il.length = V; for (var Ee = ue >> 3, Be = 0; Be < V; Be++) - al[Be] = p()[Ee + Be >>> 0]; - var Le = A < 0, he = Le ? Cm[-A - 1] : qy[A]; - return he.apply(null, al); + il[Be] = p()[Ee + Be >>> 0]; + var Le = F < 0, ge = Le ? wm[-F - 1] : Xy[F]; + return ge.apply(null, il); } - function By(A) { + function Vy(F) { try { - return oe.grow(A - We.byteLength + 65535 >>> 16), Tt(oe.buffer), 1; + return oe.grow(F - Ue.byteLength + 65535 >>> 16), Tt(oe.buffer), 1; } catch (V) { } } - function zy(A) { + function Wy(F) { var V = o().length; - if (A = A >>> 0, A <= V) + if (F = F >>> 0, F <= V) return false; - var ue = Tm(); - if (A > ue) + var ue = _m(); + if (F > ue) return false; - let Ee = (Ft, to) => Ft + (to - Ft % to) % to; + let Ee = (Ft, no) => Ft + (no - Ft % no) % no; for (var Be = 1; Be <= 4; Be *= 2) { var Le = V * (1 + 0.2 / Be); - Le = Math.min(Le, A + 100663296); - var he = Math.min(ue, Ee(Math.max(A, Le), 65536)), Ne = By(he); + Le = Math.min(Le, F + 100663296); + var ge = Math.min(ue, Ee(Math.max(F, Le), 65536)), Ne = Vy(ge); if (Ne) return true; } return false; } - function Vy() { + function Uy() { throw "unwind"; } - function _m(A) { - return S ? Si(4, 1, A) : 52; + function $m(F) { + return S ? ki(4, 1, F) : 52; } - function $m(A, V, ue, Ee, Be) { - return S ? Si(5, 1, A, V, ue, Ee, Be) : 70; + function Em(F, V, ue, Ee, Be) { + return S ? ki(5, 1, F, V, ue, Ee, Be) : 70; } - var Wy = [null, [], []]; - function Uy(A, V) { - var ue = Wy[A]; - V === 0 || V === 10 ? ((A === 1 ? U : j)(Fe(ue, 0)), ue.length = 0) : ue.push(V); + var Gy = [null, [], []]; + function Hy(F, V) { + var ue = Gy[F]; + V === 0 || V === 10 ? ((F === 1 ? U : j)(Fe(ue, 0)), ue.length = 0) : ue.push(V); } - function Em(A, V, ue, Ee) { + function Rm(F, V, ue, Ee) { if (S) - return Si(6, 1, A, V, ue, Ee); + return ki(6, 1, F, V, ue, Ee); for (var Be = 0, Le = 0; Le < ue; Le++) { - var he = a()[V >>> 2], Ne = a()[V + 4 >>> 2]; + var ge = a()[V >>> 2], Ne = a()[V + 4 >>> 2]; V += 8; for (var Ft = 0; Ft < Ne; Ft++) - Uy(A, o()[he + Ft >>> 0]); + Hy(F, o()[ge + Ft >>> 0]); Be += Ne; } return a()[Ee >>> 2] = Be, 0; } - function Rm(A) { - var V = u["_" + A]; + function Dm(F) { + var V = u["_" + F]; return V; } - function Gy(A, V) { - t10().set(A, V >>> 0); + function Ky(F, V) { + t8().set(F, V >>> 0); } - function Hy(A, V, ue, Ee, Be) { - var Le = { string: (Mr) => { - var Lp = 0; - if (Mr != null && Mr !== 0) { - var s0 = (Mr.length << 2) + 1; - Lp = zm(s0), lt(Mr, Lp, s0); + function qy(F, V, ue, Ee, Be) { + var Le = { string: (Br) => { + var Wp = 0; + if (Br != null && Br !== 0) { + var f0 = (Br.length << 2) + 1; + Wp = Vm(f0), lt(Br, Wp, f0); } - return Lp; - }, array: (Mr) => { - var Lp = zm(Mr.length); - return Gy(Mr, Lp), Lp; + return Wp; + }, array: (Br) => { + var Wp = Vm(Br.length); + return Ky(Br, Wp), Wp; } }; - function he(Mr) { - return V === "string" ? Pe(Mr) : V === "boolean" ? !!Mr : Mr; + function ge(Br) { + return V === "string" ? Pe(Br) : V === "boolean" ? !!Br : Br; } - var Ne = Rm(A), Ft = [], to = 0; + var Ne = Dm(F), Ft = [], no = 0; if (Ee) - for (var La = 0; La < Ee.length; La++) { - var n0 = Le[ue[La]]; - n0 ? (to === 0 && (to = AC()), Ft[La] = n0(Ee[La])) : Ft[La] = Ee[La]; + for (var Ua = 0; Ua < Ee.length; Ua++) { + var d0 = Le[ue[Ua]]; + d0 ? (no === 0 && (no = WC()), Ft[Ua] = d0(Ee[Ua])) : Ft[Ua] = Ee[Ua]; } - var FC = Ne.apply(null, Ft); - function LU(Mr) { - return to !== 0 && Bm(to), he(Mr); + var UC = Ne.apply(null, Ft); + function pG(Br) { + return no !== 0 && zm(no), ge(Br); } - return FC = LU(FC), FC; + return UC = pG(UC), UC; } - function Ky(A, V, ue, Ee) { + function jy(F, V, ue, Ee) { ue = ue || []; - var Be = ue.every((he) => he === "number" || he === "boolean"), Le = V !== "string"; - return Le && Be && !Ee ? Rm(A) : function() { - return Hy(A, V, ue, arguments, Ee); + var Be = ue.every((ge) => ge === "number" || ge === "boolean"), Le = V !== "string"; + return Le && Be && !Ee ? Dm(F) : function() { + return qy(F, V, ue, arguments, Ee); }; } Me.init(); - var qy = [null, Sm, vm, km, _m, $m, Em], Dm = { __emscripten_init_main_thread_js: vy, __emscripten_thread_cleanup: ky, __pthread_create_js: Nm, _emscripten_default_pthread_stack_size: Ny, _emscripten_get_now_is_monotonic: _y, _emscripten_notify_task_queue: $y, _emscripten_set_offscreencanvas_size: Ey, abort: Ry, emscripten_check_blocking_allowed: Dy, emscripten_date_now: Ay, emscripten_get_heap_max: Fy, emscripten_get_now: sl, emscripten_memcpy_big: Py, emscripten_num_logical_cores: Oy, emscripten_receive_on_main_thread_js: Ly, emscripten_resize_heap: zy, emscripten_unwind_to_js_event_loop: Vy, exit: Im, fd_close: _m, fd_seek: $m, fd_write: Em, memory: oe || u.wasmMemory }, Qv = fy(), jy = u.___wasm_call_ctors = function() { - return (jy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments); - }, Xy = u._init = function() { - return (Xy = u._init = u.asm.init).apply(null, arguments); - }, Yy = u._init_with_threads_count = function() { - return (Yy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments); - }, Qy = u._get_threads_count = function() { - return (Qy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments); - }, Zy = u._register_tensor = function() { - return (Zy = u._register_tensor = u.asm.register_tensor).apply(null, arguments); - }, Jy = u._dispose_data = function() { - return (Jy = u._dispose_data = u.asm.dispose_data).apply(null, arguments); - }, eb = u._dispose = function() { - return (eb = u._dispose = u.asm.dispose).apply(null, arguments); - }, tb = u._Abs = function() { - return (tb = u._Abs = u.asm.Abs).apply(null, arguments); - }, rb = u._Acos = function() { - return (rb = u._Acos = u.asm.Acos).apply(null, arguments); - }, ob = u._Acosh = function() { - return (ob = u._Acosh = u.asm.Acosh).apply(null, arguments); - }, nb = u._Add = function() { - return (nb = u._Add = u.asm.Add).apply(null, arguments); - }, sb = u._AddN = function() { - return (sb = u._AddN = u.asm.AddN).apply(null, arguments); - }, ab = u._All = function() { - return (ab = u._All = u.asm.All).apply(null, arguments); - }, ib = u._Any = function() { - return (ib = u._Any = u.asm.Any).apply(null, arguments); - }, ub = u._ArgMax = function() { - return (ub = u._ArgMax = u.asm.ArgMax).apply(null, arguments); - }, pb = u._ArgMin = function() { - return (pb = u._ArgMin = u.asm.ArgMin).apply(null, arguments); - }, cb = u._Asin = function() { - return (cb = u._Asin = u.asm.Asin).apply(null, arguments); - }, lb = u._Asinh = function() { - return (lb = u._Asinh = u.asm.Asinh).apply(null, arguments); - }, mb = u._Atan = function() { - return (mb = u._Atan = u.asm.Atan).apply(null, arguments); - }, db = u._Atan2 = function() { - return (db = u._Atan2 = u.asm.Atan2).apply(null, arguments); - }, fb = u._Atanh = function() { - return (fb = u._Atanh = u.asm.Atanh).apply(null, arguments); - }, hb = u._AvgPool = function() { - return (hb = u._AvgPool = u.asm.AvgPool).apply(null, arguments); - }, gb = u._AvgPool3D = function() { - return (gb = u._AvgPool3D = u.asm.AvgPool3D).apply(null, arguments); - }, xb = u._AvgPool3DGrad = function() { - return (xb = u._AvgPool3DGrad = u.asm.AvgPool3DGrad).apply(null, arguments); - }, yb = u._BatchMatMul = function() { - return (yb = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments); - }, bb = u._Bincount = function() { - return (bb = u._Bincount = u.asm.Bincount).apply(null, arguments); - }, Cb = u._Ceil = function() { - return (Cb = u._Ceil = u.asm.Ceil).apply(null, arguments); - }, wb = u._ClipByValue = function() { - return (wb = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments); - }, Sb = u._Conv2D = function() { - return (Sb = u._Conv2D = u.asm.Conv2D).apply(null, arguments); - }, Ib = u._Conv2DBackpropInput = function() { - return (Ib = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments); - }, vb = u._Conv3D = function() { - return (vb = u._Conv3D = u.asm.Conv3D).apply(null, arguments); - }, kb = u._Conv3DBackpropFilterV2 = function() { - return (kb = u._Conv3DBackpropFilterV2 = u.asm.Conv3DBackpropFilterV2).apply(null, arguments); - }, Nb = u._Conv3DBackpropInputV2 = function() { - return (Nb = u._Conv3DBackpropInputV2 = u.asm.Conv3DBackpropInputV2).apply(null, arguments); - }, Tb = u._Cos = function() { - return (Tb = u._Cos = u.asm.Cos).apply(null, arguments); - }, _b = u._Cosh = function() { - return (_b = u._Cosh = u.asm.Cosh).apply(null, arguments); - }, $b = u._CropAndResize = function() { - return ($b = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments); - }, Eb = u._Cumprod = function() { - return (Eb = u._Cumprod = u.asm.Cumprod).apply(null, arguments); - }, Rb = u._Cumsum = function() { - return (Rb = u._Cumsum = u.asm.Cumsum).apply(null, arguments); - }, Db = u._DenseBincount = function() { - return (Db = u._DenseBincount = u.asm.DenseBincount).apply(null, arguments); - }, Ab = u._DepthToSpace = function() { - return (Ab = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments); - }, Fb = u._DepthwiseConv2dNative = function() { - return (Fb = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments); - }, Pb = u._Diag = function() { - return (Pb = u._Diag = u.asm.Diag).apply(null, arguments); - }, Ob = u._Dilation2D = function() { - return (Ob = u._Dilation2D = u.asm.Dilation2D).apply(null, arguments); - }, Mb = u._Dilation2DBackpropFilter = function() { - return (Mb = u._Dilation2DBackpropFilter = u.asm.Dilation2DBackpropFilter).apply(null, arguments); - }, Lb = u._Dilation2DBackpropInput = function() { - return (Lb = u._Dilation2DBackpropInput = u.asm.Dilation2DBackpropInput).apply(null, arguments); - }, Bb = u._Elu = function() { - return (Bb = u._Elu = u.asm.Elu).apply(null, arguments); - }, zb = u._EluGrad = function() { - return (zb = u._EluGrad = u.asm.EluGrad).apply(null, arguments); - }, Vb = u._Equal = function() { - return (Vb = u._Equal = u.asm.Equal).apply(null, arguments); - }, Wb = u._Exp = function() { - return (Wb = u._Exp = u.asm.Exp).apply(null, arguments); - }, Ub = u._Expm1 = function() { - return (Ub = u._Expm1 = u.asm.Expm1).apply(null, arguments); - }, Gb = u._FlipLeftRight = function() { - return (Gb = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments); - }, Hb = u._Floor = function() { - return (Hb = u._Floor = u.asm.Floor).apply(null, arguments); - }, Kb = u._FloorDiv = function() { - return (Kb = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments); - }, qb = u._FusedBatchNorm = function() { - return (qb = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments); - }, jb = u._FusedConv2D = function() { - return (jb = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments); - }, Xb = u._FusedDepthwiseConv2D = function() { - return (Xb = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, Yb = u._Gather = function() { - return (Yb = u._Gather = u.asm.Gather).apply(null, arguments); - }, Qb = u._GatherNd = function() { - return (Qb = u._GatherNd = u.asm.GatherNd).apply(null, arguments); - }, Zb = u._Greater = function() { - return (Zb = u._Greater = u.asm.Greater).apply(null, arguments); - }, Jb = u._GreaterEqual = function() { - return (Jb = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments); - }, eC = u._IsFinite = function() { - return (eC = u._IsFinite = u.asm.IsFinite).apply(null, arguments); - }, tC = u._IsInf = function() { - return (tC = u._IsInf = u.asm.IsInf).apply(null, arguments); - }, rC = u._IsNan = function() { - return (rC = u._IsNan = u.asm.IsNan).apply(null, arguments); - }, oC = u._LRN = function() { - return (oC = u._LRN = u.asm.LRN).apply(null, arguments); - }, nC = u._LRNGrad = function() { - return (nC = u._LRNGrad = u.asm.LRNGrad).apply(null, arguments); - }, sC = u._LeakyRelu = function() { - return (sC = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments); - }, aC = u._Less = function() { - return (aC = u._Less = u.asm.Less).apply(null, arguments); - }, iC = u._LessEqual = function() { - return (iC = u._LessEqual = u.asm.LessEqual).apply(null, arguments); - }, uC = u._LinSpace = function() { - return (uC = u._LinSpace = u.asm.LinSpace).apply(null, arguments); - }, pC = u._Log = function() { - return (pC = u._Log = u.asm.Log).apply(null, arguments); - }, cC = u._Log1p = function() { - return (cC = u._Log1p = u.asm.Log1p).apply(null, arguments); - }, lC = u._LogicalAnd = function() { - return (lC = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments); - }, mC = u._LogicalNot = function() { - return (mC = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments); - }, dC = u._LogicalOr = function() { - return (dC = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments); - }, fC = u._LogicalXor = function() { - return (fC = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments); - }, hC = u._Max = function() { - return (hC = u._Max = u.asm.Max).apply(null, arguments); - }, gC = u._MaxPool = function() { - return (gC = u._MaxPool = u.asm.MaxPool).apply(null, arguments); - }, xC = u._MaxPool3D = function() { - return (xC = u._MaxPool3D = u.asm.MaxPool3D).apply(null, arguments); - }, yC = u._MaxPool3DGrad = function() { - return (yC = u._MaxPool3DGrad = u.asm.MaxPool3DGrad).apply(null, arguments); - }, bC = u._Maximum = function() { - return (bC = u._Maximum = u.asm.Maximum).apply(null, arguments); - }, CC = u._Mean = function() { - return (CC = u._Mean = u.asm.Mean).apply(null, arguments); - }, wC = u._Min = function() { - return (wC = u._Min = u.asm.Min).apply(null, arguments); - }, SC = u._Minimum = function() { - return (SC = u._Minimum = u.asm.Minimum).apply(null, arguments); - }, IC = u._MirrorPad = function() { - return (IC = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments); - }, vC = u._Multinomial = function() { - return (vC = u._Multinomial = u.asm.Multinomial).apply(null, arguments); - }, kC = u._Multiply = function() { - return (kC = u._Multiply = u.asm.Multiply).apply(null, arguments); - }, NC = u._Neg = function() { - return (NC = u._Neg = u.asm.Neg).apply(null, arguments); - }, TC = u._NonMaxSuppressionV3 = function() { - return (TC = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments); - }, Am = u._NonMaxSuppressionV4 = function() { - return (Am = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments); + var Xy = [null, Im, km, Nm, $m, Em, Rm], Am = { __emscripten_init_main_thread_js: Ny, __emscripten_thread_cleanup: Ty, __pthread_create_js: Tm, _emscripten_default_pthread_stack_size: _y, _emscripten_get_now_is_monotonic: Ey, _emscripten_notify_task_queue: Ry, _emscripten_set_offscreencanvas_size: Dy, abort: Ay, emscripten_check_blocking_allowed: Fy, emscripten_date_now: Py, emscripten_get_heap_max: Oy, emscripten_get_now: al, emscripten_memcpy_big: My, emscripten_num_logical_cores: Ly, emscripten_receive_on_main_thread_js: zy, emscripten_resize_heap: Wy, emscripten_unwind_to_js_event_loop: Uy, exit: vm, fd_close: $m, fd_seek: Em, fd_write: Rm, memory: oe || u.wasmMemory }, a0 = gy(), Yy = u.___wasm_call_ctors = function() { + return (Yy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments); + }, Qy = u._init = function() { + return (Qy = u._init = u.asm.init).apply(null, arguments); + }, Zy = u._init_with_threads_count = function() { + return (Zy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments); + }, Jy = u._get_threads_count = function() { + return (Jy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments); + }, eb = u._register_tensor = function() { + return (eb = u._register_tensor = u.asm.register_tensor).apply(null, arguments); + }, tb = u._dispose_data = function() { + return (tb = u._dispose_data = u.asm.dispose_data).apply(null, arguments); + }, rb = u._dispose = function() { + return (rb = u._dispose = u.asm.dispose).apply(null, arguments); + }, ob = u._Abs = function() { + return (ob = u._Abs = u.asm.Abs).apply(null, arguments); + }, nb = u._Acos = function() { + return (nb = u._Acos = u.asm.Acos).apply(null, arguments); + }, sb = u._Acosh = function() { + return (sb = u._Acosh = u.asm.Acosh).apply(null, arguments); + }, ab = u._Add = function() { + return (ab = u._Add = u.asm.Add).apply(null, arguments); + }, ib = u._AddN = function() { + return (ib = u._AddN = u.asm.AddN).apply(null, arguments); + }, ub = u._All = function() { + return (ub = u._All = u.asm.All).apply(null, arguments); + }, pb = u._Any = function() { + return (pb = u._Any = u.asm.Any).apply(null, arguments); + }, cb = u._ArgMax = function() { + return (cb = u._ArgMax = u.asm.ArgMax).apply(null, arguments); + }, lb = u._ArgMin = function() { + return (lb = u._ArgMin = u.asm.ArgMin).apply(null, arguments); + }, mb = u._Asin = function() { + return (mb = u._Asin = u.asm.Asin).apply(null, arguments); + }, db = u._Asinh = function() { + return (db = u._Asinh = u.asm.Asinh).apply(null, arguments); + }, fb = u._Atan = function() { + return (fb = u._Atan = u.asm.Atan).apply(null, arguments); + }, hb = u._Atan2 = function() { + return (hb = u._Atan2 = u.asm.Atan2).apply(null, arguments); + }, gb = u._Atanh = function() { + return (gb = u._Atanh = u.asm.Atanh).apply(null, arguments); + }, xb = u._AvgPool = function() { + return (xb = u._AvgPool = u.asm.AvgPool).apply(null, arguments); + }, yb = u._AvgPool3D = function() { + return (yb = u._AvgPool3D = u.asm.AvgPool3D).apply(null, arguments); + }, bb = u._AvgPool3DGrad = function() { + return (bb = u._AvgPool3DGrad = u.asm.AvgPool3DGrad).apply(null, arguments); + }, Cb = u._AvgPoolGrad = function() { + return (Cb = u._AvgPoolGrad = u.asm.AvgPoolGrad).apply(null, arguments); + }, wb = u._BatchMatMul = function() { + return (wb = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments); + }, Sb = u._Bincount = function() { + return (Sb = u._Bincount = u.asm.Bincount).apply(null, arguments); + }, Ib = u._BitwiseAnd = function() { + return (Ib = u._BitwiseAnd = u.asm.BitwiseAnd).apply(null, arguments); + }, vb = u._Ceil = function() { + return (vb = u._Ceil = u.asm.Ceil).apply(null, arguments); + }, kb = u._ClipByValue = function() { + return (kb = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments); + }, Nb = u._Conv2D = function() { + return (Nb = u._Conv2D = u.asm.Conv2D).apply(null, arguments); + }, Tb = u._Conv2DBackpropInput = function() { + return (Tb = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments); + }, _b = u._Conv3D = function() { + return (_b = u._Conv3D = u.asm.Conv3D).apply(null, arguments); + }, $b = u._Conv3DBackpropFilterV2 = function() { + return ($b = u._Conv3DBackpropFilterV2 = u.asm.Conv3DBackpropFilterV2).apply(null, arguments); + }, Eb = u._Conv3DBackpropInputV2 = function() { + return (Eb = u._Conv3DBackpropInputV2 = u.asm.Conv3DBackpropInputV2).apply(null, arguments); + }, Rb = u._Cos = function() { + return (Rb = u._Cos = u.asm.Cos).apply(null, arguments); + }, Db = u._Cosh = function() { + return (Db = u._Cosh = u.asm.Cosh).apply(null, arguments); + }, Ab = u._CropAndResize = function() { + return (Ab = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments); + }, Fb = u._Cumprod = function() { + return (Fb = u._Cumprod = u.asm.Cumprod).apply(null, arguments); + }, Pb = u._Cumsum = function() { + return (Pb = u._Cumsum = u.asm.Cumsum).apply(null, arguments); + }, Ob = u._DenseBincount = function() { + return (Ob = u._DenseBincount = u.asm.DenseBincount).apply(null, arguments); + }, Mb = u._DepthToSpace = function() { + return (Mb = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments); + }, Lb = u._DepthwiseConv2dNative = function() { + return (Lb = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments); + }, Bb = u._Diag = function() { + return (Bb = u._Diag = u.asm.Diag).apply(null, arguments); + }, zb = u._Dilation2D = function() { + return (zb = u._Dilation2D = u.asm.Dilation2D).apply(null, arguments); + }, Vb = u._Dilation2DBackpropFilter = function() { + return (Vb = u._Dilation2DBackpropFilter = u.asm.Dilation2DBackpropFilter).apply(null, arguments); + }, Wb = u._Dilation2DBackpropInput = function() { + return (Wb = u._Dilation2DBackpropInput = u.asm.Dilation2DBackpropInput).apply(null, arguments); + }, Ub = u._Elu = function() { + return (Ub = u._Elu = u.asm.Elu).apply(null, arguments); + }, Gb = u._EluGrad = function() { + return (Gb = u._EluGrad = u.asm.EluGrad).apply(null, arguments); + }, Hb = u._Equal = function() { + return (Hb = u._Equal = u.asm.Equal).apply(null, arguments); + }, Kb = u._Erf = function() { + return (Kb = u._Erf = u.asm.Erf).apply(null, arguments); + }, qb = u._Exp = function() { + return (qb = u._Exp = u.asm.Exp).apply(null, arguments); + }, jb = u._Expm1 = function() { + return (jb = u._Expm1 = u.asm.Expm1).apply(null, arguments); + }, Xb = u._FlipLeftRight = function() { + return (Xb = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments); + }, Yb = u._Floor = function() { + return (Yb = u._Floor = u.asm.Floor).apply(null, arguments); + }, Qb = u._FloorDiv = function() { + return (Qb = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments); + }, Zb = u._FusedBatchNorm = function() { + return (Zb = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments); + }, Jb = u._FusedConv2D = function() { + return (Jb = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments); + }, eC = u._FusedDepthwiseConv2D = function() { + return (eC = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments); + }, tC = u._Gather = function() { + return (tC = u._Gather = u.asm.Gather).apply(null, arguments); + }, rC = u._GatherNd = function() { + return (rC = u._GatherNd = u.asm.GatherNd).apply(null, arguments); + }, oC = u._Greater = function() { + return (oC = u._Greater = u.asm.Greater).apply(null, arguments); + }, nC = u._GreaterEqual = function() { + return (nC = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments); + }, sC = u._IsFinite = function() { + return (sC = u._IsFinite = u.asm.IsFinite).apply(null, arguments); + }, aC = u._IsInf = function() { + return (aC = u._IsInf = u.asm.IsInf).apply(null, arguments); + }, iC = u._IsNan = function() { + return (iC = u._IsNan = u.asm.IsNan).apply(null, arguments); + }, uC = u._LRN = function() { + return (uC = u._LRN = u.asm.LRN).apply(null, arguments); + }, pC = u._LRNGrad = function() { + return (pC = u._LRNGrad = u.asm.LRNGrad).apply(null, arguments); + }, cC = u._LeakyRelu = function() { + return (cC = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments); + }, lC = u._Less = function() { + return (lC = u._Less = u.asm.Less).apply(null, arguments); + }, mC = u._LessEqual = function() { + return (mC = u._LessEqual = u.asm.LessEqual).apply(null, arguments); + }, dC = u._LinSpace = function() { + return (dC = u._LinSpace = u.asm.LinSpace).apply(null, arguments); + }, fC = u._Log = function() { + return (fC = u._Log = u.asm.Log).apply(null, arguments); + }, hC = u._Log1p = function() { + return (hC = u._Log1p = u.asm.Log1p).apply(null, arguments); + }, gC = u._LogicalAnd = function() { + return (gC = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments); + }, xC = u._LogicalNot = function() { + return (xC = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments); + }, yC = u._LogicalOr = function() { + return (yC = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments); + }, bC = u._LogicalXor = function() { + return (bC = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments); + }, CC = u._Max = function() { + return (CC = u._Max = u.asm.Max).apply(null, arguments); + }, wC = u._MaxPool = function() { + return (wC = u._MaxPool = u.asm.MaxPool).apply(null, arguments); + }, SC = u._MaxPool3D = function() { + return (SC = u._MaxPool3D = u.asm.MaxPool3D).apply(null, arguments); + }, IC = u._MaxPool3DGrad = function() { + return (IC = u._MaxPool3DGrad = u.asm.MaxPool3DGrad).apply(null, arguments); + }, vC = u._MaxPoolGrad = function() { + return (vC = u._MaxPoolGrad = u.asm.MaxPoolGrad).apply(null, arguments); + }, kC = u._MaxPoolWithArgmax = function() { + return (kC = u._MaxPoolWithArgmax = u.asm.MaxPoolWithArgmax).apply(null, arguments); + }, NC = u._Maximum = function() { + return (NC = u._Maximum = u.asm.Maximum).apply(null, arguments); + }, TC = u._Mean = function() { + return (TC = u._Mean = u.asm.Mean).apply(null, arguments); + }, _C = u._Min = function() { + return (_C = u._Min = u.asm.Min).apply(null, arguments); + }, $C = u._Minimum = function() { + return ($C = u._Minimum = u.asm.Minimum).apply(null, arguments); + }, EC = u._MirrorPad = function() { + return (EC = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments); + }, RC = u._Mod = function() { + return (RC = u._Mod = u.asm.Mod).apply(null, arguments); + }, DC = u._Multinomial = function() { + return (DC = u._Multinomial = u.asm.Multinomial).apply(null, arguments); + }, AC = u._Multiply = function() { + return (AC = u._Multiply = u.asm.Multiply).apply(null, arguments); + }, FC = u._Neg = function() { + return (FC = u._Neg = u.asm.Neg).apply(null, arguments); + }, PC = u._NonMaxSuppressionV3 = function() { + return (PC = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments); + }, OC = u._NonMaxSuppressionV4 = function() { + return (OC = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments); }, Fm = u._NonMaxSuppressionV5 = function() { return (Fm = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments); - }, il = u._NotEqual = function() { - return (il = u._NotEqual = u.asm.NotEqual).apply(null, arguments); - }, _C = u._OneHot = function() { - return (_C = u._OneHot = u.asm.OneHot).apply(null, arguments); - }, $C = u._PadV2 = function() { - return ($C = u._PadV2 = u.asm.PadV2).apply(null, arguments); - }, Pp = u._Pow = function() { - return (Pp = u._Pow = u.asm.Pow).apply(null, arguments); - }, Pm = u._Prelu = function() { - return (Pm = u._Prelu = u.asm.Prelu).apply(null, arguments); - }, Op = u._Prod = function() { - return (Op = u._Prod = u.asm.Prod).apply(null, arguments); - }, Mp = u._RealDiv = function() { - return (Mp = u._RealDiv = u.asm.RealDiv).apply(null, arguments); - }, EC = u._Reciprocal = function() { - return (EC = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments); - }, G = u._Relu = function() { - return (G = u._Relu = u.asm.Relu).apply(null, arguments); - }, ae = u._Relu6 = function() { - return (ae = u._Relu6 = u.asm.Relu6).apply(null, arguments); - }, $e = u._ResizeBilinear = function() { - return ($e = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments); - }, at = u._ResizeBilinearGrad = function() { - return (at = u._ResizeBilinearGrad = u.asm.ResizeBilinearGrad).apply(null, arguments); - }, _t = u._ResizeNearestNeighbor = function() { - return (_t = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments); - }, $t = u._ResizeNearestNeighborGrad = function() { - return ($t = u._ResizeNearestNeighborGrad = u.asm.ResizeNearestNeighborGrad).apply(null, arguments); - }, Xe = u._Reverse = function() { - return (Xe = u._Reverse = u.asm.Reverse).apply(null, arguments); - }, Ge = u._RotateWithOffset = function() { - return (Ge = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments); - }, Gt = u._Round = function() { - return (Gt = u._Round = u.asm.Round).apply(null, arguments); - }, eo = u._Rsqrt = function() { - return (eo = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments); - }, Ma = u._ScatterNd = function() { - return (Ma = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments); - }, Om = u._SearchSorted = function() { - return (Om = u._SearchSorted = u.asm.SearchSorted).apply(null, arguments); - }, ul = u._SelectV2 = function() { - return (ul = u._SelectV2 = u.asm.SelectV2).apply(null, arguments); - }, RC = u._Selu = function() { - return (RC = u._Selu = u.asm.Selu).apply(null, arguments); - }, yr = u._Sigmoid = function() { - return (yr = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments); - }, Ii = u._Sign = function() { - return (Ii = u._Sign = u.asm.Sign).apply(null, arguments); - }, Mm = u._Sin = function() { - return (Mm = u._Sin = u.asm.Sin).apply(null, arguments); - }, aU = u._Softmax = function() { - return (aU = u._Softmax = u.asm.Softmax).apply(null, arguments); - }, iU = u._Softplus = function() { - return (iU = u._Softplus = u.asm.Softplus).apply(null, arguments); - }, uU = u._SparseFillEmptyRows = function() { - return (uU = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments); - }, pU = u._SparseReshape = function() { - return (pU = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments); - }, cU = u._SparseSegmentReduction = function() { - return (cU = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments); - }, lU = u._SparseToDense = function() { - return (lU = u._SparseToDense = u.asm.SparseToDense).apply(null, arguments); - }, mU = u._Sqrt = function() { - return (mU = u._Sqrt = u.asm.Sqrt).apply(null, arguments); - }, dU = u._Square = function() { - return (dU = u._Square = u.asm.Square).apply(null, arguments); - }, fU = u._SquaredDifference = function() { - return (fU = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments); - }, hU = u._Step = function() { - return (hU = u._Step = u.asm.Step).apply(null, arguments); - }, gU = u._StridedSlice = function() { - return (gU = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments); - }, xU = u._Sub = function() { - return (xU = u._Sub = u.asm.Sub).apply(null, arguments); - }, yU = u._Sum = function() { - return (yU = u._Sum = u.asm.Sum).apply(null, arguments); - }, bU = u._Tan = function() { - return (bU = u._Tan = u.asm.Tan).apply(null, arguments); - }, CU = u._Tanh = function() { - return (CU = u._Tanh = u.asm.Tanh).apply(null, arguments); - }, wU = u._TensorScatterUpdate = function() { - return (wU = u._TensorScatterUpdate = u.asm.TensorScatterUpdate).apply(null, arguments); - }, SU = u._Tile = function() { - return (SU = u._Tile = u.asm.Tile).apply(null, arguments); - }, IU = u._TopK = function() { - return (IU = u._TopK = u.asm.TopK).apply(null, arguments); - }, vU = u._Transform = function() { - return (vU = u._Transform = u.asm.Transform).apply(null, arguments); - }, kU = u._Transpose = function() { - return (kU = u._Transpose = u.asm.Transpose).apply(null, arguments); - }, NU = u.__FusedMatMul = function() { - return (NU = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments); - }, TU = u._malloc = function() { - return (TU = u._malloc = u.asm.malloc).apply(null, arguments); - }, _U = u._free = function() { - return (_U = u._free = u.asm.free).apply(null, arguments); - }, $U = u.__emscripten_tls_init = function() { - return ($U = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments); - }, Lm = u._pthread_self = function() { - return (Lm = u._pthread_self = u.asm.pthread_self).apply(null, arguments); - }, EU = u.___errno_location = function() { - return (EU = u.___errno_location = u.asm.__errno_location).apply(null, arguments); - }, Zv = u.__emscripten_thread_init = function() { - return (Zv = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments); - }, RU = u.__emscripten_thread_crashed = function() { - return (RU = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments); - }, DU = u._emscripten_main_thread_process_queued_calls = function() { - return (DU = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); - }, AU = u._emscripten_main_browser_thread_id = function() { - return (AU = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments); - }, Jv = u._emscripten_run_in_main_runtime_thread_js = function() { - return (Jv = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); - }, FU = u._emscripten_dispatch_to_thread_ = function() { - return (FU = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments); - }, e0 = u.__emscripten_proxy_execute_task_queue = function() { - return (e0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments); - }, DC = u.__emscripten_thread_free_data = function() { - return (DC = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments); - }, t0 = u.__emscripten_thread_exit = function() { - return (t0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments); - }, r0 = u._emscripten_stack_set_limits = function() { - return (r0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments); - }, AC = u.stackSave = function() { - return (AC = u.stackSave = u.asm.stackSave).apply(null, arguments); - }, Bm = u.stackRestore = function() { - return (Bm = u.stackRestore = u.asm.stackRestore).apply(null, arguments); - }, zm = u.stackAlloc = function() { - return (zm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments); - }, PU = u.dynCall_iijjiiii = function() { - return (PU = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments); - }, OU = u.dynCall_jiji = function() { - return (OU = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments); + }, Pm = u._NotEqual = function() { + return (Pm = u._NotEqual = u.asm.NotEqual).apply(null, arguments); + }, ul = u._OneHot = function() { + return (ul = u._OneHot = u.asm.OneHot).apply(null, arguments); + }, MC = u._PadV2 = function() { + return (MC = u._PadV2 = u.asm.PadV2).apply(null, arguments); + }, LC = u._Pow = function() { + return (LC = u._Pow = u.asm.Pow).apply(null, arguments); + }, Bp = u._Prelu = function() { + return (Bp = u._Prelu = u.asm.Prelu).apply(null, arguments); + }, Om = u._Prod = function() { + return (Om = u._Prod = u.asm.Prod).apply(null, arguments); + }, zp = u._RealDiv = function() { + return (zp = u._RealDiv = u.asm.RealDiv).apply(null, arguments); + }, Vp = u._Reciprocal = function() { + return (Vp = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments); + }, BC = u._Relu = function() { + return (BC = u._Relu = u.asm.Relu).apply(null, arguments); + }, G = u._Relu6 = function() { + return (G = u._Relu6 = u.asm.Relu6).apply(null, arguments); + }, ae = u._ResizeBilinear = function() { + return (ae = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments); + }, $e = u._ResizeBilinearGrad = function() { + return ($e = u._ResizeBilinearGrad = u.asm.ResizeBilinearGrad).apply(null, arguments); + }, at = u._ResizeNearestNeighbor = function() { + return (at = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments); + }, _t = u._ResizeNearestNeighborGrad = function() { + return (_t = u._ResizeNearestNeighborGrad = u.asm.ResizeNearestNeighborGrad).apply(null, arguments); + }, $t = u._Reverse = function() { + return ($t = u._Reverse = u.asm.Reverse).apply(null, arguments); + }, Qe = u._RotateWithOffset = function() { + return (Qe = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments); + }, He = u._Round = function() { + return (He = u._Round = u.asm.Round).apply(null, arguments); + }, Gt = u._Rsqrt = function() { + return (Gt = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments); + }, oo = u._ScatterNd = function() { + return (oo = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments); + }, Wa = u._SearchSorted = function() { + return (Wa = u._SearchSorted = u.asm.SearchSorted).apply(null, arguments); + }, Mm = u._SelectV2 = function() { + return (Mm = u._SelectV2 = u.asm.SelectV2).apply(null, arguments); + }, pl = u._Selu = function() { + return (pl = u._Selu = u.asm.Selu).apply(null, arguments); + }, zC = u._Sigmoid = function() { + return (zC = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments); + }, br = u._Sign = function() { + return (br = u._Sign = u.asm.Sign).apply(null, arguments); + }, Ni = u._Sin = function() { + return (Ni = u._Sin = u.asm.Sin).apply(null, arguments); + }, Lm = u._Sinh = function() { + return (Lm = u._Sinh = u.asm.Sinh).apply(null, arguments); + }, RU = u._Softmax = function() { + return (RU = u._Softmax = u.asm.Softmax).apply(null, arguments); + }, DU = u._Softplus = function() { + return (DU = u._Softplus = u.asm.Softplus).apply(null, arguments); + }, AU = u._SparseFillEmptyRows = function() { + return (AU = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments); + }, FU = u._SparseReshape = function() { + return (FU = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments); + }, PU = u._SparseSegmentReduction = function() { + return (PU = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments); + }, OU = u._SparseToDense = function() { + return (OU = u._SparseToDense = u.asm.SparseToDense).apply(null, arguments); + }, MU = u._Sqrt = function() { + return (MU = u._Sqrt = u.asm.Sqrt).apply(null, arguments); + }, LU = u._Square = function() { + return (LU = u._Square = u.asm.Square).apply(null, arguments); + }, BU = u._SquaredDifference = function() { + return (BU = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments); + }, zU = u._Step = function() { + return (zU = u._Step = u.asm.Step).apply(null, arguments); + }, VU = u._StridedSlice = function() { + return (VU = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments); + }, WU = u._Sub = function() { + return (WU = u._Sub = u.asm.Sub).apply(null, arguments); + }, UU = u._Sum = function() { + return (UU = u._Sum = u.asm.Sum).apply(null, arguments); + }, GU = u._Tan = function() { + return (GU = u._Tan = u.asm.Tan).apply(null, arguments); + }, HU = u._Tanh = function() { + return (HU = u._Tanh = u.asm.Tanh).apply(null, arguments); + }, KU = u._TensorScatterUpdate = function() { + return (KU = u._TensorScatterUpdate = u.asm.TensorScatterUpdate).apply(null, arguments); + }, qU = u._Tile = function() { + return (qU = u._Tile = u.asm.Tile).apply(null, arguments); + }, jU = u._TopK = function() { + return (jU = u._TopK = u.asm.TopK).apply(null, arguments); + }, XU = u._Transform = function() { + return (XU = u._Transform = u.asm.Transform).apply(null, arguments); + }, YU = u._Transpose = function() { + return (YU = u._Transpose = u.asm.Transpose).apply(null, arguments); + }, QU = u.__FusedMatMul = function() { + return (QU = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments); + }, ZU = u._malloc = function() { + return (ZU = u._malloc = u.asm.malloc).apply(null, arguments); + }, JU = u._free = function() { + return (JU = u._free = u.asm.free).apply(null, arguments); + }, eG = u.__emscripten_tls_init = function() { + return (eG = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments); + }, Bm = u._pthread_self = function() { + return (Bm = u._pthread_self = u.asm.pthread_self).apply(null, arguments); + }, tG = u.___errno_location = function() { + return (tG = u.___errno_location = u.asm.__errno_location).apply(null, arguments); + }, i0 = u.__emscripten_thread_init = function() { + return (i0 = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments); + }, rG = u.__emscripten_thread_crashed = function() { + return (rG = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments); + }, oG = u._emscripten_main_thread_process_queued_calls = function() { + return (oG = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); + }, nG = u._emscripten_main_browser_thread_id = function() { + return (nG = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments); + }, u0 = u._emscripten_run_in_main_runtime_thread_js = function() { + return (u0 = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); + }, sG = u._emscripten_dispatch_to_thread_ = function() { + return (sG = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments); + }, p0 = u.__emscripten_proxy_execute_task_queue = function() { + return (p0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments); + }, VC = u.__emscripten_thread_free_data = function() { + return (VC = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments); + }, c0 = u.__emscripten_thread_exit = function() { + return (c0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments); + }, l0 = u._emscripten_stack_set_limits = function() { + return (l0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments); + }, WC = u.stackSave = function() { + return (WC = u.stackSave = u.asm.stackSave).apply(null, arguments); + }, zm = u.stackRestore = function() { + return (zm = u.stackRestore = u.asm.stackRestore).apply(null, arguments); + }, Vm = u.stackAlloc = function() { + return (Vm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments); + }, aG = u.dynCall_iijjiiii = function() { + return (aG = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments); + }, iG = u.dynCall_jiji = function() { + return (iG = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments); }; - u.keepRuntimeAlive = Mo, u.wasmMemory = oe, u.cwrap = Ky, u.ExitStatus = Iu, u.PThread = Me; - var Vm; - Oa = function A() { - Vm || o0(), Vm || (Oa = A); + u.keepRuntimeAlive = Lo, u.wasmMemory = oe, u.cwrap = jy, u.ExitStatus = Tu, u.PThread = Me; + var Wm; + Va = function F() { + Wm || m0(), Wm || (Va = F); }; - function o0(A) { - if (A = A || f, wi > 0) + function m0(F) { + if (F = F || f, vi > 0) return; if (S) { - c(u), Xt(), startWorker(u); + c(u), Yt(), startWorker(u); return; } - if (Vs(), wi > 0) + if (Hs(), vi > 0) return; function V() { - Vm || (Vm = true, u.calledRun = true, !le && (Xt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), Pa())); + Wm || (Wm = true, u.calledRun = true, !le && (Yt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), za())); } u.setStatus ? (u.setStatus("Running..."), setTimeout(function() { setTimeout(function() { @@ -2172,83 +2187,83 @@ var tB = qt((Bg, kv) => { if (u.preInit) for (typeof u.preInit == "function" && (u.preInit = [u.preInit]); u.preInit.length > 0; ) u.preInit.pop()(); - o0(); - var Wm; - m && (Wm = { uncaughtException: process.listeners("uncaughtException").filter(function(A) { - return !m.uncaughtException.indexOf(A) > -1; - }), unhandledRejection: process.listeners("unhandledRejection").filter(function(A) { - return !m.unhandledRejection.indexOf(A) > -1; - }) }); + m0(); var Um; + m && (Um = { uncaughtException: process.listeners("uncaughtException").filter(function(F) { + return !m.uncaughtException.indexOf(F) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(F) { + return !m.unhandledRejection.indexOf(F) > -1; + }) }); + var Gm; if (typeof WasmBackendModule != "undefined") - Um = WasmBackendModule; + Gm = WasmBackendModule; else if (typeof e != "undefined") - Um = e; + Gm = e; else throw new Error("Could not find wasm module in post.js"); - if (Wm) { - var MU = Um._dispose; - Um._dispose = function() { - MU(), Wm.uncaughtException.forEach(function(A) { - process.removeListener("uncaughtException", A); - }), Wm.unhandledRejection.forEach(function(A) { - process.removeListener("unhandledRejection", A); + if (Um) { + var uG = Gm._dispose; + Gm._dispose = function() { + uG(), Um.uncaughtException.forEach(function(F) { + process.removeListener("uncaughtException", F); + }), Um.unhandledRejection.forEach(function(F) { + process.removeListener("unhandledRejection", F); }); }; } return e.ready; }; })(); - typeof Bg == "object" && typeof kv == "object" ? kv.exports = vv : typeof define == "function" && define.amd ? define([], function() { - return vv; - }) : typeof Bg == "object" && (Bg.WasmBackendModuleThreadedSimd = vv); + typeof zg == "object" && typeof Fv == "object" ? Fv.exports = Av : typeof define == "function" && define.amd ? define([], function() { + return Av; + }) : typeof zg == "object" && (zg.WasmBackendModuleThreadedSimd = Av); }); -var oB = qt((tAt, rB) => { - rB.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" +var _B = qt((vFt, TB) => { + TB.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" ");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`; }); -var nB = qt((zg, Tv) => { - var Nv = (() => { +var $B = qt((Vg, Ov) => { + var Pv = (() => { var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; return typeof __filename != "undefined" && (r = r || __filename), function(e) { e = e || {}; - var t10 = typeof e != "undefined" ? e : {}, o, n; - t10.ready = new Promise(function(G, ae) { + var t8 = typeof e != "undefined" ? e : {}, o, n; + t8.ready = new Promise(function(G, ae) { o = G, n = ae; }); var s; typeof process != "undefined" && process.listeners && (s = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }); - var a = Object.assign({}, t10), i = [], p = "./this.program", u = (G, ae) => { + var a = Object.assign({}, t8), i = [], p = "./this.program", u = (G, ae) => { throw ae; }, c = typeof window == "object", l = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", d = ""; function f(G) { - return t10.locateFile ? t10.locateFile(G, d) : d + G; + return t8.locateFile ? t8.locateFile(G, d) : d + G; } var h, g, x, b; - function w(G) { - if (G instanceof Dp) + function C(G) { + if (G instanceof Op) return; E("exiting due to exception: " + G); } if (m) { - var S = Sv(), k = Iv(); - l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (G, ae) => (G = Vs(G) ? new URL(G) : k.normalize(G), S.readFileSync(G, ae ? void 0 : "utf8")), x = (G) => { + var S = Rv(), k = Dv(); + l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (G, ae) => (G = Hs(G) ? new URL(G) : k.normalize(G), S.readFileSync(G, ae ? void 0 : "utf8")), x = (G) => { var ae = h(G, true); return ae.buffer || (ae = new Uint8Array(ae)), ae; }, g = (G, ae, $e) => { - G = Vs(G) ? new URL(G) : k.normalize(G), S.readFile(G, function(at, _t) { + G = Hs(G) ? new URL(G) : k.normalize(G), S.readFile(G, function(at, _t) { at ? $e(at) : ae(_t.buffer); }); }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), i = process.argv.slice(2), process.on("uncaughtException", function(G) { - if (!(G instanceof Dp)) + if (!(G instanceof Op)) throw G; }), process.on("unhandledRejection", function(G) { throw G; }), u = (G, ae) => { if (it()) throw process.exitCode = G, ae; - w(ae), process.exit(G); - }, t10.inspect = function() { + C(ae), process.exit(G); + }, t8.inspect = function() { return "[Emscripten Module object]"; }; } else @@ -2268,11 +2283,11 @@ var nB = qt((zg, Tv) => { $e(); }, at.onerror = $e, at.send(null); }, b = (G) => document.title = G); - var _ = t10.print || console.log.bind(console), E = t10.printErr || console.warn.bind(console); - Object.assign(t10, a), a = null, t10.arguments && (i = t10.arguments), t10.thisProgram && (p = t10.thisProgram), t10.quit && (u = t10.quit); + var _ = t8.print || console.log.bind(console), E = t8.printErr || console.warn.bind(console); + Object.assign(t8, a), a = null, t8.arguments && (i = t8.arguments), t8.thisProgram && (p = t8.thisProgram), t8.quit && (u = t8.quit); var R = 4, D; - t10.wasmBinary && (D = t10.wasmBinary); - var F = t10.noExitRuntime || true; + t8.wasmBinary && (D = t8.wasmBinary); + var P = t8.noExitRuntime || true; typeof WebAssembly != "object" && fr("no native wasm support detected"); var O, M = false, L; function B(G, ae) { @@ -2286,22 +2301,22 @@ var nB = qt((zg, Tv) => { if (_t - ae > 16 && G.buffer && z) return z.decode(G.subarray(ae, _t)); for (var $t = ""; ae < _t; ) { - var Xe = G[ae++]; - if (!(Xe & 128)) { - $t += String.fromCharCode(Xe); + var Qe = G[ae++]; + if (!(Qe & 128)) { + $t += String.fromCharCode(Qe); continue; } - var Ge = G[ae++] & 63; - if ((Xe & 224) == 192) { - $t += String.fromCharCode((Xe & 31) << 6 | Ge); + var He = G[ae++] & 63; + if ((Qe & 224) == 192) { + $t += String.fromCharCode((Qe & 31) << 6 | He); continue; } var Gt = G[ae++] & 63; - if ((Xe & 240) == 224 ? Xe = (Xe & 15) << 12 | Ge << 6 | Gt : Xe = (Xe & 7) << 18 | Ge << 12 | Gt << 6 | G[ae++] & 63, Xe < 65536) - $t += String.fromCharCode(Xe); + if ((Qe & 240) == 224 ? Qe = (Qe & 15) << 12 | He << 6 | Gt : Qe = (Qe & 7) << 18 | He << 12 | Gt << 6 | G[ae++] & 63, Qe < 65536) + $t += String.fromCharCode(Qe); else { - var eo = Xe - 65536; - $t += String.fromCharCode(55296 | eo >> 10, 56320 | eo & 1023); + var oo = Qe - 65536; + $t += String.fromCharCode(55296 | oo >> 10, 56320 | oo & 1023); } } return $t; @@ -2309,97 +2324,97 @@ var nB = qt((zg, Tv) => { function j(G, ae) { return G >>>= 0, G ? U(ne, G, ae) : ""; } - function H(G, ae, $e, at) { + function q(G, ae, $e, at) { if ($e >>>= 0, !(at > 0)) return 0; - for (var _t = $e, $t = $e + at - 1, Xe = 0; Xe < G.length; ++Xe) { - var Ge = G.charCodeAt(Xe); - if (Ge >= 55296 && Ge <= 57343) { - var Gt = G.charCodeAt(++Xe); - Ge = 65536 + ((Ge & 1023) << 10) | Gt & 1023; + for (var _t = $e, $t = $e + at - 1, Qe = 0; Qe < G.length; ++Qe) { + var He = G.charCodeAt(Qe); + if (He >= 55296 && He <= 57343) { + var Gt = G.charCodeAt(++Qe); + He = 65536 + ((He & 1023) << 10) | Gt & 1023; } - if (Ge <= 127) { + if (He <= 127) { if ($e >= $t) break; - ae[$e++ >>> 0] = Ge; - } else if (Ge <= 2047) { + ae[$e++ >>> 0] = He; + } else if (He <= 2047) { if ($e + 1 >= $t) break; - ae[$e++ >>> 0] = 192 | Ge >> 6, ae[$e++ >>> 0] = 128 | Ge & 63; - } else if (Ge <= 65535) { + ae[$e++ >>> 0] = 192 | He >> 6, ae[$e++ >>> 0] = 128 | He & 63; + } else if (He <= 65535) { if ($e + 2 >= $t) break; - ae[$e++ >>> 0] = 224 | Ge >> 12, ae[$e++ >>> 0] = 128 | Ge >> 6 & 63, ae[$e++ >>> 0] = 128 | Ge & 63; + ae[$e++ >>> 0] = 224 | He >> 12, ae[$e++ >>> 0] = 128 | He >> 6 & 63, ae[$e++ >>> 0] = 128 | He & 63; } else { if ($e + 3 >= $t) break; - ae[$e++ >>> 0] = 240 | Ge >> 18, ae[$e++ >>> 0] = 128 | Ge >> 12 & 63, ae[$e++ >>> 0] = 128 | Ge >> 6 & 63, ae[$e++ >>> 0] = 128 | Ge & 63; + ae[$e++ >>> 0] = 240 | He >> 18, ae[$e++ >>> 0] = 128 | He >> 12 & 63, ae[$e++ >>> 0] = 128 | He >> 6 & 63, ae[$e++ >>> 0] = 128 | He & 63; } } return ae[$e >>> 0] = 0, $e - _t; } - function X(G, ae, $e) { - return H(G, ne, ae, $e); + function Y(G, ae, $e) { + return q(G, ne, ae, $e); } - var J, re, ne, ee, oe, ie, le, ye, _e; + var J, re, ne, ee, oe, ie, le, be, _e; function ve(G) { - J = G, t10.HEAP8 = re = new Int8Array(G), t10.HEAP16 = ee = new Int16Array(G), t10.HEAP32 = ie = new Int32Array(G), t10.HEAPU8 = ne = new Uint8Array(G), t10.HEAPU16 = oe = new Uint16Array(G), t10.HEAPU32 = le = new Uint32Array(G), t10.HEAPF32 = ye = new Float32Array(G), t10.HEAPF64 = _e = new Float64Array(G); + J = G, t8.HEAP8 = re = new Int8Array(G), t8.HEAP16 = ee = new Int16Array(G), t8.HEAP32 = ie = new Int32Array(G), t8.HEAPU8 = ne = new Uint8Array(G), t8.HEAPU16 = oe = new Uint16Array(G), t8.HEAPU32 = le = new Uint32Array(G), t8.HEAPF32 = be = new Float32Array(G), t8.HEAPF64 = _e = new Float64Array(G); } - var Fe = t10.INITIAL_MEMORY || 16777216, Pe, st = [], lt = [], We = [], mt = false; + var Fe = t8.INITIAL_MEMORY || 16777216, Pe, st = [], lt = [], Ue = [], mt = false; function it() { - return F; - } - function ht() { - if (t10.preRun) - for (typeof t10.preRun == "function" && (t10.preRun = [t10.preRun]); t10.preRun.length; ) - Mt(t10.preRun.shift()); - Oa(st); + return P; } function gt() { - mt = true, Oa(lt); + if (t8.preRun) + for (typeof t8.preRun == "function" && (t8.preRun = [t8.preRun]); t8.preRun.length; ) + Mt(t8.preRun.shift()); + Va(st); } - function Or() { - if (t10.postRun) - for (typeof t10.postRun == "function" && (t10.postRun = [t10.postRun]); t10.postRun.length; ) - or(t10.postRun.shift()); - Oa(We); + function xt() { + mt = true, Va(lt); + } + function Lr() { + if (t8.postRun) + for (typeof t8.postRun == "function" && (t8.postRun = [t8.postRun]); t8.postRun.length; ) + or(t8.postRun.shift()); + Va(Ue); } function Mt(G) { st.unshift(G); } - function Qr(G) { + function eo(G) { lt.unshift(G); } function or(G) { - We.unshift(G); + Ue.unshift(G); } var Tt = 0, nr = null, sr = null; - function Zr(G) { - Tt++, t10.monitorRunDependencies && t10.monitorRunDependencies(Tt); + function to(G) { + Tt++, t8.monitorRunDependencies && t8.monitorRunDependencies(Tt); } - function Jr(G) { - if (Tt--, t10.monitorRunDependencies && t10.monitorRunDependencies(Tt), Tt == 0 && (nr !== null && (clearInterval(nr), nr = null), sr)) { + function ro(G) { + if (Tt--, t8.monitorRunDependencies && t8.monitorRunDependencies(Tt), Tt == 0 && (nr !== null && (clearInterval(nr), nr = null), sr)) { var ae = sr; sr = null, ae(); } } function fr(G) { - t10.onAbort && t10.onAbort(G), G = "Aborted(" + G + ")", E(G), M = true, L = 1, G += ". Build with -sASSERTIONS for more info."; + t8.onAbort && t8.onAbort(G), G = "Aborted(" + G + ")", E(G), M = true, L = 1, G += ". Build with -sASSERTIONS for more info."; var ae = new WebAssembly.RuntimeError(G); throw n(ae), ae; } - var Fa = "data:application/octet-stream;base64,"; - function Mo(G) { - return G.startsWith(Fa); + var Ba = "data:application/octet-stream;base64,"; + function Lo(G) { + return G.startsWith(Ba); } - function Vs(G) { + function Hs(G) { return G.startsWith("file://"); } - var Xt; - Xt = "tfjs-backend-wasm.wasm", Mo(Xt) || (Xt = f(Xt)); - function Pa(G) { + var Yt; + Yt = "tfjs-backend-wasm.wasm", Lo(Yt) || (Yt = f(Yt)); + function za(G) { try { - if (G == Xt && D) + if (G == Yt && D) return new Uint8Array(D); if (x) return x(G); @@ -2408,101 +2423,101 @@ var nB = qt((zg, Tv) => { fr(ae); } } - function el() { + function tl() { if (!D && (c || l)) { - if (typeof fetch == "function" && !Vs(Xt)) - return fetch(Xt, { credentials: "same-origin" }).then(function(G) { + if (typeof fetch == "function" && !Hs(Yt)) + return fetch(Yt, { credentials: "same-origin" }).then(function(G) { if (!G.ok) - throw "failed to load wasm binary file at '" + Xt + "'"; + throw "failed to load wasm binary file at '" + Yt + "'"; return G.arrayBuffer(); }).catch(function() { - return Pa(Xt); + return za(Yt); }); if (g) return new Promise(function(G, ae) { - g(Xt, function($e) { + g(Yt, function($e) { G(new Uint8Array($e)); }, ae); }); } return Promise.resolve().then(function() { - return Pa(Xt); + return za(Yt); }); } - function tl() { - var G = { env: rl, wasi_snapshot_preview1: rl }; - function ae(Xe, Ge) { - var Gt = Xe.exports; - t10.asm = Gt, O = t10.asm.memory, ve(O.buffer), Pe = t10.asm.__indirect_function_table, Qr(t10.asm.__wasm_call_ctors), Jr("wasm-instantiate"); + function rl() { + var G = { env: ol, wasi_snapshot_preview1: ol }; + function ae(Qe, He) { + var Gt = Qe.exports; + t8.asm = Gt, O = t8.asm.memory, ve(O.buffer), Pe = t8.asm.__indirect_function_table, eo(t8.asm.__wasm_call_ctors), ro("wasm-instantiate"); } - Zr("wasm-instantiate"); - function $e(Xe) { - ae(Xe.instance); + to("wasm-instantiate"); + function $e(Qe) { + ae(Qe.instance); } - function at(Xe) { - return el().then(function(Ge) { - return WebAssembly.instantiate(Ge, G); - }).then(function(Ge) { - return Ge; - }).then(Xe, function(Ge) { - E("failed to asynchronously prepare wasm: " + Ge), fr(Ge); + function at(Qe) { + return tl().then(function(He) { + return WebAssembly.instantiate(He, G); + }).then(function(He) { + return He; + }).then(Qe, function(He) { + E("failed to asynchronously prepare wasm: " + He), fr(He); }); } function _t() { - return !D && typeof WebAssembly.instantiateStreaming == "function" && !Mo(Xt) && !Vs(Xt) && !m && typeof fetch == "function" ? fetch(Xt, { credentials: "same-origin" }).then(function(Xe) { - var Ge = WebAssembly.instantiateStreaming(Xe, G); - return Ge.then($e, function(Gt) { + return !D && typeof WebAssembly.instantiateStreaming == "function" && !Lo(Yt) && !Hs(Yt) && !m && typeof fetch == "function" ? fetch(Yt, { credentials: "same-origin" }).then(function(Qe) { + var He = WebAssembly.instantiateStreaming(Qe, G); + return He.then($e, function(Gt) { return E("wasm streaming compile failed: " + Gt), E("falling back to ArrayBuffer instantiation"), at($e); }); }) : at($e); } - if (t10.instantiateWasm) + if (t8.instantiateWasm) try { - var $t = t10.instantiateWasm(G, ae); + var $t = t8.instantiateWasm(G, ae); return $t; - } catch (Xe) { - E("Module.instantiateWasm callback failed with error: " + Xe), n(Xe); + } catch (Qe) { + E("Module.instantiateWasm callback failed with error: " + Qe), n(Qe); } return _t().catch(n), {}; } - var jv, wi; - function Dp(G) { + var o0, vi; + function Op(G) { this.name = "ExitStatus", this.message = "Program terminated with exit(" + G + ")", this.status = G; } - function Oa(G) { + function Va(G) { for (; G.length > 0; ) - G.shift()(t10); + G.shift()(t8); } - function ly() { + function dy() { fr(""); } - function xm() { + function ym() { return 4294901760; } - function Su() { - return xm(); + function Nu() { + return ym(); } - function my(G, ae, $e) { + function fy(G, ae, $e) { ne.copyWithin(G >>> 0, ae >>> 0, ae + $e >>> 0); } - function ym(G) { + function bm(G) { try { return O.grow(G - J.byteLength + 65535 >>> 16), ve(O.buffer), 1; } catch (ae) { } } - function Ap(G) { + function Mp(G) { var ae = ne.length; G = G >>> 0; - var $e = xm(); + var $e = ym(); if (G > $e) return false; - let at = (Gt, eo) => Gt + (eo - Gt % eo) % eo; + let at = (Gt, oo) => Gt + (oo - Gt % oo) % oo; for (var _t = 1; _t <= 4; _t *= 2) { var $t = ae * (1 + 0.2 / _t); $t = Math.min($t, G + 100663296); - var Xe = Math.min($e, at(Math.max(G, $t), 65536)), Ge = ym(Xe); - if (Ge) + var Qe = Math.min($e, at(Math.max(G, $t), 65536)), He = bm(Qe); + if (He) return true; } return false; @@ -2515,397 +2530,411 @@ var nB = qt((zg, Tv) => { var ae = j(G); return ae; } }; - function bm(G) { + function Cm(G) { return 52; } - function dy(G, ae, $e, at, _t) { + function hy(G, ae, $e, at, _t) { return 70; } - var fy = [null, [], []]; - function Xv(G, ae) { - var $e = fy[G]; + var gy = [null, [], []]; + function n0(G, ae) { + var $e = gy[G]; ae === 0 || ae === 10 ? ((G === 1 ? _ : E)(U($e, 0)), $e.length = 0) : $e.push(ae); } - function Yv(G, ae, $e, at) { + function s0(G, ae, $e, at) { for (var _t = 0, $t = 0; $t < $e; $t++) { - var Xe = le[ae >>> 2], Ge = le[ae + 4 >>> 2]; + var Qe = le[ae >>> 2], He = le[ae + 4 >>> 2]; ae += 8; - for (var Gt = 0; Gt < Ge; Gt++) - Xv(G, ne[Xe + Gt >>> 0]); - _t += Ge; + for (var Gt = 0; Gt < He; Gt++) + n0(G, ne[Qe + Gt >>> 0]); + _t += He; } return le[at >>> 2] = _t, 0; } - function Cm(G) { - var ae = t10["_" + G]; + function wm(G) { + var ae = t8["_" + G]; return ae; } - function Iu(G, ae) { + function Tu(G, ae) { re.set(G, ae >>> 0); } - function hy(G, ae, $e, at, _t) { - var $t = { string: (yr) => { - var Ii = 0; - if (yr != null && yr !== 0) { - var Mm = (yr.length << 2) + 1; - Ii = il(Mm), X(yr, Ii, Mm); + function xy(G, ae, $e, at, _t) { + var $t = { string: (br) => { + var Ni = 0; + if (br != null && br !== 0) { + var Lm = (br.length << 2) + 1; + Ni = ul(Lm), Y(br, Ni, Lm); } - return Ii; - }, array: (yr) => { - var Ii = il(yr.length); - return Iu(yr, Ii), Ii; + return Ni; + }, array: (br) => { + var Ni = ul(br.length); + return Tu(br, Ni), Ni; } }; - function Xe(yr) { - return ae === "string" ? j(yr) : ae === "boolean" ? !!yr : yr; + function Qe(br) { + return ae === "string" ? j(br) : ae === "boolean" ? !!br : br; } - var Ge = Cm(G), Gt = [], eo = 0; + var He = wm(G), Gt = [], oo = 0; if (at) - for (var Ma = 0; Ma < at.length; Ma++) { - var Om = $t[$e[Ma]]; - Om ? (eo === 0 && (eo = Am()), Gt[Ma] = Om(at[Ma])) : Gt[Ma] = at[Ma]; + for (var Wa = 0; Wa < at.length; Wa++) { + var Mm = $t[$e[Wa]]; + Mm ? (oo === 0 && (oo = Fm()), Gt[Wa] = Mm(at[Wa])) : Gt[Wa] = at[Wa]; } - var ul = Ge.apply(null, Gt); - function RC(yr) { - return eo !== 0 && Fm(eo), Xe(yr); + var pl = He.apply(null, Gt); + function zC(br) { + return oo !== 0 && Pm(oo), Qe(br); } - return ul = RC(ul), ul; + return pl = zC(pl), pl; } - function gy(G, ae, $e, at) { + function yy(G, ae, $e, at) { $e = $e || []; - var _t = $e.every((Xe) => Xe === "number" || Xe === "boolean"), $t = ae !== "string"; - return $t && _t && !at ? Cm(G) : function() { - return hy(G, ae, $e, arguments, at); + var _t = $e.every((Qe) => Qe === "number" || Qe === "boolean"), $t = ae !== "string"; + return $t && _t && !at ? wm(G) : function() { + return xy(G, ae, $e, arguments, at); }; } - var rl = { abort: ly, emscripten_get_heap_max: Su, emscripten_memcpy_big: my, emscripten_resize_heap: Ap, fd_close: bm, fd_seek: dy, fd_write: Yv }, xy = tl(), wm = t10.___wasm_call_ctors = function() { - return (wm = t10.___wasm_call_ctors = t10.asm.__wasm_call_ctors).apply(null, arguments); - }, Sm = t10._init = function() { - return (Sm = t10._init = t10.asm.init).apply(null, arguments); - }, yy = t10._init_with_threads_count = function() { - return (yy = t10._init_with_threads_count = t10.asm.init_with_threads_count).apply(null, arguments); - }, Im = t10._get_threads_count = function() { - return (Im = t10._get_threads_count = t10.asm.get_threads_count).apply(null, arguments); - }, by = t10._register_tensor = function() { - return (by = t10._register_tensor = t10.asm.register_tensor).apply(null, arguments); - }, Me = t10._dispose_data = function() { - return (Me = t10._dispose_data = t10.asm.dispose_data).apply(null, arguments); - }, ol = t10._dispose = function() { - return (ol = t10._dispose = t10.asm.dispose).apply(null, arguments); - }, Cy = t10._Abs = function() { - return (Cy = t10._Abs = t10.asm.Abs).apply(null, arguments); - }, vm = t10._Acos = function() { - return (vm = t10._Acos = t10.asm.Acos).apply(null, arguments); - }, Fp = t10._Acosh = function() { - return (Fp = t10._Acosh = t10.asm.Acosh).apply(null, arguments); - }, wy = t10._Add = function() { - return (wy = t10._Add = t10.asm.Add).apply(null, arguments); - }, Sy = t10._AddN = function() { - return (Sy = t10._AddN = t10.asm.AddN).apply(null, arguments); - }, Iy = t10._All = function() { - return (Iy = t10._All = t10.asm.All).apply(null, arguments); - }, vy = t10._Any = function() { - return (vy = t10._Any = t10.asm.Any).apply(null, arguments); - }, ky = t10._ArgMax = function() { - return (ky = t10._ArgMax = t10.asm.ArgMax).apply(null, arguments); - }, km = t10._ArgMin = function() { - return (km = t10._ArgMin = t10.asm.ArgMin).apply(null, arguments); - }, Nm = t10._Asin = function() { - return (Nm = t10._Asin = t10.asm.Asin).apply(null, arguments); - }, Ny = t10._Asinh = function() { - return (Ny = t10._Asinh = t10.asm.Asinh).apply(null, arguments); - }, Ty = t10._Atan = function() { - return (Ty = t10._Atan = t10.asm.Atan).apply(null, arguments); - }, _y = t10._Atan2 = function() { - return (_y = t10._Atan2 = t10.asm.Atan2).apply(null, arguments); - }, nl = t10._Atanh = function() { - return (nl = t10._Atanh = t10.asm.Atanh).apply(null, arguments); - }, $y = t10._AvgPool = function() { - return ($y = t10._AvgPool = t10.asm.AvgPool).apply(null, arguments); - }, Ey = t10._AvgPool3D = function() { - return (Ey = t10._AvgPool3D = t10.asm.AvgPool3D).apply(null, arguments); - }, Ry = t10._AvgPool3DGrad = function() { - return (Ry = t10._AvgPool3DGrad = t10.asm.AvgPool3DGrad).apply(null, arguments); - }, vu = t10._BatchMatMul = function() { - return (vu = t10._BatchMatMul = t10.asm.BatchMatMul).apply(null, arguments); - }, Dy = t10._Bincount = function() { - return (Dy = t10._Bincount = t10.asm.Bincount).apply(null, arguments); - }, Ay = t10._Ceil = function() { - return (Ay = t10._Ceil = t10.asm.Ceil).apply(null, arguments); - }, Tm = t10._ClipByValue = function() { - return (Tm = t10._ClipByValue = t10.asm.ClipByValue).apply(null, arguments); - }, Fy = t10._Conv2D = function() { - return (Fy = t10._Conv2D = t10.asm.Conv2D).apply(null, arguments); - }, sl = t10._Conv2DBackpropInput = function() { - return (sl = t10._Conv2DBackpropInput = t10.asm.Conv2DBackpropInput).apply(null, arguments); - }, Py = t10._Conv3D = function() { - return (Py = t10._Conv3D = t10.asm.Conv3D).apply(null, arguments); - }, Oy = t10._Conv3DBackpropFilterV2 = function() { - return (Oy = t10._Conv3DBackpropFilterV2 = t10.asm.Conv3DBackpropFilterV2).apply(null, arguments); - }, My = t10._Conv3DBackpropInputV2 = function() { - return (My = t10._Conv3DBackpropInputV2 = t10.asm.Conv3DBackpropInputV2).apply(null, arguments); - }, Si = t10._Cos = function() { - return (Si = t10._Cos = t10.asm.Cos).apply(null, arguments); - }, al = t10._Cosh = function() { - return (al = t10._Cosh = t10.asm.Cosh).apply(null, arguments); - }, Ly = t10._CropAndResize = function() { - return (Ly = t10._CropAndResize = t10.asm.CropAndResize).apply(null, arguments); - }, By = t10._Cumprod = function() { - return (By = t10._Cumprod = t10.asm.Cumprod).apply(null, arguments); - }, zy = t10._Cumsum = function() { - return (zy = t10._Cumsum = t10.asm.Cumsum).apply(null, arguments); - }, Vy = t10._DenseBincount = function() { - return (Vy = t10._DenseBincount = t10.asm.DenseBincount).apply(null, arguments); - }, _m = t10._DepthToSpace = function() { - return (_m = t10._DepthToSpace = t10.asm.DepthToSpace).apply(null, arguments); - }, $m = t10._DepthwiseConv2dNative = function() { - return ($m = t10._DepthwiseConv2dNative = t10.asm.DepthwiseConv2dNative).apply(null, arguments); - }, Wy = t10._Diag = function() { - return (Wy = t10._Diag = t10.asm.Diag).apply(null, arguments); - }, Uy = t10._Dilation2D = function() { - return (Uy = t10._Dilation2D = t10.asm.Dilation2D).apply(null, arguments); - }, Em = t10._Dilation2DBackpropFilter = function() { - return (Em = t10._Dilation2DBackpropFilter = t10.asm.Dilation2DBackpropFilter).apply(null, arguments); - }, Rm = t10._Dilation2DBackpropInput = function() { - return (Rm = t10._Dilation2DBackpropInput = t10.asm.Dilation2DBackpropInput).apply(null, arguments); - }, Gy = t10._Elu = function() { - return (Gy = t10._Elu = t10.asm.Elu).apply(null, arguments); - }, Hy = t10._EluGrad = function() { - return (Hy = t10._EluGrad = t10.asm.EluGrad).apply(null, arguments); - }, Ky = t10._Equal = function() { - return (Ky = t10._Equal = t10.asm.Equal).apply(null, arguments); - }, qy = t10._Exp = function() { - return (qy = t10._Exp = t10.asm.Exp).apply(null, arguments); - }, Dm = t10._Expm1 = function() { - return (Dm = t10._Expm1 = t10.asm.Expm1).apply(null, arguments); - }, Qv = t10._FlipLeftRight = function() { - return (Qv = t10._FlipLeftRight = t10.asm.FlipLeftRight).apply(null, arguments); - }, jy = t10._Floor = function() { - return (jy = t10._Floor = t10.asm.Floor).apply(null, arguments); - }, Xy = t10._FloorDiv = function() { - return (Xy = t10._FloorDiv = t10.asm.FloorDiv).apply(null, arguments); - }, Yy = t10._FusedBatchNorm = function() { - return (Yy = t10._FusedBatchNorm = t10.asm.FusedBatchNorm).apply(null, arguments); - }, Qy = t10._FusedConv2D = function() { - return (Qy = t10._FusedConv2D = t10.asm.FusedConv2D).apply(null, arguments); - }, Zy = t10._FusedDepthwiseConv2D = function() { - return (Zy = t10._FusedDepthwiseConv2D = t10.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, Jy = t10._Gather = function() { - return (Jy = t10._Gather = t10.asm.Gather).apply(null, arguments); - }, eb = t10._GatherNd = function() { - return (eb = t10._GatherNd = t10.asm.GatherNd).apply(null, arguments); - }, tb = t10._Greater = function() { - return (tb = t10._Greater = t10.asm.Greater).apply(null, arguments); - }, rb = t10._GreaterEqual = function() { - return (rb = t10._GreaterEqual = t10.asm.GreaterEqual).apply(null, arguments); - }, ob = t10._IsFinite = function() { - return (ob = t10._IsFinite = t10.asm.IsFinite).apply(null, arguments); - }, nb = t10._IsInf = function() { - return (nb = t10._IsInf = t10.asm.IsInf).apply(null, arguments); - }, sb = t10._IsNan = function() { - return (sb = t10._IsNan = t10.asm.IsNan).apply(null, arguments); - }, ab = t10._LRN = function() { - return (ab = t10._LRN = t10.asm.LRN).apply(null, arguments); - }, ib = t10._LRNGrad = function() { - return (ib = t10._LRNGrad = t10.asm.LRNGrad).apply(null, arguments); - }, ub = t10._LeakyRelu = function() { - return (ub = t10._LeakyRelu = t10.asm.LeakyRelu).apply(null, arguments); - }, pb = t10._Less = function() { - return (pb = t10._Less = t10.asm.Less).apply(null, arguments); - }, cb = t10._LessEqual = function() { - return (cb = t10._LessEqual = t10.asm.LessEqual).apply(null, arguments); - }, lb = t10._LinSpace = function() { - return (lb = t10._LinSpace = t10.asm.LinSpace).apply(null, arguments); - }, mb = t10._Log = function() { - return (mb = t10._Log = t10.asm.Log).apply(null, arguments); - }, db = t10._Log1p = function() { - return (db = t10._Log1p = t10.asm.Log1p).apply(null, arguments); - }, fb = t10._LogicalAnd = function() { - return (fb = t10._LogicalAnd = t10.asm.LogicalAnd).apply(null, arguments); - }, hb = t10._LogicalNot = function() { - return (hb = t10._LogicalNot = t10.asm.LogicalNot).apply(null, arguments); - }, gb = t10._LogicalOr = function() { - return (gb = t10._LogicalOr = t10.asm.LogicalOr).apply(null, arguments); - }, xb = t10._LogicalXor = function() { - return (xb = t10._LogicalXor = t10.asm.LogicalXor).apply(null, arguments); - }, yb = t10._Max = function() { - return (yb = t10._Max = t10.asm.Max).apply(null, arguments); - }, bb = t10._MaxPool = function() { - return (bb = t10._MaxPool = t10.asm.MaxPool).apply(null, arguments); - }, Cb = t10._MaxPool3D = function() { - return (Cb = t10._MaxPool3D = t10.asm.MaxPool3D).apply(null, arguments); - }, wb = t10._MaxPool3DGrad = function() { - return (wb = t10._MaxPool3DGrad = t10.asm.MaxPool3DGrad).apply(null, arguments); - }, Sb = t10._Maximum = function() { - return (Sb = t10._Maximum = t10.asm.Maximum).apply(null, arguments); - }, Ib = t10._Mean = function() { - return (Ib = t10._Mean = t10.asm.Mean).apply(null, arguments); - }, vb = t10._Min = function() { - return (vb = t10._Min = t10.asm.Min).apply(null, arguments); - }, kb = t10._Minimum = function() { - return (kb = t10._Minimum = t10.asm.Minimum).apply(null, arguments); - }, Nb = t10._MirrorPad = function() { - return (Nb = t10._MirrorPad = t10.asm.MirrorPad).apply(null, arguments); - }, Tb = t10._Multinomial = function() { - return (Tb = t10._Multinomial = t10.asm.Multinomial).apply(null, arguments); - }, _b = t10._Multiply = function() { - return (_b = t10._Multiply = t10.asm.Multiply).apply(null, arguments); - }, $b = t10._Neg = function() { - return ($b = t10._Neg = t10.asm.Neg).apply(null, arguments); - }, Eb = t10._NonMaxSuppressionV3 = function() { - return (Eb = t10._NonMaxSuppressionV3 = t10.asm.NonMaxSuppressionV3).apply(null, arguments); - }, Rb = t10._NonMaxSuppressionV4 = function() { - return (Rb = t10._NonMaxSuppressionV4 = t10.asm.NonMaxSuppressionV4).apply(null, arguments); - }, Db = t10._NonMaxSuppressionV5 = function() { - return (Db = t10._NonMaxSuppressionV5 = t10.asm.NonMaxSuppressionV5).apply(null, arguments); - }, Ab = t10._NotEqual = function() { - return (Ab = t10._NotEqual = t10.asm.NotEqual).apply(null, arguments); - }, Fb = t10._OneHot = function() { - return (Fb = t10._OneHot = t10.asm.OneHot).apply(null, arguments); - }, Pb = t10._PadV2 = function() { - return (Pb = t10._PadV2 = t10.asm.PadV2).apply(null, arguments); - }, Ob = t10._Pow = function() { - return (Ob = t10._Pow = t10.asm.Pow).apply(null, arguments); - }, Mb = t10._Prelu = function() { - return (Mb = t10._Prelu = t10.asm.Prelu).apply(null, arguments); - }, Lb = t10._Prod = function() { - return (Lb = t10._Prod = t10.asm.Prod).apply(null, arguments); - }, Bb = t10._RealDiv = function() { - return (Bb = t10._RealDiv = t10.asm.RealDiv).apply(null, arguments); - }, zb = t10._Reciprocal = function() { - return (zb = t10._Reciprocal = t10.asm.Reciprocal).apply(null, arguments); - }, Vb = t10._Relu = function() { - return (Vb = t10._Relu = t10.asm.Relu).apply(null, arguments); - }, Wb = t10._Relu6 = function() { - return (Wb = t10._Relu6 = t10.asm.Relu6).apply(null, arguments); - }, Ub = t10._ResizeBilinear = function() { - return (Ub = t10._ResizeBilinear = t10.asm.ResizeBilinear).apply(null, arguments); - }, Gb = t10._ResizeBilinearGrad = function() { - return (Gb = t10._ResizeBilinearGrad = t10.asm.ResizeBilinearGrad).apply(null, arguments); - }, Hb = t10._ResizeNearestNeighbor = function() { - return (Hb = t10._ResizeNearestNeighbor = t10.asm.ResizeNearestNeighbor).apply(null, arguments); - }, Kb = t10._ResizeNearestNeighborGrad = function() { - return (Kb = t10._ResizeNearestNeighborGrad = t10.asm.ResizeNearestNeighborGrad).apply(null, arguments); - }, qb = t10._Reverse = function() { - return (qb = t10._Reverse = t10.asm.Reverse).apply(null, arguments); - }, jb = t10._RotateWithOffset = function() { - return (jb = t10._RotateWithOffset = t10.asm.RotateWithOffset).apply(null, arguments); - }, Xb = t10._Round = function() { - return (Xb = t10._Round = t10.asm.Round).apply(null, arguments); - }, Yb = t10._Rsqrt = function() { - return (Yb = t10._Rsqrt = t10.asm.Rsqrt).apply(null, arguments); - }, Qb = t10._ScatterNd = function() { - return (Qb = t10._ScatterNd = t10.asm.ScatterNd).apply(null, arguments); - }, Zb = t10._SearchSorted = function() { - return (Zb = t10._SearchSorted = t10.asm.SearchSorted).apply(null, arguments); - }, Jb = t10._SelectV2 = function() { - return (Jb = t10._SelectV2 = t10.asm.SelectV2).apply(null, arguments); - }, eC = t10._Selu = function() { - return (eC = t10._Selu = t10.asm.Selu).apply(null, arguments); - }, tC = t10._Sigmoid = function() { - return (tC = t10._Sigmoid = t10.asm.Sigmoid).apply(null, arguments); - }, rC = t10._Sign = function() { - return (rC = t10._Sign = t10.asm.Sign).apply(null, arguments); - }, oC = t10._Sin = function() { - return (oC = t10._Sin = t10.asm.Sin).apply(null, arguments); - }, nC = t10._Softmax = function() { - return (nC = t10._Softmax = t10.asm.Softmax).apply(null, arguments); - }, sC = t10._Softplus = function() { - return (sC = t10._Softplus = t10.asm.Softplus).apply(null, arguments); - }, aC = t10._SparseFillEmptyRows = function() { - return (aC = t10._SparseFillEmptyRows = t10.asm.SparseFillEmptyRows).apply(null, arguments); - }, iC = t10._SparseReshape = function() { - return (iC = t10._SparseReshape = t10.asm.SparseReshape).apply(null, arguments); - }, uC = t10._SparseSegmentReduction = function() { - return (uC = t10._SparseSegmentReduction = t10.asm.SparseSegmentReduction).apply(null, arguments); - }, pC = t10._SparseToDense = function() { - return (pC = t10._SparseToDense = t10.asm.SparseToDense).apply(null, arguments); - }, cC = t10._Sqrt = function() { - return (cC = t10._Sqrt = t10.asm.Sqrt).apply(null, arguments); - }, lC = t10._Square = function() { - return (lC = t10._Square = t10.asm.Square).apply(null, arguments); - }, mC = t10._SquaredDifference = function() { - return (mC = t10._SquaredDifference = t10.asm.SquaredDifference).apply(null, arguments); - }, dC = t10._Step = function() { - return (dC = t10._Step = t10.asm.Step).apply(null, arguments); - }, fC = t10._StridedSlice = function() { - return (fC = t10._StridedSlice = t10.asm.StridedSlice).apply(null, arguments); - }, hC = t10._Sub = function() { - return (hC = t10._Sub = t10.asm.Sub).apply(null, arguments); - }, gC = t10._Sum = function() { - return (gC = t10._Sum = t10.asm.Sum).apply(null, arguments); - }, xC = t10._Tan = function() { - return (xC = t10._Tan = t10.asm.Tan).apply(null, arguments); - }, yC = t10._Tanh = function() { - return (yC = t10._Tanh = t10.asm.Tanh).apply(null, arguments); - }, bC = t10._TensorScatterUpdate = function() { - return (bC = t10._TensorScatterUpdate = t10.asm.TensorScatterUpdate).apply(null, arguments); - }, CC = t10._Tile = function() { - return (CC = t10._Tile = t10.asm.Tile).apply(null, arguments); - }, wC = t10._TopK = function() { - return (wC = t10._TopK = t10.asm.TopK).apply(null, arguments); - }, SC = t10._Transform = function() { - return (SC = t10._Transform = t10.asm.Transform).apply(null, arguments); - }, IC = t10._Transpose = function() { - return (IC = t10._Transpose = t10.asm.Transpose).apply(null, arguments); - }, vC = t10.__FusedMatMul = function() { - return (vC = t10.__FusedMatMul = t10.asm._FusedMatMul).apply(null, arguments); - }, kC = t10._malloc = function() { - return (kC = t10._malloc = t10.asm.malloc).apply(null, arguments); - }, NC = t10._free = function() { - return (NC = t10._free = t10.asm.free).apply(null, arguments); - }, TC = t10.___errno_location = function() { - return (TC = t10.___errno_location = t10.asm.__errno_location).apply(null, arguments); - }, Am = t10.stackSave = function() { - return (Am = t10.stackSave = t10.asm.stackSave).apply(null, arguments); - }, Fm = t10.stackRestore = function() { - return (Fm = t10.stackRestore = t10.asm.stackRestore).apply(null, arguments); - }, il = t10.stackAlloc = function() { - return (il = t10.stackAlloc = t10.asm.stackAlloc).apply(null, arguments); - }, _C = t10.dynCall_iijjiiii = function() { - return (_C = t10.dynCall_iijjiiii = t10.asm.dynCall_iijjiiii).apply(null, arguments); - }, $C = t10.dynCall_jiji = function() { - return ($C = t10.dynCall_jiji = t10.asm.dynCall_jiji).apply(null, arguments); + var ol = { abort: dy, emscripten_get_heap_max: Nu, emscripten_memcpy_big: fy, emscripten_resize_heap: Mp, fd_close: Cm, fd_seek: hy, fd_write: s0 }, by = rl(), Sm = t8.___wasm_call_ctors = function() { + return (Sm = t8.___wasm_call_ctors = t8.asm.__wasm_call_ctors).apply(null, arguments); + }, Im = t8._init = function() { + return (Im = t8._init = t8.asm.init).apply(null, arguments); + }, Cy = t8._init_with_threads_count = function() { + return (Cy = t8._init_with_threads_count = t8.asm.init_with_threads_count).apply(null, arguments); + }, vm = t8._get_threads_count = function() { + return (vm = t8._get_threads_count = t8.asm.get_threads_count).apply(null, arguments); + }, wy = t8._register_tensor = function() { + return (wy = t8._register_tensor = t8.asm.register_tensor).apply(null, arguments); + }, Me = t8._dispose_data = function() { + return (Me = t8._dispose_data = t8.asm.dispose_data).apply(null, arguments); + }, nl = t8._dispose = function() { + return (nl = t8._dispose = t8.asm.dispose).apply(null, arguments); + }, Sy = t8._Abs = function() { + return (Sy = t8._Abs = t8.asm.Abs).apply(null, arguments); + }, km = t8._Acos = function() { + return (km = t8._Acos = t8.asm.Acos).apply(null, arguments); + }, Lp = t8._Acosh = function() { + return (Lp = t8._Acosh = t8.asm.Acosh).apply(null, arguments); + }, Iy = t8._Add = function() { + return (Iy = t8._Add = t8.asm.Add).apply(null, arguments); + }, vy = t8._AddN = function() { + return (vy = t8._AddN = t8.asm.AddN).apply(null, arguments); + }, ky = t8._All = function() { + return (ky = t8._All = t8.asm.All).apply(null, arguments); + }, Ny = t8._Any = function() { + return (Ny = t8._Any = t8.asm.Any).apply(null, arguments); + }, Ty = t8._ArgMax = function() { + return (Ty = t8._ArgMax = t8.asm.ArgMax).apply(null, arguments); + }, Nm = t8._ArgMin = function() { + return (Nm = t8._ArgMin = t8.asm.ArgMin).apply(null, arguments); + }, Tm = t8._Asin = function() { + return (Tm = t8._Asin = t8.asm.Asin).apply(null, arguments); + }, _y = t8._Asinh = function() { + return (_y = t8._Asinh = t8.asm.Asinh).apply(null, arguments); + }, $y = t8._Atan = function() { + return ($y = t8._Atan = t8.asm.Atan).apply(null, arguments); + }, Ey = t8._Atan2 = function() { + return (Ey = t8._Atan2 = t8.asm.Atan2).apply(null, arguments); + }, sl = t8._Atanh = function() { + return (sl = t8._Atanh = t8.asm.Atanh).apply(null, arguments); + }, Ry = t8._AvgPool = function() { + return (Ry = t8._AvgPool = t8.asm.AvgPool).apply(null, arguments); + }, Dy = t8._AvgPool3D = function() { + return (Dy = t8._AvgPool3D = t8.asm.AvgPool3D).apply(null, arguments); + }, Ay = t8._AvgPool3DGrad = function() { + return (Ay = t8._AvgPool3DGrad = t8.asm.AvgPool3DGrad).apply(null, arguments); + }, _u = t8._AvgPoolGrad = function() { + return (_u = t8._AvgPoolGrad = t8.asm.AvgPoolGrad).apply(null, arguments); + }, Fy = t8._BatchMatMul = function() { + return (Fy = t8._BatchMatMul = t8.asm.BatchMatMul).apply(null, arguments); + }, Py = t8._Bincount = function() { + return (Py = t8._Bincount = t8.asm.Bincount).apply(null, arguments); + }, _m = t8._BitwiseAnd = function() { + return (_m = t8._BitwiseAnd = t8.asm.BitwiseAnd).apply(null, arguments); + }, Oy = t8._Ceil = function() { + return (Oy = t8._Ceil = t8.asm.Ceil).apply(null, arguments); + }, al = t8._ClipByValue = function() { + return (al = t8._ClipByValue = t8.asm.ClipByValue).apply(null, arguments); + }, My = t8._Conv2D = function() { + return (My = t8._Conv2D = t8.asm.Conv2D).apply(null, arguments); + }, Ly = t8._Conv2DBackpropInput = function() { + return (Ly = t8._Conv2DBackpropInput = t8.asm.Conv2DBackpropInput).apply(null, arguments); + }, By = t8._Conv3D = function() { + return (By = t8._Conv3D = t8.asm.Conv3D).apply(null, arguments); + }, ki = t8._Conv3DBackpropFilterV2 = function() { + return (ki = t8._Conv3DBackpropFilterV2 = t8.asm.Conv3DBackpropFilterV2).apply(null, arguments); + }, il = t8._Conv3DBackpropInputV2 = function() { + return (il = t8._Conv3DBackpropInputV2 = t8.asm.Conv3DBackpropInputV2).apply(null, arguments); + }, zy = t8._Cos = function() { + return (zy = t8._Cos = t8.asm.Cos).apply(null, arguments); + }, Vy = t8._Cosh = function() { + return (Vy = t8._Cosh = t8.asm.Cosh).apply(null, arguments); + }, Wy = t8._CropAndResize = function() { + return (Wy = t8._CropAndResize = t8.asm.CropAndResize).apply(null, arguments); + }, Uy = t8._Cumprod = function() { + return (Uy = t8._Cumprod = t8.asm.Cumprod).apply(null, arguments); + }, $m = t8._Cumsum = function() { + return ($m = t8._Cumsum = t8.asm.Cumsum).apply(null, arguments); + }, Em = t8._DenseBincount = function() { + return (Em = t8._DenseBincount = t8.asm.DenseBincount).apply(null, arguments); + }, Gy = t8._DepthToSpace = function() { + return (Gy = t8._DepthToSpace = t8.asm.DepthToSpace).apply(null, arguments); + }, Hy = t8._DepthwiseConv2dNative = function() { + return (Hy = t8._DepthwiseConv2dNative = t8.asm.DepthwiseConv2dNative).apply(null, arguments); + }, Rm = t8._Diag = function() { + return (Rm = t8._Diag = t8.asm.Diag).apply(null, arguments); + }, Dm = t8._Dilation2D = function() { + return (Dm = t8._Dilation2D = t8.asm.Dilation2D).apply(null, arguments); + }, Ky = t8._Dilation2DBackpropFilter = function() { + return (Ky = t8._Dilation2DBackpropFilter = t8.asm.Dilation2DBackpropFilter).apply(null, arguments); + }, qy = t8._Dilation2DBackpropInput = function() { + return (qy = t8._Dilation2DBackpropInput = t8.asm.Dilation2DBackpropInput).apply(null, arguments); + }, jy = t8._Elu = function() { + return (jy = t8._Elu = t8.asm.Elu).apply(null, arguments); + }, Xy = t8._EluGrad = function() { + return (Xy = t8._EluGrad = t8.asm.EluGrad).apply(null, arguments); + }, Am = t8._Equal = function() { + return (Am = t8._Equal = t8.asm.Equal).apply(null, arguments); + }, a0 = t8._Erf = function() { + return (a0 = t8._Erf = t8.asm.Erf).apply(null, arguments); + }, Yy = t8._Exp = function() { + return (Yy = t8._Exp = t8.asm.Exp).apply(null, arguments); + }, Qy = t8._Expm1 = function() { + return (Qy = t8._Expm1 = t8.asm.Expm1).apply(null, arguments); + }, Zy = t8._FlipLeftRight = function() { + return (Zy = t8._FlipLeftRight = t8.asm.FlipLeftRight).apply(null, arguments); + }, Jy = t8._Floor = function() { + return (Jy = t8._Floor = t8.asm.Floor).apply(null, arguments); + }, eb = t8._FloorDiv = function() { + return (eb = t8._FloorDiv = t8.asm.FloorDiv).apply(null, arguments); + }, tb = t8._FusedBatchNorm = function() { + return (tb = t8._FusedBatchNorm = t8.asm.FusedBatchNorm).apply(null, arguments); + }, rb = t8._FusedConv2D = function() { + return (rb = t8._FusedConv2D = t8.asm.FusedConv2D).apply(null, arguments); + }, ob = t8._FusedDepthwiseConv2D = function() { + return (ob = t8._FusedDepthwiseConv2D = t8.asm.FusedDepthwiseConv2D).apply(null, arguments); + }, nb = t8._Gather = function() { + return (nb = t8._Gather = t8.asm.Gather).apply(null, arguments); + }, sb = t8._GatherNd = function() { + return (sb = t8._GatherNd = t8.asm.GatherNd).apply(null, arguments); + }, ab = t8._Greater = function() { + return (ab = t8._Greater = t8.asm.Greater).apply(null, arguments); + }, ib = t8._GreaterEqual = function() { + return (ib = t8._GreaterEqual = t8.asm.GreaterEqual).apply(null, arguments); + }, ub = t8._IsFinite = function() { + return (ub = t8._IsFinite = t8.asm.IsFinite).apply(null, arguments); + }, pb = t8._IsInf = function() { + return (pb = t8._IsInf = t8.asm.IsInf).apply(null, arguments); + }, cb = t8._IsNan = function() { + return (cb = t8._IsNan = t8.asm.IsNan).apply(null, arguments); + }, lb = t8._LRN = function() { + return (lb = t8._LRN = t8.asm.LRN).apply(null, arguments); + }, mb = t8._LRNGrad = function() { + return (mb = t8._LRNGrad = t8.asm.LRNGrad).apply(null, arguments); + }, db = t8._LeakyRelu = function() { + return (db = t8._LeakyRelu = t8.asm.LeakyRelu).apply(null, arguments); + }, fb = t8._Less = function() { + return (fb = t8._Less = t8.asm.Less).apply(null, arguments); + }, hb = t8._LessEqual = function() { + return (hb = t8._LessEqual = t8.asm.LessEqual).apply(null, arguments); + }, gb = t8._LinSpace = function() { + return (gb = t8._LinSpace = t8.asm.LinSpace).apply(null, arguments); + }, xb = t8._Log = function() { + return (xb = t8._Log = t8.asm.Log).apply(null, arguments); + }, yb = t8._Log1p = function() { + return (yb = t8._Log1p = t8.asm.Log1p).apply(null, arguments); + }, bb = t8._LogicalAnd = function() { + return (bb = t8._LogicalAnd = t8.asm.LogicalAnd).apply(null, arguments); + }, Cb = t8._LogicalNot = function() { + return (Cb = t8._LogicalNot = t8.asm.LogicalNot).apply(null, arguments); + }, wb = t8._LogicalOr = function() { + return (wb = t8._LogicalOr = t8.asm.LogicalOr).apply(null, arguments); + }, Sb = t8._LogicalXor = function() { + return (Sb = t8._LogicalXor = t8.asm.LogicalXor).apply(null, arguments); + }, Ib = t8._Max = function() { + return (Ib = t8._Max = t8.asm.Max).apply(null, arguments); + }, vb = t8._MaxPool = function() { + return (vb = t8._MaxPool = t8.asm.MaxPool).apply(null, arguments); + }, kb = t8._MaxPool3D = function() { + return (kb = t8._MaxPool3D = t8.asm.MaxPool3D).apply(null, arguments); + }, Nb = t8._MaxPool3DGrad = function() { + return (Nb = t8._MaxPool3DGrad = t8.asm.MaxPool3DGrad).apply(null, arguments); + }, Tb = t8._MaxPoolGrad = function() { + return (Tb = t8._MaxPoolGrad = t8.asm.MaxPoolGrad).apply(null, arguments); + }, _b = t8._MaxPoolWithArgmax = function() { + return (_b = t8._MaxPoolWithArgmax = t8.asm.MaxPoolWithArgmax).apply(null, arguments); + }, $b = t8._Maximum = function() { + return ($b = t8._Maximum = t8.asm.Maximum).apply(null, arguments); + }, Eb = t8._Mean = function() { + return (Eb = t8._Mean = t8.asm.Mean).apply(null, arguments); + }, Rb = t8._Min = function() { + return (Rb = t8._Min = t8.asm.Min).apply(null, arguments); + }, Db = t8._Minimum = function() { + return (Db = t8._Minimum = t8.asm.Minimum).apply(null, arguments); + }, Ab = t8._MirrorPad = function() { + return (Ab = t8._MirrorPad = t8.asm.MirrorPad).apply(null, arguments); + }, Fb = t8._Mod = function() { + return (Fb = t8._Mod = t8.asm.Mod).apply(null, arguments); + }, Pb = t8._Multinomial = function() { + return (Pb = t8._Multinomial = t8.asm.Multinomial).apply(null, arguments); + }, Ob = t8._Multiply = function() { + return (Ob = t8._Multiply = t8.asm.Multiply).apply(null, arguments); + }, Mb = t8._Neg = function() { + return (Mb = t8._Neg = t8.asm.Neg).apply(null, arguments); + }, Lb = t8._NonMaxSuppressionV3 = function() { + return (Lb = t8._NonMaxSuppressionV3 = t8.asm.NonMaxSuppressionV3).apply(null, arguments); + }, Bb = t8._NonMaxSuppressionV4 = function() { + return (Bb = t8._NonMaxSuppressionV4 = t8.asm.NonMaxSuppressionV4).apply(null, arguments); + }, zb = t8._NonMaxSuppressionV5 = function() { + return (zb = t8._NonMaxSuppressionV5 = t8.asm.NonMaxSuppressionV5).apply(null, arguments); + }, Vb = t8._NotEqual = function() { + return (Vb = t8._NotEqual = t8.asm.NotEqual).apply(null, arguments); + }, Wb = t8._OneHot = function() { + return (Wb = t8._OneHot = t8.asm.OneHot).apply(null, arguments); + }, Ub = t8._PadV2 = function() { + return (Ub = t8._PadV2 = t8.asm.PadV2).apply(null, arguments); + }, Gb = t8._Pow = function() { + return (Gb = t8._Pow = t8.asm.Pow).apply(null, arguments); + }, Hb = t8._Prelu = function() { + return (Hb = t8._Prelu = t8.asm.Prelu).apply(null, arguments); + }, Kb = t8._Prod = function() { + return (Kb = t8._Prod = t8.asm.Prod).apply(null, arguments); + }, qb = t8._RealDiv = function() { + return (qb = t8._RealDiv = t8.asm.RealDiv).apply(null, arguments); + }, jb = t8._Reciprocal = function() { + return (jb = t8._Reciprocal = t8.asm.Reciprocal).apply(null, arguments); + }, Xb = t8._Relu = function() { + return (Xb = t8._Relu = t8.asm.Relu).apply(null, arguments); + }, Yb = t8._Relu6 = function() { + return (Yb = t8._Relu6 = t8.asm.Relu6).apply(null, arguments); + }, Qb = t8._ResizeBilinear = function() { + return (Qb = t8._ResizeBilinear = t8.asm.ResizeBilinear).apply(null, arguments); + }, Zb = t8._ResizeBilinearGrad = function() { + return (Zb = t8._ResizeBilinearGrad = t8.asm.ResizeBilinearGrad).apply(null, arguments); + }, Jb = t8._ResizeNearestNeighbor = function() { + return (Jb = t8._ResizeNearestNeighbor = t8.asm.ResizeNearestNeighbor).apply(null, arguments); + }, eC = t8._ResizeNearestNeighborGrad = function() { + return (eC = t8._ResizeNearestNeighborGrad = t8.asm.ResizeNearestNeighborGrad).apply(null, arguments); + }, tC = t8._Reverse = function() { + return (tC = t8._Reverse = t8.asm.Reverse).apply(null, arguments); + }, rC = t8._RotateWithOffset = function() { + return (rC = t8._RotateWithOffset = t8.asm.RotateWithOffset).apply(null, arguments); + }, oC = t8._Round = function() { + return (oC = t8._Round = t8.asm.Round).apply(null, arguments); + }, nC = t8._Rsqrt = function() { + return (nC = t8._Rsqrt = t8.asm.Rsqrt).apply(null, arguments); + }, sC = t8._ScatterNd = function() { + return (sC = t8._ScatterNd = t8.asm.ScatterNd).apply(null, arguments); + }, aC = t8._SearchSorted = function() { + return (aC = t8._SearchSorted = t8.asm.SearchSorted).apply(null, arguments); + }, iC = t8._SelectV2 = function() { + return (iC = t8._SelectV2 = t8.asm.SelectV2).apply(null, arguments); + }, uC = t8._Selu = function() { + return (uC = t8._Selu = t8.asm.Selu).apply(null, arguments); + }, pC = t8._Sigmoid = function() { + return (pC = t8._Sigmoid = t8.asm.Sigmoid).apply(null, arguments); + }, cC = t8._Sign = function() { + return (cC = t8._Sign = t8.asm.Sign).apply(null, arguments); + }, lC = t8._Sin = function() { + return (lC = t8._Sin = t8.asm.Sin).apply(null, arguments); + }, mC = t8._Sinh = function() { + return (mC = t8._Sinh = t8.asm.Sinh).apply(null, arguments); + }, dC = t8._Softmax = function() { + return (dC = t8._Softmax = t8.asm.Softmax).apply(null, arguments); + }, fC = t8._Softplus = function() { + return (fC = t8._Softplus = t8.asm.Softplus).apply(null, arguments); + }, hC = t8._SparseFillEmptyRows = function() { + return (hC = t8._SparseFillEmptyRows = t8.asm.SparseFillEmptyRows).apply(null, arguments); + }, gC = t8._SparseReshape = function() { + return (gC = t8._SparseReshape = t8.asm.SparseReshape).apply(null, arguments); + }, xC = t8._SparseSegmentReduction = function() { + return (xC = t8._SparseSegmentReduction = t8.asm.SparseSegmentReduction).apply(null, arguments); + }, yC = t8._SparseToDense = function() { + return (yC = t8._SparseToDense = t8.asm.SparseToDense).apply(null, arguments); + }, bC = t8._Sqrt = function() { + return (bC = t8._Sqrt = t8.asm.Sqrt).apply(null, arguments); + }, CC = t8._Square = function() { + return (CC = t8._Square = t8.asm.Square).apply(null, arguments); + }, wC = t8._SquaredDifference = function() { + return (wC = t8._SquaredDifference = t8.asm.SquaredDifference).apply(null, arguments); + }, SC = t8._Step = function() { + return (SC = t8._Step = t8.asm.Step).apply(null, arguments); + }, IC = t8._StridedSlice = function() { + return (IC = t8._StridedSlice = t8.asm.StridedSlice).apply(null, arguments); + }, vC = t8._Sub = function() { + return (vC = t8._Sub = t8.asm.Sub).apply(null, arguments); + }, kC = t8._Sum = function() { + return (kC = t8._Sum = t8.asm.Sum).apply(null, arguments); + }, NC = t8._Tan = function() { + return (NC = t8._Tan = t8.asm.Tan).apply(null, arguments); + }, TC = t8._Tanh = function() { + return (TC = t8._Tanh = t8.asm.Tanh).apply(null, arguments); + }, _C = t8._TensorScatterUpdate = function() { + return (_C = t8._TensorScatterUpdate = t8.asm.TensorScatterUpdate).apply(null, arguments); + }, $C = t8._Tile = function() { + return ($C = t8._Tile = t8.asm.Tile).apply(null, arguments); + }, EC = t8._TopK = function() { + return (EC = t8._TopK = t8.asm.TopK).apply(null, arguments); + }, RC = t8._Transform = function() { + return (RC = t8._Transform = t8.asm.Transform).apply(null, arguments); + }, DC = t8._Transpose = function() { + return (DC = t8._Transpose = t8.asm.Transpose).apply(null, arguments); + }, AC = t8.__FusedMatMul = function() { + return (AC = t8.__FusedMatMul = t8.asm._FusedMatMul).apply(null, arguments); + }, FC = t8._malloc = function() { + return (FC = t8._malloc = t8.asm.malloc).apply(null, arguments); + }, PC = t8._free = function() { + return (PC = t8._free = t8.asm.free).apply(null, arguments); + }, OC = t8.___errno_location = function() { + return (OC = t8.___errno_location = t8.asm.__errno_location).apply(null, arguments); + }, Fm = t8.stackSave = function() { + return (Fm = t8.stackSave = t8.asm.stackSave).apply(null, arguments); + }, Pm = t8.stackRestore = function() { + return (Pm = t8.stackRestore = t8.asm.stackRestore).apply(null, arguments); + }, ul = t8.stackAlloc = function() { + return (ul = t8.stackAlloc = t8.asm.stackAlloc).apply(null, arguments); + }, MC = t8.dynCall_iijjiiii = function() { + return (MC = t8.dynCall_iijjiiii = t8.asm.dynCall_iijjiiii).apply(null, arguments); + }, LC = t8.dynCall_jiji = function() { + return (LC = t8.dynCall_jiji = t8.asm.dynCall_jiji).apply(null, arguments); }; - t10.cwrap = gy; - var Pp; + t8.cwrap = yy; + var Bp; sr = function G() { - Pp || Pm(), Pp || (sr = G); + Bp || Om(), Bp || (sr = G); }; - function Pm(G) { - if (G = G || i, Tt > 0 || (ht(), Tt > 0)) + function Om(G) { + if (G = G || i, Tt > 0 || (gt(), Tt > 0)) return; function ae() { - Pp || (Pp = true, t10.calledRun = true, !M && (gt(), o(t10), t10.onRuntimeInitialized && t10.onRuntimeInitialized(), Or())); + Bp || (Bp = true, t8.calledRun = true, !M && (xt(), o(t8), t8.onRuntimeInitialized && t8.onRuntimeInitialized(), Lr())); } - t10.setStatus ? (t10.setStatus("Running..."), setTimeout(function() { + t8.setStatus ? (t8.setStatus("Running..."), setTimeout(function() { setTimeout(function() { - t10.setStatus(""); + t8.setStatus(""); }, 1), ae(); }, 1)) : ae(); } - if (t10.preInit) - for (typeof t10.preInit == "function" && (t10.preInit = [t10.preInit]); t10.preInit.length > 0; ) - t10.preInit.pop()(); - Pm(); - var Op; - s && (Op = { uncaughtException: process.listeners("uncaughtException").filter(function(G) { + if (t8.preInit) + for (typeof t8.preInit == "function" && (t8.preInit = [t8.preInit]); t8.preInit.length > 0; ) + t8.preInit.pop()(); + Om(); + var zp; + s && (zp = { uncaughtException: process.listeners("uncaughtException").filter(function(G) { return !s.uncaughtException.indexOf(G) > -1; }), unhandledRejection: process.listeners("unhandledRejection").filter(function(G) { return !s.unhandledRejection.indexOf(G) > -1; }) }); - var Mp; + var Vp; if (typeof e != "undefined") - Mp = e; + Vp = e; else if (typeof WasmBackendModuleThreadedSimd != "undefined") - Mp = WasmBackendModuleThreadedSimd; + Vp = WasmBackendModuleThreadedSimd; else throw new Error("Could not find wasm module in post.js"); - if (Op) { - var EC = Mp._dispose; - Mp._dispose = function() { - EC(), Op.uncaughtException.forEach(function(G) { + if (zp) { + var BC = Vp._dispose; + Vp._dispose = function() { + BC(), zp.uncaughtException.forEach(function(G) { process.removeListener("uncaughtException", G); - }), Op.unhandledRejection.forEach(function(G) { + }), zp.unhandledRejection.forEach(function(G) { process.removeListener("unhandledRejection", G); }); }; @@ -2913,19 +2942,19 @@ var nB = qt((zg, Tv) => { return e.ready; }; })(); - typeof zg == "object" && typeof Tv == "object" ? Tv.exports = Nv : typeof define == "function" && define.amd ? define([], function() { - return Nv; - }) : typeof zg == "object" && (zg.WasmBackendModule = Nv); + typeof Vg == "object" && typeof Ov == "object" ? Ov.exports = Pv : typeof define == "function" && define.amd ? define([], function() { + return Pv; + }) : typeof Vg == "object" && (Vg.WasmBackendModule = Pv); }); -var Lo = class { - constructor(e, t10) { - this.backend = e, this.dataMover = t10, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0; +var Bo = class { + constructor(e, t8) { + this.backend = e, this.dataMover = t8, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0; } get(e) { return this.data.has(e) || this.dataMover.moveData(this.backend, e), this.data.get(e); } - set(e, t10) { - this.dataIdsCount++, this.data.set(e, t10); + set(e, t8) { + this.dataIdsCount++, this.data.set(e, t8); } has(e) { return this.data.has(e); @@ -2937,144 +2966,144 @@ var Lo = class { return this.dataIdsCount; } }; -var ro = class { +var so = class { refCount(e) { - return Lr("refCount"); + return zr("refCount"); } incRef(e) { - return Lr("incRef"); + return zr("incRef"); } timerAvailable() { return true; } time(e) { - return Lr("time"); + return zr("time"); } read(e) { - return Lr("read"); + return zr("read"); } readSync(e) { - return Lr("readSync"); + return zr("readSync"); } - readToGPU(e, t10) { - return Lr("readToGPU"); + readToGPU(e, t8) { + return zr("readToGPU"); } numDataIds() { - return Lr("numDataIds"); + return zr("numDataIds"); } - disposeData(e, t10) { - return Lr("disposeData"); + disposeData(e, t8) { + return zr("disposeData"); } - write(e, t10, o) { - return Lr("write"); + write(e, t8, o) { + return zr("write"); } - move(e, t10, o, n, s) { - return Lr("move"); + move(e, t8, o, n, s) { + return zr("move"); } - createTensorFromGPUData(e, t10, o) { - return Lr("createTensorFromGPUData"); + createTensorFromGPUData(e, t8, o) { + return zr("createTensorFromGPUData"); } memory() { - return Lr("memory"); + return zr("memory"); } floatPrecision() { - return Lr("floatPrecision"); + return zr("floatPrecision"); } epsilon() { return this.floatPrecision() === 32 ? 1e-7 : 1e-4; } dispose() { - return Lr("dispose"); + return zr("dispose"); } }; -function Lr(r) { +function zr(r) { throw new Error(`'${r}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); } -function a0(r) { - let e = r.length, t10 = 0; +function h0(r) { + let e = r.length, t8 = 0; for (; e > 0; ) - t10 = Math.random() * e | 0, e--, Gm(r, e, t10); + t8 = Math.random() * e | 0, e--, Hm(r, e, t8); } -function HU(r, e) { +function gG(r, e) { if (r.length !== e.length) throw new Error(`Array sizes must match to be shuffled together First array length was ${r.length}Second array length was ${e.length}`); - let t10 = r.length, o = 0; - for (; t10 > 0; ) - o = Math.random() * t10 | 0, t10--, Gm(r, t10, o), Gm(e, t10, o); + let t8 = r.length, o = 0; + for (; t8 > 0; ) + o = Math.random() * t8 | 0, t8--, Hm(r, t8, o), Hm(e, t8, o); } -function zp(r, e, t10) { - return Math.max(r, Math.min(e, t10)); +function Gp(r, e, t8) { + return Math.max(r, Math.min(e, t8)); } -function KU(r) { +function xG(r) { return r % 2 === 0 ? r : r + 1; } -function Gm(r, e, t10) { +function Hm(r, e, t8) { let o = r[e]; - r[e] = r[t10], r[t10] = o; + r[e] = r[t8], r[t8] = o; } -function qU(r) { +function yG(r) { let e = 0; - for (let t10 = 0; t10 < r.length; t10++) - e += r[t10]; + for (let t8 = 0; t8 < r.length; t8++) + e += r[t8]; return e; } -function jU(r, e) { - let t10 = Math.random(); - return e * t10 + (1 - t10) * r; +function bG(r, e) { + let t8 = Math.random(); + return e * t8 + (1 - t8) * r; } -function XU(r, e) { - let t10 = 0; +function CG(r, e) { + let t8 = 0; for (let o = 0; o < r.length; o++) { let n = Number(r[o]) - Number(e[o]); - t10 += n * n; + t8 += n * n; } - return t10; + return t8; } function $(r, e) { if (!r) throw new Error(typeof e == "string" ? e : e()); } -function xt(r, e, t10 = "") { - $(br(r, e), () => t10 + ` Shapes ${r} and ${e} must match`); +function yt(r, e, t8 = "") { + $(Cr(r, e), () => t8 + ` Shapes ${r} and ${e} must match`); } -function oo(r) { +function ao(r) { $(r != null, () => "The input to the tensor constructor must be a non-null value."); } -function Ue(r) { +function Ge(r) { if (r.length === 0) return 1; let e = r[0]; - for (let t10 = 1; t10 < r.length; t10++) - e *= r[t10]; + for (let t8 = 1; t8 < r.length; t8++) + e *= r[t8]; return e; } -function YU(r) { +function wG(r) { return r.length === 0; } -function OC(r, e) { +function HC(r, e) { if (r === e) return true; if (r == null || e == null || r.length !== e.length) return false; - for (let t10 = 0; t10 < r.length; t10++) - if (r[t10] !== null && e[t10] !== null && r[t10] !== e[t10]) + for (let t8 = 0; t8 < r.length; t8++) + if (r[t8] !== null && e[t8] !== null && r[t8] !== e[t8]) return false; return true; } -function br(r, e) { +function Cr(r, e) { if (r === e) return true; if (r == null || e == null || r.length !== e.length) return false; - for (let t10 = 0; t10 < r.length; t10++) - if (r[t10] !== e[t10]) + for (let t8 = 0; t8 < r.length; t8++) + if (r[t8] !== e[t8]) return false; return true; } -function Ba(r) { +function Ga(r) { return r % 1 === 0; } -function QU(r) { +function SG(r) { if (Math.tanh != null) return Math.tanh(r); if (r === 1 / 0) @@ -3086,20 +3115,20 @@ function QU(r) { return (e - 1) / (e + 1); } } -function ZU(r) { +function IG(r) { let e = Math.ceil(Math.sqrt(r)); return [e, Math.ceil(r / e)]; } -function JU(r) { +function vG(r) { let e = new Uint32Array(r); - for (let t10 = 0; t10 < r; ++t10) - e[t10] = t10; - return a0(e), e; + for (let t8 = 0; t8 < r; ++t8) + e[t8] = t8; + return h0(e), e; } -function Nu(r, e) { +function Eu(r, e) { return e <= r.length ? r : r + " ".repeat(e - r.length); } -function eG(r, e = (n) => 0, t10, o) { +function kG(r, e = (n) => 0, t8, o) { return new Promise((n, s) => { let a = 0, i = () => { if (r()) { @@ -3108,7 +3137,7 @@ function eG(r, e = (n) => 0, t10, o) { } a++; let p = e(a); - if (t10 != null && a >= t10) { + if (t8 != null && a >= t8) { s(); return; } @@ -3117,11 +3146,11 @@ function eG(r, e = (n) => 0, t10, o) { i(); }); } -function tG(r, e) { - let t10 = 1, o = -1; +function NG(r, e) { + let t8 = 1, o = -1; for (let s = 0; s < r.length; ++s) if (r[s] >= 0) - t10 *= r[s]; + t8 *= r[s]; else if (r[s] === -1) { if (o !== -1) throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${o} and dim ${s}`); @@ -3129,64 +3158,64 @@ function tG(r, e) { } else if (r[s] < 0) throw Error(`Shapes can not be < 0. Found ${r[s]} at dim ${s}`); if (o === -1) { - if (e > 0 && e !== t10) + if (e > 0 && e !== t8) throw Error(`Size(${e}) must match the product of shape ${r}`); return r; } - if (t10 === 0) + if (t8 === 0) throw Error(`Cannot infer the missing size in [${r}] when there are 0 elements`); - if (e % t10 !== 0) - throw Error(`The implicit shape can't be a fractional number. Got ${e} / ${t10}`); + if (e % t8 !== 0) + throw Error(`The implicit shape can't be a fractional number. Got ${e} / ${t8}`); let n = r.slice(); - return n[o] = e / t10, n; + return n[o] = e / t8, n; } -function vi(r, e) { - let t10 = e.length; - return r = r == null ? e.map((o, n) => n) : [].concat(r), $(r.every((o) => o >= -t10 && o < t10), () => `All values in axis param must be in range [-${t10}, ${t10}) but got axis ${r}`), $(r.every((o) => Ba(o)), () => `All values in axis param must be integers but got axis ${r}`), r.map((o) => o < 0 ? t10 + o : o); +function Ti(r, e) { + let t8 = e.length; + return r = r == null ? e.map((o, n) => n) : [].concat(r), $(r.every((o) => o >= -t8 && o < t8), () => `All values in axis param must be in range [-${t8}, ${t8}) but got axis ${r}`), $(r.every((o) => Ga(o)), () => `All values in axis param must be integers but got axis ${r}`), r.map((o) => o < 0 ? t8 + o : o); } -function MC(r, e) { - let t10 = [], o = [], n = e != null && Array.isArray(e) && e.length === 0, s = e == null || n ? null : vi(e, r).sort(), a = 0; +function KC(r, e) { + let t8 = [], o = [], n = e != null && Array.isArray(e) && e.length === 0, s = e == null || n ? null : Ti(e, r).sort(), a = 0; for (let i = 0; i < r.length; ++i) { if (s != null) { if (s[a] === i && r[i] !== 1) throw new Error(`Can't squeeze axis ${i} since its dim '${r[i]}' is not 1`); - (s[a] == null || s[a] > i) && r[i] === 1 && (t10.push(r[i]), o.push(i)), s[a] <= i && a++; + (s[a] == null || s[a] > i) && r[i] === 1 && (t8.push(r[i]), o.push(i)), s[a] <= i && a++; } - r[i] !== 1 && (t10.push(r[i]), o.push(i)); + r[i] !== 1 && (t8.push(r[i]), o.push(i)); } - return { newShape: t10, keptDims: o }; + return { newShape: t8, keptDims: o }; } -function LC(r, e) { - return Hm(r, e); +function qC(r, e) { + return Km(r, e); } -function Hm(r, e) { - let t10 = null; +function Km(r, e) { + let t8 = null; if (r == null || r === "float32") - t10 = new Float32Array(e); + t8 = new Float32Array(e); else if (r === "int32") - t10 = new Int32Array(e); + t8 = new Int32Array(e); else if (r === "bool") - t10 = new Uint8Array(e); + t8 = new Uint8Array(e); else if (r === "string") - t10 = new Array(e); + t8 = new Array(e); else throw new Error(`Unknown data type ${r}`); - return t10; + return t8; } -function BC(r, e) { - for (let t10 = 0; t10 < r.length; t10++) { - let o = r[t10]; +function jC(r, e) { + for (let t8 = 0; t8 < r.length; t8++) { + let o = r[t8]; if (isNaN(o) || !isFinite(o)) throw Error(`A tensor of type ${e} being uploaded contains ${o}.`); } } -function zC(r) { +function XC(r) { return r === "bool" || r === "complex64" || r === "float32" || r === "int32" || r === "string"; } -function rG(r, e) { +function TG(r, e) { return !(e === "complex64" || e === "float32" && r !== "complex64" || e === "int32" && r !== "float32" && r !== "complex64" || e === "bool" && r === "bool"); } -function Vp(r) { +function Hp(r) { if (r === "float32" || r === "int32") return 4; if (r === "complex64") @@ -3195,67 +3224,67 @@ function Vp(r) { return 1; throw new Error(`Unknown dtype ${r}`); } -function VC(r) { +function YC(r) { if (r == null) return 0; let e = 0; - return r.forEach((t10) => e += t10.length), e; + return r.forEach((t8) => e += t8.length), e; } -function Bo(r) { +function zo(r) { return typeof r == "string" || r instanceof String; } -function i0(r) { +function g0(r) { return typeof r == "boolean"; } -function u0(r) { +function x0(r) { return typeof r == "number"; } -function ki(r) { - return Array.isArray(r) ? ki(r[0]) : r instanceof Float32Array ? "float32" : r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray ? "int32" : u0(r) ? "float32" : Bo(r) ? "string" : i0(r) ? "bool" : "float32"; +function _i(r) { + return Array.isArray(r) ? _i(r[0]) : r instanceof Float32Array ? "float32" : r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray ? "int32" : x0(r) ? "float32" : zo(r) ? "string" : g0(r) ? "bool" : "float32"; } -function Ws(r) { +function Ks(r) { return !!(r && r.constructor && r.call && r.apply); } -function Wp(r, e) { - for (let t10 = e; t10 < r; ++t10) - if (r % t10 === 0) - return t10; +function Kp(r, e) { + for (let t8 = e; t8 < r; ++t8) + if (r % t8 === 0) + return t8; return r; } -function Us(r) { +function qs(r) { let e = r.length; if (e < 2) return []; - let t10 = new Array(e - 1); - t10[e - 2] = r[e - 1]; + let t8 = new Array(e - 1); + t8[e - 2] = r[e - 1]; for (let o = e - 3; o >= 0; --o) - t10[o] = t10[o + 1] * r[o + 1]; - return t10; + t8[o] = t8[o + 1] * r[o + 1]; + return t8; } -function p0(r, e, t10, o = false) { +function y0(r, e, t8, o = false) { let n = new Array(); if (e.length === 1) { let s = e[0] * (o ? 2 : 1); for (let a = 0; a < s; a++) - n[a] = t10[r + a]; + n[a] = t8[r + a]; } else { let s = e[0], a = e.slice(1), i = a.reduce((p, u) => p * u) * (o ? 2 : 1); for (let p = 0; p < s; p++) - n[p] = p0(r + p * i, a, t10, o); + n[p] = y0(r + p * i, a, t8, o); } return n; } -function ku(r, e, t10 = false) { +function $u(r, e, t8 = false) { if (r.length === 0) return e[0]; - let o = r.reduce((n, s) => n * s) * (t10 ? 2 : 1); + let o = r.reduce((n, s) => n * s) * (t8 ? 2 : 1); if (o === 0) return []; if (o !== e.length) - throw new Error(`[${r}] does not match the input size ${e.length}${t10 ? " for a complex tensor" : ""}.`); - return p0(0, r, e, t10); + throw new Error(`[${r}] does not match the input size ${e.length}${t8 ? " for a complex tensor" : ""}.`); + return y0(0, r, e, t8); } -function oG(r, e) { +function _G(r, e) { if (Array.isArray(r)) return r; if (e === "float32") @@ -3266,13 +3295,13 @@ function oG(r, e) { return Uint8Array.from(new Int32Array(r)); throw new Error(`Unknown dtype ${e}`); } -function pl(r, e) { - let t10 = Up(r, e); - for (let o = 0; o < t10.length; o++) - t10[o] = 1; - return t10; +function cl(r, e) { + let t8 = qp(r, e); + for (let o = 0; o < t8.length; o++) + t8[o] = 1; + return t8; } -function Up(r, e) { +function qp(r, e) { if (e == null || e === "float32" || e === "complex64") return new Float32Array(r); if (e === "int32") @@ -3281,56 +3310,56 @@ function Up(r, e) { return new Uint8Array(r); throw new Error(`Unknown data type ${e}`); } -function nG(r, e) { - let t10 = r.reduce((o, n) => o * n, 1); +function $G(r, e) { + let t8 = r.reduce((o, n) => o * n, 1); if (e == null || e === "float32") - return ku(r, new Float32Array(t10)); + return $u(r, new Float32Array(t8)); if (e === "int32") - return ku(r, new Int32Array(t10)); + return $u(r, new Int32Array(t8)); if (e === "bool") - return ku(r, new Uint8Array(t10)); + return $u(r, new Uint8Array(t8)); throw new Error(`Unknown data type ${e}`); } -function Ct(r) { +function wt(r) { r.forEach((e) => { $(Number.isInteger(e) && e >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${r}].`); }); } -function sG(r, e, t10) { +function EG(r, e, t8) { if (e === 0) return 0; if (e === 1) return r[0]; let o = r[r.length - 1]; for (let n = 0; n < r.length - 1; ++n) - o += t10[n] * r[n]; + o += t8[n] * r[n]; return o; } -function aG(r, e, t10) { +function RG(r, e, t8) { if (e === 0) return []; if (e === 1) return [r]; let o = new Array(e); for (let n = 0; n < o.length - 1; ++n) - o[n] = Math.floor(r / t10[n]), r -= o[n] * t10[n]; + o[n] = Math.floor(r / t8[n]), r -= o[n] * t8[n]; return o[o.length - 1] = r, o; } -function Tu(r) { +function Ru(r) { return r && r.then && typeof r.then == "function"; } -var c0 = "tfjsflags"; -var cl = class { +var b0 = "tfjsflags"; +var ll = class { constructor(e) { - this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = uG, this.populateURLFlags(); + this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = AG, this.populateURLFlags(); } - setPlatform(e, t10) { - this.platform != null && (P().getBool("IS_TEST") || P().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t10; + setPlatform(e, t8) { + this.platform != null && (A().getBool("IS_TEST") || A().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t8; } - registerFlag(e, t10, o) { - if (this.flagRegistry[e] = { evaluationFn: t10, setHook: o }, this.urlFlags[e] != null) { + registerFlag(e, t8, o) { + if (this.flagRegistry[e] = { evaluationFn: t8, setHook: o }, this.urlFlags[e] != null) { let n = this.urlFlags[e]; - P().getBool("IS_TEST") || P().getBool("PROD") || console.warn(`Setting feature override from URL ${e}: ${n}.`), this.set(e, n); + A().getBool("IS_TEST") || A().getBool("PROD") || console.warn(`Setting feature override from URL ${e}: ${n}.`), this.set(e, n); } } async getAsync(e) { @@ -3339,10 +3368,10 @@ var cl = class { get(e) { if (e in this.flags) return this.flags[e]; - let t10 = this.evaluateFlag(e); - if (Tu(t10)) + let t8 = this.evaluateFlag(e); + if (Ru(t8)) throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`); - return this.flags[e] = t10, this.flags[e]; + return this.flags[e] = t8, this.flags[e]; } getNumber(e) { return this.get(e); @@ -3359,10 +3388,10 @@ var cl = class { get features() { return this.flags; } - set(e, t10) { + set(e, t8) { if (this.flagRegistry[e] == null) throw new Error(`Cannot set flag ${e} as it has not been registered.`); - this.flags[e] = t10, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t10); + this.flags[e] = t8, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t8); } evaluateFlag(e) { if (this.flagRegistry[e] == null) @@ -3379,33 +3408,33 @@ var cl = class { if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined") return; let e = this.getQueryParams(this.global.location.search); - c0 in e && e[c0].split(",").forEach((o) => { + b0 in e && e[b0].split(",").forEach((o) => { let [n, s] = o.split(":"); - this.urlFlags[n] = cG(n, s); + this.urlFlags[n] = PG(n, s); }); } }; -function uG(r) { +function AG(r) { let e = {}; - return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (t10, ...o) => (pG(e, o[0], o[1]), o.join("="))), e; + return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (t8, ...o) => (FG(e, o[0], o[1]), o.join("="))), e; } -function pG(r, e, t10) { - r[decodeURIComponent(e)] = decodeURIComponent(t10 || ""); +function FG(r, e, t8) { + r[decodeURIComponent(e)] = decodeURIComponent(t8 || ""); } -function cG(r, e) { - let t10 = e.toLowerCase(); - return t10 === "true" || t10 === "false" ? t10 === "true" : `${+t10}` === t10 ? +t10 : e; +function PG(r, e) { + let t8 = e.toLowerCase(); + return t8 === "true" || t8 === "false" ? t8 === "true" : `${+t8}` === t8 ? +t8 : e; } -function P() { - return WC; +function A() { + return QC; } -var WC = null; -function l0(r) { - WC = r; +var QC = null; +function C0(r) { + QC = r; } -var UC; -function GC() { - if (UC == null) { +var ZC; +function JC() { + if (ZC == null) { let r; if (typeof window != "undefined") r = window; @@ -3417,448 +3446,449 @@ function GC() { r = self; else throw new Error("Could not find a global object"); - UC = r; + ZC = r; } - return UC; + return ZC; } -function lG() { - let r = GC(); +function OG() { + let r = JC(); return r._tfGlobals == null && (r._tfGlobals = /* @__PURE__ */ new Map()), r._tfGlobals; } -function ll(r, e) { - let t10 = lG(); - if (t10.has(r)) - return t10.get(r); +function ml(r, e) { + let t8 = OG(); + if (t8.has(r)) + return t8.get(r); { let o = e(); - return t10.set(r, o), t10.get(r); + return t8.set(r, o), t8.get(r); } } -var Gs = "Abs"; -var zo = "Acos"; -var Vo = "Acosh"; -var no = "Add"; -var Wo = "AddN"; -var Uo = "All"; -var Go = "Any"; -var Hs = "ArgMax"; -var Ks = "ArgMin"; -var Ho = "Asin"; -var Ko = "Asinh"; -var qo = "Atan"; -var jo = "Atanh"; -var Xo = "Atan2"; -var Yo = "AvgPool"; -var Gp = "AvgPoolGrad"; -var qs = "AvgPool3D"; -var Ni = "AvgPool3DGrad"; -var Qo = "BatchMatMul"; -var js = "BatchToSpaceND"; -var Zo = "Bincount"; -var ml = "BitwiseAnd"; -var Kpe = "BroadcastTo"; -var Xs = "BroadcastArgs"; -var ho = "Cast"; -var Jo = "Ceil"; -var go = "ClipByValue"; -var Ti = "Complex"; -var _i = "ComplexAbs"; -var Ys = "Concat"; -var en = "Conv2D"; -var $i = "Conv2DBackpropFilter"; -var tn = "Conv2DBackpropInput"; -var rn = "Conv3D"; -var za = "Conv3DBackpropFilterV2"; -var on = "Conv3DBackpropInputV2"; -var nn = "Cos"; -var sn = "Cosh"; -var an = "Cumprod"; -var un = "Cumsum"; -var pn = "CropAndResize"; -var Qs = "DenseBincount"; -var cn = "DepthToSpace"; -var ln = "DepthwiseConv2dNative"; -var Ei = "DepthwiseConv2dNativeBackpropFilter"; -var Ri = "DepthwiseConv2dNativeBackpropInput"; -var Zs = "Diag"; -var mn = "Dilation2D"; -var Di = "Dilation2DBackpropInput"; -var Ai = "Dilation2DBackpropFilter"; -var dn = "RealDiv"; -var Fi = "Einsum"; -var fn = "Elu"; -var Va = "EluGrad"; -var Wa = "Erf"; -var hn = "Equal"; -var gn = "Exp"; -var Js = "ExpandDims"; -var xn = "Expm1"; -var Pi = "FFT"; -var ea = "Fill"; -var yn = "FlipLeftRight"; -var bn = "Floor"; -var Cn = "FloorDiv"; -var wn = "FusedBatchNorm"; -var ta = "GatherV2"; -var Sn = "GatherNd"; -var In = "Greater"; -var vn = "GreaterEqual"; -var xo = "Identity"; -var Oi = "IFFT"; -var Mi = "Imag"; -var kn = "IsFinite"; -var Nn = "IsInf"; -var Tn = "IsNan"; -var _n = "LeakyRelu"; -var $n = "Less"; -var En = "LessEqual"; -var Rn = "LinSpace"; -var Dn = "Log"; -var An = "Log1p"; -var Fn = "LogicalAnd"; -var Pn = "LogicalNot"; -var On = "LogicalOr"; -var m0 = "LogicalXor"; -var qpe = "LogSoftmax"; -var jpe = "LowerBound"; -var Mn = "LRN"; -var Ua = "LRNGrad"; -var Xpe = "MatrixBandPart"; -var Ln = "Max"; -var Bn = "Maximum"; -var zn = "MaxPool"; -var Hp = "MaxPoolGrad"; -var ra = "MaxPool3D"; -var Li = "MaxPool3DGrad"; -var Bi = "MaxPoolWithArgmax"; -var Vn = "Mean"; -var Wn = "Min"; -var Un = "Minimum"; -var Gn = "MirrorPad"; -var Ga = "Mod"; -var Hn = "Multinomial"; -var Kn = "Multiply"; -var oa = "Neg"; -var qn = "NotEqual"; -var jn = "NonMaxSuppressionV3"; -var Ha = "NonMaxSuppressionV4"; -var Xn = "NonMaxSuppressionV5"; -var na = "OnesLike"; -var Yn = "OneHot"; -var sa = "Pack"; -var Qn = "PadV2"; -var Ype = "Pool"; -var Zn = "Pow"; -var Jn = "Prelu"; -var es = "Prod"; -var Kp = "RaggedGather"; -var qp = "RaggedRange"; -var jp = "RaggedTensorToTensor"; -var aa = "Range"; -var zi = "Real"; -var ts = "Reciprocal"; -var rs = "Relu"; -var ia = "Reshape"; -var os = "ResizeNearestNeighbor"; -var Ka = "ResizeNearestNeighborGrad"; -var ns = "ResizeBilinear"; -var qa = "ResizeBilinearGrad"; -var ss = "Relu6"; -var as = "Reverse"; -var is = "Round"; -var us = "Rsqrt"; -var ps = "ScatterNd"; -var cs = "TensorScatterUpdate"; -var ls = "SearchSorted"; -var ua = "Select"; -var ms = "Selu"; -var pa = "Slice"; -var ds = "Sin"; -var ja = "Sinh"; -var fs = "Sign"; -var hs = "Sigmoid"; -var gs = "Softplus"; -var xs = "Sqrt"; -var ys = "Sum"; -var ca = "SpaceToBatchND"; -var la = "SplitV"; -var bs = "Softmax"; -var Vi = "SparseFillEmptyRows"; -var Xa = "SparseReshape"; -var Wi = "SparseSegmentMean"; -var Ui = "SparseSegmentSum"; -var Cs = "SparseToDense"; -var ws = "SquaredDifference"; -var Gi = "Square"; -var _u = "StaticRegexReplace"; -var Ss = "StridedSlice"; -var ma = "StringNGrams"; -var Hi = "StringSplit"; -var Ki = "StringToHashBucketFast"; -var Is = "Sub"; -var vs = "Tan"; -var ks = "Tanh"; -var so = "Tile"; -var Ns = "TopK"; -var Ts = "Transform"; -var ao = "Transpose"; -var qi = "Unique"; -var da = "Unpack"; -var ji = "UnsortedSegmentSum"; -var Qpe = "UpperBound"; -var fa = "ZerosLike"; -var yo = "Step"; -var $u = "FromPixels"; -var _s = "RotateWithOffset"; -var bo = "_FusedMatMul"; -var Co = "FusedConv2D"; -var wo = "FusedDepthwiseConv2D"; -function ha(...r) { - P().getBool("IS_TEST") || P().getBool("PROD") || console.warn(...r); +var js = "Abs"; +var Vo = "Acos"; +var Wo = "Acosh"; +var io = "Add"; +var Uo = "AddN"; +var Go = "All"; +var Ho = "Any"; +var Xs = "ArgMax"; +var Ys = "ArgMin"; +var Ko = "Asin"; +var qo = "Asinh"; +var jo = "Atan"; +var Xo = "Atanh"; +var Yo = "Atan2"; +var Qo = "AvgPool"; +var $i = "AvgPoolGrad"; +var Qs = "AvgPool3D"; +var Ei = "AvgPool3DGrad"; +var Zo = "BatchMatMul"; +var Zs = "BatchToSpaceND"; +var Jo = "Bincount"; +var Ha = "BitwiseAnd"; +var _ce = "BroadcastTo"; +var Js = "BroadcastArgs"; +var yo = "Cast"; +var en = "Ceil"; +var bo = "ClipByValue"; +var Ri = "Complex"; +var Di = "ComplexAbs"; +var ea = "Concat"; +var tn = "Conv2D"; +var Ai = "Conv2DBackpropFilter"; +var rn = "Conv2DBackpropInput"; +var on = "Conv3D"; +var Ka = "Conv3DBackpropFilterV2"; +var nn = "Conv3DBackpropInputV2"; +var sn = "Cos"; +var an = "Cosh"; +var un = "Cumprod"; +var pn = "Cumsum"; +var cn = "CropAndResize"; +var ta = "DenseBincount"; +var ln = "DepthToSpace"; +var mn = "DepthwiseConv2dNative"; +var Fi = "DepthwiseConv2dNativeBackpropFilter"; +var Pi = "DepthwiseConv2dNativeBackpropInput"; +var ra = "Diag"; +var dn = "Dilation2D"; +var Oi = "Dilation2DBackpropInput"; +var Mi = "Dilation2DBackpropFilter"; +var qm = "Draw"; +var fn = "RealDiv"; +var Li = "Einsum"; +var hn = "Elu"; +var qa = "EluGrad"; +var gn = "Erf"; +var xn = "Equal"; +var yn = "Exp"; +var oa = "ExpandDims"; +var bn = "Expm1"; +var Bi = "FFT"; +var na = "Fill"; +var Cn = "FlipLeftRight"; +var wn = "Floor"; +var Sn = "FloorDiv"; +var In = "FusedBatchNorm"; +var sa = "GatherV2"; +var vn = "GatherNd"; +var kn = "Greater"; +var Nn = "GreaterEqual"; +var Co = "Identity"; +var zi = "IFFT"; +var Vi = "Imag"; +var Tn = "IsFinite"; +var _n = "IsInf"; +var $n = "IsNan"; +var En = "LeakyRelu"; +var Rn = "Less"; +var Dn = "LessEqual"; +var An = "LinSpace"; +var Fn = "Log"; +var Pn = "Log1p"; +var On = "LogicalAnd"; +var Mn = "LogicalNot"; +var Ln = "LogicalOr"; +var w0 = "LogicalXor"; +var $ce = "LogSoftmax"; +var Ece = "LowerBound"; +var Bn = "LRN"; +var ja = "LRNGrad"; +var Rce = "MatrixBandPart"; +var zn = "Max"; +var Vn = "Maximum"; +var Wn = "MaxPool"; +var Wi = "MaxPoolGrad"; +var aa = "MaxPool3D"; +var Ui = "MaxPool3DGrad"; +var ia = "MaxPoolWithArgmax"; +var Un = "Mean"; +var Gn = "Min"; +var Hn = "Minimum"; +var Kn = "MirrorPad"; +var qn = "Mod"; +var jn = "Multinomial"; +var Xn = "Multiply"; +var ua = "Neg"; +var Yn = "NotEqual"; +var Qn = "NonMaxSuppressionV3"; +var Xa = "NonMaxSuppressionV4"; +var Zn = "NonMaxSuppressionV5"; +var pa = "OnesLike"; +var Jn = "OneHot"; +var ca = "Pack"; +var es = "PadV2"; +var Dce = "Pool"; +var ts = "Pow"; +var rs = "Prelu"; +var os = "Prod"; +var jp = "RaggedGather"; +var Xp = "RaggedRange"; +var Yp = "RaggedTensorToTensor"; +var la = "Range"; +var Gi = "Real"; +var ns = "Reciprocal"; +var ss = "Relu"; +var ma = "Reshape"; +var as = "ResizeNearestNeighbor"; +var Ya = "ResizeNearestNeighborGrad"; +var is = "ResizeBilinear"; +var Qa = "ResizeBilinearGrad"; +var us = "Relu6"; +var ps = "Reverse"; +var cs = "Round"; +var ls = "Rsqrt"; +var ms = "ScatterNd"; +var ds = "TensorScatterUpdate"; +var fs = "SearchSorted"; +var da = "Select"; +var hs = "Selu"; +var fa = "Slice"; +var gs = "Sin"; +var xs = "Sinh"; +var ys = "Sign"; +var bs = "Sigmoid"; +var Cs = "Softplus"; +var ws = "Sqrt"; +var Ss = "Sum"; +var ha = "SpaceToBatchND"; +var ga = "SplitV"; +var Is = "Softmax"; +var Hi = "SparseFillEmptyRows"; +var Za = "SparseReshape"; +var Ki = "SparseSegmentMean"; +var qi = "SparseSegmentSum"; +var vs = "SparseToDense"; +var ks = "SquaredDifference"; +var ji = "Square"; +var Du = "StaticRegexReplace"; +var Ns = "StridedSlice"; +var xa = "StringNGrams"; +var Xi = "StringSplit"; +var Yi = "StringToHashBucketFast"; +var Ts = "Sub"; +var _s = "Tan"; +var $s = "Tanh"; +var uo = "Tile"; +var Es = "TopK"; +var Rs = "Transform"; +var po = "Transpose"; +var Qi = "Unique"; +var ya = "Unpack"; +var Zi = "UnsortedSegmentSum"; +var Ace = "UpperBound"; +var ba = "ZerosLike"; +var wo = "Step"; +var Au = "FromPixels"; +var Ds = "RotateWithOffset"; +var So = "_FusedMatMul"; +var Io = "FusedConv2D"; +var vo = "FusedDepthwiseConv2D"; +function Ca(...r) { + A().getBool("IS_TEST") || A().getBool("PROD") || console.warn(...r); } -function mG(...r) { - P().getBool("IS_TEST") || P().getBool("PROD") || console.log(...r); +function MG(...r) { + A().getBool("IS_TEST") || A().getBool("PROD") || console.log(...r); } -var Xp = ll("kernelRegistry", () => /* @__PURE__ */ new Map()); -var dl = ll("gradRegistry", () => /* @__PURE__ */ new Map()); +var Qp = ml("kernelRegistry", () => /* @__PURE__ */ new Map()); +var dl = ml("gradRegistry", () => /* @__PURE__ */ new Map()); function fl(r, e) { - let t10 = KC(r, e); - return Xp.get(t10); + let t8 = tw(r, e); + return Qp.get(t8); } -function HC(r) { +function ew(r) { return dl.get(r); } -function Km(r) { - let e = Xp.entries(), t10 = []; +function jm(r) { + let e = Qp.entries(), t8 = []; for (; ; ) { let { done: o, value: n } = e.next(); if (o) break; let [s, a] = n, [i] = s.split("_"); - i === r && t10.push(a); + i === r && t8.push(a); } - return t10; + return t8; } -function Ya(r) { - let { kernelName: e, backendName: t10 } = r, o = KC(e, t10); - Xp.has(o) && ha(`The kernel '${e}' for backend '${t10}' is already registered`), Xp.set(o, r); +function Ja(r) { + let { kernelName: e, backendName: t8 } = r, o = tw(e, t8); + Qp.has(o) && Ca(`The kernel '${e}' for backend '${t8}' is already registered`), Qp.set(o, r); } -function rce(r) { +function Lce(r) { let { kernelName: e } = r; - dl.has(e) && P().getBool("DEBUG") && ha(`Overriding the gradient for '${e}'`), dl.set(e, r); + dl.has(e) && A().getBool("DEBUG") && Ca(`Overriding the gradient for '${e}'`), dl.set(e, r); } -function oce(r, e) { - let t10 = KC(r, e); - if (!Xp.has(t10)) +function Bce(r, e) { + let t8 = tw(r, e); + if (!Qp.has(t8)) throw new Error(`The kernel '${r}' for backend '${e}' is not registered`); - Xp.delete(t10); + Qp.delete(t8); } -function nce(r) { +function zce(r) { if (!dl.has(r)) throw new Error(`The gradient '${r}' for backend is not registered`); dl.delete(r); } -function sce(r, e) { - Km(r).forEach((o) => { +function Vce(r, e) { + jm(r).forEach((o) => { let n = Object.assign({}, o, { backendName: e }); - Ya(n); + Ja(n); }); } -function KC(r, e) { +function tw(r, e) { return `${e}_${r}`; } var y = {}; -He(y, { arraysEqual: () => br, arraysEqualWithNull: () => OC, assert: () => $, assertNonNegativeIntegerDimensions: () => Ct, assertNonNull: () => oo, assertShapesMatch: () => xt, bytesFromStringArray: () => VC, bytesPerElement: () => Vp, checkConversionForErrors: () => BC, clamp: () => zp, computeStrides: () => Us, convertBackendValuesAndArrayBuffer: () => oG, createScalarValue: () => bG, createShuffledIndices: () => JU, decodeString: () => Jp, distSquared: () => XU, encodeString: () => Yi, fetch: () => wG, fingerPrint64: () => yG, flatten: () => Es, getArrayFromDType: () => Hm, getTypedArrayFromDType: () => LC, hasEncodingLoss: () => rG, hexToLong: () => hl, indexToLoc: () => aG, inferDtype: () => ki, inferFromImplicitShape: () => tG, isBoolean: () => i0, isFunction: () => Ws, isInt: () => Ba, isNumber: () => u0, isPromise: () => Tu, isScalarShape: () => YU, isString: () => Bo, isTypedArray: () => Pt, isValidDtype: () => zC, locToIndex: () => sG, makeOnesTypedArray: () => pl, makeZerosNestedTypedArray: () => nG, makeZerosTypedArray: () => Up, nearestDivisor: () => Wp, nearestLargerEven: () => KU, now: () => Fu, parseAxisParam: () => vi, randUniform: () => jU, repeatedTry: () => eG, rightPad: () => Nu, shuffle: () => a0, shuffleCombo: () => HU, sizeFromShape: () => Ue, sizeToSquarishShape: () => ZU, squeezeShape: () => MC, sum: () => qU, swap: () => Gm, tanh: () => QU, toNestedArray: () => ku, toTypedArray: () => Zp }); -function qm(r) { +Ke(y, { arraysEqual: () => Cr, arraysEqualWithNull: () => HC, assert: () => $, assertNonNegativeIntegerDimensions: () => wt, assertNonNull: () => ao, assertShapesMatch: () => yt, bytesFromStringArray: () => YC, bytesPerElement: () => Hp, checkConversionForErrors: () => jC, clamp: () => Gp, computeStrides: () => qs, convertBackendValuesAndArrayBuffer: () => _G, createScalarValue: () => GG, createShuffledIndices: () => vG, decodeString: () => tc, distSquared: () => CG, encodeString: () => eu, fetch: () => KG, fingerPrint64: () => UG, flatten: () => Fs, getArrayFromDType: () => Km, getTypedArrayFromDType: () => qC, hasEncodingLoss: () => TG, hexToLong: () => hl, indexToLoc: () => RG, inferDtype: () => _i, inferFromImplicitShape: () => NG, isBoolean: () => g0, isFunction: () => Ks, isInt: () => Ga, isNumber: () => x0, isPromise: () => Ru, isScalarShape: () => wG, isString: () => zo, isTypedArray: () => Pt, isValidDtype: () => XC, locToIndex: () => EG, makeOnesTypedArray: () => cl, makeZerosNestedTypedArray: () => $G, makeZerosTypedArray: () => qp, nearestDivisor: () => Kp, nearestLargerEven: () => xG, now: () => Lu, parseAxisParam: () => Ti, randUniform: () => bG, repeatedTry: () => kG, rightPad: () => Eu, shuffle: () => h0, shuffleCombo: () => gG, sizeFromShape: () => Ge, sizeToSquarishShape: () => IG, squeezeShape: () => KC, sum: () => yG, swap: () => Hm, tanh: () => SG, toNestedArray: () => $u, toTypedArray: () => ec }); +function Xm(r) { return r instanceof Float32Array || r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray; } -var YC = Bp(v0()); -var Au = YC.default || YC; +var sw = Up(A0()); +var Mu = sw.default || sw; function hl(r) { - return Au.fromString(r, true, 16); + return Mu.fromString(r, true, 16); } -var N0 = hl("c3a5c85c97cb3127"); -var Du = hl("b492b66fbe98f273"); -var Cr = hl("9ae16a3b2f90404f"); -function XC(r) { +var P0 = hl("c3a5c85c97cb3127"); +var Ou = hl("b492b66fbe98f273"); +var wr = hl("9ae16a3b2f90404f"); +function nw(r) { return r.xor(r.shru(47)); } -function T0(r, e, t10) { - let o = r.slice(e, e + t10); - return Au.fromBytes(Array.from(o), true, true); +function O0(r, e, t8) { + let o = r.slice(e, e + t8); + return Mu.fromBytes(Array.from(o), true, true); } -function wt(r, e) { - return T0(r, e, 8); +function St(r, e) { + return O0(r, e, 8); } -function k0(r, e) { - return T0(r, e, 4); +function F0(r, e) { + return O0(r, e, 4); } -function Yt(r, e) { +function Qt(r, e) { return e === 0 ? r : r.shru(e).or(r.shl(64 - e)); } -function Xi(r, e, t10 = hl("9ddfea08eb382d69")) { - let o = r.xor(e).mul(t10); +function Ji(r, e, t8 = hl("9ddfea08eb382d69")) { + let o = r.xor(e).mul(t8); o = o.xor(o.shru(47)); - let n = e.xor(o).mul(t10); - return n = n.xor(n.shru(47)), n = n.mul(t10), n; + let n = e.xor(o).mul(t8); + return n = n.xor(n.shru(47)), n = n.mul(t8), n; } -function fG(r, e, t10, o, n, s) { - n = n.add(r), s = Yt(s.add(n).add(o), 21); +function BG(r, e, t8, o, n, s) { + n = n.add(r), s = Qt(s.add(n).add(o), 21); let a = n; - return n = n.add(e), n = n.add(t10), s = s.add(Yt(n, 44)), [n.add(o), s.add(a)]; + return n = n.add(e), n = n.add(t8), s = s.add(Qt(n, 44)), [n.add(o), s.add(a)]; } -function Xm(r, e, t10, o) { - return fG(wt(r, e), wt(r, e + 8), wt(r, e + 16), wt(r, e + 24), t10, o); +function Qm(r, e, t8, o) { + return BG(St(r, e), St(r, e + 8), St(r, e + 16), St(r, e + 24), t8, o); } -function hG(r, e = r.length) { +function zG(r, e = r.length) { if (e >= 8) { - let t10 = Cr.add(e * 2), o = wt(r, 0).add(Cr), n = wt(r, e - 8), s = Yt(n, 37).mul(t10).add(o), a = Yt(o, 25).add(n).mul(t10); - return Xi(s, a, t10); + let t8 = wr.add(e * 2), o = St(r, 0).add(wr), n = St(r, e - 8), s = Qt(n, 37).mul(t8).add(o), a = Qt(o, 25).add(n).mul(t8); + return Ji(s, a, t8); } if (e >= 4) { - let t10 = Cr.add(e * 2), o = k0(r, 0); - return Xi(o.shl(3).add(e), k0(r, e - 4), t10); + let t8 = wr.add(e * 2), o = F0(r, 0); + return Ji(o.shl(3).add(e), F0(r, e - 4), t8); } if (e > 0) { - let t10 = r[0], o = r[e >> 1], n = r[e - 1], s = t10 + (o << 8), a = e + (n << 2); - return XC(Cr.mul(s).xor(N0.mul(a))).mul(Cr); + let t8 = r[0], o = r[e >> 1], n = r[e - 1], s = t8 + (o << 8), a = e + (n << 2); + return nw(wr.mul(s).xor(P0.mul(a))).mul(wr); } - return Cr; + return wr; } -function gG(r, e = r.length) { - let t10 = Cr.add(e * 2), o = wt(r, 0).mul(Du), n = wt(r, 8), s = wt(r, e - 8).mul(t10), a = wt(r, e - 16).mul(Cr); - return Xi(Yt(o.add(n), 43).add(Yt(s, 30)).add(a), o.add(Yt(n.add(Cr), 18)).add(s), t10); +function VG(r, e = r.length) { + let t8 = wr.add(e * 2), o = St(r, 0).mul(Ou), n = St(r, 8), s = St(r, e - 8).mul(t8), a = St(r, e - 16).mul(wr); + return Ji(Qt(o.add(n), 43).add(Qt(s, 30)).add(a), o.add(Qt(n.add(wr), 18)).add(s), t8); } -function xG(r, e = r.length) { - let t10 = Cr.add(e * 2), o = wt(r, 0).mul(Cr), n = wt(r, 8), s = wt(r, e - 8).mul(t10), a = wt(r, e - 16).mul(Cr), i = Yt(o.add(n), 43).add(Yt(s, 30)).add(a), p = Xi(i, o.add(Yt(n.add(Cr), 18)).add(s), t10), u = wt(r, 16).mul(t10), c = wt(r, 24), l = i.add(wt(r, e - 32)).mul(t10), m = p.add(wt(r, e - 24)).mul(t10); - return Xi(Yt(u.add(c), 43).add(Yt(l, 30)).add(m), u.add(Yt(c.add(o), 18)).add(l), t10); +function WG(r, e = r.length) { + let t8 = wr.add(e * 2), o = St(r, 0).mul(wr), n = St(r, 8), s = St(r, e - 8).mul(t8), a = St(r, e - 16).mul(wr), i = Qt(o.add(n), 43).add(Qt(s, 30)).add(a), p = Ji(i, o.add(Qt(n.add(wr), 18)).add(s), t8), u = St(r, 16).mul(t8), c = St(r, 24), l = i.add(St(r, e - 32)).mul(t8), m = p.add(St(r, e - 24)).mul(t8); + return Ji(Qt(u.add(c), 43).add(Qt(l, 30)).add(m), u.add(Qt(c.add(o), 18)).add(l), t8); } -function yG(r, e = r.length) { - let t10 = Au.fromNumber(81, true); +function UG(r, e = r.length) { + let t8 = Mu.fromNumber(81, true); if (e <= 32) - return e <= 16 ? hG(r, e) : gG(r, e); + return e <= 16 ? zG(r, e) : VG(r, e); if (e <= 64) - return xG(r, e); - let o = t10, n = t10.mul(Du).add(113), s = XC(n.mul(Cr).add(113)).mul(Cr), a = [Au.UZERO, Au.UZERO], i = [Au.UZERO, Au.UZERO]; - o = o.mul(Cr).add(wt(r, 0)); + return WG(r, e); + let o = t8, n = t8.mul(Ou).add(113), s = nw(n.mul(wr).add(113)).mul(wr), a = [Mu.UZERO, Mu.UZERO], i = [Mu.UZERO, Mu.UZERO]; + o = o.mul(wr).add(St(r, 0)); let p = 0, u = (e - 1 >> 6) * 64, c = u + (e - 1 & 63) - 63; do - o = Yt(o.add(n).add(a[0]).add(wt(r, p + 8)), 37).mul(Du), n = Yt(n.add(a[1]).add(wt(r, p + 48)), 42).mul(Du), o = o.xor(i[1]), n = n.add(a[0]).add(wt(r, p + 40)), s = Yt(s.add(i[0]), 33).mul(Du), a = Xm(r, p, a[1].mul(Du), o.add(i[0])), i = Xm(r, p + 32, s.add(i[1]), n.add(wt(r, p + 16))), [s, o] = [o, s], p += 64; + o = Qt(o.add(n).add(a[0]).add(St(r, p + 8)), 37).mul(Ou), n = Qt(n.add(a[1]).add(St(r, p + 48)), 42).mul(Ou), o = o.xor(i[1]), n = n.add(a[0]).add(St(r, p + 40)), s = Qt(s.add(i[0]), 33).mul(Ou), a = Qm(r, p, a[1].mul(Ou), o.add(i[0])), i = Qm(r, p + 32, s.add(i[1]), n.add(St(r, p + 16))), [s, o] = [o, s], p += 64; while (p !== u); - let l = Du.add(s.and(255).shl(1)); - return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = Yt(o.add(n).add(a[0]).add(wt(r, p + 8)), 37).mul(l), n = Yt(n.add(a[1]).add(wt(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(wt(r, p + 40))), s = Yt(s.add(i[0]), 33).mul(l), a = Xm(r, p, a[1].mul(l), o.add(i[0])), i = Xm(r, p + 32, s.add(i[1]), n.add(wt(r, p + 16))), [s, o] = [o, s], Xi(Xi(a[0], i[0], l).add(XC(n).mul(N0)).add(s), Xi(a[1], i[1], l).add(o), l); + let l = Ou.add(s.and(255).shl(1)); + return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = Qt(o.add(n).add(a[0]).add(St(r, p + 8)), 37).mul(l), n = Qt(n.add(a[1]).add(St(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(St(r, p + 40))), s = Qt(s.add(i[0]), 33).mul(l), a = Qm(r, p, a[1].mul(l), o.add(i[0])), i = Qm(r, p + 32, s.add(i[1]), n.add(St(r, p + 16))), [s, o] = [o, s], Ji(Ji(a[0], i[0], l).add(nw(n).mul(P0)).add(s), Ji(a[1], i[1], l).add(o), l); } -function bG(r, e) { - return e === "string" ? Yi(r) : Zp([r], e); +function GG(r, e) { + return e === "string" ? eu(r) : ec([r], e); } -function CG(r, e) { +function HG(r, e) { return r instanceof Float32Array && e === "float32" || r instanceof Int32Array && e === "int32" || r instanceof Uint8Array && e === "bool"; } -function Zp(r, e) { +function ec(r, e) { if (e === "string") throw new Error("Cannot convert a string[] to a TypedArray"); - if (Array.isArray(r) && (r = Es(r)), P().getBool("DEBUG") && BC(r, e), CG(r, e)) + if (Array.isArray(r) && (r = Fs(r)), A().getBool("DEBUG") && jC(r, e), HG(r, e)) return r; if (e == null || e === "float32" || e === "complex64") return new Float32Array(r); if (e === "int32") return new Int32Array(r); if (e === "bool") { - let t10 = new Uint8Array(r.length); - for (let o = 0; o < t10.length; ++o) - Math.round(r[o]) !== 0 && (t10[o] = 1); - return t10; + let t8 = new Uint8Array(r.length); + for (let o = 0; o < t8.length; ++o) + Math.round(r[o]) !== 0 && (t8[o] = 1); + return t8; } else throw new Error(`Unknown data type ${e}`); } -function Fu() { - return P().platform.now(); +function Lu() { + return A().platform.now(); } -function wG(r, e) { - return P().platform.fetch(r, e); +function KG(r, e) { + return A().platform.fetch(r, e); } -function Yi(r, e = "utf-8") { - return e = e || "utf-8", P().platform.encode(r, e); +function eu(r, e = "utf-8") { + return e = e || "utf-8", A().platform.encode(r, e); } -function Jp(r, e = "utf-8") { - return e = e || "utf-8", P().platform.decode(r, e); +function tc(r, e = "utf-8") { + return e = e || "utf-8", A().platform.decode(r, e); } function Pt(r) { - return P().platform.isTypedArray != null ? P().platform.isTypedArray(r) : qm(r); + return A().platform.isTypedArray != null ? A().platform.isTypedArray(r) : Xm(r); } -function Es(r, e = [], t10 = false) { - if (e == null && (e = []), typeof r == "boolean" || typeof r == "number" || typeof r == "string" || Tu(r) || r == null || Pt(r) && t10) +function Fs(r, e = [], t8 = false) { + if (e == null && (e = []), typeof r == "boolean" || typeof r == "number" || typeof r == "string" || Ru(r) || r == null || Pt(r) && t8) e.push(r); else if (Array.isArray(r) || Pt(r)) for (let o = 0; o < r.length; ++o) - Es(r[o], e, t10); + Fs(r[o], e, t8); else { let o = -1; for (let n of Object.keys(r)) /^([1-9]+[0-9]*|0)$/.test(n) && (o = Math.max(o, Number(n))); for (let n = 0; n <= o; n++) - Es(r[n], e, t10); + Fs(r[n], e, t8); } return e; } -var Ym = class { - constructor(e, t10) { - this.backendTimer = e, this.logger = t10, t10 == null && (this.logger = new QC()); +var Zm = class { + constructor(e, t8) { + this.backendTimer = e, this.logger = t8, t8 == null && (this.logger = new aw()); } - profileKernel(e, t10, o) { + profileKernel(e, t8, o) { let n, s = () => { n = o(); - }, a, i = Fu(); + }, a, i = Lu(); if (this.backendTimer.timerAvailable()) a = this.backendTimer.time(s); else { s(); for (let u of n) u.dataSync(); - a = Promise.resolve({ kernelMs: Fu() - i }); + a = Promise.resolve({ kernelMs: Lu() - i }); } - if (P().getBool("CHECK_COMPUTATION_FOR_ERRORS")) + if (A().getBool("CHECK_COMPUTATION_FOR_ERRORS")) for (let u = 0; u < n.length; u++) { let c = n[u]; c.data().then((l) => { - SG(l, c.dtype, e); + qG(l, c.dtype, e); }); } - return { kernelName: e, outputs: n, inputs: t10, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") }; + return { kernelName: e, outputs: n, inputs: t8, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") }; } logKernelProfile(e) { - let { kernelName: t10, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e; + let { kernelName: t8, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e; o.forEach((i) => { Promise.all([i.data(), n, a]).then((p) => { - this.logger.logKernelProfile(t10, i, p[0], p[1], s, p[2]); + this.logger.logKernelProfile(t8, i, p[0], p[1], s, p[2]); }); }); } }; -function SG(r, e, t10) { +function qG(r, e, t8) { if (e !== "float32") return false; for (let o = 0; o < r.length; o++) { let n = r[o]; if (isNaN(n) || !isFinite(n)) - return console.warn(`Found ${n} in the result of '${t10}'`), true; + return console.warn(`Found ${n} in the result of '${t8}'`), true; } return false; } -var QC = class { - logKernelProfile(e, t10, o, n, s, a) { - let i = typeof n == "number" ? Nu(`${n}ms`, 9) : n.error, p = Nu(e, 25), u = t10.rank, c = t10.size, l = Nu(t10.shape.toString(), 14), m = ""; +var aw = class { + logKernelProfile(e, t8, o, n, s, a) { + let i = typeof n == "number" ? Eu(`${n}ms`, 9) : n.error, p = Eu(e, 25), u = t8.rank, c = t8.size, l = Eu(t8.shape.toString(), 14), m = ""; for (let d in s) { let f = s[d]; if (f != null) { - let h = f.shape || t10.shape, g = h.length; + let h = f.shape || t8.shape, g = h.length; m += `${d}: ${g}D ${g > 0 ? h : ""} `; } } console.log(`%c${p} %c${i} %c${u}D ${l} %c${c} %c${m} %c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); } }; -function _0(r, e, t10) { +function M0(r, e, t8) { let o = {}, n = {}; for (let p = 0; p < e.length; p++) o[e[p].id] = true; @@ -3876,7 +3906,7 @@ function _0(r, e, t10) { } } let s = {}; - s[t10.id] = true; + s[t8.id] = true; let a = {}; for (let p = r.length - 1; p >= 0; p--) { let u = r[p], c = u.inputs; @@ -3902,7 +3932,7 @@ function _0(r, e, t10) { } return i; } -function $0(r, e, t10, o) { +function L0(r, e, t8, o) { for (let n = e.length - 1; n >= 0; n--) { let s = e[n], a = []; if (s.outputs.forEach((p) => { @@ -3914,11 +3944,11 @@ function $0(r, e, t10, o) { for (let p in s.inputs) { if (!(p in i)) throw new Error(`Cannot backprop through input ${p}. Available gradients found: ${Object.keys(i)}.`); - let u = t10(() => i[p]()); + let u = t8(() => i[p]()); if (u.dtype !== "float32") throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${u.dtype}'`); let c = s.inputs[p]; - if (!br(u.shape, c.shape)) + if (!Cr(u.shape, c.shape)) throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`); if (r[c.id] == null) r[c.id] = u; @@ -3929,63 +3959,63 @@ function $0(r, e, t10, o) { } } } -var E0 = 20; +var B0 = 20; var gl = 3; -var ZC = 7; -function R0(r, e, t10, o) { - let n = Us(e), s = IG(r, e, t10, n), a = e.length, i = Qm(r, e, t10, n, s), p = ["Tensor"]; - return o && (p.push(` dtype: ${t10}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(` +var iw = 7; +function z0(r, e, t8, o) { + let n = qs(e), s = jG(r, e, t8, n), a = e.length, i = Jm(r, e, t8, n, s), p = ["Tensor"]; + return o && (p.push(` dtype: ${t8}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(` `)), p.join(` `); } -function IG(r, e, t10, o) { - let n = Ue(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t10 === "complex64" ? yl(r) : r; +function jG(r, e, t8, o) { + let n = Ge(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t8 === "complex64" ? yl(r) : r; if (i > 1) for (let u = 0; u < n / s; u++) { let c = u * s; for (let l = 0; l < s; l++) - a[l] = Math.max(a[l], xl(p[c + l], 0, t10).length); + a[l] = Math.max(a[l], xl(p[c + l], 0, t8).length); } return a; } -function xl(r, e, t10) { +function xl(r, e, t8) { let o; - return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(ZC))} + ${parseFloat(r[1].toFixed(ZC))}j` : Bo(r) ? o = `'${r}'` : t10 === "bool" ? o = D0(r) : o = parseFloat(r.toFixed(ZC)).toString(), Nu(o, e); + return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(iw))} + ${parseFloat(r[1].toFixed(iw))}j` : zo(r) ? o = `'${r}'` : t8 === "bool" ? o = V0(r) : o = parseFloat(r.toFixed(iw)).toString(), Eu(o, e); } -function D0(r) { +function V0(r) { return r === 0 ? "false" : "true"; } -function Qm(r, e, t10, o, n, s = true) { - let a = t10 === "complex64" ? 2 : 1, i = e[0], p = e.length; +function Jm(r, e, t8, o, n, s = true) { + let a = t8 === "complex64" ? 2 : 1, i = e[0], p = e.length; if (p === 0) { - if (t10 === "complex64") { + if (t8 === "complex64") { let h = yl(r); - return [xl(h[0], 0, t10)]; + return [xl(h[0], 0, t8)]; } - return t10 === "bool" ? [D0(r[0])] : [r[0].toString()]; + return t8 === "bool" ? [V0(r[0])] : [r[0].toString()]; } if (p === 1) { - if (i > E0) { + if (i > B0) { let g = gl * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - gl) * a, i * a)); - return t10 === "complex64" && (x = yl(x), b = yl(b)), ["[" + x.map((w, S) => xl(w, n[S], t10)).join(", ") + ", ..., " + b.map((w, S) => xl(w, n[i - gl + S], t10)).join(", ") + "]"]; + return t8 === "complex64" && (x = yl(x), b = yl(b)), ["[" + x.map((C, S) => xl(C, n[S], t8)).join(", ") + ", ..., " + b.map((C, S) => xl(C, n[i - gl + S], t8)).join(", ") + "]"]; } - return ["[" + (t10 === "complex64" ? yl(r) : Array.from(r)).map((g, x) => xl(g, n[x], t10)).join(", ") + "]"]; + return ["[" + (t8 === "complex64" ? yl(r) : Array.from(r)).map((g, x) => xl(g, n[x], t8)).join(", ") + "]"]; } let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = []; - if (i > E0) { + if (i > B0) { for (let h = 0; h < gl; h++) { let g = h * l, x = g + l; - m.push(...Qm(r.slice(g, x), u, t10, c, n, false)); + m.push(...Jm(r.slice(g, x), u, t8, c, n, false)); } m.push("..."); for (let h = i - gl; h < i; h++) { let g = h * l, x = g + l; - m.push(...Qm(r.slice(g, x), u, t10, c, n, h === i - 1)); + m.push(...Jm(r.slice(g, x), u, t8, c, n, h === i - 1)); } } else for (let h = 0; h < i; h++) { let g = h * l, x = g + l; - m.push(...Qm(r.slice(g, x), u, t10, c, n, h === i - 1)); + m.push(...Jm(r.slice(g, x), u, t8, c, n, h === i - 1)); } let d = p === 2 ? "," : ""; m[0] = "[" + (i > 0 ? m[0] + d : ""); @@ -4000,34 +4030,34 @@ function Qm(r, e, t10, o, n, s = true) { } function yl(r) { let e = []; - for (let t10 = 0; t10 < r.length; t10 += 2) - e.push([r[t10], r[t10 + 1]]); + for (let t8 = 0; t8 < r.length; t8 += 2) + e.push([r[t8], r[t8 + 1]]); return e; } var tt = class { - constructor(e, t10, o) { - if (this.dtype = t10, this.shape = e.slice(), this.size = Ue(e), o != null) { + constructor(e, t8, o) { + if (this.dtype = t8, this.shape = e.slice(), this.size = Ge(e), o != null) { let n = o.length; $(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); } - if (t10 === "complex64") + if (t8 === "complex64") throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag)."); - this.values = o || Hm(t10, this.size), this.strides = Us(e); + this.values = o || Km(t8, this.size), this.strides = qs(e); } - set(e, ...t10) { - t10.length === 0 && (t10 = [0]), $(t10.length === this.rank, () => `The number of provided coordinates (${t10.length}) must match the rank (${this.rank})`); - let o = this.locToIndex(t10); + set(e, ...t8) { + t8.length === 0 && (t8 = [0]), $(t8.length === this.rank, () => `The number of provided coordinates (${t8.length}) must match the rank (${this.rank})`); + let o = this.locToIndex(t8); this.values[o] = e; } get(...e) { e.length === 0 && (e = [0]); - let t10 = 0; + let t8 = 0; for (let n of e) { - if (n < 0 || n >= this.shape[t10]) { + if (n < 0 || n >= this.shape[t8]) { let s = `Requested out of range element at ${e}. Buffer shape=${this.shape}`; throw new Error(s); } - t10++; + t8++; } let o = e[e.length - 1]; for (let n = 0; n < e.length - 1; ++n) @@ -4039,68 +4069,68 @@ var tt = class { return 0; if (this.rank === 1) return e[0]; - let t10 = e[e.length - 1]; + let t8 = e[e.length - 1]; for (let o = 0; o < e.length - 1; ++o) - t10 += this.strides[o] * e[o]; - return t10; + t8 += this.strides[o] * e[o]; + return t8; } indexToLoc(e) { if (this.rank === 0) return []; if (this.rank === 1) return [e]; - let t10 = new Array(this.shape.length); - for (let o = 0; o < t10.length - 1; ++o) - t10[o] = Math.floor(e / this.strides[o]), e -= t10[o] * this.strides[o]; - return t10[t10.length - 1] = e, t10; + let t8 = new Array(this.shape.length); + for (let o = 0; o < t8.length - 1; ++o) + t8[o] = Math.floor(e / this.strides[o]), e -= t8[o] * this.strides[o]; + return t8[t8.length - 1] = e, t8; } get rank() { return this.shape.length; } toTensor() { - return Rs().makeTensor(this.values, this.shape, this.dtype); + return Ps().makeTensor(this.values, this.shape, this.dtype); } }; -var Rs = null; -var ec = null; -var vG = null; -function A0(r) { - Rs = r; +var Ps = null; +var rc = null; +var XG = null; +function W0(r) { + Ps = r; } -function F0(r) { - ec = r; +function U0(r) { + rc = r; } -function P0(r) { - vG = r; +function G0(r) { + XG = r; } -var pt = class { - constructor(e, t10, o, n) { - this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t10 || "float32", this.size = Ue(e), this.strides = Us(e), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; +var ut = class { + constructor(e, t8, o, n) { + this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t8 || "float32", this.size = Ge(e), this.strides = qs(e), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; } get rank() { return this.shape.length; } async buffer() { let e = await this.data(); - return ec.buffer(this.shape, this.dtype, e); + return rc.buffer(this.shape, this.dtype, e); } bufferSync() { - return ec.buffer(this.shape, this.dtype, this.dataSync()); + return rc.buffer(this.shape, this.dtype, this.dataSync()); } async array() { let e = await this.data(); - return ku(this.shape, e, this.dtype === "complex64"); + return $u(this.shape, e, this.dtype === "complex64"); } arraySync() { - return ku(this.shape, this.dataSync(), this.dtype === "complex64"); + return $u(this.shape, this.dataSync(), this.dtype === "complex64"); } async data() { this.throwIfDisposed(); - let e = Rs().read(this.dataId); + let e = Ps().read(this.dataId); if (this.dtype === "string") { - let t10 = await e; + let t8 = await e; try { - return t10.map((o) => Jp(o)); + return t8.map((o) => tc(o)); } catch (o) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } @@ -4108,26 +4138,26 @@ var pt = class { return e; } dataToGPU(e) { - return this.throwIfDisposed(), Rs().readToGPU(this.dataId, e); + return this.throwIfDisposed(), Ps().readToGPU(this.dataId, e); } dataSync() { this.throwIfDisposed(); - let e = Rs().readSync(this.dataId); + let e = Ps().readSync(this.dataId); if (this.dtype === "string") try { - return e.map((t10) => Jp(t10)); - } catch (t10) { + return e.map((t8) => tc(t8)); + } catch (t8) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } return e; } async bytes() { this.throwIfDisposed(); - let e = await Rs().read(this.dataId); + let e = await Ps().read(this.dataId); return this.dtype === "string" ? e : new Uint8Array(e.buffer); } dispose() { - this.isDisposed || (Rs().disposeTensor(this), this.isDisposedInternal = true); + this.isDisposed || (Ps().disposeTensor(this), this.isDisposedInternal = true); } get isDisposed() { return this.isDisposedInternal; @@ -4137,121 +4167,121 @@ var pt = class { throw new Error("Tensor is disposed."); } print(e = false) { - return ec.print(this, e); + return rc.print(this, e); } clone() { - return this.throwIfDisposed(), ec.clone(this); + return this.throwIfDisposed(), rc.clone(this); } toString(e = false) { - let t10 = this.dataSync(); - return R0(t10, this.shape, this.dtype, e); + let t8 = this.dataSync(); + return z0(t8, this.shape, this.dtype, e); } cast(e) { - return this.throwIfDisposed(), ec.cast(this, e); + return this.throwIfDisposed(), rc.cast(this, e); } - variable(e = true, t10, o) { - return this.throwIfDisposed(), Rs().makeVariable(this, e, t10, o); + variable(e = true, t8, o) { + return this.throwIfDisposed(), Ps().makeVariable(this, e, t8, o); } }; -Object.defineProperty(pt, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null }); -function kG() { - return ll("Tensor", () => pt); +Object.defineProperty(ut, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null }); +function YG() { + return ml("Tensor", () => ut); } -kG(); -var Qa = class extends pt { - constructor(e, t10, o, n) { - super(e.shape, e.dtype, e.dataId, n), this.trainable = t10, this.name = o; +YG(); +var ei = class extends ut { + constructor(e, t8, o, n) { + super(e.shape, e.dtype, e.dataId, n), this.trainable = t8, this.name = o; } assign(e) { if (e.dtype !== this.dtype) throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`); - if (!br(e.shape, this.shape)) + if (!Cr(e.shape, this.shape)) throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`); - Rs().disposeTensor(this), this.dataId = e.dataId, Rs().incRef(this, null); + Ps().disposeTensor(this), this.dataId = e.dataId, Ps().incRef(this, null); } dispose() { - Rs().disposeVariable(this), this.isDisposedInternal = true; + Ps().disposeVariable(this), this.isDisposedInternal = true; } }; -Object.defineProperty(Qa, Symbol.hasInstance, { value: (r) => r instanceof pt && r.assign != null && r.assign instanceof Function }); -var M0 = {}; -He(M0, { assertTypesMatch: () => nw, getTensorsInContainer: () => bl, isTensorInList: () => TG, makeTypesMatch: () => Oe }); -var JC; +Object.defineProperty(ei, Symbol.hasInstance, { value: (r) => r instanceof ut && r.assign != null && r.assign instanceof Function }); +var K0 = {}; +Ke(K0, { assertTypesMatch: () => dw, getTensorsInContainer: () => bl, isTensorInList: () => ZG, makeTypesMatch: () => Oe }); +var uw; (function(r) { r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6"; -})(JC || (JC = {})); -var ew; +})(uw || (uw = {})); +var pw; (function(r) { r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64"; -})(ew || (ew = {})); -var tw; +})(pw || (pw = {})); +var cw; (function(r) { r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64"; -})(tw || (tw = {})); -var rw; +})(cw || (cw = {})); +var lw; (function(r) { r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64"; -})(rw || (rw = {})); -var ow; +})(lw || (lw = {})); +var mw; (function(r) { r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64"; -})(ow || (ow = {})); -var NG = { float32: rw, int32: ew, bool: tw, complex64: ow }; +})(mw || (mw = {})); +var QG = { float32: lw, int32: pw, bool: cw, complex64: mw }; function dt(r, e) { if (r === "string" || e === "string") { if (r === "string" && e === "string") return "string"; throw new Error(`Can not upcast ${r} with ${e}`); } - return NG[r][e]; + return QG[r][e]; } -function Za(r) { +function ti(r) { return dt(r, "int32"); } -function Zm(r) { +function ed(r) { return r != null && typeof r == "object" && "texture" in r && r.texture instanceof WebGLTexture; } -function Jm(r) { +function td(r) { return typeof GPUBuffer != "undefined" && r != null && typeof r == "object" && "buffer" in r && r.buffer instanceof GPUBuffer; } function Oe(r, e) { if (r.dtype === e.dtype) return [r, e]; - let t10 = dt(r.dtype, e.dtype); - return [r.cast(t10), e.cast(t10)]; + let t8 = dt(r.dtype, e.dtype); + return [r.cast(t8), e.cast(t8)]; } -function nw(r, e) { +function dw(r, e) { $(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`); } -function TG(r, e) { - return e.some((t10) => t10.id === r.id); +function ZG(r, e) { + return e.some((t8) => t8.id === r.id); } function bl(r) { let e = []; - return O0(r, e, /* @__PURE__ */ new Set()), e; + return H0(r, e, /* @__PURE__ */ new Set()), e; } -function O0(r, e, t10) { +function H0(r, e, t8) { if (r == null) return; - if (r instanceof pt) { + if (r instanceof ut) { e.push(r); return; } - if (!_G(r)) + if (!JG(r)) return; let o = r; for (let n in o) { let s = o[n]; - t10.has(s) || (t10.add(s), O0(s, e, t10)); + t8.has(s) || (t8.add(s), H0(s, e, t8)); } } -function _G(r) { +function JG(r) { return Array.isArray(r) || typeof r == "object"; } -function sw(r) { +function fw(r) { return r.kernelName != null; } -var ed = class { +var rd = class { constructor() { this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() { return Array.from(new Set(this.kernels.map((e) => e.name))); @@ -4262,9 +4292,9 @@ var ed = class { this.registeredVariables[e].dispose(); } }; -var Qi = class { +var tu = class { constructor(e) { - this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new ed(); + this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new rd(); } async ready() { if (this.pendingBackendInit != null) @@ -4273,8 +4303,8 @@ var Qi = class { if (this.backendInstance != null) return; let e = this.getSortedBackends(); - for (let t10 = 0; t10 < e.length; t10++) { - let o = e[t10]; + for (let t8 = 0; t8 < e.length; t8++) { + let o = e[t8]; if (await this.initializeBackend(o).success) { await this.setBackend(o); return; @@ -4286,8 +4316,8 @@ var Qi = class { if (this.pendingBackendInit != null) throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); if (this.backendInstance == null) { - let { name: e, asyncInit: t10 } = this.initializeBackendsAndReturnBest(); - if (t10) + let { name: e, asyncInit: t8 } = this.initializeBackendsAndReturnBest(); + if (t8) throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); this.setBackend(e); } @@ -4299,8 +4329,8 @@ var Qi = class { findBackend(e) { if (!(e in this.registry)) if (e in this.registryFactory) { - let { asyncInit: t10 } = this.initializeBackend(e); - if (t10) + let { asyncInit: t8 } = this.initializeBackend(e); + if (t8) return null; } else return null; @@ -4309,43 +4339,43 @@ var Qi = class { findBackendFactory(e) { return e in this.registryFactory ? this.registryFactory[e].factory : null; } - registerBackend(e, t10, o = 1) { - return e in this.registryFactory ? (ha(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t10, priority: o }, true); + registerBackend(e, t8, o = 1) { + return e in this.registryFactory ? (Ca(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t8, priority: o }, true); } async setBackend(e) { if (this.registryFactory[e] == null) throw new Error(`Backend name '${e}' not found in registry`); if (this.backendName = e, this.registry[e] == null) { this.backendInstance = null; - let { success: t10, asyncInit: o } = this.initializeBackend(e); - if (!(o ? await t10 : t10)) + let { success: t8, asyncInit: o } = this.initializeBackend(e); + if (!(o ? await t8 : t8)) return false; } - return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Ym(this.backendInstance), true; + return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Zm(this.backendInstance), true; } setupRegisteredKernels() { - Km(this.backendName).forEach((t10) => { - t10.setupFunc != null && t10.setupFunc(this.backendInstance); + jm(this.backendName).forEach((t8) => { + t8.setupFunc != null && t8.setupFunc(this.backendInstance); }); } disposeRegisteredKernels(e) { - Km(e).forEach((o) => { + jm(e).forEach((o) => { o.disposeFunc != null && o.disposeFunc(this.registry[e]); }); } initializeBackend(e) { - let t10 = this.registryFactory[e]; - if (t10 == null) + let t8 = this.registryFactory[e]; + if (t8 == null) throw new Error(`Cannot initialize backend ${e}, no registration found.`); try { - let o = t10.factory(); - if (o && !(o instanceof ro) && typeof o.then == "function") { - let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, ha(`Initialization of backend ${e} failed`), ha(a.stack || a.message)), false)); + let o = t8.factory(); + if (o && !(o instanceof so) && typeof o.then == "function") { + let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, Ca(`Initialization of backend ${e} failed`), Ca(a.stack || a.message)), false)); return this.pendingBackendInit = s, { success: s, asyncInit: true }; } else return this.registry[e] = o, { success: true, asyncInit: false }; } catch (o) { - return ha(`Initialization of backend ${e} failed`), ha(o.stack || o.message), { success: false, asyncInit: false }; + return Ca(`Initialization of backend ${e} failed`), Ca(o.stack || o.message), { success: false, asyncInit: false }; } } removeBackend(e) { @@ -4356,92 +4386,92 @@ var Qi = class { getSortedBackends() { if (Object.keys(this.registryFactory).length === 0) throw new Error("No backend found in registry."); - return Object.keys(this.registryFactory).sort((e, t10) => this.registryFactory[t10].priority - this.registryFactory[e].priority); + return Object.keys(this.registryFactory).sort((e, t8) => this.registryFactory[t8].priority - this.registryFactory[e].priority); } initializeBackendsAndReturnBest() { let e = this.getSortedBackends(); - for (let t10 = 0; t10 < e.length; t10++) { - let o = e[t10], { success: n, asyncInit: s } = this.initializeBackend(o); + for (let t8 = 0; t8 < e.length; t8++) { + let o = e[t8], { success: n, asyncInit: s } = this.initializeBackend(o); if (s || n) return { name: o, asyncInit: s }; } throw new Error("Could not initialize any backends, all backend initializations failed."); } - moveData(e, t10) { - let o = this.state.tensorInfo.get(t10), n = o.backend, s = this.readSync(t10), a = n.refCount(t10); - n.disposeData(t10, true), o.backend = e, e.move(t10, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + moveData(e, t8) { + let o = this.state.tensorInfo.get(t8), n = o.backend, s = this.readSync(t8), a = n.refCount(t8); + n.disposeData(t8, true), o.backend = e, e.move(t8, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; } - tidy(e, t10) { + tidy(e, t8) { let o = null; - if (t10 == null) { + if (t8 == null) { if (typeof e != "function") throw new Error("Please provide a function to tidy()"); - t10 = e; + t8 = e; } else { if (typeof e != "string" && !(e instanceof String)) throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); - if (typeof t10 != "function") + if (typeof t8 != "function") throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); o = e; } let n; - return this.scopedRun(() => this.startScope(o), () => this.endScope(n), () => (n = t10(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n)); + return this.scopedRun(() => this.startScope(o), () => this.endScope(n), () => (n = t8(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n)); } - scopedRun(e, t10, o) { + scopedRun(e, t8, o) { e(); try { let n = o(); - return t10(), n; + return t8(), n; } catch (n) { - throw t10(), n; + throw t8(), n; } } nextTensorId() { - return Qi.nextTensorId++; + return tu.nextTensorId++; } nextVariableId() { - return Qi.nextVariableId++; + return tu.nextVariableId++; } clone(e) { - let t10 = T.runKernel(xo, { x: e }), o = { x: e }, n = (a) => ({ x: () => { + let t8 = T.runKernel(Co, { x: e }), o = { x: e }, n = (a) => ({ x: () => { let i = "float32", p = { x: a }, u = { dtype: i }; - return T.runKernel(ho, p, u); + return T.runKernel(yo, p, u); } }), s = []; - return this.addTapeNode(this.state.activeScope.name, o, [t10], n, s, {}), t10; + return this.addTapeNode(this.state.activeScope.name, o, [t8], n, s, {}), t8; } - runKernel(e, t10, o) { + runKernel(e, t8, o) { if (this.backendName == null && this.backend, !(fl(e, this.backendName) != null)) throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`); - return this.runKernelFunc({ kernelName: e, inputs: t10, attrs: o }); + return this.runKernelFunc({ kernelName: e, inputs: t8, attrs: o }); } shouldCheckForMemLeaks() { return this.ENV.getBool("IS_TEST"); } - checkKernelForMemLeak(e, t10, o) { + checkKernelForMemLeak(e, t8, o) { let n = this.backend.numDataIds(), s = 0; o.forEach((p) => { s += p.dtype === "complex64" ? 3 : 1; }); - let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = n - t10 - s - a; + let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = n - t8 - s - a; if (i > 0) throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`); } runKernelFunc(e) { - let t10, o = [], n = this.isTapeOn(), s = this.state.numBytes, a = this.state.numTensors; + let t8, o = [], n = this.isTapeOn(), s = this.state.numBytes, a = this.state.numTensors; this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0); let i; this.backendName == null && this.backend; - let p, u = sw(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; - if (sw(e)) { + let p, u = fw(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (fw(e)) { let { kernelName: f, inputs: h, attrs: g } = e; this.backendName == null && this.backend; let x = fl(f, this.backendName); $(x != null, () => `Cannot find registered kernel '${f}' for backend '${this.backendName}'`), i = () => { let b = this.backend.numDataIds(); p = x.kernelFunc({ inputs: h, attrs: g, backend: this.backend }); - let w = Array.isArray(p) ? p : [p]; - this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(f, b, w); - let S = w.map((k) => k.rank != null ? k : this.makeTensorFromTensorInfo(k)); + let C = Array.isArray(p) ? p : [p]; + this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(f, b, C); + let S = C.map((k) => k.rank != null ? k : this.makeTensorFromTensorInfo(k)); if (n) { let k = this.getTensorsForGradient(f, h, S); o = this.saveTensorsForBackwardMode(k); @@ -4459,78 +4489,78 @@ var Qi = class { return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, g, x), x; }; } - let { inputs: c, attrs: l } = e, m = sw(e) ? null : e.backwardsFunc, d; + let { inputs: c, attrs: l } = e, m = fw(e) ? null : e.backwardsFunc, d; return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { - !this.ENV.getBool("DEBUG") && !this.state.profiling ? t10 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t10 = d.outputs); - }), n && this.addTapeNode(u, c, t10, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t10.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t10 : t10[0]; + !this.ENV.getBool("DEBUG") && !this.state.profiling ? t8 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t8 = d.outputs); + }), n && this.addTapeNode(u, c, t8, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t8.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t8 : t8[0]; } saveTensorsForBackwardMode(e) { return e.map((o) => this.keep(this.clone(o))); } - getTensorsForGradient(e, t10, o) { - let n = HC(e); + getTensorsForGradient(e, t8, o) { + let n = ew(e); if (n != null) { let s = n.inputsToSave || [], a = n.outputsToSave || [], i; - n.saveAllInputs ? ($(Array.isArray(t10), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t10).map((u) => t10[u])) : i = s.map((u) => t10[u]); + n.saveAllInputs ? ($(Array.isArray(t8), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t8).map((u) => t8[u])) : i = s.map((u) => t8[u]); let p = o.filter((u, c) => a[c]); return i.concat(p); } return []; } - makeTensor(e, t10, o, n) { + makeTensor(e, t8, o, n) { if (e == null) throw new Error("Values passed to engine.makeTensor() are null"); o = o || "float32", n = n || this.backend; let s = e; - o === "string" && Bo(e[0]) && (s = e.map((p) => Yi(p))); - let a = n.write(s, t10, o), i = new pt(t10, o, a, this.nextTensorId()); + o === "string" && zo(e[0]) && (s = e.map((p) => eu(p))); + let a = n.write(s, t8, o), i = new ut(t8, o, a, this.nextTensorId()); if (this.trackTensor(i, n), o === "string") { - let p = this.state.tensorInfo.get(a), u = VC(s); + let p = this.state.tensorInfo.get(a), u = YC(s); this.state.numBytes += u - p.bytes, p.bytes = u; } return i; } - makeTensorFromDataId(e, t10, o, n) { + makeTensorFromDataId(e, t8, o, n) { o = o || "float32"; - let s = { dataId: e, shape: t10, dtype: o }; + let s = { dataId: e, shape: t8, dtype: o }; return this.makeTensorFromTensorInfo(s, n); } - makeTensorFromTensorInfo(e, t10) { - let { dataId: o, shape: n, dtype: s } = e, a = new pt(n, s, o, this.nextTensorId()); - return this.trackTensor(a, t10), a; + makeTensorFromTensorInfo(e, t8) { + let { dataId: o, shape: n, dtype: s } = e, a = new ut(n, s, o, this.nextTensorId()); + return this.trackTensor(a, t8), a; } - makeVariable(e, t10 = true, o, n) { + makeVariable(e, t8 = true, o, n) { o = o || this.nextVariableId().toString(), n != null && n !== e.dtype && (e = e.cast(n)); - let s = new Qa(e, t10, o, this.nextTensorId()); + let s = new ei(e, t8, o, this.nextTensorId()); if (this.state.registeredVariables[s.name] != null) throw new Error(`Variable with name ${s.name} was already registered`); return this.state.registeredVariables[s.name] = s, this.incRef(s, this.backend), s; } - trackTensor(e, t10) { + trackTensor(e, t8) { this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++; let o = 0; - e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * Vp(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t10 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof Qa || this.track(e); + e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * Hp(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t8 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof ei || this.track(e); } - incRef(e, t10) { - this.trackTensor(e, t10), this.backend.incRef(e.dataId); + incRef(e, t8) { + this.trackTensor(e, t8), this.backend.incRef(e.dataId); } - removeDataId(e, t10) { - this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t10 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--); + removeDataId(e, t8) { + this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t8 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--); } disposeTensor(e) { if (!this.state.tensorInfo.has(e.dataId)) return; - let t10 = this.state.tensorInfo.get(e.dataId); - if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t10.bytes), e.dtype !== "complex64" && e.dtype !== "string") { - let o = e.size * Vp(e.dtype); + let t8 = this.state.tensorInfo.get(e.dataId); + if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t8.bytes), e.dtype !== "complex64" && e.dtype !== "string") { + let o = e.size * Hp(e.dtype); this.state.numBytes -= o; } - t10.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t10.backend); + t8.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t8.backend); } disposeVariables() { for (let e in this.state.registeredVariables) { - let t10 = this.state.registeredVariables[e]; - this.disposeVariable(t10); + let t8 = this.state.registeredVariables[e]; + this.disposeVariable(t8); } } disposeVariable(e) { @@ -4542,8 +4572,8 @@ var Qi = class { } async profile(e) { this.state.profiling = true; - let t10 = this.state.numBytes, o = this.state.numTensors; - this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((n) => n.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t10, this.state.activeProfile.newTensors = this.state.numTensors - o; + let t8 = this.state.numBytes, o = this.state.numTensors; + this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((n) => n.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t8, this.state.activeProfile.newTensors = this.state.numTensors - o; for (let n of this.state.activeProfile.kernels) n.kernelTimeMs = await n.kernelTimeMs, n.extraInfo = await n.extraInfo; return this.state.activeProfile; @@ -4551,11 +4581,11 @@ var Qi = class { isTapeOn() { return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; } - addTapeNode(e, t10, o, n, s, a) { - let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t10, outputs: o, saved: s }, p = HC(e); + addTapeNode(e, t8, o, n, s, a) { + let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t8, outputs: o, saved: s }, p = ew(e); p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => { if (c == null) { - let m = o[l], d = Up(m.size, m.dtype); + let m = o[l], d = qp(m.size, m.dtype); return this.makeTensor(d, m.shape, m.dtype); } return c; @@ -4571,32 +4601,32 @@ var Qi = class { this.state.gradientDepth--; } startScope(e) { - let t10 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ }; - e && (t10.name = e), this.state.scopeStack.push(t10), this.state.activeScope = t10; + let t8 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ }; + e && (t8.name = e), this.state.scopeStack.push(t8), this.state.activeScope = t8; } endScope(e) { - let t10 = bl(e), o = new Set(t10.map((s) => s.id)); + let t8 = bl(e), o = new Set(t8.map((s) => s.id)); for (let s = 0; s < this.state.activeScope.track.length; s++) { let a = this.state.activeScope.track[s]; !a.kept && !o.has(a.id) && a.dispose(); } let n = this.state.scopeStack.pop(); - this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t10.forEach((s) => { + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t8.forEach((s) => { !s.kept && s.scopeId === n.id && this.track(s); }); } - gradients(e, t10, o, n = false) { - if ($(t10.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32") + gradients(e, t8, o, n = false) { + if ($(t8.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32") throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`); let s = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e)); - $(s instanceof pt, () => "The result y returned by f() must be a tensor."); - let a = _0(this.state.activeTape, t10, s); - if (!n && a.length === 0 && t10.length > 0) + $(s instanceof ut, () => "The result y returned by f() must be a tensor."); + let a = M0(this.state.activeTape, t8, s); + if (!n && a.length === 0 && t8.length > 0) throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); return this.tidy("backward", () => { let i = {}; - i[s.id] = o == null ? $G(s.shape) : o, $0(i, a, (u) => this.tidy(u), EG); - let p = t10.map((u) => i[u.id]); + i[s.id] = o == null ? e4(s.shape) : o, L0(i, a, (u) => this.tidy(u), t4); + let p = t8.map((u) => i[u.id]); return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => { for (let c of u.saved) c.dispose(); @@ -4604,15 +4634,15 @@ var Qi = class { }); } customGrad(e) { - return $(Ws(e), () => "The f passed in customGrad(f) must be a function."), (...t10) => { - $(t10.every((i) => i instanceof pt), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + return $(Ks(e), () => "The f passed in customGrad(f) must be a function."), (...t8) => { + $(t8.every((i) => i instanceof ut), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); let o, n = {}; - t10.forEach((i, p) => { + t8.forEach((i, p) => { n[p] = i; }); - let s = (i, p) => (o = e(...t10, p), $(o.value instanceof pt, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(Ws(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => { + let s = (i, p) => (o = e(...t8, p), $(o.value instanceof ut, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(Ks(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => { let u = o.gradFunc(i, p), c = Array.isArray(u) ? u : [u]; - $(c.length === t10.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), $(c.every((m) => m instanceof pt), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + $(c.length === t8.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), $(c.every((m) => m instanceof ut), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); let l = {}; return c.forEach((m, d) => { l[d] = () => m; @@ -4627,12 +4657,12 @@ var Qi = class { read(e) { return this.state.tensorInfo.get(e).backend.read(e); } - readToGPU(e, t10) { - return this.state.tensorInfo.get(e).backend.readToGPU(e, t10); + readToGPU(e, t8) { + return this.state.tensorInfo.get(e).backend.readToGPU(e, t8); } async time(e) { - let t10 = Fu(), o = await this.backend.time(e); - return o.wallMs = Fu() - t10, o; + let t8 = Lu(), o = await this.backend.time(e); + return o.wallMs = Lu() - t8, o; } track(e) { return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e; @@ -4641,182 +4671,239 @@ var Qi = class { return this.state.registeredVariables; } reset() { - this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new ed(); + this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new rd(); for (let e in this.registry) this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e]; this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null; } }; -Qi.nextTensorId = 0; -Qi.nextVariableId = 0; -function $G(r) { - let e = pl(Ue(r), "float32"); +tu.nextTensorId = 0; +tu.nextVariableId = 0; +function e4(r) { + let e = cl(Ge(r), "float32"); return T.makeTensor(e, r, "float32"); } -function aw() { - let r = GC(); +function hw() { + let r = JC(); if (r._tfengine == null) { - let e = new cl(r); - r._tfengine = new Qi(e); + let e = new ll(r); + r._tfengine = new tu(e); } - return l0(r._tfengine.ENV), A0(() => r._tfengine), r._tfengine; + return C0(r._tfengine.ENV), W0(() => r._tfengine), r._tfengine; } -var T = aw(); -function EG(r, e) { - let t10 = { a: r, b: e }; - return T.runKernel(no, t10); +var T = hw(); +function t4(r, e) { + let t8 = { a: r, b: e }; + return T.runKernel(io, t8); } -var Zi = {}; -He(Zi, { isBrowser: () => uw, isMobile: () => AG, mockIsMobile: () => DG }); -function RG() { +var ru = {}; +Ke(ru, { isBrowser: () => xw, isMobile: () => n4, mockIsMobile: () => o4 }); +function r4() { return typeof navigator != "undefined" && navigator != null; } -var iw; -function DG(r) { - iw = r; +var gw; +function o4(r) { + gw = r; } -function AG(r) { - if (iw !== void 0) - return iw; - if (r || RG()) { +function n4(r) { + if (gw !== void 0) + return gw; + if (r || r4()) { if (r || (r = navigator), r.product === "ReactNative") return true; let e = r.userAgent || r.vendor || (typeof window != "undefined" ? window.opera : ""); if (!e) { - let t10 = r; - return t10.userAgentData && t10.userAgentData.mobile; + let t8 = r; + return t8.userAgentData && t8.userAgentData.mobile; } return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0, 4)); } return false; } -function uw() { +function xw() { return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined"; } -var _r = P(); -_r.registerFlag("DEBUG", () => false, (r) => { +var $r = A(); +$r.registerFlag("DEBUG", () => false, (r) => { r && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); }); -_r.registerFlag("IS_BROWSER", () => uw()); -_r.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined"); -_r.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); -_r.registerFlag("IS_SAFARI", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor)); -_r.registerFlag("PROD", () => false); -_r.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => _r.getBool("DEBUG")); -_r.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); -_r.registerFlag("IS_TEST", () => false); -_r.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => _r.getBool("DEBUG")); -_r.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); -_r.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); -_r.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); +$r.registerFlag("IS_BROWSER", () => xw()); +$r.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined"); +$r.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +$r.registerFlag("IS_SAFARI", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor)); +$r.registerFlag("PROD", () => false); +$r.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => $r.getBool("DEBUG")); +$r.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +$r.registerFlag("IS_TEST", () => false); +$r.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => $r.getBool("DEBUG")); +$r.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +$r.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); +$r.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); function ar(r, e) { - let t10 = r; + let t8 = r; if (Pt(r)) return e === "string" ? [] : [r.length]; - if (Zm(r)) { + if (ed(r)) { let n = r.channels || "RGBA"; return [r.height, r.width * n.length]; - } else if (Jm(r)) - return [r.buffer.size / (e == null ? 4 : Vp(e))]; + } else if (td(r)) + return [r.buffer.size / (e == null ? 4 : Hp(e))]; if (!Array.isArray(r)) return []; let o = []; - for (; Array.isArray(t10) || Pt(t10) && e !== "string"; ) - o.push(t10.length), t10 = t10[0]; - return Array.isArray(r) && P().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && B0(r, o, []), o; + for (; Array.isArray(t8) || Pt(t8) && e !== "string"; ) + o.push(t8.length), t8 = t8[0]; + return Array.isArray(r) && A().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && j0(r, o, []), o; } -function B0(r, e, t10) { - if (t10 = t10 || [], !Array.isArray(r) && !Pt(r)) { - $(e.length === 0, () => `Element arr[${t10.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`); +function j0(r, e, t8) { + if (t8 = t8 || [], !Array.isArray(r) && !Pt(r)) { + $(e.length === 0, () => `Element arr[${t8.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`); return; } - $(e.length > 0, () => `Element arr[${t10.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === e[0], () => `Element arr[${t10.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`); + $(e.length > 0, () => `Element arr[${t8.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === e[0], () => `Element arr[${t8.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`); let o = e.slice(1); for (let n = 0; n < r.length; ++n) - B0(r[n], o, t10.concat(n)); + j0(r[n], o, t8.concat(n)); } -function L0(r, e, t10, o) { +function q0(r, e, t8, o) { if (r !== "string_or_numeric") { if (r == null) throw new Error("Expected dtype cannot be null."); if (r !== "numeric" && r !== e || r === "numeric" && e === "string") - throw new Error(`Argument '${t10}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`); + throw new Error(`Argument '${t8}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`); } } -function v(r, e, t10, o = "numeric") { - if (r instanceof pt) - return L0(o, r.dtype, e, t10), r; - let n = ki(r); - if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), L0(o, n, e, t10), r == null || !Pt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { +function v(r, e, t8, o = "numeric") { + if (r instanceof ut) + return q0(o, r.dtype, e, t8), r; + let n = _i(r); + if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), q0(o, n, e, t8), r == null || !Pt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { let p = r == null ? "null" : r.constructor.name; - throw new Error(`Argument '${e}' passed to '${t10}' must be a Tensor or TensorLike, but got '${p}'`); + throw new Error(`Argument '${e}' passed to '${t8}' must be a Tensor or TensorLike, but got '${p}'`); } let s = ar(r, n); !Pt(r) && !Array.isArray(r) && (r = [r]); - let i = n !== "string" ? Zp(r, n) : Es(r, [], true); + let i = n !== "string" ? ec(r, n) : Fs(r, [], true); return T.makeTensor(i, s, n); } -function Ja(r, e, t10, o = "numeric") { +function ri(r, e, t8, o = "numeric") { if (!Array.isArray(r)) - throw new Error(`Argument ${e} passed to ${t10} must be a \`Tensor[]\` or \`TensorLike[]\``); - return r.map((s, a) => v(s, `${e}[${a}]`, t10, o)); + throw new Error(`Argument ${e} passed to ${t8} must be a \`Tensor[]\` or \`TensorLike[]\``); + return r.map((s, a) => v(s, `${e}[${a}]`, t8, o)); } -var pw = "__op"; +var yw = "__op"; function N(r) { let e = Object.keys(r); if (e.length !== 1) throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${e.length} keys.`); - let t10 = e[0], o = r[t10]; - t10.endsWith("_") && (t10 = t10.substring(0, t10.length - 1)), t10 = t10 + pw; + let t8 = e[0], o = r[t8]; + t8.endsWith("_") && (t8 = t8.substring(0, t8.length - 1)), t8 = t8 + yw; let n = (...s) => { - T.startScope(t10); + T.startScope(t8); try { let a = o(...s); - return Tu(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a; + return Ru(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a; } catch (a) { throw T.endScope(null), a; } }; - return Object.defineProperty(n, "name", { value: t10, configurable: true }), n; + return Object.defineProperty(n, "name", { value: t8, configurable: true }), n; } -function FG(r, e) { - let t10 = v(r, "real", "complex"), o = v(e, "imag", "complex"); - xt(t10.shape, o.shape, `real and imag shapes, ${t10.shape} and ${o.shape}, must match in call to tf.complex().`); - let n = { real: t10, imag: o }; - return T.runKernel(Ti, n); +function s4(r, e) { + let t8 = v(r, "real", "complex"), o = v(e, "imag", "complex"); + yt(t8.shape, o.shape, `real and imag shapes, ${t8.shape} and ${o.shape}, must match in call to tf.complex().`); + let n = { real: t8, imag: o }; + return T.runKernel(Ri, n); } -var $r = N({ complex_: FG }); -function wr(r, e, t10, o) { +var Er = N({ complex_: s4 }); +function Sr(r, e, t8, o) { if (o == null) - o = ki(r); + o = _i(r); else if (o === "complex64") throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag)."); - if (Jm(r) || Zm(r)) { + if (td(r) || ed(r)) { if (o !== "float32" && o !== "int32") throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${o}.`); - return T.backend.createTensorFromGPUData(r, e || t10, o); + return T.backend.createTensorFromGPUData(r, e || t8, o); } if (!Pt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); if (e != null) { - Ct(e); - let n = Ue(e), s = Ue(t10); + wt(e); + let n = Ge(e), s = Ge(t8); $(n === s, () => `Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`); - for (let a = 0; a < t10.length; ++a) { - let i = t10[a], p = a === t10.length - 1 ? i !== Ue(e.slice(a)) : true; - $(t10[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t10}) does not match the provided shape (${e}). `); + for (let a = 0; a < t8.length; ++a) { + let i = t8[a], p = a === t8.length - 1 ? i !== Ge(e.slice(a)) : true; + $(t8[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t8}) does not match the provided shape (${e}). `); } } - return !Pt(r) && !Array.isArray(r) && (r = [r]), e = e || t10, r = o !== "string" ? Zp(r, o) : Es(r, [], true), T.makeTensor(r, e, o); + return !Pt(r) && !Array.isArray(r) && (r = [r]), e = e || t8, r = o !== "string" ? ec(r, o) : Fs(r, [], true), T.makeTensor(r, e, o); } -function ir(r, e, t10) { - let o = ar(r, t10); - return wr(r, e, o, t10); +function ir(r, e, t8) { + let o = ar(r, t8); + return Sr(r, e, o, t8); } var Cl = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 }; -var td = 4; -async function V0(r, e) { - let t10 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r); +var jt = class { + static join(e) { + return new jt(e).slice(); + } + constructor(e) { + if (this.shards = [], this.previousShardIndex = 0, e == null || (e instanceof Array || (e = [e]), e = e.map((o) => Pt(o) ? o.buffer : o), e.length === 0)) + return; + this.bufferUniformSize = e[0].byteLength; + let t8 = 0; + for (let o = 0; o < e.length; o++) { + let n = e[o]; + o !== e.length - 1 && n.byteLength !== this.bufferUniformSize && (this.bufferUniformSize = void 0); + let s = t8 + n.byteLength; + this.shards.push({ buffer: n, start: t8, end: s }), t8 = s; + } + this.shards.length === 0 && (this.byteLength = 0), this.byteLength = this.shards[this.shards.length - 1].end; + } + slice(e = 0, t8 = this.byteLength) { + if (this.shards.length === 0) + return new ArrayBuffer(0); + if (e = isNaN(Number(e)) ? 0 : e, t8 = isNaN(Number(t8)) ? 0 : t8, e = Math.max(0, e), t8 = Math.min(this.byteLength, t8), t8 <= e) + return new ArrayBuffer(0); + let o = this.findShardForByte(e); + if (o === -1) + throw new Error(`Could not find start shard for byte ${e}`); + let n = t8 - e, s = new ArrayBuffer(n), a = new Uint8Array(s), i = 0; + for (let p = o; p < this.shards.length; p++) { + let u = this.shards[p], l = e + i - u.start, m = i, f = Math.min(t8, u.end) - u.start, h = new Uint8Array(u.buffer, l, f - l); + if (a.set(h, m), i += h.length, t8 < u.end) + break; + } + return s; + } + findShardForByte(e) { + if (this.shards.length === 0 || e < 0 || e >= this.byteLength) + return -1; + if (this.bufferUniformSize != null) + return this.previousShardIndex = Math.floor(e / this.bufferUniformSize), this.previousShardIndex; + function t8(n) { + return e < n.start ? -1 : e >= n.end ? 1 : 0; + } + if (t8(this.shards[this.previousShardIndex]) === 0) + return this.previousShardIndex; + let o = a4(this.shards, t8); + return o === -1 ? -1 : (this.previousShardIndex = o, this.previousShardIndex); + } +}; +function a4(r, e) { + let t8 = 0, o = r.length; + for (; t8 <= o; ) { + let n = Math.floor((o - t8) / 2) + t8, s = e(r[n]); + if (s === 0) + return n; + s < 0 ? o = n : t8 = n + 1; + } + return -1; +} +var od = 4; +async function Y0(r, e) { + let t8 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r); for (let a = 0; a < n.length; ++a) { let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i]; if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64") @@ -4824,189 +4911,180 @@ async function V0(r, e) { let u = { name: i, shape: p.shape, dtype: p.dtype }; if (p.dtype === "string") { let c = new Promise(async (l) => { - let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + td * m.length, f = new Uint8Array(d), h = 0; + let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + od * m.length, f = new Uint8Array(d), h = 0; for (let g = 0; g < m.length; g++) { let x = m[g], b = new Uint8Array(new Uint32Array([x.length]).buffer); - f.set(b, h), h += td, f.set(x, h), h += x.length; + f.set(b, h), h += od, f.set(x, h), h += x.length; } l(f); }); o.push(c); } else o.push(p.data()); - e != null && (u.group = e), t10.push(u); + e != null && (u.group = e), t8.push(u); } let s = await Promise.all(o); - return { data: PG(s), specs: t10 }; + return { data: i4(s), specs: t8 }; } -function rd(r, e) { - let t10 = {}, o, n = 0; - for (let s of e) { - let a = s.name, i = s.dtype, p = s.shape, u = Ue(p), c; - if ("quantization" in s) { - let l = s.quantization; - if (l.dtype === "uint8" || l.dtype === "uint16") { - if (!("min" in l && "scale" in l)) - throw new Error(`Weight ${s.name} with quantization ${l.dtype} doesn't have corresponding metadata min and scale.`); - } else if (l.dtype === "float16") { - if (i !== "float32") - throw new Error(`Weight ${s.name} is quantized with ${l.dtype} which only supports weights of type float32 not ${i}.`); +function nd(r, e) { + let t8 = new jt(r), o = {}, n, s = 0; + for (let a of e) { + let i = a.name, p = a.dtype, u = a.shape, c = Ge(u), l; + if ("quantization" in a) { + let m = a.quantization; + if (m.dtype === "uint8" || m.dtype === "uint16") { + if (!("min" in m && "scale" in m)) + throw new Error(`Weight ${a.name} with quantization ${m.dtype} doesn't have corresponding metadata min and scale.`); + } else if (m.dtype === "float16") { + if (p !== "float32") + throw new Error(`Weight ${a.name} is quantized with ${m.dtype} which only supports weights of type float32 not ${p}.`); } else - throw new Error(`Weight ${s.name} has unknown quantization dtype ${l.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); - let m = Cl[l.dtype], d = r.slice(n, n + u * m), f = l.dtype === "uint8" ? new Uint8Array(d) : new Uint16Array(d); - if (i === "float32") - if (l.dtype === "uint8" || l.dtype === "uint16") { - c = new Float32Array(f.length); - for (let h = 0; h < f.length; h++) { - let g = f[h]; - c[h] = g * l.scale + l.min; + throw new Error(`Weight ${a.name} has unknown quantization dtype ${m.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); + let d = Cl[m.dtype], f = t8.slice(s, s + c * d), h = m.dtype === "uint8" ? new Uint8Array(f) : new Uint16Array(f); + if (p === "float32") + if (m.dtype === "uint8" || m.dtype === "uint16") { + l = new Float32Array(h.length); + for (let g = 0; g < h.length; g++) { + let x = h[g]; + l[g] = x * m.scale + m.min; } - } else if (l.dtype === "float16") - o === void 0 && (o = BG()), c = o(f); + } else if (m.dtype === "float16") + n === void 0 && (n = l4()), l = n(h); else - throw new Error(`Unsupported quantization type ${l.dtype} for weight type float32.`); - else if (i === "int32") { - if (l.dtype !== "uint8" && l.dtype !== "uint16") - throw new Error(`Unsupported quantization type ${l.dtype} for weight type int32.`); - c = new Int32Array(f.length); - for (let h = 0; h < f.length; h++) { - let g = f[h]; - c[h] = Math.round(g * l.scale + l.min); + throw new Error(`Unsupported quantization type ${m.dtype} for weight type float32.`); + else if (p === "int32") { + if (m.dtype !== "uint8" && m.dtype !== "uint16") + throw new Error(`Unsupported quantization type ${m.dtype} for weight type int32.`); + l = new Int32Array(h.length); + for (let g = 0; g < h.length; g++) { + let x = h[g]; + l[g] = Math.round(x * m.scale + m.min); } } else - throw new Error(`Unsupported dtype in weight '${a}': ${i}`); - n += u * m; - } else if (i === "string") { - let l = Ue(s.shape); - c = []; - for (let m = 0; m < l; m++) { - let d = new Uint32Array(r.slice(n, n + td))[0]; - n += td; - let f = new Uint8Array(r.slice(n, n + d)); - c.push(f), n += d; + throw new Error(`Unsupported dtype in weight '${i}': ${p}`); + s += c * d; + } else if (p === "string") { + let m = Ge(a.shape); + l = []; + for (let d = 0; d < m; d++) { + let f = new Uint32Array(t8.slice(s, s + od))[0]; + s += od; + let h = new Uint8Array(t8.slice(s, s + f)); + l.push(h), s += f; } } else { - let l = Cl[i], m = r.slice(n, n + u * l); - if (i === "float32") - c = new Float32Array(m); - else if (i === "int32") - c = new Int32Array(m); - else if (i === "bool") - c = new Uint8Array(m); - else if (i === "complex64") { - c = new Float32Array(m); - let d = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2); - for (let x = 0; x < d.length; x++) - d[x] = c[x * 2], f[x] = c[x * 2 + 1]; - let h = ir(d, p, "float32"), g = ir(f, p, "float32"); - t10[a] = $r(h, g), h.dispose(), g.dispose(); + let m = Cl[p], d = t8.slice(s, s + c * m); + if (p === "float32") + l = new Float32Array(d); + else if (p === "int32") + l = new Int32Array(d); + else if (p === "bool") + l = new Uint8Array(d); + else if (p === "complex64") { + l = new Float32Array(d); + let f = new Float32Array(l.length / 2), h = new Float32Array(l.length / 2); + for (let b = 0; b < f.length; b++) + f[b] = l[b * 2], h[b] = l[b * 2 + 1]; + let g = ir(f, u, "float32"), x = ir(h, u, "float32"); + o[i] = Er(g, x), g.dispose(), x.dispose(); } else - throw new Error(`Unsupported dtype in weight '${a}': ${i}`); - n += u * l; + throw new Error(`Unsupported dtype in weight '${i}': ${p}`); + s += c * m; } - i !== "complex64" && (t10[a] = ir(c, p, i)); + p !== "complex64" && (o[i] = ir(l, u, p)); } - return t10; + return o; } -function PG(r) { +function i4(r) { if (r === null) throw new Error(`Invalid input value: ${JSON.stringify(r)}`); - let e = 0, t10 = []; + let e = 0, t8 = []; r.forEach((s) => { - if (e += s.byteLength, t10.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array)) + if (e += s.byteLength, t8.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array)) throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`); }); let o = new Uint8Array(e), n = 0; - return t10.forEach((s) => { + return t8.forEach((s) => { o.set(new Uint8Array(s.buffer), n), n += s.byteLength; }), o.buffer; } -var cw = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); -function z0(r) { - return cw ? Buffer.byteLength(r) : new Blob([r]).size; +var bw = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); +function X0(r) { + return bw ? Buffer.byteLength(r, "utf8") : new Blob([r]).size; } -function W0(r) { - if (cw) +function Q0(r) { + if (bw) return Buffer.from(r).toString("base64"); - let e = new Uint8Array(r), t10 = ""; + let e = new Uint8Array(r), t8 = ""; for (let o = 0, n = e.length; o < n; o++) - t10 += String.fromCharCode(e[o]); - return btoa(t10); + t8 += String.fromCharCode(e[o]); + return btoa(t8); } -function U0(r) { - if (cw) { +function Z0(r) { + if (bw) { let o = Buffer.from(r, "base64"); return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength); } - let e = atob(r), t10 = new Uint8Array(e.length); + let e = atob(r), t8 = new Uint8Array(e.length); for (let o = 0; o < e.length; ++o) - t10.set([e.charCodeAt(o)], o); - return t10.buffer; + t8.set([e.charCodeAt(o)], o); + return t8.buffer; } -function tc(r) { - if (r.length === 1) - return r[0]; - let e = 0; - r.forEach((n) => { - e += n.byteLength; - }); - let t10 = new Uint8Array(e), o = 0; - return r.forEach((n) => { - t10.set(new Uint8Array(n), o), o += n.byteLength; - }), t10.buffer; +function J0(r) { + return jt.join(r); } -function lw(r) { +function Cw(r) { let e = "/"; for (r = r.trim(); r.endsWith(e); ) r = r.slice(0, r.length - 1); - let t10 = r.split(e); - return t10[t10.length - 1]; + let t8 = r.split(e); + return t8[t8.length - 1]; } -function od(r, e) { - let t10 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e }; - return r.signature != null && (t10.signature = r.signature), r.userDefinedMetadata != null && (t10.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t10.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t10.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t10.trainingConfig = r.trainingConfig), t10; +function sd(r, e) { + let t8 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e }; + return r.signature != null && (t8.signature = r.signature), r.userDefinedMetadata != null && (t8.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t8.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t8.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t8.trainingConfig = r.trainingConfig), t8; } -function mw(r, e, t10) { +function ww(r, e, t8) { let o = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy }; if (r.trainingConfig != null && (o.trainingConfig = r.trainingConfig), r.weightsManifest != null) { if (!e) throw new Error("modelJSON has weightsManifest but weightSpecs is null"); - if (!t10) + if (!t8) throw new Error("modelJSON has weightsManifest but weightData is null"); - o.weightSpecs = e, o.weightData = t10; + o.weightSpecs = e, o.weightData = t8; } return r.signature != null && (o.signature = r.signature), r.userDefinedMetadata != null && (o.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (o.modelInitializer = r.modelInitializer), r.initializerSignature != null && (o.initializerSignature = r.initializerSignature), o; } -async function rc(r, e) { - let t10, o; - return r.weightsManifest != null && ([t10, o] = await e(r.weightsManifest)), mw(r, t10, o); +async function oc(r, e) { + let t8, o; + return r.weightsManifest != null && ([t8, o] = await e(r.weightsManifest)), ww(r, t8, o); } -function ga(r) { +function wa(r) { if (r.modelTopology instanceof ArrayBuffer) throw new Error("Expected JSON model topology, received ArrayBuffer."); - return { dateSaved: /* @__PURE__ */ new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : z0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : z0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength }; + return { dateSaved: /* @__PURE__ */ new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : X0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : X0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : new jt(r.weightData).byteLength }; } -function nd(r) { +function ad(r) { let e = []; - for (let t10 of r) - e.push(...t10.weights); + for (let t8 of r) + e.push(...t8.weights); return e; } -function OG() { - let r = (t10) => { - let o = t10 << 13, n = 0; +function u4() { + let r = (t8) => { + let o = t8 << 13, n = 0; for (; !(o & 8388608); ) n -= 8388608, o <<= 1; return o &= -8388609, n += 947912704, o | n; }, e = new Uint32Array(2048); e[0] = 0; - for (let t10 = 1; t10 < 1024; t10++) - e[t10] = r(t10); - for (let t10 = 1024; t10 < 2048; t10++) - e[t10] = 939524096 + (t10 - 1024 << 13); + for (let t8 = 1; t8 < 1024; t8++) + e[t8] = r(t8); + for (let t8 = 1024; t8 < 2048; t8++) + e[t8] = 939524096 + (t8 - 1024 << 13); return e; } -function MG() { +function p4() { let r = new Uint32Array(64); r[0] = 0, r[31] = 1199570944, r[32] = 2147483648, r[63] = 3347054592; for (let e = 1; e < 31; e++) @@ -5015,18 +5093,18 @@ function MG() { r[e] = 2147483648 + (e - 32 << 23); return r; } -function LG() { +function c4() { let r = new Uint32Array(64); for (let e = 0; e < 64; e++) r[e] = 1024; return r[0] = r[32] = 0, r; } -function BG() { - let r = OG(), e = MG(), t10 = LG(); +function l4() { + let r = u4(), e = p4(), t8 = c4(); return (o) => { let n = new ArrayBuffer(4 * o.length), s = new Uint32Array(n); for (let a = 0; a < o.length; a++) { - let i = o[a], p = r[t10[i >> 10] + (i & 1023)] + e[i >> 10]; + let i = o[a], p = r[t8[i >> 10] + (i & 1023)] + e[i >> 10]; s[a] = p; } return new Float32Array(n); @@ -5048,40 +5126,40 @@ var ft = class { static getSaveHandlers(e) { return ft.getHandlers(e, "save"); } - static getLoadHandlers(e, t10) { - return ft.getHandlers(e, "load", t10); + static getLoadHandlers(e, t8) { + return ft.getHandlers(e, "load", t8); } - static getHandlers(e, t10, o) { + static getHandlers(e, t8, o) { let n = []; - return (t10 === "load" ? ft.getInstance().loadRouters : ft.getInstance().saveRouters).forEach((a) => { + return (t8 === "load" ? ft.getInstance().loadRouters : ft.getInstance().saveRouters).forEach((a) => { let i = a(e, o); i !== null && n.push(i); }), n; } }; -var G0 = (r) => ft.registerSaveRouter(r); -var H0 = (r) => ft.registerLoadRouter(r); -var K0 = (r) => ft.getSaveHandlers(r); -var q0 = (r, e) => ft.getLoadHandlers(r, e); -var dw = "tensorflowjs"; -var fw = 1; -var Pu = "models_store"; -var Ji = "model_info_store"; -function j0() { - if (!P().getBool("IS_BROWSER")) +var ek = (r) => ft.registerSaveRouter(r); +var tk = (r) => ft.registerLoadRouter(r); +var rk = (r) => ft.getSaveHandlers(r); +var ok = (r, e) => ft.getLoadHandlers(r, e); +var Sw = "tensorflowjs"; +var Iw = 1; +var Bu = "models_store"; +var ou = "model_info_store"; +function nk() { + if (!A().getBool("IS_BROWSER")) throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); let r = typeof window == "undefined" ? self : window, e = r.indexedDB || r.mozIndexedDB || r.webkitIndexedDB || r.msIndexedDB || r.shimIndexedDB; if (e == null) throw new Error("The current browser does not appear to support IndexedDB."); return e; } -function hw(r) { +function vw(r) { let e = r.result; - e.createObjectStore(Pu, { keyPath: "modelPath" }), e.createObjectStore(Ji, { keyPath: "modelPath" }); + e.createObjectStore(Bu, { keyPath: "modelPath" }), e.createObjectStore(ou, { keyPath: "modelPath" }); } -var xa = class { +var Sa = class { constructor(e) { - if (this.indexedDB = j0(), e == null || !e) + if (this.indexedDB = nk(), e == null || !e) throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); this.modelPath = e; } @@ -5093,20 +5171,21 @@ var xa = class { async load() { return this.databaseAction(this.modelPath); } - databaseAction(e, t10) { + databaseAction(e, t8) { return new Promise((o, n) => { - let s = this.indexedDB.open(dw, fw); - s.onupgradeneeded = () => hw(s), s.onsuccess = () => { + let s = this.indexedDB.open(Sw, Iw); + s.onupgradeneeded = () => vw(s), s.onsuccess = () => { let a = s.result; - if (t10 == null) { - let i = a.transaction(Pu, "readonly"), u = i.objectStore(Pu).get(this.modelPath); + if (t8 == null) { + let i = a.transaction(Bu, "readonly"), u = i.objectStore(Bu).get(this.modelPath); u.onsuccess = () => { if (u.result == null) return a.close(), n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); o(u.result.modelArtifacts); }, u.onerror = (c) => (a.close(), n(u.error)), i.oncomplete = () => a.close(); } else { - let i = ga(t10), p = a.transaction(Ji, "readwrite"), u = p.objectStore(Ji), c; + t8.weightData = jt.join(t8.weightData); + let i = wa(t8), p = a.transaction(ou, "readwrite"), u = p.objectStore(ou), c; try { c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }); } catch (m) { @@ -5114,15 +5193,15 @@ var xa = class { } let l; c.onsuccess = () => { - l = a.transaction(Pu, "readwrite"); - let m = l.objectStore(Pu), d; + l = a.transaction(Bu, "readwrite"); + let m = l.objectStore(Bu), d; try { - d = m.put({ modelPath: this.modelPath, modelArtifacts: t10, modelArtifactsInfo: i }); + d = m.put({ modelPath: this.modelPath, modelArtifacts: t8, modelArtifactsInfo: i }); } catch (f) { return n(f); } d.onsuccess = () => o({ modelArtifactsInfo: i }), d.onerror = (f) => { - u = p.objectStore(Ji); + u = p.objectStore(ou); let h = u.delete(this.modelPath); h.onsuccess = () => (a.close(), n(d.error)), h.onerror = (g) => (a.close(), n(d.error)); }; @@ -5134,47 +5213,47 @@ var xa = class { }); } }; -xa.URL_SCHEME = "indexeddb://"; -var X0 = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(xa.URL_SCHEME) ? zG(r.slice(xa.URL_SCHEME.length)) : null; -ft.registerSaveRouter(X0); -ft.registerLoadRouter(X0); -function zG(r) { - return new xa(r); +Sa.URL_SCHEME = "indexeddb://"; +var sk = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Sa.URL_SCHEME) ? m4(r.slice(Sa.URL_SCHEME.length)) : null; +ft.registerSaveRouter(sk); +ft.registerLoadRouter(sk); +function m4(r) { + return new Sa(r); } -function VG(r) { - return r.startsWith(xa.URL_SCHEME) ? r.slice(xa.URL_SCHEME.length) : r; +function d4(r) { + return r.startsWith(Sa.URL_SCHEME) ? r.slice(Sa.URL_SCHEME.length) : r; } -var sd = class { +var id = class { constructor() { - this.indexedDB = j0(); + this.indexedDB = nk(); } async listModels() { - return new Promise((e, t10) => { - let o = this.indexedDB.open(dw, fw); - o.onupgradeneeded = () => hw(o), o.onsuccess = () => { - let n = o.result, s = n.transaction(Ji, "readonly"), i = s.objectStore(Ji).getAll(); + return new Promise((e, t8) => { + let o = this.indexedDB.open(Sw, Iw); + o.onupgradeneeded = () => vw(o), o.onsuccess = () => { + let n = o.result, s = n.transaction(ou, "readonly"), i = s.objectStore(ou).getAll(); i.onsuccess = () => { let p = {}; for (let u of i.result) p[u.modelPath] = u.modelArtifactsInfo; e(p); - }, i.onerror = (p) => (n.close(), t10(i.error)), s.oncomplete = () => n.close(); - }, o.onerror = (n) => t10(o.error); + }, i.onerror = (p) => (n.close(), t8(i.error)), s.oncomplete = () => n.close(); + }, o.onerror = (n) => t8(o.error); }); } async removeModel(e) { - return e = VG(e), new Promise((t10, o) => { - let n = this.indexedDB.open(dw, fw); - n.onupgradeneeded = () => hw(n), n.onsuccess = () => { - let s = n.result, a = s.transaction(Ji, "readwrite"), i = a.objectStore(Ji), p = i.get(e), u; + return e = d4(e), new Promise((t8, o) => { + let n = this.indexedDB.open(Sw, Iw); + n.onupgradeneeded = () => vw(n), n.onsuccess = () => { + let s = n.result, a = s.transaction(ou, "readwrite"), i = a.objectStore(ou), p = i.get(e), u; p.onsuccess = () => { if (p.result == null) return s.close(), o(new Error(`Cannot find model with path '${e}' in IndexedDB.`)); { let c = i.delete(e), l = () => { - u = s.transaction(Pu, "readwrite"); - let d = u.objectStore(Pu).delete(e); - d.onsuccess = () => t10(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error); + u = s.transaction(Bu, "readwrite"); + let d = u.objectStore(Bu).delete(e); + d.onsuccess = () => t8(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error); }; c.onsuccess = l, c.onerror = (m) => (l(), s.close(), o(p.error)); } @@ -5185,48 +5264,48 @@ var sd = class { }); } }; -var ei = "/"; -var oc = "tensorflowjs_models"; -var Y0 = "info"; -var WG = "model_topology"; -var UG = "weight_specs"; -var GG = "weight_data"; -var HG = "model_metadata"; -function Q0(r) { - return { info: [oc, r, Y0].join(ei), topology: [oc, r, WG].join(ei), weightSpecs: [oc, r, UG].join(ei), weightData: [oc, r, GG].join(ei), modelMetadata: [oc, r, HG].join(ei) }; +var oi = "/"; +var nc = "tensorflowjs_models"; +var ak = "info"; +var f4 = "model_topology"; +var h4 = "weight_specs"; +var g4 = "weight_data"; +var x4 = "model_metadata"; +function ik(r) { + return { info: [nc, r, ak].join(oi), topology: [nc, r, f4].join(oi), weightSpecs: [nc, r, h4].join(oi), weightData: [nc, r, g4].join(oi), modelMetadata: [nc, r, x4].join(oi) }; } -function Z0(r) { +function uk(r) { for (let e of Object.values(r)) window.localStorage.removeItem(e); } -function KG(r) { - let e = r.split(ei); +function y4(r) { + let e = r.split(oi); if (e.length < 3) throw new Error(`Invalid key format: ${r}`); - return e.slice(1, e.length - 1).join(ei); + return e.slice(1, e.length - 1).join(oi); } -function qG(r) { - return r.startsWith(ya.URL_SCHEME) ? r.slice(ya.URL_SCHEME.length) : r; +function b4(r) { + return r.startsWith(Ia.URL_SCHEME) ? r.slice(Ia.URL_SCHEME.length) : r; } -var ya = class { +var Ia = class { constructor(e) { - if (!P().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined") + if (!A().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined") throw new Error("The current environment does not support local storage."); if (this.LS = window.localStorage, e == null || !e) throw new Error("For local storage, modelPath must not be null, undefined or empty."); - this.modelPath = e, this.keys = Q0(this.modelPath); + this.modelPath = e, this.keys = ik(this.modelPath); } async save(e) { if (e.modelTopology instanceof ArrayBuffer) throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); { - let t10 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = ga(e); + let t8 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = wa(e), s = jt.join(e.weightData); try { - this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t10), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, W0(e.weightData)); - let s = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 }; - return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(s)), { modelArtifactsInfo: n }; - } catch (s) { - throw Z0(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); + this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t8), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, Q0(s)); + let a = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 }; + return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(a)), { modelArtifactsInfo: n }; + } catch (a) { + throw uk(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); } } } @@ -5236,142 +5315,142 @@ var ya = class { throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); if (e.modelTopologyType !== "JSON") throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); - let t10 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology)); + let t8 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology)); if (o == null) throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); - t10.modelTopology = o; + t8.modelTopology = o; let n = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); if (n == null) throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); - t10.weightSpecs = n; + t8.weightSpecs = n; let s = this.LS.getItem(this.keys.modelMetadata); if (s != null) { let i = JSON.parse(s); - t10.format = i.format, t10.generatedBy = i.generatedBy, t10.convertedBy = i.convertedBy, i.signature != null && (t10.signature = i.signature), i.userDefinedMetadata != null && (t10.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t10.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t10.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t10.trainingConfig = i.trainingConfig); + t8.format = i.format, t8.generatedBy = i.generatedBy, t8.convertedBy = i.convertedBy, i.signature != null && (t8.signature = i.signature), i.userDefinedMetadata != null && (t8.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t8.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t8.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t8.trainingConfig = i.trainingConfig); } let a = this.LS.getItem(this.keys.weightData); if (a == null) throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); - return t10.weightData = U0(a), t10; + return t8.weightData = Z0(a), t8; } }; -ya.URL_SCHEME = "localstorage://"; -var J0 = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(ya.URL_SCHEME) ? jG(r.slice(ya.URL_SCHEME.length)) : null; -ft.registerSaveRouter(J0); -ft.registerLoadRouter(J0); -function jG(r) { - return new ya(r); +Ia.URL_SCHEME = "localstorage://"; +var pk = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ia.URL_SCHEME) ? C4(r.slice(Ia.URL_SCHEME.length)) : null; +ft.registerSaveRouter(pk); +ft.registerLoadRouter(pk); +function C4(r) { + return new Ia(r); } -var ad = class { +var ud = class { constructor() { - $(P().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; + $(A().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; } async listModels() { - let e = {}, t10 = oc + ei, o = ei + Y0; + let e = {}, t8 = nc + oi, o = oi + ak; for (let n = 0; n < this.LS.length; ++n) { let s = this.LS.key(n); - if (s.startsWith(t10) && s.endsWith(o)) { - let a = KG(s); + if (s.startsWith(t8) && s.endsWith(o)) { + let a = y4(s); e[a] = JSON.parse(this.LS.getItem(s)); } } return e; } async removeModel(e) { - e = qG(e); - let t10 = Q0(e); - if (this.LS.getItem(t10.info) == null) + e = b4(e); + let t8 = ik(e); + if (this.LS.getItem(t8.info) == null) throw new Error(`Cannot find model at path '${e}'`); - let o = JSON.parse(this.LS.getItem(t10.info)); - return Z0(t10), o; + let o = JSON.parse(this.LS.getItem(t8.info)); + return uk(t8), o; } }; -var nc = "://"; -var Qt = class { +var sc = "://"; +var Zt = class { constructor() { this.managers = {}; } static getInstance() { - return Qt.instance == null && (Qt.instance = new Qt()), Qt.instance; + return Zt.instance == null && (Zt.instance = new Zt()), Zt.instance; } - static registerManager(e, t10) { - $(e != null, () => "scheme must not be undefined or null."), e.endsWith(nc) && (e = e.slice(0, e.indexOf(nc))), $(e.length > 0, () => "scheme must not be an empty string."); - let o = Qt.getInstance(); - $(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t10; + static registerManager(e, t8) { + $(e != null, () => "scheme must not be undefined or null."), e.endsWith(sc) && (e = e.slice(0, e.indexOf(sc))), $(e.length > 0, () => "scheme must not be an empty string."); + let o = Zt.getInstance(); + $(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t8; } static getManager(e) { - let t10 = Qt.getInstance().managers[e]; - if (t10 == null) + let t8 = Zt.getInstance().managers[e]; + if (t8 == null) throw new Error(`Cannot find model manager for scheme '${e}'`); - return t10; + return t8; } static getSchemes() { - return Object.keys(Qt.getInstance().managers); + return Object.keys(Zt.getInstance().managers); } }; -function id(r) { - if (r.indexOf(nc) === -1) - throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Qt.getSchemes().join(",")}`); - return { scheme: r.split(nc)[0], path: r.split(nc)[1] }; +function pd(r) { + if (r.indexOf(sc) === -1) + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Zt.getSchemes().join(",")}`); + return { scheme: r.split(sc)[0], path: r.split(sc)[1] }; } -async function ek(r, e, t10 = false) { +async function ck(r, e, t8 = false) { $(r !== e, () => `Old path and new path are the same: '${r}'`); let o = ft.getLoadHandlers(r); $(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), $(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`); let n = o[0], s = ft.getSaveHandlers(e); $(s.length > 0, () => `Copying failed because no save handler is found for destination URL ${e}.`), $(s.length < 2, () => `Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`); - let a = s[0], i = id(r).scheme, p = id(r).path, u = i === id(r).scheme, c = await n.load(); - t10 && u && await Qt.getManager(i).removeModel(p); + let a = s[0], i = pd(r).scheme, p = pd(r).path, u = i === pd(r).scheme, c = await n.load(); + t8 && u && await Zt.getManager(i).removeModel(p); let l = await a.save(c); - return t10 && !u && await Qt.getManager(i).removeModel(p), l.modelArtifactsInfo; + return t8 && !u && await Zt.getManager(i).removeModel(p), l.modelArtifactsInfo; } -async function tk() { - let r = Qt.getSchemes(), e = {}; - for (let t10 of r) { - let o = await Qt.getManager(t10).listModels(); +async function lk() { + let r = Zt.getSchemes(), e = {}; + for (let t8 of r) { + let o = await Zt.getManager(t8).listModels(); for (let n in o) { - let s = t10 + nc + n; + let s = t8 + sc + n; e[s] = o[n]; } } return e; } -async function rk(r) { - let e = id(r); - return Qt.getManager(e.scheme).removeModel(e.path); +async function mk(r) { + let e = pd(r); + return Zt.getManager(e.scheme).removeModel(e.path); } -async function ok(r, e) { - return ek(r, e, false); +async function dk(r, e) { + return ck(r, e, false); } -async function nk(r, e) { - return ek(r, e, true); +async function fk(r, e) { + return ck(r, e, true); } -var gw = class { +var kw = class { constructor() { this.messageName = "setTimeoutCustom", this.functionRefs = [], this.handledMessageCount = 0, this.hasEventListener = false; } - fetch(e, t10) { - return fetch(e, t10); + fetch(e, t8) { + return fetch(e, t8); } now() { return performance.now(); } - encode(e, t10) { - if (t10 !== "utf-8" && t10 !== "utf8") - throw new Error(`Browser's encoder only supports utf-8, but got ${t10}`); + encode(e, t8) { + if (t8 !== "utf-8" && t8 !== "utf8") + throw new Error(`Browser's encoder only supports utf-8, but got ${t8}`); return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e); } - decode(e, t10) { - return new TextDecoder(t10).decode(e); + decode(e, t8) { + return new TextDecoder(t8).decode(e); } - setTimeoutCustom(e, t10) { - if (typeof window == "undefined" || !P().getBool("USE_SETTIMEOUTCUSTOM")) { - setTimeout(e, t10); + setTimeoutCustom(e, t8) { + if (typeof window == "undefined" || !A().getBool("USE_SETTIMEOUTCUSTOM")) { + setTimeout(e, t8); return; } this.functionRefs.push(e), setTimeout(() => { window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*"); - }, t10), this.hasEventListener || (this.hasEventListener = true, window.addEventListener("message", (o) => { + }, t8), this.hasEventListener || (this.hasEventListener = true, window.addEventListener("message", (o) => { if (o.source === window && o.data.name === this.messageName) { o.stopPropagation(); let n = this.functionRefs[o.data.index]; @@ -5380,250 +5459,250 @@ var gw = class { }, true)); } isTypedArray(e) { - return qm(e); + return Xm(e); } }; -if (P().get("IS_BROWSER")) { - P().setPlatform("browser", new gw()); +if (A().get("IS_BROWSER")) { + A().setPlatform("browser", new kw()); try { - Qt.registerManager(ya.URL_SCHEME, new ad()); + Zt.registerManager(Ia.URL_SCHEME, new ud()); } catch (r) { } try { - Qt.registerManager(xa.URL_SCHEME, new sd()); + Zt.registerManager(Sa.URL_SCHEME, new id()); } catch (r) { } } -var XG = { importFetch: () => sk() }; -var xw; -var yw = class { +var w4 = { importFetch: () => hk() }; +var Nw; +var Tw = class { constructor() { - this.util = ak(), this.textEncoder = new this.util.TextEncoder(); + this.util = gk(), this.textEncoder = new this.util.TextEncoder(); } - fetch(e, t10) { - return P().global.fetch != null ? P().global.fetch(e, t10) : (xw == null && (xw = XG.importFetch()), xw(e, t10)); + fetch(e, t8) { + return A().global.fetch != null ? A().global.fetch(e, t8) : (Nw == null && (Nw = w4.importFetch()), Nw(e, t8)); } now() { let e = process.hrtime(); return e[0] * 1e3 + e[1] / 1e6; } - encode(e, t10) { - if (t10 !== "utf-8" && t10 !== "utf8") - throw new Error(`Node built-in encoder only supports utf-8, but got ${t10}`); + encode(e, t8) { + if (t8 !== "utf-8" && t8 !== "utf8") + throw new Error(`Node built-in encoder only supports utf-8, but got ${t8}`); return this.textEncoder.encode(e); } - decode(e, t10) { - return e.length === 0 ? "" : new this.util.TextDecoder(t10).decode(e); + decode(e, t8) { + return e.length === 0 ? "" : new this.util.TextDecoder(t8).decode(e); } isTypedArray(e) { return this.util.types.isFloat32Array(e) || this.util.types.isInt32Array(e) || this.util.types.isUint8Array(e) || this.util.types.isUint8ClampedArray(e); } }; -P().get("IS_NODE") && !P().get("IS_BROWSER") && P().setPlatform("node", new yw()); -function me(r, e = "float32", t10) { - return e = e || "float32", Ct(r), new tt(r, e, t10); +A().get("IS_NODE") && !A().get("IS_BROWSER") && A().setPlatform("node", new Tw()); +function me(r, e = "float32", t8) { + return e = e || "float32", wt(r), new tt(r, e, t8); } -function YG(r, e) { - let t10 = v(r, "x", "cast"); - if (!zC(e)) +function S4(r, e) { + let t8 = v(r, "x", "cast"); + if (!XC(e)) throw new Error(`Failed to cast to unknown dtype ${e}`); - if (e === "string" && t10.dtype !== "string" || e !== "string" && t10.dtype === "string") + if (e === "string" && t8.dtype !== "string" || e !== "string" && t8.dtype === "string") throw new Error("Only strings can be casted to strings"); - let o = { x: t10 }, n = { dtype: e }; - return T.runKernel(ho, o, n); + let o = { x: t8 }, n = { dtype: e }; + return T.runKernel(yo, o, n); } -var Ye = N({ cast_: YG }); -function QG(r) { - let t10 = { x: v(r, "x", "clone", "string_or_numeric") }; - return T.runKernel(xo, t10); +var qe = N({ cast_: S4 }); +function I4(r) { + let t8 = { x: v(r, "x", "clone", "string_or_numeric") }; + return T.runKernel(Co, t8); } -var Vr = N({ clone_: QG }); -function ud(r, e = false) { +var Ur = N({ clone_: I4 }); +function cd(r, e = false) { console.log(r.toString(e)); } -aw(); -var ZG = { buffer: me, cast: Ye, clone: Vr, print: ud }; -F0(ZG); -function fme() { - P().set("PROD", true); +hw(); +var v4 = { buffer: me, cast: qe, clone: Ur, print: cd }; +U0(v4); +function ede() { + A().set("PROD", true); } -function hme() { - P().set("DEBUG", true); +function tde() { + A().set("DEBUG", true); } -function gme() { - P().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled."); +function rde() { + A().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled."); } -function bw(r) { - P().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); +function _w(r) { + A().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); } -P0(bw); -function xme() { +G0(_w); +function ode() { T.disposeVariables(); } function ur() { return T; } -function yme() { +function nde() { return T.memory(); } -function bme(r) { +function sde(r) { return T.profile(r); } function De(r, e) { return T.tidy(r, e); } function Ot(r) { - bl(r).forEach((t10) => t10.dispose()); + bl(r).forEach((t8) => t8.dispose()); } -function Er(r) { +function Rr(r) { return T.keep(r); } -function Cme(r) { +function ade(r) { return T.time(r); } -function wme(r) { +function ide(r) { return T.setBackend(r); } -function Sme() { +function ude() { return T.ready(); } -function Ime() { +function pde() { return T.backendName; } -function vme(r) { +function cde(r) { T.removeBackend(r); } -function kme(r) { +function lde(r) { return T.findBackend(r); } -function Nme(r) { +function mde(r) { return T.findBackendFactory(r); } -function eu(r, e, t10 = 1) { - return T.registerBackend(r, e, t10); +function nu(r, e, t8 = 1) { + return T.registerBackend(r, e, t8); } -function Tme() { +function dde() { return T.backend; } -function _me(r, e) { - P().setPlatform(r, e); +function fde(r, e) { + A().setPlatform(r, e); } -function JG(r, e) { - let t10 = v(r, "a", "add"), o = v(e, "b", "add"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(no, n); +function k4(r, e) { + let t8 = v(r, "a", "add"), o = v(e, "b", "add"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(io, n); } -var be = N({ add_: JG }); -function e4(r, e) { - let t10 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(Cn, n); +var Ce = N({ add_: k4 }); +function N4(r, e) { + let t8 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(Sn, n); } -var pd = N({ floorDiv_: e4 }); -function t4(r, e) { - let t10 = v(r, "a", "div"), o = v(e, "b", "div"); - if ([t10, o] = Oe(t10, o), t10.dtype === "int32" && o.dtype === "int32") - return pd(t10, o); - let n = { a: t10, b: o }, s = {}; - return T.runKernel(dn, n, s); +var ld = N({ floorDiv_: N4 }); +function T4(r, e) { + let t8 = v(r, "a", "div"), o = v(e, "b", "div"); + if ([t8, o] = Oe(t8, o), t8.dtype === "int32" && o.dtype === "int32") + return ld(t8, o); + let n = { a: t8, b: o }, s = {}; + return T.runKernel(fn, n, s); } -var Ke = N({ div_: t4 }); -function r4(r, e) { - let t10 = v(r, "a", "mul"), o = v(e, "b", "mul"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(Kn, n); +var je = N({ div_: T4 }); +function _4(r, e) { + let t8 = v(r, "a", "mul"), o = v(e, "b", "mul"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(Xn, n); } -var se = N({ mul_: r4 }); -function o4(r) { +var se = N({ mul_: _4 }); +function $4(r) { let e = v(r, "x", "abs"); if (e.dtype === "complex64") { - let t10 = { x: e }; - return T.runKernel(_i, t10); + let t8 = { x: e }; + return T.runKernel(Di, t8); } else { - let t10 = { x: e }; - return T.runKernel(Gs, t10); + let t8 = { x: e }; + return T.runKernel(js, t8); } } -var Zt = N({ abs_: o4 }); -function n4(r) { - let t10 = { x: v(r, "x", "acos") }; - return T.runKernel(zo, t10); +var Jt = N({ abs_: $4 }); +function E4(r) { + let t8 = { x: v(r, "x", "acos") }; + return T.runKernel(Vo, t8); } -var ik = N({ acos_: n4 }); -function s4(r) { - let t10 = { x: v(r, "x", "acosh") }; - return T.runKernel(Vo, t10); +var xk = N({ acos_: E4 }); +function R4(r) { + let t8 = { x: v(r, "x", "acosh") }; + return T.runKernel(Wo, t8); } -var uk = N({ acosh_: s4 }); -function a4(r) { +var yk = N({ acosh_: R4 }); +function D4(r) { $(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), $(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`); - let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t10 = e[0]; + let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t8 = e[0]; e.forEach((n) => { - if (n.dtype !== t10.dtype) + if (n.dtype !== t8.dtype) throw new Error("All tensors passed to tf.addN() must have the same dtype"); }), e.forEach((n) => { - if (!br(n.shape, t10.shape)) + if (!Cr(n.shape, t8.shape)) throw new Error("All tensors passed to tf.addN() must have the same shape"); }); let o = e; - return T.runKernel(Wo, o); + return T.runKernel(Uo, o); } -var pk = N({ addN_: a4 }); -function i4(r, e = null, t10 = false) { - let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Uo, n, s); -} -var ck = N({ all_: i4 }); -function u4(r, e = null, t10 = false) { - let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t10 }; +var bk = N({ addN_: D4 }); +function A4(r, e = null, t8 = false) { + let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t8 }; return T.runKernel(Go, n, s); } -var lk = N({ any_: u4 }); -function p4(r, e = 0) { +var Ck = N({ all_: A4 }); +function F4(r, e = null, t8 = false) { + let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t8 }; + return T.runKernel(Ho, n, s); +} +var wk = N({ any_: F4 }); +function P4(r, e = 0) { let o = { x: v(r, "x", "argMax") }, n = { axis: e }; - return T.runKernel(Hs, o, n); + return T.runKernel(Xs, o, n); } -var mk = N({ argMax_: p4 }); -function c4(r, e = 0) { +var Sk = N({ argMax_: P4 }); +function O4(r, e = 0) { let o = { x: v(r, "x", "argMin") }, n = { axis: e }; - return T.runKernel(Ks, o, n); + return T.runKernel(Ys, o, n); } -var dk = N({ argMin_: c4 }); -function l4(r) { - let t10 = { x: v(r, "x", "asin") }; - return T.runKernel(Ho, t10); +var Ik = N({ argMin_: O4 }); +function M4(r) { + let t8 = { x: v(r, "x", "asin") }; + return T.runKernel(Ko, t8); } -var fk = N({ asin_: l4 }); -function m4(r) { - let t10 = { x: v(r, "x", "asinh") }; - return T.runKernel(Ko, t10); +var vk = N({ asin_: M4 }); +function L4(r) { + let t8 = { x: v(r, "x", "asinh") }; + return T.runKernel(qo, t8); } -var hk = N({ asinh_: m4 }); -function d4(r) { - let t10 = { x: v(r, "x", "atan") }; - return T.runKernel(qo, t10); +var kk = N({ asinh_: L4 }); +function B4(r) { + let t8 = { x: v(r, "x", "atan") }; + return T.runKernel(jo, t8); } -var gk = N({ atan_: d4 }); -function f4(r, e) { - let t10 = v(r, "a", "atan2"), o = v(e, "b", "atan2"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(Xo, n); +var Nk = N({ atan_: B4 }); +function z4(r, e) { + let t8 = v(r, "a", "atan2"), o = v(e, "b", "atan2"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(Yo, n); } -var xk = N({ atan2_: f4 }); -function h4(r) { - let t10 = { x: v(r, "x", "atanh") }; - return T.runKernel(jo, t10); +var Tk = N({ atan2_: z4 }); +function V4(r) { + let t8 = { x: v(r, "x", "atanh") }; + return T.runKernel(Xo, t8); } -var yk = N({ atanh_: h4 }); -function g4(r, e, t10, o, n = "NHWC", s) { - let a = r[3], i = [...e, a], p = Ck(n); - return Mu(r, i, t10, s, o, null, null, p); +var _k = N({ atanh_: V4 }); +function W4(r, e, t8, o, n = "NHWC", s) { + let a = r[3], i = [...e, a], p = Ek(n); + return Vu(r, i, t8, s, o, null, null, p); } -function ww(r, e, t10, o, n, s, a = "channelsLast") { +function Ew(r, e, t8, o, n, s, a = "channelsLast") { let [i, p] = wl(e), u; if (a === "channelsLast") u = [i, p, r[3], r[3]]; @@ -5631,19 +5710,19 @@ function ww(r, e, t10, o, n, s, a = "channelsLast") { u = [i, p, r[1], r[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return Mu(r, u, t10, o, n, s, false, a); + return Vu(r, u, t8, o, n, s, false, a); } -function x4(r, e, t10, o, n, s, a = "NDHWC") { - let [i, p, u] = Cw(e), c, l; +function U4(r, e, t8, o, n, s, a = "NDHWC") { + let [i, p, u] = $w(e), c, l; if (a === "NDHWC") l = "channelsLast", c = [i, p, u, r[4], r[4]]; else if (a === "NCDHW") l = "channelsFirst", c = [i, p, u, r[1], r[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return bk(r, c, t10, o, n, false, l, s); + return $k(r, c, t8, o, n, false, l, s); } -function Mu(r, e, t10, o, n, s, a = false, i = "channelsLast") { +function Vu(r, e, t8, o, n, s, a = false, i = "channelsLast") { let [p, u, c, l] = [-1, -1, -1, -1]; if (i === "channelsLast") [p, u, c, l] = r; @@ -5651,10 +5730,10 @@ function Mu(r, e, t10, o, n, s, a = false, i = "channelsLast") { [p, l, u, c] = r; else throw new Error(`Unknown dataFormat ${i}`); - let [m, d, , f] = e, [h, g] = wl(t10), [x, b] = wl(o), w = sc(m, x), S = sc(d, b), { padInfo: k, outHeight: _, outWidth: E } = C4(n, u, c, h, g, w, S, s, i), R = a ? f * l : f, D; - return i === "channelsFirst" ? D = [p, R, _, E] : i === "channelsLast" && (D = [p, _, E, R]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: R, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: w, effectiveFilterWidth: S, dilationHeight: x, dilationWidth: b, inShape: r, outShape: D, filterShape: e }; + let [m, d, , f] = e, [h, g] = wl(t8), [x, b] = wl(o), C = ac(m, x), S = ac(d, b), { padInfo: k, outHeight: _, outWidth: E } = K4(n, u, c, h, g, C, S, s, i), R = a ? f * l : f, D; + return i === "channelsFirst" ? D = [p, R, _, E] : i === "channelsLast" && (D = [p, _, E, R]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: R, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: S, dilationHeight: x, dilationWidth: b, inShape: r, outShape: D, filterShape: e }; } -function bk(r, e, t10, o, n, s = false, a = "channelsLast", i) { +function $k(r, e, t8, o, n, s = false, a = "channelsLast", i) { let [p, u, c, l, m] = [-1, -1, -1, -1, -1]; if (a === "channelsLast") [p, u, c, l, m] = r; @@ -5662,63 +5741,63 @@ function bk(r, e, t10, o, n, s = false, a = "channelsLast", i) { [p, m, u, c, l] = r; else throw new Error(`Unknown dataFormat ${a}`); - let [d, f, h, , g] = e, [x, b, w] = Cw(t10), [S, k, _] = Cw(o), E = sc(d, S), R = sc(f, k), D = sc(h, _), { padInfo: F, outDepth: O, outHeight: M, outWidth: L } = w4(n, u, c, l, x, b, w, E, R, D, i), B = s ? g * m : g, z; - return a === "channelsFirst" ? z = [p, B, O, M, L] : a === "channelsLast" && (z = [p, O, M, L, B]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: O, outHeight: M, outWidth: L, outChannels: B, padInfo: F, strideDepth: x, strideHeight: b, strideWidth: w, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: D, dilationDepth: S, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: e }; + let [d, f, h, , g] = e, [x, b, C] = $w(t8), [S, k, _] = $w(o), E = ac(d, S), R = ac(f, k), D = ac(h, _), { padInfo: P, outDepth: O, outHeight: M, outWidth: L } = q4(n, u, c, l, x, b, C, E, R, D, i), B = s ? g * m : g, z; + return a === "channelsFirst" ? z = [p, B, O, M, L] : a === "channelsLast" && (z = [p, O, M, L, B]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: O, outHeight: M, outWidth: L, outChannels: B, padInfo: P, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: D, dilationDepth: S, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: e }; } -function y4(r, e, t10, o, n) { - o == null && (o = Sw(r, e, t10)); - let s = r[0], a = r[1], i = Sl((s - e + 2 * o) / t10 + 1, n), p = Sl((a - e + 2 * o) / t10 + 1, n); +function G4(r, e, t8, o, n) { + o == null && (o = Rw(r, e, t8)); + let s = r[0], a = r[1], i = Sl((s - e + 2 * o) / t8 + 1, n), p = Sl((a - e + 2 * o) / t8 + 1, n); return [i, p]; } -function b4(r, e, t10, o, n, s) { - n == null && (n = Sw(r, e[0], o[0])); - let a = [0, 0, 0, t10]; +function H4(r, e, t8, o, n, s) { + n == null && (n = Rw(r, e[0], o[0])); + let a = [0, 0, 0, t8]; for (let i = 0; i < 3; i++) r[i] + 2 * n >= e[i] && (a[i] = Sl((r[i] - e[i] + 2 * n) / o[i] + 1, s)); return a; } -function Sw(r, e, t10, o = 1) { - let n = sc(e, o); - return Math.floor((r[0] * (t10 - 1) - t10 + n) / 2); +function Rw(r, e, t8, o = 1) { + let n = ac(e, o); + return Math.floor((r[0] * (t8 - 1) - t8 + n) / 2); } function wl(r) { return typeof r == "number" ? [r, r, r] : r.length === 2 ? [r[0], r[1], 1] : r; } -function Cw(r) { +function $w(r) { return typeof r == "number" ? [r, r, r] : r; } -function sc(r, e) { +function ac(r, e) { return e <= 1 ? r : r + (r - 1) * (e - 1); } -function C4(r, e, t10, o, n, s, a, i, p) { +function K4(r, e, t8, o, n, s, a, i, p) { let u, c, l; if (typeof r == "number") { u = { top: r, bottom: r, left: r, right: r, type: r === 0 ? "VALID" : "NUMBER" }; - let d = y4([e, t10], s, o, r, i); + let d = G4([e, t8], s, o, r, i); c = d[0], l = d[1]; } else if (r === "same") { - c = Math.ceil(e / o), l = Math.ceil(t10 / n); - let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t10), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g; + c = Math.ceil(e / o), l = Math.ceil(t8 / n); + let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t8), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g; u = { top: f, bottom: h, left: g, right: x, type: "SAME" }; } else if (r === "valid") - u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t10 - a + 1) / n); + u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t8 - a + 1) / n); else if (typeof r == "object") { let m = p === "channelsLast" ? r[1][0] : r[2][0], d = p === "channelsLast" ? r[1][1] : r[2][1], f = p === "channelsLast" ? r[2][0] : r[3][0], h = p === "channelsLast" ? r[2][1] : r[3][1]; - u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = Sl((e - s + m + d) / o + 1, i), l = Sl((t10 - a + f + h) / n + 1, i); + u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = Sl((e - s + m + d) / o + 1, i), l = Sl((t8 - a + f + h) / n + 1, i); } else throw Error(`Unknown padding parameter: ${r}`); return { padInfo: u, outHeight: c, outWidth: l }; } -function w4(r, e, t10, o, n, s, a, i, p, u, c) { +function q4(r, e, t8, o, n, s, a, i, p, u, c) { let l, m, d, f; if (r === "valid" && (r = 0), typeof r == "number") { l = { top: r, bottom: r, left: r, right: r, front: r, back: r, type: r === 0 ? "VALID" : "NUMBER" }; - let g = b4([e, t10, o, 1], [i, p, u], 1, [n, s, a], r, c); + let g = H4([e, t8, o, 1], [i, p, u], 1, [n, s, a], r, c); m = g[0], d = g[1], f = g[2]; } else if (r === "same") { - m = Math.ceil(e / n), d = Math.ceil(t10 / s), f = Math.ceil(o / a); - let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t10, x = (f - 1) * a + u - o, b = Math.floor(h / 2), w = h - b, S = Math.floor(g / 2), k = g - S, _ = Math.floor(x / 2), E = x - _; - l = { top: S, bottom: k, left: _, right: E, front: b, back: w, type: "SAME" }; + m = Math.ceil(e / n), d = Math.ceil(t8 / s), f = Math.ceil(o / a); + let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t8, x = (f - 1) * a + u - o, b = Math.floor(h / 2), C = h - b, S = Math.floor(g / 2), k = g - S, _ = Math.floor(x / 2), E = x - _; + l = { top: S, bottom: k, left: _, right: E, front: b, back: C, type: "SAME" }; } else throw Error(`Unknown padding parameter: ${r}`); return { padInfo: l, outDepth: m, outHeight: d, outWidth: f }; @@ -5737,744 +5816,744 @@ function Sl(r, e) { throw new Error(`Unknown roundingMode ${e}`); } } -function Ou(r) { - let [e, t10, o] = wl(r); - return e === 1 && t10 === 1 && o === 1; +function zu(r) { + let [e, t8, o] = wl(r); + return e === 1 && t8 === 1 && o === 1; } function gr(r, e) { - return Ou(r) || Ou(e); + return zu(r) || zu(e); } -function ba(r) { +function va(r) { return wl(r).every((e) => e > 0); } -function Ck(r) { +function Ek(r) { if (r === "NHWC") return "channelsLast"; if (r === "NCHW") return "channelsFirst"; throw new Error(`Unknown dataFormat ${r}`); } -function Lt(r, e, t10) { - if (t10 != null) { +function Lt(r, e, t8) { + if (t8 != null) { if (typeof e == "string") - throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`); + throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t8} but got pad ${e}.`); if (typeof e == "number") - $(Ba(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`); + $(Ga(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t8} but got pad ${e}.`); else if (typeof e == "object") e.forEach((o) => { o.forEach((n) => { - $(Ba(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${n}.`); + $(Ga(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t8} but got pad ${n}.`); }); }); else throw Error(`Error in ${r}: Unknown padding parameter: ${e}`); } } -function S4(r, e) { +function j4(r, e) { let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: e }; - return T.runKernel(ia, o, n); + return T.runKernel(ma, o, n); } -var W = N({ reshape_: S4 }); -function I4(r, e, t10, o, n) { +var W = N({ reshape_: j4 }); +function X4(r, e, t8, o, n) { let s = v(r, "x", "avgPool", "float32"), a = 1; - $(gr(t10, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`); + $(gr(t8, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t8} and dilations '${a}'`); let i = s, p = false; s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Lt("avgPool", o, n); - let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(Yo, u, c); - return l = Ye(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; + let u = { x: i }, c = { filterSize: e, strides: t8, pad: o, dimRoundingMode: n }, l = T.runKernel(Qo, u, c); + return l = qe(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var cd = N({ avgPool_: I4 }); -function v4(r, e, t10, o, n, s = "NDHWC") { +var md = N({ avgPool_: X4 }); +function Y4(r, e, t8, o, n, s = "NDHWC") { let a = v(r, "x", "avgPool3d", "float32"), i = a, p = false; - a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof t10 == "number" && t10 > 0 || Array.isArray(t10) && t10[0] > 0 && t10[1] > 0 && t10[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${t10}'`), Lt("avgPool3d", o, n); - let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(qs, u, c); - return l = Ye(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; + a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof t8 == "number" && t8 > 0 || Array.isArray(t8) && t8[0] > 0 && t8[1] > 0 && t8[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${t8}'`), Lt("avgPool3d", o, n); + let u = { x: i }, c = { filterSize: e, strides: t8, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(Qs, u, c); + return l = qe(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var wk = N({ avgPool3d_: v4 }); -function k4(r, e = 0) { +var Rk = N({ avgPool3d_: Y4 }); +function Q4(r, e = 0) { $(r.length >= 1, () => "Pass at least one tensor to concat"); - let t10 = Ja(r, "tensors", "concat", "string_or_numeric"); - if (t10[0].dtype === "complex64" && t10.forEach((s) => { + let t8 = ri(r, "tensors", "concat", "string_or_numeric"); + if (t8[0].dtype === "complex64" && t8.forEach((s) => { if (s.dtype !== "complex64") throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `); - }), t10.length === 1) - return Vr(t10[0]); - let o = t10, n = { axis: e }; - return T.runKernel(Ys, o, n); + }), t8.length === 1) + return Ur(t8[0]); + let o = t8, n = { axis: e }; + return T.runKernel(ea, o, n); } -var yt = N({ concat_: k4 }); -function N4(r, e, t10 = false, o = false) { +var bt = N({ concat_: Q4 }); +function Z4(r, e, t8 = false, o = false) { let n = v(r, "a", "matMul"), s = v(e, "b", "matMul"); [n, s] = Oe(n, s); - let a = { a: n, b: s }, i = { transposeA: t10, transposeB: o }; - return T.runKernel(Qo, a, i); + let a = { a: n, b: s }, i = { transposeA: t8, transposeB: o }; + return T.runKernel(Zo, a, i); } -var Qe = N({ matMul_: N4 }); -function T4(r) { - let t10 = { x: v(r, "x", "sigmoid", "float32") }; - return T.runKernel(hs, t10); +var Ze = N({ matMul_: Z4 }); +function J4(r) { + let t8 = { x: v(r, "x", "sigmoid", "float32") }; + return T.runKernel(bs, t8); } -var wa = N({ sigmoid_: T4 }); -function _4(r, e, t10) { +var Na = N({ sigmoid_: J4 }); +function eH(r, e, t8) { let o = v(r, "x", "slice", "string_or_numeric"); if (o.rank === 0) throw new Error("Slicing scalar is not possible"); - let n = { x: o }, s = { begin: e, size: t10 }; - return T.runKernel(pa, n, s); + let n = { x: o }, s = { begin: e, size: t8 }; + return T.runKernel(fa, n, s); } -var qe = N({ slice_: _4 }); -function $4(r) { - let t10 = { x: v(r, "x", "tanh", "float32") }; - return T.runKernel(ks, t10); +var Xe = N({ slice_: eH }); +function tH(r) { + let t8 = { x: v(r, "x", "tanh", "float32") }; + return T.runKernel($s, t8); } -var Il = N({ tanh_: $4 }); -function E4(r, e, t10, o, n, s) { - let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t10, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = yt([u, l], 1), d = Qe(m, i), f = be(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = qe(f, [0, 0], x), w = qe(f, [0, g], x), S = qe(f, [0, g * 2], x), k = qe(f, [0, g * 3], x), _ = be(se(wa(b), Il(w)), se(c, wa(be(a, S)))), E = se(Il(_), wa(k)); +var Il = N({ tanh_: tH }); +function rH(r, e, t8, o, n, s) { + let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t8, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = bt([u, l], 1), d = Ze(m, i), f = Ce(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = Xe(f, [0, 0], x), C = Xe(f, [0, g], x), S = Xe(f, [0, g * 2], x), k = Xe(f, [0, g * 3], x), _ = Ce(se(Na(b), Il(C)), se(c, Na(Ce(a, S)))), E = se(Il(_), Na(k)); return [_, E]; } -var Sk = N({ basicLSTMCell_: E4 }); -function R4(r, e, t10) { +var Dk = N({ basicLSTMCell_: rH }); +function oH(r, e, t8) { let o = v(r, "x", "batchToSpaceND"), n = e.reduce((i, p) => i * p); - $(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), $(t10.length === e.length, () => `crops.length is ${t10.length} but should be equal to blockShape.length ${e.length}`), $(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`); - let s = { x: o }, a = { blockShape: e, crops: t10 }; - return T.runKernel(js, s, a); + $(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), $(t8.length === e.length, () => `crops.length is ${t8.length} but should be equal to blockShape.length ${e.length}`), $(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`); + let s = { x: o }, a = { blockShape: e, crops: t8 }; + return T.runKernel(Zs, s, a); } -var ld = N({ batchToSpaceND_: R4 }); -function Ik(r) { +var dd = N({ batchToSpaceND_: oH }); +function Ak(r) { let e; return r.rank === 0 || r.rank === 1 ? e = W(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = W(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e; } -function D4(r, e, t10, o, n, s) { +function nH(r, e, t8, o, n, s) { s == null && (s = 1e-3); - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; o != null && (c = v(o, "offset", "batchNorm")), $(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), $(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), $(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - let m = { x: Ik(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(wn, m, d); + let m = { x: Ak(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(In, m, d); return W(f, a.shape); } -var tu = N({ batchNorm_: D4 }); -function A4(r, e, t10, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; +var su = N({ batchNorm_: nH }); +function sH(r, e, t8, o, n, s) { + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), tu(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), su(a, i, p, c, u, s); } -var vk = N({ batchNorm2d_: A4 }); -function F4(r, e, t10, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; +var Fk = N({ batchNorm2d_: sH }); +function aH(r, e, t8, o, n, s) { + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), tu(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), su(a, i, p, c, u, s); } -var kk = N({ batchNorm3d_: F4 }); -function P4(r, e, t10, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; +var Pk = N({ batchNorm3d_: aH }); +function iH(r, e, t8, o, n, s) { + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), tu(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), su(a, i, p, c, u, s); } -var Nk = N({ batchNorm4d_: P4 }); -function O4(r, e, t10) { +var Ok = N({ batchNorm4d_: iH }); +function uH(r, e, t8) { let o = v(r, "x", "bincount"), n = v(e, "weights", "bincount"); - $(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`); - let s = { x: o, weights: n }, a = { size: t10 }; - return T.runKernel(Zo, s, a); + $(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), $(t8 >= 0, () => `size must be non-negative, but got ${t8}.`), $(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`); + let s = { x: o, weights: n }, a = { size: t8 }; + return T.runKernel(Jo, s, a); } -var md = N({ bincount_: O4 }); -function M4(r, e) { - let t10 = v(r, "x", "bitwiseAnd"), o = v(e, "y", "bitwiseAnd"); - if (!br(t10.shape, o.shape)) - throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t10.shape}, y: ${o.shape}`); - if (t10.dtype !== "int32" || o.dtype !== "int32") - throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t10.dtype} and type of y: ${o.dtype}`); - let n = { a: t10, b: o }; - return T.runKernel(ml, n); +var fd = N({ bincount_: uH }); +function pH(r, e) { + let t8 = v(r, "x", "bitwiseAnd"), o = v(e, "y", "bitwiseAnd"); + if (!Cr(t8.shape, o.shape)) + throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t8.shape}, y: ${o.shape}`); + if (t8.dtype !== "int32" || o.dtype !== "int32") + throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t8.dtype} and type of y: ${o.dtype}`); + let n = { a: t8, b: o }; + return T.runKernel(Ha, n); } -var Tk = N({ bitwiseAnd_: M4 }); -function L4(r, e) { - let t10 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32"); - if (t10.rank !== 1) - throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t10.rank}`); +var Mk = N({ bitwiseAnd_: pH }); +function cH(r, e) { + let t8 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32"); + if (t8.rank !== 1) + throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t8.rank}`); if (o.rank !== 1) throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`); - let n = { s0: t10, s1: o }; - return T.runKernel(Xs, n); + let n = { s0: t8, s1: o }; + return T.runKernel(Js, n); } -var _k = N({ broadcastArgs_: L4 }); -function B4(r, e) { - let t10 = v(r, "broadcastTo", "x"), o = t10.shape; - if (Ct(e), e.length < t10.rank) - throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t10.rank}.`); - if (e.length > t10.rank) { - let u = t10.shape.slice(); +var Lk = N({ broadcastArgs_: cH }); +function lH(r, e) { + let t8 = v(r, "broadcastTo", "x"), o = t8.shape; + if (wt(e), e.length < t8.rank) + throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t8.rank}.`); + if (e.length > t8.rank) { + let u = t8.shape.slice(); for (; u.length < e.length; ) u.unshift(1); - t10 = W(t10, u); + t8 = W(t8, u); } - let n = t10.shape, s = Array.from(e); + let n = t8.shape, s = Array.from(e); for (let u = e.length - 1; u >= 0; u--) if (n[u] === e[u]) s[u] = 1; - else if (t10.shape[u] !== 1) + else if (t8.shape[u] !== 1) throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`); if (s.map((u, c) => u > 1 ? c : -1).filter((u) => u >= 0).length === 0) - return Vr(t10); - let i = { x: t10 }, p = { reps: s }; - return T.runKernel(so, i, p); + return Ur(t8); + let i = { x: t8 }, p = { reps: s }; + return T.runKernel(uo, i, p); } -var ru = N({ broadcastTo_: B4 }); -function z4(r) { - let t10 = { x: v(r, "x", "ceil", "float32") }; - return T.runKernel(Jo, t10); +var au = N({ broadcastTo_: lH }); +function mH(r) { + let t8 = { x: v(r, "x", "ceil", "float32") }; + return T.runKernel(en, t8); } -var $k = N({ ceil_: z4 }); -function Sa(r, e, t10) { - Ct(r), t10 = t10 || ki(e); - let o = { shape: r, value: e, dtype: t10 }; - return T.runKernel(ea, {}, o); +var Bk = N({ ceil_: mH }); +function Ta(r, e, t8) { + wt(r), t8 = t8 || _i(e); + let o = { shape: r, value: e, dtype: t8 }; + return T.runKernel(na, {}, o); } -function V4(r, e, t10) { +function dH(r, e, t8) { let o = v(r, "x", "clipByValue"); - if ($(e <= t10, () => `Error in clip: min (${e}) must be less than or equal to max (${t10}).`), e === t10) - return Sa(o.shape, e, o.dtype); - let n = { x: o }, s = { clipValueMin: e, clipValueMax: t10 }; - return T.runKernel(go, n, s); + if ($(e <= t8, () => `Error in clip: min (${e}) must be less than or equal to max (${t8}).`), e === t8) + return Ta(o.shape, e, o.dtype); + let n = { x: o }, s = { clipValueMin: e, clipValueMax: t8 }; + return T.runKernel(bo, n, s); } -var Ek = N({ clipByValue_: V4 }); -function W4(r) { - return yt(r, 0); +var zk = N({ clipByValue_: dH }); +function fH(r) { + return bt(r, 0); } -var Rk = N({ concat1d_: W4 }); -function U4(r, e) { - return yt(r, e); +var Vk = N({ concat1d_: fH }); +function hH(r, e) { + return bt(r, e); } -var Dk = N({ concat2d_: U4 }); -function G4(r, e) { - return yt(r, e); +var Wk = N({ concat2d_: hH }); +function gH(r, e) { + return bt(r, e); } -var Ak = N({ concat3d_: G4 }); -function H4(r, e) { - return yt(r, e); +var Uk = N({ concat3d_: gH }); +function xH(r, e) { + return bt(r, e); } -var Fk = N({ concat4d_: H4 }); -function K4(r, e, t10, o, n = "NHWC", s = [1, 1], a) { +var Gk = N({ concat4d_: xH }); +function yH(r, e, t8, o, n = "NHWC", s = [1, 1], a) { let i = v(r, "x", "conv2d", "float32"), p = v(e, "filter", "conv2d", "float32"), u = i, c = false; i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Lt("conv2d", o, a); let l = n === "NHWC" ? u.shape[3] : u.shape[1]; - $(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(gr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(ba(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(ba(t10), () => "Error in conv2D: Strides should be larger than 0."); - let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(en, m, d); + $(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(gr(t8, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`), $(va(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(va(t8), () => "Error in conv2D: Strides should be larger than 0."); + let m = { x: u, filter: p }, d = { strides: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(tn, m, d); return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var ou = N({ conv2d_: K4 }); -function q4(r, e, t10, o, n = "NWC", s = 1, a) { +var iu = N({ conv2d_: yH }); +function bH(r, e, t8, o, n = "NWC", s = 1, a) { let i = v(r, "x", "conv1d"), p = v(e, "filter", "conv1d"), u = i, c = false; - i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Lt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(gr(t10, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t10} and dilation '${s}'`), $(ba(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(ba(t10), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`); - let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = ou(m, l, [1, t10], o, "NHWC", [1, s], a); + i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Lt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(gr(t8, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t8} and dilation '${s}'`), $(va(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(va(t8), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`); + let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = iu(m, l, [1, t8], o, "NHWC", [1, s], a); return c ? W(g, [g.shape[2], g.shape[3]]) : W(g, [g.shape[0], g.shape[2], g.shape[3]]); } -var Pk = N({ conv1d_: q4 }); -function j4(r, e, t10, o, n, s = "NHWC", a) { +var Hk = N({ conv1d_: bH }); +function CH(r, e, t8, o, n, s = "NHWC", a) { $(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); let i = r, p = e, u = false; - e.rank === 3 && (u = true, p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]), i = [1, r[0], r[1], r[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(t10.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t10.rank}`); + e.rank === 3 && (u = true, p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]), i = [1, r[0], r[1], r[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(t8.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t8.rank}`); let c = s === "NHWC" ? i[3] : i[1], l = s === "NHWC" ? p.shape[3] : p.shape[1]; - $(c === t10.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t10.shape[2]}.`), $(l === t10.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t10.shape[3]}.`), Lt("conv2dDerInput", n, a); - let m = { dy: p, filter: t10 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(tn, m, d); + $(c === t8.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t8.shape[2]}.`), $(l === t8.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t8.shape[3]}.`), Lt("conv2dDerInput", n, a); + let m = { dy: p, filter: t8 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(rn, m, d); return u ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var dd = N({ conv2DBackpropInput_: j4 }); -function X4(r, e, t10, o, n, s) { +var hd = N({ conv2DBackpropInput_: CH }); +function wH(r, e, t8, o, n, s) { let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose"); - return dd(t10, a, i, o, n, "NHWC", s); + return hd(t8, a, i, o, n, "NHWC", s); } -var Ok = N({ conv2dTranspose_: X4 }); -function Y4(r, e, t10, o, n = "NDHWC", s = [1, 1, 1]) { +var Kk = N({ conv2dTranspose_: wH }); +function SH(r, e, t8, o, n = "NDHWC", s = [1, 1, 1]) { let a = v(r, "x", "conv3d"), i = v(e, "filter", "conv3d"), p = a, u = false; - a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(gr(t10, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(ba(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(ba(t10), () => "Error in conv3D: Strides should be larger than 0."); - let c = { x: p, filter: i }, l = { strides: t10, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(rn, c, l); + a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(gr(t8, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(va(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(va(t8), () => "Error in conv3D: Strides should be larger than 0."); + let c = { x: p, filter: i }, l = { strides: t8, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(on, c, l); return u ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var Mk = N({ conv3d_: Y4 }); -function Q4(r, e, t10, o, n) { +var qk = N({ conv3d_: SH }); +function IH(r, e, t8, o, n) { $(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); let s = r, a = e, i = false; e.rank === 4 && (i = true, a = W(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]), s = [1, r[0], r[1], r[2], r[3]]); let p = s[4], u = a.shape[4]; - $(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), $(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), $(t10.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t10.rank}`), $(p === t10.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t10.shape[3]}.`), $(u === t10.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t10.shape[4]}.`); - let c = { dy: a, filter: t10 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(on, c, l); + $(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), $(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), $(t8.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t8.rank}`), $(p === t8.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t8.shape[3]}.`), $(u === t8.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t8.shape[4]}.`); + let c = { dy: a, filter: t8 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(nn, c, l); return i ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var Lk = N({ conv3DBackpropInput_: Q4 }); -function Z4(r, e, t10, o, n) { +var jk = N({ conv3DBackpropInput_: IH }); +function vH(r, e, t8, o, n) { let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose"); - return Lk(t10, s, a, o, n); + return jk(t8, s, a, o, n); } -var Bk = N({ conv3dTranspose_: Z4 }); -function J4(r) { - let t10 = { x: v(r, "x", "cos", "float32") }; - return T.runKernel(nn, t10); +var Xk = N({ conv3dTranspose_: vH }); +function kH(r) { + let t8 = { x: v(r, "x", "cos", "float32") }; + return T.runKernel(sn, t8); } -var zk = N({ cos_: J4 }); -function eH(r) { - let t10 = { x: v(r, "x", "cosh", "float32") }; - return T.runKernel(sn, t10); +var Yk = N({ cos_: kH }); +function NH(r) { + let t8 = { x: v(r, "x", "cosh", "float32") }; + return T.runKernel(an, t8); } -var Vk = N({ cosh_: eH }); -function tH(r, e = 0, t10 = false, o = false) { - let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t10, reverse: o }; - return T.runKernel(an, s, a); -} -var Wk = N({ cumprod_: tH }); -function rH(r, e = 0, t10 = false, o = false) { - let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t10, reverse: o }; +var Qk = N({ cosh_: NH }); +function TH(r, e = 0, t8 = false, o = false) { + let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t8, reverse: o }; return T.runKernel(un, s, a); } -var Uk = N({ cumsum_: rH }); -function oH(r, e, t10, o = false) { - let n = v(r, "x", "denseBincount"), s = v(e, "weights", "denseBincount"); - $(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), $(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`); - let a = { x: n, weights: s }, i = { size: t10, binaryOutput: o }; - return T.runKernel(Qs, a, i); +var Zk = N({ cumprod_: TH }); +function _H(r, e = 0, t8 = false, o = false) { + let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t8, reverse: o }; + return T.runKernel(pn, s, a); } -var Gk = N({ denseBincount_: oH }); -function nH(r, e, t10 = "NHWC") { - let o = v(r, "x", "depthToSpace", "float32"), n = t10 === "NHWC" ? o.shape[1] : o.shape[2], s = t10 === "NHWC" ? o.shape[2] : o.shape[3], a = t10 === "NHWC" ? o.shape[3] : o.shape[1]; +var Jk = N({ cumsum_: _H }); +function $H(r, e, t8, o = false) { + let n = v(r, "x", "denseBincount"), s = v(e, "weights", "denseBincount"); + $(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), $(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), $(t8 >= 0, () => `size must be non-negative, but got ${t8}.`), $(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`); + let a = { x: n, weights: s }, i = { size: t8, binaryOutput: o }; + return T.runKernel(ta, a, i); +} +var e2 = N({ denseBincount_: $H }); +function EH(r, e, t8 = "NHWC") { + let o = v(r, "x", "depthToSpace", "float32"), n = t8 === "NHWC" ? o.shape[1] : o.shape[2], s = t8 === "NHWC" ? o.shape[2] : o.shape[3], a = t8 === "NHWC" ? o.shape[3] : o.shape[1]; $(e > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${e}`), $(n * e >= 0, () => `Negative dimension size caused by overflow when multiplying ${n} and ${e} for depthToSpace with input shape ${o.shape}`), $(s * e >= 0, () => `Negative dimension size caused by overflow when multiplying ${s} and ${e} for depthToSpace with input shape ${o.shape}`), $(a % (e * e) === 0, () => `Dimension size must be evenly divisible by ${e * e} but is ${a} for depthToSpace with input shape ${o.shape}`); - let i = { x: o }, p = { blockSize: e, dataFormat: t10 }; - return T.runKernel(cn, i, p); + let i = { x: o }, p = { blockSize: e, dataFormat: t8 }; + return T.runKernel(ln, i, p); } -var Hk = N({ depthToSpace_: nH }); -function sH(r, e, t10, o, n = "NHWC", s = [1, 1], a) { +var t2 = N({ depthToSpace_: EH }); +function RH(r, e, t8, o, n = "NHWC", s = [1, 1], a) { let i = v(r, "x", "depthwiseConv2d", "float32"), p = v(e, "filter", "depthwiseConv2d", "float32"), u = i, c = false; i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`); let l = n === "NHWC" ? u.shape[3] : u.shape[1]; $(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Lt("depthwiseConv2d", o, a); - let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(ln, m, d); + let m = { x: u, filter: p }, d = { strides: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(mn, m, d); return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var ac = N({ depthwiseConv2d_: sH }); -function aH(r) { - let t10 = { x: v(r, "x", "diag") }; - return T.runKernel(Zs, t10); +var ic = N({ depthwiseConv2d_: RH }); +function DH(r) { + let t8 = { x: v(r, "x", "diag") }; + return T.runKernel(ra, t8); } -var Kk = N({ diag_: aH }); -function iH(r, e, t10, o, n = [1, 1], s = "NHWC") { +var r2 = N({ diag_: DH }); +function AH(r, e, t8, o, n = [1, 1], s = "NHWC") { let a = v(r, "x", "dilation2d"), i = v(e, "filter", "dilation2d"); $(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), $(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), $(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`); let p = a, u = false; a.rank === 3 && (p = W(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true), $(p.shape[3] === i.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs ${i.shape[2]}`); - let c = { x: p, filter: i }, l = { strides: t10, pad: o, dilations: n }, m = T.runKernel(mn, c, l); + let c = { x: p, filter: i }, l = { strides: t8, pad: o, dilations: n }, m = T.runKernel(dn, c, l); return u ? W(m, [m.shape[1], m.shape[2], m.shape[3]]) : m; } -var qk = N({ dilation2d_: iH }); -var Sr = {}; -He(Sr, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => jk, getReductionAxes: () => fd }); -function jk(r, e) { - let t10 = r.length, o = []; - for (let n = 0; n < t10; n++) { - let s = t10 - 1 - n, a = r[s] || 1; +var o2 = N({ dilation2d_: AH }); +var Ir = {}; +Ke(Ir, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => n2, getReductionAxes: () => gd }); +function n2(r, e) { + let t8 = r.length, o = []; + for (let n = 0; n < t8; n++) { + let s = t8 - 1 - n, a = r[s] || 1; (e[e.length - 1 - n] || 1) > 1 && a === 1 && o.unshift(s); } return o; } -function fd(r, e) { - let t10 = []; +function gd(r, e) { + let t8 = []; for (let o = 0; o < e.length; o++) { let n = r[r.length - o - 1], s = e.length - o - 1, a = e[s]; - (n == null || n === 1 && a > 1) && t10.unshift(s); + (n == null || n === 1 && a > 1) && t8.unshift(s); } - return t10; + return t8; } function rt(r, e) { - let t10 = Math.max(r.length, e.length), o = new Array(t10); - for (let n = 0; n < t10; n++) { + let t8 = Math.max(r.length, e.length), o = new Array(t8); + for (let n = 0; n < t8; n++) { let s = r[r.length - n - 1]; s == null && (s = 1); let a = e[e.length - n - 1]; if (a == null && (a = 1), s === 1) - o[t10 - n - 1] = a; + o[t8 - n - 1] = a; else if (a === 1) - o[t10 - n - 1] = s; + o[t8 - n - 1] = s; else if (s !== a) { let i = `Operands could not be broadcast together with shapes ${r} and ${e}.`; throw Error(i); } else - o[t10 - n - 1] = s; + o[t8 - n - 1] = s; } return o; } -function uH(r, e) { - let t10 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(hn, n); +function FH(r, e) { + let t8 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(xn, n); } -var hd = N({ equal_: uH }); -function pH(r, e, t10) { - let o = v(e, "a", "where"), n = v(t10, "b", "where"), s = v(r, "condition", "where", "bool"), a = rt(rt(s.shape, o.shape), n.shape), i = ru(s, a), p = ru(o, a), u = ru(n, a), c = { condition: i, t: p, e: u }; - return T.runKernel(ua, c); +var xd = N({ equal_: FH }); +function PH(r, e, t8) { + let o = v(e, "a", "where"), n = v(t8, "b", "where"), s = v(r, "condition", "where", "bool"), a = rt(rt(s.shape, o.shape), n.shape), i = au(s, a), p = au(o, a), u = au(n, a), c = { condition: i, t: p, e: u }; + return T.runKernel(da, c); } -var io = N({ where_: pH }); -function cH(r) { - let t10 = { x: v(r, "x", "zerosLike") }; - return T.runKernel(fa, t10); +var co = N({ where_: PH }); +function OH(r) { + let t8 = { x: v(r, "x", "zerosLike") }; + return T.runKernel(ba, t8); } -var Ht = N({ zerosLike_: cH }); -function lH(r, e) { - let t10 = v(r, "a", "div"), o = v(e, "b", "div"); - [t10, o] = Oe(t10, o); - let n = Ke(t10, o), s = Ht(n), a = hd(o, s); - return io(a, s, n); +var Ht = N({ zerosLike_: OH }); +function MH(r, e) { + let t8 = v(r, "a", "div"), o = v(e, "b", "div"); + [t8, o] = Oe(t8, o); + let n = je(t8, o), s = Ht(n), a = xd(o, s); + return co(a, s, n); } -var Xk = N({ divNoNan_: lH }); -function mH(r, e) { - let t10 = v(r, "t1", "dot"), o = v(e, "t2", "dot"); - $((t10.rank === 1 || t10.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t10.rank} and ${o.rank}.`); - let n = t10.rank === 1 ? t10.size : t10.shape[1], s = o.rank === 1 ? o.size : o.shape[0]; - if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t10.rank === 1 && o.rank === 1) { - let a = W(t10, [1, -1]), i = W(o, [-1, 1]), p = Qe(a, i); +var s2 = N({ divNoNan_: MH }); +function LH(r, e) { + let t8 = v(r, "t1", "dot"), o = v(e, "t2", "dot"); + $((t8.rank === 1 || t8.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t8.rank} and ${o.rank}.`); + let n = t8.rank === 1 ? t8.size : t8.shape[1], s = o.rank === 1 ? o.size : o.shape[0]; + if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t8.rank === 1 && o.rank === 1) { + let a = W(t8, [1, -1]), i = W(o, [-1, 1]), p = Ze(a, i); return W(p, []); - } else if (t10.rank === 1 && o.rank === 2) { - let a = W(t10, [1, -1]), i = W(o, [o.shape[0], o.shape[1]]), p = Qe(a, i); + } else if (t8.rank === 1 && o.rank === 2) { + let a = W(t8, [1, -1]), i = W(o, [o.shape[0], o.shape[1]]), p = Ze(a, i); return W(p, [p.size]); - } else if (t10.rank === 2 && o.rank === 1) { - let a = W(o, [-1, 1]), i = Qe(t10, a); + } else if (t8.rank === 2 && o.rank === 1) { + let a = W(o, [-1, 1]), i = Ze(t8, a); return W(i, [i.size]); } else { let a = W(o, [o.shape[0], o.shape[1]]); - return Qe(t10, a); + return Ze(t8, a); } } -var Yk = N({ dot_: mH }); -function dH(r, ...e) { - let t10 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r }; - return T.runKernel(Fi, t10, o); +var a2 = N({ dot_: LH }); +function BH(r, ...e) { + let t8 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r }; + return T.runKernel(Li, t8, o); } -var Qk = N({ einsum_: dH }); -function fH(r) { - let t10 = { x: v(r, "x", "elu", "float32") }; - return T.runKernel(fn, t10); +var i2 = N({ einsum_: BH }); +function zH(r) { + let t8 = { x: v(r, "x", "elu", "float32") }; + return T.runKernel(hn, t8); } -var gd = N({ elu_: fH }); -function hH(r, e) { - let t10 = v(r, "x", "ensureShape", "string_or_numeric"); - if (!OC(t10.shape, e)) - throw new Error(`EnsureShape: Shape of tensor ${t10.shape} is not compatible with expected shape ${e}`); +var yd = N({ elu_: zH }); +function VH(r, e) { + let t8 = v(r, "x", "ensureShape", "string_or_numeric"); + if (!HC(t8.shape, e)) + throw new Error(`EnsureShape: Shape of tensor ${t8.shape} is not compatible with expected shape ${e}`); return r; } -var Zk = N({ ensureShape_: hH }); -function gH(r) { +var u2 = N({ ensureShape_: VH }); +function WH(r) { let e = v(r, "x", "erf"); - $(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = Ye(e, "float32")); - let t10 = { x: e }; - return T.runKernel(Wa, t10); + $(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = qe(e, "float32")); + let t8 = { x: e }; + return T.runKernel(gn, t8); } -var Jk = N({ erf_: gH }); -function Iw(r, e) { - for (let t10 = 0; t10 < r.length; ++t10) - if (r[r.length - t10 - 1] !== e - 1 - t10) +var p2 = N({ erf_: WH }); +function Dw(r, e) { + for (let t8 = 0; t8 < r.length; ++t8) + if (r[r.length - t8 - 1] !== e - 1 - t8) return false; return true; } -function e2(r, e, t10) { +function c2(r, e, t8) { let o = r.length + e.length, n = [], s = 0, a = 0; for (let i = 0; i < o; i++) - t10.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]); + t8.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]); return n; } -function xH(r, e) { - let t10 = [], o = r.length; +function UH(r, e) { + let t8 = [], o = r.length; for (let s = 0; s < o; s++) - e.indexOf(s) === -1 && t10.push(r[s]); + e.indexOf(s) === -1 && t8.push(r[s]); let n = e.map((s) => r[s]); - return [t10, n]; + return [t8, n]; } -function ti(r, e) { - let t10 = e.map((o) => 1); - return e2(r, t10, e); +function ni(r, e) { + let t8 = e.map((o) => 1); + return c2(r, t8, e); } -function yH(r, e, t10) { - $(Iw(e, t10), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t10} input.`); +function GH(r, e, t8) { + $(Dw(e, t8), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t8} input.`); } -function bH(r, e) { - if (Iw(r, e)) +function HH(r, e) { + if (Dw(r, e)) return null; - let t10 = []; + let t8 = []; for (let o = 0; o < e; ++o) - r.indexOf(o) === -1 && t10.push(o); - return r.forEach((o) => t10.push(o)), t10; + r.indexOf(o) === -1 && t8.push(o); + return r.forEach((o) => t8.push(o)), t8; } -function CH(r) { - return r.map((e, t10) => [t10, e]).sort((e, t10) => e[1] - t10[1]).map((e) => e[0]); +function KH(r) { + return r.map((e, t8) => [t8, e]).sort((e, t8) => e[1] - t8[1]).map((e) => e[0]); } -function wH(r, e) { - let t10 = []; +function qH(r, e) { + let t8 = []; for (let o = e - r; o < e; ++o) - t10.push(o); - return t10; + t8.push(o); + return t8; } -function IH(r, e = null, t10 = false) { - let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t10 }; - return T.runKernel(Ln, n, s); +function XH(r, e = null, t8 = false) { + let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t8 }; + return T.runKernel(zn, n, s); } -var Ia = N({ max_: IH }); -function vH(r, e = null, t10 = false) { - let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Wn, n, s); +var _a = N({ max_: XH }); +function YH(r, e = null, t8 = false) { + let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t8 }; + return T.runKernel(Gn, n, s); } -var vl = N({ min_: vH }); -function kH(r, e) { - let t10 = v(r, "base", "pow"), o = v(e, "exp", "pow"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(Zn, n); +var vl = N({ min_: YH }); +function QH(r, e) { + let t8 = v(r, "base", "pow"), o = v(e, "exp", "pow"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(ts, n); } -var ri = N({ pow_: kH }); +var si = N({ pow_: QH }); function ke(r, e) { if ((Pt(r) && e !== "string" || Array.isArray(r)) && e !== "complex64") throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)"); if (e === "string" && Pt(r) && !(r instanceof Uint8Array)) throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`."); - return wr(r, [], [], e); + return Sr(r, [], [], e); } -function NH(r) { - let t10 = { x: v(r, "x", "sqrt", "float32") }; - return T.runKernel(xs, t10); +function ZH(r) { + let t8 = { x: v(r, "x", "sqrt", "float32") }; + return T.runKernel(ws, t8); } -var Rr = N({ sqrt_: NH }); -function TH(r) { - let e = v(r, "x", "square"), t10 = {}; - return T.runKernel("Square", { x: e }, t10); +var Dr = N({ sqrt_: ZH }); +function JH(r) { + let e = v(r, "x", "square"), t8 = {}; + return T.runKernel("Square", { x: e }, t8); } -var Jt = N({ square_: TH }); -function _H(r, e = null, t10 = false) { +var er = N({ square_: JH }); +function eK(r, e = null, t8 = false) { let o = v(r, "x", "sum"); - o.dtype === "bool" && (o = Ye(o, "int32")); - let n = { x: o }, s = { axis: e, keepDims: t10 }; - return T.runKernel(ys, n, s); + o.dtype === "bool" && (o = qe(o, "int32")); + let n = { x: o }, s = { axis: e, keepDims: t8 }; + return T.runKernel(Ss, n, s); } -var ot = N({ sum_: _H }); -function $H(r, e = "euclidean", t10 = null, o = false) { +var ot = N({ sum_: eK }); +function tK(r, e = "euclidean", t8 = null, o = false) { r = v(r, "x", "norm"); - let n = t2(r, e, t10), s = n.shape; + let n = l2(r, e, t8), s = n.shape; if (o) { - let a = vi(t10, r.shape); - s = ti(n.shape, a); + let a = Ti(t8, r.shape); + s = ni(n.shape, a); } return W(n, s); } -function t2(r, e, t10 = null) { +function l2(r, e, t8 = null) { if (r.rank === 0) - return Zt(r); - if (r.rank !== 1 && t10 === null) - return t2(W(r, [-1]), e, t10); - if (r.rank === 1 || typeof t10 == "number" || Array.isArray(t10) && t10.length === 1) { + return Jt(r); + if (r.rank !== 1 && t8 === null) + return l2(W(r, [-1]), e, t8); + if (r.rank === 1 || typeof t8 == "number" || Array.isArray(t8) && t8.length === 1) { if (e === 1) - return ot(Zt(r), t10); + return ot(Jt(r), t8); if (e === 1 / 0) - return Ia(Zt(r), t10); + return _a(Jt(r), t8); if (e === -1 / 0) - return vl(Zt(r), t10); + return vl(Jt(r), t8); if (e === "euclidean" || e === 2) - return Rr(ot(ri(Zt(r), ke(2, "int32")), t10)); + return Dr(ot(si(Jt(r), ke(2, "int32")), t8)); throw new Error(`Error in norm: invalid ord value: ${e}`); } - if (Array.isArray(t10) && t10.length === 2) { + if (Array.isArray(t8) && t8.length === 2) { if (e === 1) - return Ia(ot(Zt(r), t10[0]), t10[1] - 1); + return _a(ot(Jt(r), t8[0]), t8[1] - 1); if (e === 1 / 0) - return Ia(ot(Zt(r), t10[1]), t10[0]); + return _a(ot(Jt(r), t8[1]), t8[0]); if (e === -1 / 0) - return vl(ot(Zt(r), t10[1]), t10[0]); + return vl(ot(Jt(r), t8[1]), t8[0]); if (e === "fro" || e === "euclidean") - return Rr(ot(Jt(r), t10)); + return Dr(ot(er(r), t8)); throw new Error(`Error in norm: invalid ord value: ${e}`); } - throw new Error(`Error in norm: invalid axis: ${t10}`); + throw new Error(`Error in norm: invalid axis: ${t8}`); } -var Lu = N({ norm_: $H }); -function EH(r, e = null, t10 = false) { - return Lu(r, "euclidean", e, t10); +var Wu = N({ norm_: tK }); +function rK(r, e = null, t8 = false) { + return Wu(r, "euclidean", e, t8); } -var r2 = N({ euclideanNorm_: EH }); -function RH(r) { - let t10 = { x: v(r, "x", "exp") }; - return T.runKernel(gn, t10); +var m2 = N({ euclideanNorm_: rK }); +function oK(r) { + let t8 = { x: v(r, "x", "exp") }; + return T.runKernel(yn, t8); } -var ko = N({ exp_: RH }); -function DH(r, e = 0) { - let t10 = v(r, "x", "expandDims", "string_or_numeric"); - $(e <= t10.rank, () => "Axis must be <= rank of the tensor"); - let o = { input: t10 }, n = { dim: e }; - return T.runKernel(Js, o, n); +var _o = N({ exp_: oK }); +function nK(r, e = 0) { + let t8 = v(r, "x", "expandDims", "string_or_numeric"); + $(e <= t8.rank, () => "Axis must be <= rank of the tensor"); + let o = { input: t8 }, n = { dim: e }; + return T.runKernel(oa, o, n); } -var oi = N({ expandDims_: DH }); -function AH(r) { - let t10 = { x: v(r, "x", "expm1") }; - return T.runKernel(xn, t10); +var ai = N({ expandDims_: nK }); +function sK(r) { + let t8 = { x: v(r, "x", "expm1") }; + return T.runKernel(bn, t8); } -var o2 = N({ expm1_: AH }); -function FH(r, e) { - let t10 = v(r, "x", "tile", "string_or_numeric"); - $(t10.rank === e.length, () => `Error in transpose: rank of input ${t10.rank} must match length of reps ${e}.`); - let o = { x: t10 }, n = { reps: e }; - return T.runKernel(so, o, n); +var d2 = N({ expm1_: sK }); +function aK(r, e) { + let t8 = v(r, "x", "tile", "string_or_numeric"); + $(t8.rank === e.length, () => `Error in transpose: rank of input ${t8.rank} must match length of reps ${e}.`); + let o = { x: t8 }, n = { reps: e }; + return T.runKernel(uo, o, n); } -var nu = N({ tile_: FH }); -function PH(r, e, t10, o = "float32") { +var uu = N({ tile_: aK }); +function iK(r, e, t8, o = "float32") { e == null && (e = r); let n = me([r, e], o), s = r <= e ? r : e; for (let i = 0; i < s; ++i) n.set(1, i, i); let a = W(n.toTensor(), [r, e]); - if (t10 == null) + if (t8 == null) return a; - if (t10.length === 1) - return nu(oi(a, 0), [t10[0], 1, 1]); - if (t10.length === 2) - return nu(oi(oi(a, 0), 0), [t10[0], t10[1], 1, 1]); - if (t10.length === 3) - return nu(oi(oi(oi(a, 0), 0), 0), [t10[0], t10[1], t10[2], 1, 1]); - throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t10.length}D.`); + if (t8.length === 1) + return uu(ai(a, 0), [t8[0], 1, 1]); + if (t8.length === 2) + return uu(ai(ai(a, 0), 0), [t8[0], t8[1], 1, 1]); + if (t8.length === 3) + return uu(ai(ai(ai(a, 0), 0), 0), [t8[0], t8[1], t8[2], 1, 1]); + throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t8.length}D.`); } -var xd = N({ eye_: PH }); -function OH(r) { - let t10 = { x: v(r, "x", "floor", "float32") }; - return T.runKernel(bn, t10); +var bd = N({ eye_: iK }); +function uK(r) { + let t8 = { x: v(r, "x", "floor", "float32") }; + return T.runKernel(wn, t8); } -var yd = N({ floor_: OH }); -function MH(r, e, t10 = 0, o = 0) { - let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t10, batchDims: o }; - return T.runKernel(ta, a, i); +var Cd = N({ floor_: uK }); +function pK(r, e, t8 = 0, o = 0) { + let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t8, batchDims: o }; + return T.runKernel(sa, a, i); } -var bd = N({ gather_: MH }); -function LH(r, e) { - let t10 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(In, n); +var wd = N({ gather_: pK }); +function cK(r, e) { + let t8 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(kn, n); } -var Bu = N({ greater_: LH }); -function BH(r, e) { - let t10 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(vn, n); +var Uu = N({ greater_: cK }); +function lK(r, e) { + let t8 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Nn, n); } -var Cd = N({ greaterEqual_: BH }); -function zH(r) { - let t10 = { input: v(r, "input", "imag") }; - return T.runKernel(Mi, t10); +var Sd = N({ greaterEqual_: lK }); +function mK(r) { + let t8 = { input: v(r, "input", "imag") }; + return T.runKernel(Vi, t8); } -var su = N({ imag_: zH }); -function VH(r) { - let t10 = { x: v(r, "x", "isFinite") }; - return T.runKernel(kn, t10); +var pu = N({ imag_: mK }); +function dK(r) { + let t8 = { x: v(r, "x", "isFinite") }; + return T.runKernel(Tn, t8); } -var n2 = N({ isFinite_: VH }); -function WH(r) { - let t10 = { x: v(r, "x", "isInf") }; - return T.runKernel(Nn, t10); +var f2 = N({ isFinite_: dK }); +function fK(r) { + let t8 = { x: v(r, "x", "isInf") }; + return T.runKernel(_n, t8); } -var s2 = N({ isInf_: WH }); -function UH(r) { - let t10 = { x: v(r, "x", "isNaN") }; - return T.runKernel(Tn, t10); +var h2 = N({ isInf_: fK }); +function hK(r) { + let t8 = { x: v(r, "x", "isNaN") }; + return T.runKernel($n, t8); } -var a2 = N({ isNaN_: UH }); -function GH(r, e = 0.2) { +var g2 = N({ isNaN_: hK }); +function gK(r, e = 0.2) { let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: e }; - return T.runKernel(_n, o, n); + return T.runKernel(En, o, n); } -var wd = N({ leakyRelu_: GH }); -function HH(r, e) { - let t10 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel($n, n); +var Id = N({ leakyRelu_: gK }); +function xK(r, e) { + let t8 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Rn, n); } -var kl = N({ less_: HH }); -function KH(r, e) { - let t10 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(En, n); +var kl = N({ less_: xK }); +function yK(r, e) { + let t8 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Dn, n); } -var ic = N({ lessEqual_: KH }); -function i2(r, e, t10) { - if (t10 <= 0) +var uc = N({ lessEqual_: yK }); +function x2(r, e, t8) { + if (t8 <= 0) throw new Error("The number of values should be positive."); - let o = { start: r, stop: e, num: t10 }; - return T.runKernel(Rn, {}, o); + let o = { start: r, stop: e, num: t8 }; + return T.runKernel(An, {}, o); } -function qH(r, e = 5, t10 = 1, o = 1, n = 0.5) { +function bK(r, e = 5, t8 = 1, o = 1, n = 0.5) { let s = v(r, "x", "localResponseNormalization"); $(s.rank === 4 || s.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got - rank ${s.rank}.`), $(Ba(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`); + rank ${s.rank}.`), $(Ga(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`); let a = s, i = false; s.rank === 3 && (i = true, a = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])); - let p = { x: a }, u = { depthRadius: e, bias: t10, alpha: o, beta: n }, c = T.runKernel(Mn, p, u); + let p = { x: a }, u = { depthRadius: e, bias: t8, alpha: o, beta: n }, c = T.runKernel(Bn, p, u); return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c; } -var u2 = N({ localResponseNormalization_: qH }); -function jH(r) { - let t10 = { x: v(r, "x", "log", "float32") }; - return T.runKernel(Dn, t10); +var y2 = N({ localResponseNormalization_: bK }); +function CK(r) { + let t8 = { x: v(r, "x", "log", "float32") }; + return T.runKernel(Fn, t8); } -var ni = N({ log_: jH }); -function XH(r) { - let t10 = { x: v(r, "x", "log1p") }; - return T.runKernel(An, t10); +var ii = N({ log_: CK }); +function wK(r) { + let t8 = { x: v(r, "x", "log1p") }; + return T.runKernel(Pn, t8); } -var Sd = N({ log1p_: XH }); -function YH(r) { - return $(Ws(r), () => "The f passed in grad(f) must be a function"), (e, t10) => { - let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grad") : null; +var vd = N({ log1p_: wK }); +function SK(r) { + return $(Ks(r), () => "The f passed in grad(f) must be a function"), (e, t8) => { + let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t8 != null ? v(t8, "dy", "tf.grad") : null; return T.tidy(() => { let { value: s, grads: a } = T.gradients(() => r(o), [o], n); - return n != null && xt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), Id(a), a[0]; + return n != null && yt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), kd(a), a[0]; }); }; } -function QH(r) { - return $(Ws(r), () => "The f passed in grads(f) must be a function"), (e, t10) => { +function IK(r) { + return $(Ks(r), () => "The f passed in grads(f) must be a function"), (e, t8) => { $(Array.isArray(e), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"); - let o = Ja(e, "args", "tf.grads", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grads") : null; + let o = ri(e, "args", "tf.grads", "string_or_numeric"), n = t8 != null ? v(t8, "dy", "tf.grads") : null; return T.tidy(() => { let { value: s, grads: a } = T.gradients(() => r(...o), o, n); - return n != null && xt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Id(a), a; + return n != null && yt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), kd(a), a; }); }; } -function ZH(r) { - return $(Ws(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t10) => { - $(e instanceof pt, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t10 == null || t10 instanceof pt, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); - let { grads: o, value: n } = T.gradients(() => r(e), [e], t10); - return Id(o), { grad: o[0], value: n }; +function vK(r) { + return $(Ks(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t8) => { + $(e instanceof ut, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t8 == null || t8 instanceof ut, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); + let { grads: o, value: n } = T.gradients(() => r(e), [e], t8); + return kd(o), { grad: o[0], value: n }; }; } -function JH(r) { - return $(Ws(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t10) => { - $(Array.isArray(e) && e.every((n) => n instanceof pt), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t10 == null || t10 instanceof pt, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); - let o = T.gradients(() => r(...e), e, t10); - return t10 != null && xt(o.value.shape, t10.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Id(o.grads), o; +function kK(r) { + return $(Ks(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t8) => { + $(Array.isArray(e) && e.every((n) => n instanceof ut), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t8 == null || t8 instanceof ut, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); + let o = T.gradients(() => r(...e), e, t8); + return t8 != null && yt(o.value.shape, t8.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), kd(o.grads), o; }; } -function vw(r, e) { - $(Ws(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof Qa), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); - let t10 = e != null; - if (!t10) { +function Aw(r, e) { + $(Ks(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof ei), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); + let t8 = e != null; + if (!t8) { e = []; for (let u in T.registeredVariables) e.push(T.registeredVariables[u]); } - let o = t10 ? e.filter((u) => !u.trainable) : null, n = e.length; + let o = t8 ? e.filter((u) => !u.trainable) : null, n = e.length; e = e.filter((u) => u.trainable), $(e.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`); let s = true, { value: a, grads: i } = T.gradients(r, e, null, s); $(i.some((u) => u != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), $(a.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`); @@ -6483,195 +6562,195 @@ function vw(r, e) { i[c] != null && (p[u.name] = i[c]); }), o != null && o.forEach((u) => p[u.name] = null), { value: a, grads: p }; } -function Ir(r) { +function vr(r) { return T.customGrad(r); } -function Id(r) { - if (r.filter((t10) => t10 == null).length > 0) +function kd(r) { + if (r.filter((t8) => t8 == null).length > 0) throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.`); } -function eK(r) { - let t10 = { x: v(r, "x", "neg") }; - return T.runKernel(oa, t10); +function NK(r) { + let t8 = { x: v(r, "x", "neg") }; + return T.runKernel(ua, t8); } -var pr = N({ neg_: eK }); -function tK(r) { - let t10 = { x: v(r, "x", "softplus") }; - return T.runKernel(gs, t10); +var pr = N({ neg_: NK }); +function TK(r) { + let t8 = { x: v(r, "x", "softplus") }; + return T.runKernel(Cs, t8); } -var vd = N({ softplus_: tK }); -function rK(r) { +var Nd = N({ softplus_: TK }); +function _K(r) { let e = v(r, "x", "logSigmoid"); - return Ir((o) => ({ value: pr(vd(pr(o))), gradFunc: (a) => se(a, wa(pr(o))) }))(e); + return vr((o) => ({ value: pr(Nd(pr(o))), gradFunc: (a) => se(a, Na(pr(o))) }))(e); } -var p2 = N({ logSigmoid_: rK }); -function oK(r, e) { - let t10 = v(r, "a", "sub"), o = v(e, "b", "sub"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(Is, n); +var b2 = N({ logSigmoid_: _K }); +function $K(r, e) { + let t8 = v(r, "a", "sub"), o = v(e, "b", "sub"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(Ts, n); } -var Te = N({ sub_: oK }); -function nK(r, e = -1) { - let t10 = v(r, "logits", "logSoftmax"); - if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1) - throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and axis was ${e}`); - return Ir((n, s) => { - let i = Ia(n, e, true), p = Te(n, i), u = Te(Ye(p, "float32"), ni(ot(ko(p), e, true))); +var Te = N({ sub_: $K }); +function EK(r, e = -1) { + let t8 = v(r, "logits", "logSoftmax"); + if (e === -1 && (e = t8.rank - 1), e !== t8.rank - 1) + throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t8.rank} and axis was ${e}`); + return vr((n, s) => { + let i = _a(n, e, true), p = Te(n, i), u = Te(qe(p, "float32"), ii(ot(_o(p), e, true))); return s([u]), { value: u, gradFunc: (l, m) => { - let [d] = m, f = true, h = ko(d); + let [d] = m, f = true, h = _o(d); return Te(l, se(ot(l, e, f), h)); } }; - })(t10); + })(t8); } -var c2 = N({ logSoftmax_: nK }); -function sK(r, e = null, t10 = false) { - let o = v(r, "x", "logSumExp"), n = vi(e, o.shape), s = Ia(o, n, true), a = Te(o, s), i = ko(a), p = ot(i, n), u = ni(p), c = be(W(s, u.shape), u); - if (t10) { - let l = ti(c.shape, n); +var C2 = N({ logSoftmax_: EK }); +function RK(r, e = null, t8 = false) { + let o = v(r, "x", "logSumExp"), n = Ti(e, o.shape), s = _a(o, n, true), a = Te(o, s), i = _o(a), p = ot(i, n), u = ii(p), c = Ce(W(s, u.shape), u); + if (t8) { + let l = ni(c.shape, n); return W(c, l); } return c; } -var kd = N({ logSumExp_: sK }); -function aK(r, e) { - let t10 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool"); - rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(Fn, n); -} -var zu = N({ logicalAnd_: aK }); -function iK(r) { - let t10 = { x: v(r, "x", "logicalNot", "bool") }; - return T.runKernel(Pn, t10); -} -var Nd = N({ logicalNot_: iK }); -function uK(r, e) { - let t10 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool"); - rt(t10.shape, o.shape); - let n = { a: t10, b: o }; +var Td = N({ logSumExp_: RK }); +function DK(r, e) { + let t8 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool"); + rt(t8.shape, o.shape); + let n = { a: t8, b: o }; return T.runKernel(On, n); } -var Td = N({ logicalOr_: uK }); -function pK(r, e) { - let t10 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool"); - return rt(t10.shape, o.shape), zu(Td(r, e), Nd(zu(r, e))); +var Gu = N({ logicalAnd_: DK }); +function AK(r) { + let t8 = { x: v(r, "x", "logicalNot", "bool") }; + return T.runKernel(Mn, t8); } -var l2 = N({ logicalXor_: pK }); -var _d = 2147483648; -function cK(r, e, t10 = "left") { +var _d = N({ logicalNot_: AK }); +function FK(r, e) { + let t8 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool"); + rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Ln, n); +} +var $d = N({ logicalOr_: FK }); +function PK(r, e) { + let t8 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool"); + return rt(t8.shape, o.shape), Gu($d(r, e), _d(Gu(r, e))); +} +var w2 = N({ logicalXor_: PK }); +var Ed = 2147483648; +function OK(r, e, t8 = "left") { let o = v(r, "sortedSequence", "searchSorted"), n = v(e, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = W(o, [-1, s]), p = W(n, [-1, a]); if (i.rank < 2) throw new Error("Sorted input argument must be at least 2-dimensional"); if (i.shape[0] !== p.shape[0]) throw new Error("Leading dimension of 'sortedSequence' and 'values' must match."); - if (Ue(p.shape) >= _d) - throw new Error(`values tensor size must less than ${_d}`); - if (i.shape[1] >= _d) - throw new Error(`trailing dim_size must less than ${_d} for int32 output type, was ${i.shape[1]}`); - let u = { sortedSequence: i, values: p }, c = { side: t10 }; - return T.runKernel(ls, u, c); + if (Ge(p.shape) >= Ed) + throw new Error(`values tensor size must less than ${Ed}`); + if (i.shape[1] >= Ed) + throw new Error(`trailing dim_size must less than ${Ed} for int32 output type, was ${i.shape[1]}`); + let u = { sortedSequence: i, values: p }, c = { side: t8 }; + return T.runKernel(fs, u, c); } -var Nl = N({ searchSorted_: cK }); -function m2(r, e) { +var Nl = N({ searchSorted_: OK }); +function S2(r, e) { return Nl(r, e, "left"); } -function lK(r, e, t10, o, n) { +function MK(r, e, t8, o, n) { let s = v(r, "x", "maxPool"), a = 1, i = s, p = false; - s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(gr(t10, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`), Lt("maxPool", o, n); - let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(zn, u, c); + s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(gr(t8, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t8} and dilations '${a}'`), Lt("maxPool", o, n); + let u = { x: i }, c = { filterSize: e, strides: t8, pad: o, dimRoundingMode: n }, l = T.runKernel(Wn, u, c); return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var $d = N({ maxPool_: lK }); -function mK(r, e = [1, 1, 1], t10, o, n, s = "NDHWC") { +var Rd = N({ maxPool_: MK }); +function LK(r, e = [1, 1, 1], t8, o, n, s = "NDHWC") { let a = v(r, "x", "maxPool3d"), i = a, p = false; a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Lt("maxPool3d", o, n); - let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(ra, u, c); + let u = { x: i }, c = { filterSize: e, strides: t8, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(aa, u, c); return p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var d2 = N({ maxPool3d_: mK }); -function dK(r, e, t10, o, n = false) { - let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t10, pad: o, includeBatchInIndex: n }, p = T.runKernel(Bi, a, i); +var I2 = N({ maxPool3d_: LK }); +function BK(r, e, t8, o, n = false) { + let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t8, pad: o, includeBatchInIndex: n }, p = T.runKernel(ia, a, i); return { result: p[0], indexes: p[1] }; } -var f2 = N({ maxPoolWithArgmax_: dK }); -function fK(r, e) { - let t10 = v(r, "a", "maximum"), o = v(e, "b", "maximum"); - [t10, o] = Oe(t10, o), t10.dtype === "bool" && (t10 = Ye(t10, "int32"), o = Ye(o, "int32")), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(Bn, n); +var v2 = N({ maxPoolWithArgmax_: BK }); +function zK(r, e) { + let t8 = v(r, "a", "maximum"), o = v(e, "b", "maximum"); + [t8, o] = Oe(t8, o), t8.dtype === "bool" && (t8 = qe(t8, "int32"), o = qe(o, "int32")), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Vn, n); } -var Ed = N({ maximum_: fK }); -function hK(r, e = null, t10 = false) { - let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Vn, n, s); +var Dd = N({ maximum_: zK }); +function VK(r, e = null, t8 = false) { + let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t8 }; + return T.runKernel(Un, n, s); } -var Vu = N({ mean_: hK }); -function Wr(r, e = "float32") { - if (Ct(r), e === "complex64") { - let o = Wr(r, "float32"), n = Wr(r, "float32"); - return $r(o, n); +var Hu = N({ mean_: VK }); +function Gr(r, e = "float32") { + if (wt(r), e === "complex64") { + let o = Gr(r, "float32"), n = Gr(r, "float32"); + return Er(o, n); } - let t10 = Up(Ue(r), e); - return T.makeTensor(t10, r, e); + let t8 = qp(Ge(r), e); + return T.makeTensor(t8, r, e); } -function va(r, e = "float32") { - if (Ct(r), e === "complex64") { - let o = va(r, "float32"), n = Wr(r, "float32"); - return $r(o, n); +function $a(r, e = "float32") { + if (wt(r), e === "complex64") { + let o = $a(r, "float32"), n = Gr(r, "float32"); + return Er(o, n); } - let t10 = pl(Ue(r), e); - return T.makeTensor(t10, r, e); + let t8 = cl(Ge(r), e); + return T.makeTensor(t8, r, e); } -function h2(r, e, { indexing: t10 = "xy" } = {}) { - if (t10 !== "xy" && t10 !== "ij") - throw new TypeError(`${t10} is not a valid third argument to meshgrid`); +function k2(r, e, { indexing: t8 = "xy" } = {}) { + if (t8 !== "xy" && t8 !== "ij") + throw new TypeError(`${t8} is not a valid third argument to meshgrid`); if (r === void 0) return []; - let o = v(r, "x", "meshgrid", r instanceof pt ? r.dtype : "float32"); + let o = v(r, "x", "meshgrid", r instanceof ut ? r.dtype : "float32"); if (e === void 0) return [o]; - let n = v(e, "y", "meshgrid", e instanceof pt ? e.dtype : "float32"), s = Ue(o.shape), a = Ue(n.shape); - return t10 === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Qe(va([a, 1], o.dtype), o), Qe(n, va([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Qe(o, va([1, a], o.dtype)), Qe(va([s, 1], n.dtype), n)]); + let n = v(e, "y", "meshgrid", e instanceof ut ? e.dtype : "float32"), s = Ge(o.shape), a = Ge(n.shape); + return t8 === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Ze($a([a, 1], o.dtype), o), Ze(n, $a([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Ze(o, $a([1, a], o.dtype)), Ze($a([s, 1], n.dtype), n)]); } -function gK(r, e) { - let t10 = v(r, "a", "minimum"), o = v(e, "b", "minimum"); - [t10, o] = Oe(t10, o), t10.dtype === "bool" && (t10 = Ye(t10, "int32"), o = Ye(o, "int32")), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(Un, n); +function WK(r, e) { + let t8 = v(r, "a", "minimum"), o = v(e, "b", "minimum"); + [t8, o] = Oe(t8, o), t8.dtype === "bool" && (t8 = qe(t8, "int32"), o = qe(o, "int32")), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Hn, n); } -var Wu = N({ minimum_: gK }); -function xK(r, e, t10) { - $(t10 === "reflect" || t10 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t10}.`); +var Ku = N({ minimum_: WK }); +function UK(r, e, t8) { + $(t8 === "reflect" || t8 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t8}.`); let o = v(r, "x", "mirrorPad"); if (o.rank === 0) throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad"); $(e.length === o.rank, () => `Padding doesn't match input. Must be ${o.rank}. Got ${e.length}.`); - let n = t10 === "reflect" ? 1 : 0; + let n = t8 === "reflect" ? 1 : 0; for (let i = 0; i < o.rank; i++) $(e[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), $(e[i][0] >= 0 && e[i][0] <= o.shape[i] - n && e[i][1] >= 0 && e[i][1] <= o.shape[i] - n, () => `Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i] - n} or less than 0 for input of shape ${o.shape}`); - let s = { paddings: e, mode: t10 }, a = { x: o }; - return T.runKernel(Gn, a, s); + let s = { paddings: e, mode: t8 }, a = { x: o }; + return T.runKernel(Kn, a, s); } -var g2 = N({ mirrorPad_: xK }); -function yK(r, e) { - let t10 = v(r, "a", "mod"), o = v(e, "b", "mod"); - [t10, o] = Oe(t10, o); - let n = { a: t10, b: o }; - return T.runKernel(Ga, n); +var N2 = N({ mirrorPad_: UK }); +function GK(r, e) { + let t8 = v(r, "a", "mod"), o = v(e, "b", "mod"); + [t8, o] = Oe(t8, o); + let n = { a: t8, b: o }; + return T.runKernel(qn, n); } -var x2 = N({ mod_: yK }); -function bK(r, e = null, t10 = false) { +var T2 = N({ mod_: GK }); +function HK(r, e = null, t8 = false) { r = v(r, "x", "moments"); - let o = vi(e, r.shape), n = Vu(r, o, t10), s = n.shape; - t10 || (s = ti(n.shape, o)); - let a = Jt(Te(Ye(r, "float32"), W(n, s))), i = Vu(a, o, t10); + let o = Ti(e, r.shape), n = Hu(r, o, t8), s = n.shape; + t8 || (s = ni(n.shape, o)); + let a = er(Te(qe(r, "float32"), W(n, s))), i = Hu(a, o, t8); return { mean: n, variance: i }; } -var y2 = N({ moments_: bK }); -function CK(r, e, t10, o) { - let n = v(e, "data", "multiRNNCell"), s = Ja(t10, "c", "multiRNNCell"), a = Ja(o, "h", "multiRNNCell"), i = n, p = []; +var _2 = N({ moments_: HK }); +function KK(r, e, t8, o) { + let n = v(e, "data", "multiRNNCell"), s = ri(t8, "c", "multiRNNCell"), a = ri(o, "h", "multiRNNCell"), i = n, p = []; for (let l = 0; l < r.length; l++) { let m = r[l](i, s[l], a[l]); p.push(m[0]), p.push(m[1]), i = m[1]; @@ -6681,148 +6760,148 @@ function CK(r, e, t10, o) { u.push(p[l]), c.push(p[l + 1]); return [u, c]; } -var b2 = N({ multiRNNCell_: CK }); -function wK(r, e, t10, o = false) { +var $2 = N({ multiRNNCell_: KK }); +function qK(r, e, t8, o = false) { let n = v(r, "logits", "multinomial"), s = n.size, a = n.rank; if (s < 2) throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`); if (a > 2) throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`); - t10 = t10 || Math.random(); - let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: e, seed: t10, normalized: o }, c = T.runKernel(Hn, p, u); + t8 = t8 || Math.random(); + let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: e, seed: t8, normalized: o }, c = T.runKernel(jn, p, u); return a === 1 ? W(c, [c.size]) : c; } -var C2 = N({ multinomial_: wK }); -function SK(r, e) { - let t10 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }; - return T.runKernel(qn, n); +var E2 = N({ multinomial_: qK }); +function jK(r, e) { + let t8 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }; + return T.runKernel(Yn, n); } -var Rd = N({ notEqual_: SK }); -function IK(r, e, t10 = 1, o = 0, n = "int32") { +var Ad = N({ notEqual_: jK }); +function XK(r, e, t8 = 1, o = 0, n = "int32") { if (e < 2) throw new Error(`Error in oneHot: depth must be >=2, but it is ${e}`); - let a = { indices: v(r, "indices", "oneHot", "int32") }, i = { dtype: n, depth: e, onValue: t10, offValue: o }; - return T.runKernel(Yn, a, i); + let a = { indices: v(r, "indices", "oneHot", "int32") }, i = { dtype: n, depth: e, onValue: t8, offValue: o }; + return T.runKernel(Jn, a, i); } -var Tl = N({ oneHot_: IK }); -function vK(r) { - let t10 = { x: v(r, "x", "onesLike") }; - return T.runKernel(na, t10); +var Tl = N({ oneHot_: XK }); +function YK(r) { + let t8 = { x: v(r, "x", "onesLike") }; + return T.runKernel(pa, t8); } -var w2 = N({ onesLike_: vK }); -function kK(r, e) { - let t10 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct"); - $(t10.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t10.rank} and ${o.rank}.`); - let n = W(t10, [-1, 1]), s = W(o, [1, -1]); - return Qe(n, s); +var R2 = N({ onesLike_: YK }); +function QK(r, e) { + let t8 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct"); + $(t8.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t8.rank} and ${o.rank}.`); + let n = W(t8, [-1, 1]), s = W(o, [1, -1]); + return Ze(n, s); } -var S2 = N({ outerProduct_: kK }); -function NK(r, e, t10 = 0) { +var D2 = N({ outerProduct_: QK }); +function ZK(r, e, t8 = 0) { let o = v(r, "x", "pad"); if (o.rank === 0) throw new Error("pad(scalar) is not defined. Pass non-scalar to pad"); - let n = { paddings: e, constantValue: t10 }, s = { x: o }; - return T.runKernel(Qn, s, n); + let n = { paddings: e, constantValue: t8 }, s = { x: o }; + return T.runKernel(es, s, n); } -var ka = N({ pad_: NK }); -function TK(r, e, t10 = 0) { - return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), ka(r, [e], t10); +var Ea = N({ pad_: ZK }); +function JK(r, e, t8 = 0) { + return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), Ea(r, [e], t8); } -var I2 = N({ pad1d_: TK }); -function _K(r, e, t10 = 0) { - return $(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), ka(r, e, t10); +var A2 = N({ pad1d_: JK }); +function eq(r, e, t8 = 0) { + return $(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ea(r, e, t8); } -var v2 = N({ pad2d_: _K }); -function $K(r, e, t10 = 0) { - return $(e.length === 3 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), ka(r, e, t10); +var F2 = N({ pad2d_: eq }); +function tq(r, e, t8 = 0) { + return $(e.length === 3 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ea(r, e, t8); } -var k2 = N({ pad3d_: $K }); -function EK(r, e, t10 = 0) { - return $(e.length === 4 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2 && e[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), ka(r, e, t10); +var P2 = N({ pad3d_: tq }); +function rq(r, e, t8 = 0) { + return $(e.length === 4 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2 && e[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ea(r, e, t8); } -var N2 = N({ pad4d_: EK }); -function RK(r, e, t10) { +var O2 = N({ pad4d_: rq }); +function oq(r, e, t8) { let o = v(r, "x", "spaceToBatchND"); - $(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), $(t10.length === e.length, () => `paddings.shape[0] ${t10.length} must be equal to [blockShape] ${e.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t10[p - 1][0] + t10[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t10.toString()} must be divisible by blockShapes ${e.toString()}`); - let n = { x: o }, s = { blockShape: e, paddings: t10 }; - return T.runKernel(ca, n, s); + $(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), $(t8.length === e.length, () => `paddings.shape[0] ${t8.length} must be equal to [blockShape] ${e.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t8[p - 1][0] + t8[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t8.toString()} must be divisible by blockShapes ${e.toString()}`); + let n = { x: o }, s = { blockShape: e, paddings: t8 }; + return T.runKernel(ha, n, s); } -var Dd = N({ spaceToBatchND_: RK }); -function DK(r, e, t10, o, n, s, a) { +var Fd = N({ spaceToBatchND_: oq }); +function nq(r, e, t8, o, n, s, a) { n == null && (n = [1, 1]), s == null && (s = 1), o === 0 && (o = "valid"); let i = v(r, "x", "maxPool"), p = i, u = false; i.rank === 3 && (u = true, p = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(gr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`); - let c = ww(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m; - o === "same" ? m = FK([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]]; - let d = l[0] === 1 && l[1] === 1, [f, h] = AK([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Dd(p, l, f), w = (t10 === "avg" ? () => cd(x, e, s, g, a) : () => $d(x, e, s, g, a))(), S = d ? w : ld(w, l, h); + let c = Ew(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m; + o === "same" ? m = aq([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]]; + let d = l[0] === 1 && l[1] === 1, [f, h] = sq([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Fd(p, l, f), C = (t8 === "avg" ? () => md(x, e, s, g, a) : () => Rd(x, e, s, g, a))(), S = d ? C : dd(C, l, h); return u ? W(S, [S.shape[1], S.shape[2], S.shape[3]]) : S; } -function AK(r, e, t10) { - let o = t10.map((c) => c[0]), n = t10.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]); +function sq(r, e, t8) { + let o = t8.map((c) => c[0]), n = t8.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]); return [p, u]; } -function FK(r, e) { +function aq(r, e) { let o = r.map((a, i) => a + (a - 1) * (e[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]); return o.map((a, i) => [n[i], s[i]]); } -var T2 = N({ pool_: DK }); -function PK(r, e) { - let t10 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t10, alpha: o }; - return T.runKernel(Jn, n); +var M2 = N({ pool_: nq }); +function iq(r, e) { + let t8 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t8, alpha: o }; + return T.runKernel(rs, n); } -var Ad = N({ prelu_: PK }); -function OK(r, e = null, t10 = false) { +var Pd = N({ prelu_: iq }); +function uq(r, e = null, t8 = false) { let o = v(r, "x", "prod"); - o.dtype === "bool" && (o = Ye(o, "int32")); - let n = { x: o }, s = { axis: e, keepDims: t10 }; - return T.runKernel(es, n, s); + o.dtype === "bool" && (o = qe(o, "int32")); + let n = { x: o }, s = { axis: e, keepDims: t8 }; + return T.runKernel(os, n, s); } -var _2 = N({ prod_: OK }); -function MK(r, e, t10, o) { - let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t10, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(Kp, i, p); +var L2 = N({ prod_: uq }); +function pq(r, e, t8, o) { + let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t8, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(jp, i, p); return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] }; } -var $2 = N({ raggedGather_: MK }); -function LK(r, e, t10) { - let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t10, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(qp, a); +var B2 = N({ raggedGather_: pq }); +function cq(r, e, t8) { + let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t8, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(Xp, a); return { rtNestedSplits: i[0], rtDenseValues: i[1] }; } -var E2 = N({ raggedRange_: LK }); -function BK(r, e, t10, o, n) { - let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t10, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n }; - return T.runKernel(jp, u, c); +var z2 = N({ raggedRange_: cq }); +function lq(r, e, t8, o, n) { + let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t8, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n }; + return T.runKernel(Yp, u, c); } -var R2 = N({ raggedTensorToTensor_: BK }); -function zK(r, e, t10) { - Ct(r); - let o = Ue(r), n = null; - if (t10 == null || t10 === "float32") +var V2 = N({ raggedTensorToTensor_: lq }); +function mq(r, e, t8) { + wt(r); + let o = Ge(r), n = null; + if (t8 == null || t8 === "float32") n = new Float32Array(o); - else if (t10 === "int32") + else if (t8 === "int32") n = new Int32Array(o); - else if (t10 === "bool") + else if (t8 === "bool") n = new Uint8Array(o); else - throw new Error(`Unknown data type ${t10}`); + throw new Error(`Unknown data type ${t8}`); for (let s = 0; s < o; s++) n[s] = e(); - return T.makeTensor(n, r, t10); + return T.makeTensor(n, r, t8); } -var D2 = N({ rand_: zK }); -var Ld = Bp(Rw()); -var Z2 = {}; -He(Z2, { TEST_EPSILON_FLOAT16: () => X2, createVideoElement: () => JK, encodeStrings: () => Q2, expectArrayBuffersEqual: () => ZK, expectArraysClose: () => jK, expectArraysEqual: () => YK, expectNumbersClose: () => Y2, expectPromiseToFail: () => XK, expectValuesInRange: () => QK, play: () => eq, testEpsilon: () => Pd }); -var qK = 1e-3; -var X2 = 0.1; -function jK(r, e, t10) { - return t10 == null && (t10 = Pd()), Dw(r, e, (o, n) => Aw(o, n, t10)); +var W2 = N({ rand_: mq }); +var zd = Up(zw()); +var u1 = {}; +Ke(u1, { TEST_EPSILON_FLOAT16: () => s1, createVideoElement: () => kq, encodeStrings: () => i1, expectArrayBuffersEqual: () => vq, expectArraysClose: () => Cq, expectArraysEqual: () => Sq, expectNumbersClose: () => a1, expectPromiseToFail: () => wq, expectValuesInRange: () => Iq, play: () => Nq, testEpsilon: () => Md }); +var bq = 1e-3; +var s1 = 0.1; +function Cq(r, e, t8) { + return t8 == null && (t8 = Md()), Vw(r, e, (o, n) => Ww(o, n, t8)); } -function Pd() { - return T.backend.floatPrecision() === 32 ? qK : X2; +function Md() { + return T.backend.floatPrecision() === 32 ? bq : s1; } -function Dw(r, e, t10) { +function Vw(r, e, t8) { let o = true; if ((Pt(r) || Pt(e)) && (o = false), Pt(r) && Pt(e) && (o = true), o) { let a = r.constructor.name, i = e.constructor.name; @@ -6831,90 +6910,90 @@ function Dw(r, e, t10) { } if (Array.isArray(r) && Array.isArray(e)) { let a = ar(r), i = ar(e); - if (!br(a, i)) + if (!Cr(a, i)) throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`); } - let n = Pt(r) ? r : Es(r), s = Pt(e) ? e : Es(e); + let n = Pt(r) ? r : Fs(r), s = Pt(e) ? e : Fs(e); if (n.length !== s.length) throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}. Actual: ${n}. Expected: ${s}.`); for (let a = 0; a < s.length; ++a) { let i = n[a], p = s[a]; - if (!t10(i, p)) + if (!t8(i, p)) throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${p}. Actual: ${n}. Expected: ${s}.`); } typeof expect != "undefined" && expect().nothing(); } -function XK(r, e) { +function wq(r, e) { r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing(); } -function YK(r, e) { - let t10 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e; - return Bo(r) || Bo(r[0]) || Bo(e) || Bo(e[0]) ? Dw(r, t10, (o, n) => o == n) : Dw(r, e, (o, n) => Aw(o, n, 0)); +function Sq(r, e) { + let t8 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e; + return zo(r) || zo(r[0]) || zo(e) || zo(e[0]) ? Vw(r, t8, (o, n) => o == n) : Vw(r, e, (o, n) => Ww(o, n, 0)); } -function Y2(r, e, t10) { - if (t10 == null && (t10 = Pd()), !Aw(r, e, t10)) +function a1(r, e, t8) { + if (t8 == null && (t8 = Md()), !Ww(r, e, t8)) throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`); typeof expect != "undefined" && expect().nothing(); } -function Aw(r, e, t10) { - return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t10); +function Ww(r, e, t8) { + return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t8); } -function QK(r, e, t10) { +function Iq(r, e, t8) { for (let o = 0; o < r.length; o++) - if (r[o] < e || r[o] > t10) - throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t10}`); + if (r[o] < e || r[o] > t8) + throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t8}`); } -function ZK(r, e) { - let t10 = new Float32Array(r), o = new Float32Array(e); - if (t10.length !== o.length) - throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t10.length}`); +function vq(r, e) { + let t8 = new Float32Array(r), o = new Float32Array(e); + if (t8.length !== o.length) + throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t8.length}`); for (let n = 0; n < o.length; n++) - if (t10[n] !== o[n]) - throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t10[n]} instead`); + if (t8[n] !== o[n]) + throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t8[n]} instead`); } -function Q2(r) { +function i1(r) { for (let e = 0; e < r.length; e++) { - let t10 = r[e]; - Array.isArray(t10) ? Q2(t10) : r[e] = Yi(t10); + let t8 = r[e]; + Array.isArray(t8) ? i1(t8) : r[e] = eu(t8); } return r; } -function JK(r) { +function kq(r) { let e = document.createElement("video"); - return "playsInline" in e && (e.playsInline = true), e.muted = true, e.loop = true, e.style.position = "fixed", e.style.left = "0px", e.style.top = "0px", e.preload = "auto", e.appendChild(r), new Promise((t10) => { - e.addEventListener("loadeddata", (o) => t10(e)), e.load(); + return "playsInline" in e && (e.playsInline = true), e.muted = true, e.loop = true, e.style.position = "fixed", e.style.left = "0px", e.style.top = "0px", e.preload = "auto", e.appendChild(r), new Promise((t8) => { + e.addEventListener("loadeddata", (o) => t8(e)), e.load(); }); } -async function eq(r) { +async function Nq(r) { await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => { r.requestVideoFrameCallback(e); }); } -var Gu = class { - constructor(e, t10, o, n, s) { - this.mean = e, this.stdDev = t10, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2); +var ju = class { + constructor(e, t8, o, n, s) { + this.mean = e, this.stdDev = t8, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2); let a = s || Math.random(); - this.random = Ld.alea(a.toString()); + this.random = zd.alea(a.toString()); } nextValue() { if (!isNaN(this.nextVal)) { let n = this.nextVal; return this.nextVal = NaN, n; } - let e, t10, o = false; + let e, t8, o = false; for (; !o; ) { let n, s, a; do n = 2 * this.random() - 1, s = 2 * this.random() - 1, a = n * n + s * s; while (a >= 1 || a === 0); let i = Math.sqrt(-2 * Math.log(a) / a); - e = this.mean + this.stdDev * n * i, t10 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true); + e = this.mean + this.stdDev * n * i, t8 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true); } - return (!this.truncated || this.isValidTruncated(t10)) && (this.nextVal = this.convertValue(t10)), this.convertValue(e); + return (!this.truncated || this.isValidTruncated(t8)) && (this.nextVal = this.convertValue(t8)), this.convertValue(e); } convertValue(e) { return this.dtype == null || this.dtype === "float32" ? e : Math.round(e); @@ -6923,19 +7002,19 @@ var Gu = class { return e <= this.upper && e >= this.lower; } }; -var Od = class { - constructor(e, t10, o, n) { - this.alpha = e, this.beta = 1 / t10, this.dtype = o; +var Ld = class { + constructor(e, t8, o, n) { + this.alpha = e, this.beta = 1 / t8, this.dtype = o; let s = n || Math.random(); - this.randu = Ld.alea(s.toString()), this.randn = new Gu(0, 1, o, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d); + this.randu = zd.alea(s.toString()), this.randn = new ju(0, 1, o, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d); } nextValue() { - let e, t10, o, n, s, a; + let e, t8, o, n, s, a; for (; ; ) { do n = this.randn.nextValue(), a = 1 + this.c * n; while (a <= 0); - if (a *= a * a, e = n * n, t10 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t10 || Math.log(s) < o) + if (a *= a * a, e = n * n, t8 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t8 || Math.log(s) < o) break; } return a = 1 / this.beta * this.d * a, this.alpha < 1 && (a *= Math.pow(this.randu(), 1 / this.alpha)), this.convertValue(a); @@ -6944,11 +7023,11 @@ var Od = class { return this.dtype === "float32" ? e : Math.round(e); } }; -var Md = class { - constructor(e = 0, t10 = 1, o, n) { - if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t10 - e, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1) - throw new Error(`The difference between ${e} - ${t10} <= 1 and dtype is not float`); - this.random = Ld.alea(n); +var Bd = class { + constructor(e = 0, t8 = 1, o, n) { + if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t8 - e, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1) + throw new Error(`The difference between ${e} - ${t8} <= 1 and dtype is not float`); + this.random = zd.alea(n); } convertValue(e) { return this.canReturnFloat() ? e : Math.round(e); @@ -6957,363 +7036,363 @@ var Md = class { return this.convertValue(this.min + this.range * this.random()); } }; -function tq(r, e, t10 = 1, o = "float32", n) { - if (Ct(r), t10 == null && (t10 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32") +function Tq(r, e, t8 = 1, o = "float32", n) { + if (wt(r), t8 == null && (t8 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32") throw new Error(`Unsupported data type ${o}`); - let s = new Od(e, t10, o, n), a = me(r, o); + let s = new Ld(e, t8, o, n), a = me(r, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var J2 = N({ randomGamma_: tq }); -function rq(r, e = 0, t10 = 1, o, n) { - if (Ct(r), o != null && o === "bool") +var p1 = N({ randomGamma_: Tq }); +function _q(r, e = 0, t8 = 1, o, n) { + if (wt(r), o != null && o === "bool") throw new Error(`Unsupported data type ${o}`); - let s = new Gu(e, t10, o, false, n), a = me(r, o); + let s = new ju(e, t8, o, false, n), a = me(r, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var Bd = N({ randomNormal_: rq }); -function oq(r, e, t10) { +var Vd = N({ randomNormal_: _q }); +function $q(r, e, t8) { if (e != null && e === "bool") throw new Error(`Unsupported data type ${e}`); - return Bd(r, 0, 1, e, t10); + return Vd(r, 0, 1, e, t8); } -var e1 = N({ randomStandardNormal_: oq }); -function nq(r, e = 0, t10 = 1, o = "float32", n) { - Ct(r); - let s = me(r, o), a = new Md(e, t10, null, n); +var c1 = N({ randomStandardNormal_: $q }); +function Eq(r, e = 0, t8 = 1, o = "float32", n) { + wt(r); + let s = me(r, o), a = new Bd(e, t8, null, n); for (let i = 0; i < s.values.length; i++) s.values[i] = a.nextValue(); return s.toTensor(); } -var uc = N({ randomUniform_: nq }); -function sq(r, e, t10, o) { - return uc(r, e, t10, "int32", o); +var pc = N({ randomUniform_: Eq }); +function Rq(r, e, t8, o) { + return pc(r, e, t8, "int32", o); } -var t1 = N({ randomUniformInt_: sq }); -function au(r, e, t10 = 1, o = "float32") { - if (t10 === 0) +var l1 = N({ randomUniformInt_: Rq }); +function cu(r, e, t8 = 1, o = "float32") { + if (t8 === 0) throw new Error("Cannot have a step of zero"); - let n = { start: r, stop: e, step: t10, dtype: o }; - return T.runKernel(aa, {}, n); + let n = { start: r, stop: e, step: t8, dtype: o }; + return T.runKernel(la, {}, n); } -function aq(r) { - let t10 = { input: v(r, "input", "real") }; - return T.runKernel(zi, t10); +function Dq(r) { + let t8 = { input: v(r, "input", "real") }; + return T.runKernel(Gi, t8); } -var si = N({ real_: aq }); -function iq(r) { - let t10 = { x: v(r, "x", "reciprocal") }; - return T.runKernel(ts, t10); +var ui = N({ real_: Dq }); +function Aq(r) { + let t8 = { x: v(r, "x", "reciprocal") }; + return T.runKernel(ns, t8); } -var r1 = N({ reciprocal_: iq }); -function uq(r) { - let t10 = { x: v(r, "x", "relu") }; - return T.runKernel(rs, t10); +var m1 = N({ reciprocal_: Aq }); +function Fq(r) { + let t8 = { x: v(r, "x", "relu") }; + return T.runKernel(ss, t8); } -var iu = N({ relu_: uq }); -function pq(r) { - let t10 = { x: v(r, "x", "relu6") }; - return T.runKernel(ss, t10); +var lu = N({ relu_: Fq }); +function Pq(r) { + let t8 = { x: v(r, "x", "relu6") }; + return T.runKernel(us, t8); } -var zd = N({ relu6_: pq }); -function cq(r, e) { +var Wd = N({ relu6_: Pq }); +function Oq(r, e) { let o = { x: v(r, "x", "reverse") }, n = { dims: e }; - return T.runKernel(as, o, n); + return T.runKernel(ps, o, n); } -var uo = N({ reverse_: cq }); -function lq(r) { +var lo = N({ reverse_: Oq }); +function Mq(r) { let e = v(r, "x", "reverse"); - return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), uo(e, 0); + return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), lo(e, 0); } -var o1 = N({ reverse1d_: lq }); -function mq(r, e) { - let t10 = v(r, "x", "reverse"); - return $(t10.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t10.rank}.`), uo(t10, e); +var d1 = N({ reverse1d_: Mq }); +function Lq(r, e) { + let t8 = v(r, "x", "reverse"); + return $(t8.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t8.rank}.`), lo(t8, e); } -var n1 = N({ reverse2d_: mq }); -function dq(r, e) { - let t10 = v(r, "x", "reverse"); - return $(t10.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t10.rank}.`), uo(t10, e); +var f1 = N({ reverse2d_: Lq }); +function Bq(r, e) { + let t8 = v(r, "x", "reverse"); + return $(t8.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t8.rank}.`), lo(t8, e); } -var s1 = N({ reverse3d_: dq }); -function fq(r, e) { - let t10 = v(r, "x", "reverse"); - return $(t10.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t10.rank}.`), uo(t10, e); +var h1 = N({ reverse3d_: Bq }); +function zq(r, e) { + let t8 = v(r, "x", "reverse"); + return $(t8.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t8.rank}.`), lo(t8, e); } -var a1 = N({ reverse4d_: fq }); -function hq(r) { - let t10 = { x: v(r, "x", "round") }; - return T.runKernel(is, t10); +var g1 = N({ reverse4d_: zq }); +function Vq(r) { + let t8 = { x: v(r, "x", "round") }; + return T.runKernel(cs, t8); } -var Vd = N({ round_: hq }); -function gq(r) { - let t10 = { x: v(r, "x", "rsqrt", "float32") }; - return T.runKernel(us, t10); +var Ud = N({ round_: Vq }); +function Wq(r) { + let t8 = { x: v(r, "x", "rsqrt", "float32") }; + return T.runKernel(ls, t8); } -var i1 = N({ rsqrt_: gq }); -function xq(r) { - let t10 = { x: v(r, "x", "selu") }; - return T.runKernel(ms, t10); +var x1 = N({ rsqrt_: Wq }); +function Uq(r) { + let t8 = { x: v(r, "x", "selu") }; + return T.runKernel(hs, t8); } -var u1 = N({ selu_: xq }); -function yq(r, e, t10, o, n, s = [1, 1], a = "NHWC") { - let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t10, "pointwiseFilter", "separableConv2d"), c = i, l = false; +var y1 = N({ selu_: Uq }); +function Gq(r, e, t8, o, n, s = [1, 1], a = "NHWC") { + let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t8, "pointwiseFilter", "separableConv2d"), c = i, l = false; if (i.rank === 3 && (l = true, c = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), a === "NCHW") throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported"); $(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), $(p.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`), $(u.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`), $(u.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`), $(u.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`); let m = p.shape[2], d = p.shape[3]; $(u.shape[2] === m * d, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * d}, but got ${u.shape[2]}.`); - let f = ac(c, p, o, n, a, s), g = ou(f, u, 1, "valid", a); + let f = ic(c, p, o, n, a, s), g = iu(f, u, 1, "valid", a); return l ? W(g, [g.shape[1], g.shape[2], g.shape[3]]) : g; } -var p1 = N({ separableConv2d_: yq }); -async function bq(r, e) { - let t10 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d"); - $(t10.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t10.dtype}) and y (${o.dtype}).`), $(t10.rank === 1, () => `x should be 1D tensor, but got x (${t10.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`); - let n = await t10.data(), s = await o.data(), a = new Set(s), i = 0; +var b1 = N({ separableConv2d_: Gq }); +async function Hq(r, e) { + let t8 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d"); + $(t8.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t8.dtype}) and y (${o.dtype}).`), $(t8.rank === 1, () => `x should be 1D tensor, but got x (${t8.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`); + let n = await t8.data(), s = await o.data(), a = new Set(s), i = 0; for (let c = 0; c < n.length; c++) a.has(n[c]) || i++; - let p = new tt([i], t10.dtype), u = new tt([i], "int32"); + let p = new tt([i], t8.dtype), u = new tt([i], "int32"); for (let c = 0, l = 0; c < n.length; c++) a.has(n[c]) || (p.values[l] = n[c], u.values[l] = c, l++); return [p.toTensor(), u.toTensor()]; } -var c1 = bq; -function Cq(r) { - let t10 = { x: v(r, "x", "sign") }; - return T.runKernel(fs, t10); +var C1 = Hq; +function Kq(r) { + let t8 = { x: v(r, "x", "sign") }; + return T.runKernel(ys, t8); } -var l1 = N({ sign_: Cq }); -function wq(r) { - let t10 = { x: v(r, "x", "sin", "float32") }; - return T.runKernel(ds, t10); +var w1 = N({ sign_: Kq }); +function qq(r) { + let t8 = { x: v(r, "x", "sin", "float32") }; + return T.runKernel(gs, t8); } -var m1 = N({ sin_: wq }); -function Sq(r) { - let t10 = { x: v(r, "x", "sinh") }; - return T.runKernel(ja, t10); +var S1 = N({ sin_: qq }); +function jq(r) { + let t8 = { x: v(r, "x", "sinh") }; + return T.runKernel(xs, t8); } -var d1 = N({ sinh_: Sq }); -function Iq(r, e, t10) { +var I1 = N({ sinh_: jq }); +function Xq(r, e, t8) { let o = v(r, "x", "slice1d"); - return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), qe(o, [e], [t10]); + return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), Xe(o, [e], [t8]); } -var f1 = N({ slice1d_: Iq }); -function vq(r, e, t10) { +var v1 = N({ slice1d_: Xq }); +function Yq(r, e, t8) { let o = v(r, "x", "slice2d"); - return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), qe(o, e, t10); + return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), Xe(o, e, t8); } -var h1 = N({ slice2d_: vq }); -function kq(r, e, t10) { +var k1 = N({ slice2d_: Yq }); +function Qq(r, e, t8) { let o = v(r, "x", "slice3d"); - return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), qe(o, e, t10); + return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), Xe(o, e, t8); } -var g1 = N({ slice3d_: kq }); -function Nq(r, e, t10) { +var N1 = N({ slice3d_: Qq }); +function Zq(r, e, t8) { let o = v(r, "x", "slice4d"); - return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), qe(o, e, t10); + return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), Xe(o, e, t8); } -var x1 = N({ slice4d_: Nq }); -function Tq(r, e = -1) { - let t10 = v(r, "logits", "softmax", "float32"); - if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1) - throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and dim was ${e}`); - let o = { logits: t10 }, n = { dim: e }; - return T.runKernel(bs, o, n); +var T1 = N({ slice4d_: Zq }); +function Jq(r, e = -1) { + let t8 = v(r, "logits", "softmax", "float32"); + if (e === -1 && (e = t8.rank - 1), e !== t8.rank - 1) + throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t8.rank} and dim was ${e}`); + let o = { logits: t8 }, n = { dim: e }; + return T.runKernel(Is, o, n); } -var y1 = N({ softmax_: Tq }); -function _q(r) { +var _1 = N({ softmax_: Jq }); +function e6(r) { $(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`); let e = { input: r }; - return T.runKernel(Pi, e); + return T.runKernel(Bi, e); } -var pc = N({ fft_: _q }); -function $q(r) { +var cc = N({ fft_: e6 }); +function t6(r) { $(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`); let e = { input: r }; - return T.runKernel(Oi, e); + return T.runKernel(zi, e); } -var Hu = N({ ifft_: $q }); -function Eq(r) { - let e = r.shape[r.shape.length - 1], t10 = r.size / e, o; +var Xu = N({ ifft_: t6 }); +function r6(r) { + let e = r.shape[r.shape.length - 1], t8 = r.size / e, o; if (e <= 2) { - let n = W(r, [t10, e]); - o = Hu(n); + let n = W(r, [t8, e]); + o = Xu(n); } else { - let n = [t10, 2 * (e - 1)], s = W(si(r), [t10, e]), a = W(su(r), [t10, e]), i = uo(qe(s, [0, 1], [t10, e - 2]), 1), p = se(uo(qe(a, [0, 1], [t10, e - 2]), 1), ke(-1)), u = yt([s, i], 1), c = yt([a, p], 1), l = W($r(u, c), [n[0], n[1]]); - o = Hu(l); + let n = [t8, 2 * (e - 1)], s = W(ui(r), [t8, e]), a = W(pu(r), [t8, e]), i = lo(Xe(s, [0, 1], [t8, e - 2]), 1), p = se(lo(Xe(a, [0, 1], [t8, e - 2]), 1), ke(-1)), u = bt([s, i], 1), c = bt([a, p], 1), l = W(Er(u, c), [n[0], n[1]]); + o = Xu(l); } - if (o = si(o), r.rank === 3 && r.shape[0] !== 0) { + if (o = ui(o), r.rank === 3 && r.shape[0] !== 0) { let n = o, s = r.shape[0]; o = W(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose(); } return o; } -var Wd = N({ irfft_: Eq }); -function Rq(r, e, t10 = 0) { - let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t10 }; - return T.runKernel(la, n, s); +var Gd = N({ irfft_: r6 }); +function o6(r, e, t8 = 0) { + let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t8 }; + return T.runKernel(ga, n, s); } -var ai = N({ split_: Rq }); -function Dq(r, e) { +var pi = N({ split_: o6 }); +function n6(r, e) { $(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`); - let t10 = r.shape[r.shape.length - 1], o = r.size / t10, n; - if (e != null && e < t10) { + let t8 = r.shape[r.shape.length - 1], o = r.size / t8, n; + if (e != null && e < t8) { let f = r.shape.map((g) => 0), h = r.shape.map((g) => g); - h[r.shape.length - 1] = e, n = qe(r, f, h), t10 = e; - } else if (e != null && e > t10) { + h[r.shape.length - 1] = e, n = Xe(r, f, h), t8 = e; + } else if (e != null && e > t8) { let f = r.shape.map((h) => h); - f[r.shape.length - 1] = e - t10, n = yt([r, Wr(f)], r.shape.length - 1), t10 = e; + f[r.shape.length - 1] = e - t8, n = bt([r, Gr(f)], r.shape.length - 1), t8 = e; } else n = r; - let s = Ht(n), a = W($r(n, s), [o, t10]), i = pc(a), p = Math.floor(t10 / 2) + 1, u = si(i), c = su(i), l = ai(u, [p, t10 - p], u.shape.length - 1), m = ai(c, [p, t10 - p], c.shape.length - 1), d = n.shape.slice(); - return d[n.shape.length - 1] = p, W($r(l[0], m[0]), d); + let s = Ht(n), a = W(Er(n, s), [o, t8]), i = cc(a), p = Math.floor(t8 / 2) + 1, u = ui(i), c = pu(i), l = pi(u, [p, t8 - p], u.shape.length - 1), m = pi(c, [p, t8 - p], c.shape.length - 1), d = n.shape.slice(); + return d[n.shape.length - 1] = p, W(Er(l[0], m[0]), d); } -var cc = N({ rfft_: Dq }); -function Aq(r, e) { - let t10 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference"); - [t10, o] = Oe(t10, o), rt(t10.shape, o.shape); - let n = { a: t10, b: o }, s = {}; - return T.runKernel(ws, n, s); +var lc = N({ rfft_: n6 }); +function s6(r, e) { + let t8 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference"); + [t8, o] = Oe(t8, o), rt(t8.shape, o.shape); + let n = { a: t8, b: o }, s = {}; + return T.runKernel(ks, n, s); } -var Ud = N({ squaredDifference_: Aq }); -function Fq(r, e) { - let t10 = v(r, "x", "squeeze", "string_or_numeric"); - return W(t10, MC(t10.shape, e).newShape); +var Hd = N({ squaredDifference_: s6 }); +function a6(r, e) { + let t8 = v(r, "x", "squeeze", "string_or_numeric"); + return W(t8, KC(t8.shape, e).newShape); } -var lc = N({ squeeze_: Fq }); -function Pq(r, e = 0) { - let t10 = Ja(r, "tensors", "stack", "string_or_numeric"); - $(t10.length >= 1, () => "Pass at least one tensor to tf.stack"), t10.length > 0 && $(e <= t10[0].rank, () => "Axis must be <= rank of the tensor"); - let o = t10, n = { axis: e }; - return T.runKernel(sa, o, n); +var mc = N({ squeeze_: a6 }); +function i6(r, e = 0) { + let t8 = ri(r, "tensors", "stack", "string_or_numeric"); + $(t8.length >= 1, () => "Pass at least one tensor to tf.stack"), t8.length > 0 && $(e <= t8[0].rank, () => "Axis must be <= rank of the tensor"); + let o = t8, n = { axis: e }; + return T.runKernel(ca, o, n); } -var vr = N({ stack_: Pq }); -function Oq(r, e = 0) { +var kr = N({ stack_: i6 }); +function u6(r, e = 0) { let o = { x: v(r, "x", "step") }, n = { alpha: e }; - return T.runKernel(yo, o, n); + return T.runKernel(wo, o, n); } -var Gd = N({ step_: Oq }); -function Mq(r, e, t10, o, n = 0, s = 0, a = 0, i = 0, p = 0) { - let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: e, end: t10, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; - return T.runKernel(Ss, c, l); +var Kd = N({ step_: u6 }); +function p6(r, e, t8, o, n = 0, s = 0, a = 0, i = 0, p = 0) { + let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: e, end: t8, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; + return T.runKernel(Ns, c, l); } -var b1 = N({ stridedSlice_: Mq }); -function Lq(r) { - let t10 = { x: v(r, "x", "tan", "float32") }; - return T.runKernel(vs, t10); +var $1 = N({ stridedSlice_: p6 }); +function c6(r) { + let t8 = { x: v(r, "x", "tan", "float32") }; + return T.runKernel(_s, t8); } -var C1 = N({ tan_: Lq }); +var E1 = N({ tan_: c6 }); function xr(r, e) { - oo(r); - let t10 = ar(r, e); - if (t10.length !== 1) + ao(r); + let t8 = ar(r, e); + if (t8.length !== 1) throw new Error("tensor1d() requires values to be a flat/TypedArray"); - return wr(r, null, t10, e); + return Sr(r, null, t8, e); } -function uu(r, e, t10) { - if (oo(r), e != null && e.length !== 2) +function mu(r, e, t8) { + if (ao(r), e != null && e.length !== 2) throw new Error("tensor2d() requires shape to have two numbers"); - let o = ar(r, t10); + let o = ar(r, t8); if (o.length !== 2 && o.length !== 1) throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray"); - return wr(r, e, o, t10); + return Sr(r, e, o, t8); } -function Hd(r, e, t10) { - if (oo(r), e != null && e.length !== 3) +function qd(r, e, t8) { + if (ao(r), e != null && e.length !== 3) throw new Error("tensor3d() requires shape to have three numbers"); - let o = ar(r, t10); + let o = ar(r, t8); if (o.length !== 3 && o.length !== 1) throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); - return wr(r, e, o, t10); + return Sr(r, e, o, t8); } -function w1(r, e, t10) { - if (oo(r), e != null && e.length !== 4) +function R1(r, e, t8) { + if (ao(r), e != null && e.length !== 4) throw new Error("tensor4d() requires shape to have four numbers"); - let o = ar(r, t10); + let o = ar(r, t8); if (o.length !== 4 && o.length !== 1) throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor4d() requires shape to be provided when `values` are a flat array"); - return wr(r, e, o, t10); + return Sr(r, e, o, t8); } -function S1(r, e, t10) { - if (oo(r), e != null && e.length !== 5) +function D1(r, e, t8) { + if (ao(r), e != null && e.length !== 5) throw new Error("tensor5d() requires shape to have five numbers"); - let o = ar(r, t10); + let o = ar(r, t8); if (o.length !== 5 && o.length !== 1) throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor5d() requires shape to be provided when `values` are a flat array"); - return wr(r, e, o, t10); + return Sr(r, e, o, t8); } -function I1(r, e, t10) { - if (oo(r), e != null && e.length !== 6) +function A1(r, e, t8) { + if (ao(r), e != null && e.length !== 6) throw new Error("tensor6d() requires shape to have six numbers"); - let o = ar(r, t10); + let o = ar(r, t8); if (o.length !== 6 && o.length !== 1) throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor6d() requires shape to be provided when `values` are a flat array"); - return e = e || o, wr(r, e, o, t10); + return e = e || o, Sr(r, e, o, t8); } -var pu = {}; -He(pu, { calculateShapes: () => v1, validateInput: () => mc, validateUpdateShape: () => Fw }); -function Fw(r, e, t10) { - let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t10.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`; - if (t10.rank < n) +var du = {}; +Ke(du, { calculateShapes: () => F1, validateInput: () => dc, validateUpdateShape: () => Uw }); +function Uw(r, e, t8) { + let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t8.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`; + if (t8.rank < n) throw new Error(s + ` update.rank < ${n}. `); - if (r.length < o + (t10.rank - n)) - throw new Error(s + ` Output shape length < ${o + (t10.rank - n)}`); - if (t10.rank !== n + r.length - o) + if (r.length < o + (t8.rank - n)) + throw new Error(s + ` Output shape length < ${o + (t8.rank - n)}`); + if (t8.rank !== n + r.length - o) throw new Error(s + ` update.rank != ${n + r.length - o}`); for (let a = 0; a < n; ++a) - if (t10.shape[a] !== e.shape[a]) - throw new Error(s + ` updates.shape[${a}] (${t10.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`); - for (let a = 0; a < t10.rank - n; ++a) - if (t10.shape[a + n] !== r[a + o]) - throw new Error(s + ` updates.shape[${a + n}] (${t10.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`); + if (t8.shape[a] !== e.shape[a]) + throw new Error(s + ` updates.shape[${a}] (${t8.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`); + for (let a = 0; a < t8.rank - n; ++a) + if (t8.shape[a + n] !== r[a + o]) + throw new Error(s + ` updates.shape[${a + n}] (${t8.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`); } -function mc(r, e, t10) { +function dc(r, e, t8) { if (e.rank < 1) throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${e.rank}.`); if (r.rank < 1) throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`); if (e.dtype !== "int32") throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${e.dtype}`); - if (t10.length < 1) - throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t10}`); - if (t10.length === 0) { + if (t8.length < 1) + throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t8}`); + if (t8.length === 0) { if (e.size === 0) throw new Error(`Indices specified for empty output. indices shape: ${e.shape}`); if (r.size === 0) throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`); } - Fw(t10, e, r); + Uw(t8, e, r); } -function v1(r, e, t10) { - let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t10.length, a = 1; +function F1(r, e, t8) { + let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t8.length, a = 1; for (let l = n; l < s; ++l) - a *= t10[l]; - let i = n < 1 ? 1 : n, p = Ue(e.shape) / i, u = [...Us(t10.slice(0, n)), 1], c = Ue(t10); + a *= t8[l]; + let i = n < 1 ? 1 : n, p = Ge(e.shape) / i, u = [...qs(t8.slice(0, n)), 1], c = Ge(t8); return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c }; } -function Bq(r, e, t10) { - let o = v(r, "tensor", "tensorScatterupdate"), n = v(e, "indices", "tensorScatterupdate", "int32"), s = v(t10, "updates", "tensorScatterupdate"); - if (mc(s, n, o.shape), o.dtype !== s.dtype) +function l6(r, e, t8) { + let o = v(r, "tensor", "tensorScatterupdate"), n = v(e, "indices", "tensorScatterupdate", "int32"), s = v(t8, "updates", "tensorScatterupdate"); + if (dc(s, n, o.shape), o.dtype !== s.dtype) throw new Error(`tensor and updates must have the same dtype, instead they are ${o.dtype} and ${s.dtype}.`); let a = { tensor: o, indices: n, updates: s }, i = {}; - return T.runKernel(cs, a, i); + return T.runKernel(ds, a, i); } -var k1 = N({ tensorScatterUpdate_: Bq }); -function zq(r, e = 1, t10 = true) { +var P1 = N({ tensorScatterUpdate_: l6 }); +function m6(r, e = 1, t8 = true) { let o = v(r, "x", "topk"); if (o.rank === 0) throw new Error("topk() expects the input to be of rank 1 or higher"); @@ -7322,73 +7401,73 @@ function zq(r, e = 1, t10 = true) { throw new Error(`'k' passed to topk() must be >= 0 but got ${e}`); if (e > n) throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${e}`); - let s = { x: o }, a = { k: e, sorted: t10 }, [i, p] = T.runKernel(Ns, s, a); + let s = { x: o }, a = { k: e, sorted: t8 }, [i, p] = T.runKernel(Es, s, a); return { values: i, indices: p }; } -var N1 = N({ topk_: zq }); -function Vq(r, e = 0, t10 = 1, o, n) { - if (Ct(r), o != null && o === "bool") +var O1 = N({ topk_: m6 }); +function d6(r, e = 0, t8 = 1, o, n) { + if (wt(r), o != null && o === "bool") throw new Error("Unsupported data type $ { dtype }"); - let s = new Gu(e, t10, o, true, n), a = me(r, o); + let s = new ju(e, t8, o, true, n), a = me(r, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var T1 = N({ truncatedNormal_: Vq }); -function Wq(r, e = 0) { - let t10 = v(r, "x", "unique", "string_or_numeric"); - $(t10.rank > 0, () => "The input tensor must be at least 1D"); - let o = { x: t10 }, n = { axis: e }, [s, a] = T.runKernel(qi, o, n); +var M1 = N({ truncatedNormal_: d6 }); +function f6(r, e = 0) { + let t8 = v(r, "x", "unique", "string_or_numeric"); + $(t8.rank > 0, () => "The input tensor must be at least 1D"); + let o = { x: t8 }, n = { axis: e }, [s, a] = T.runKernel(Qi, o, n); return { values: s, indices: a }; } -var _1 = N({ unique_: Wq }); -function Uq(r, e, t10) { +var L1 = N({ unique_: f6 }); +function h6(r, e, t8) { let o = v(r, "x", "unsortedSegmentSum"), n = v(e, "segmentIds", "unsortedSegmentSum", "int32"); - $(Ba(t10), () => "numSegments must be of dtype int"); - let s = { x: o, segmentIds: n }, a = { numSegments: t10 }; - return T.runKernel(ji, s, a); + $(Ga(t8), () => "numSegments must be of dtype int"); + let s = { x: o, segmentIds: n }, a = { numSegments: t8 }; + return T.runKernel(Zi, s, a); } -var $1 = N({ unsortedSegmentSum_: Uq }); -function Gq(r, e = 0) { - let t10 = v(r, "x", "unstack", "string_or_numeric"); - $(e >= -t10.shape.length && e < t10.shape.length, () => `Axis = ${e} is not in [-${t10.shape.length}, ${t10.shape.length})`); - let o = { value: t10 }, n = { axis: e }; - return T.runKernel(da, o, n); +var B1 = N({ unsortedSegmentSum_: h6 }); +function g6(r, e = 0) { + let t8 = v(r, "x", "unstack", "string_or_numeric"); + $(e >= -t8.shape.length && e < t8.shape.length, () => `Axis = ${e} is not in [-${t8.shape.length}, ${t8.shape.length})`); + let o = { value: t8 }, n = { axis: e }; + return T.runKernel(ya, o, n); } -var po = N({ unstack_: Gq }); -function E1(r, e) { +var mo = N({ unstack_: g6 }); +function z1(r, e) { return Nl(r, e, "right"); } -function R1(r, e = true, t10, o) { - return T.makeVariable(r, e, t10, o); +function V1(r, e = true, t8, o) { + return T.makeVariable(r, e, t8, o); } -function Kd(r, e) { - let t10 = []; +function jd(r, e) { + let t8 = []; for (let s = 0; s < e.length; s++) - e[s] && t10.push(s); - let o = me(r, "int32"), n = me([t10.length, r.length], "int32"); - for (let s = 0; s < t10.length; s++) { - let a = o.indexToLoc(t10[s]), i = s * r.length; + e[s] && t8.push(s); + let o = me(r, "int32"), n = me([t8.length, r.length], "int32"); + for (let s = 0; s < t8.length; s++) { + let a = o.indexToLoc(t8[s]), i = s * r.length; n.values.set(a, i); } return n.toTensor(); } -async function Hq(r) { - let e = v(r, "condition", "whereAsync", "bool"), t10 = await e.data(), o = Kd(e.shape, t10); +async function x6(r) { + let e = v(r, "condition", "whereAsync", "bool"), t8 = await e.data(), o = jd(e.shape, t8); return r !== e && e.dispose(), o; } -var qd = Hq; -async function Kq(r, e, t10) { - let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t10 == null ? 0 : t10, a = n.rank, i = o.shape; - $(a > 0, () => "mask cannot be scalar"), xt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,"); +var Xd = x6; +async function y6(r, e, t8) { + let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t8 == null ? 0 : t8, a = n.rank, i = o.shape; + $(a > 0, () => "mask cannot be scalar"), yt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,"); let p = 1; for (let h = s; h < s + a; h++) p *= i[h]; - let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await qd(l), d = lc(m, [1]), f = bd(c, d, s); + let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await Xd(l), d = mc(m, [1]), f = wd(c, d, s); return r !== o && o.dispose(), e !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f; } -var qq = Kq; -function jq(r, e, t10) { +var b6 = y6; +function C6(r, e, t8) { let o = v(r, "x", "transpose"); if (e == null && (e = o.shape.map((a, i) => i).reverse()), $(o.rank === e.length, () => `Error in transpose: rank of input ${o.rank} must match length of perm ${e}.`), e.forEach((a) => { $(a >= 0 && a < o.rank, () => `All entries in 'perm' must be between 0 and ${o.rank - 1} but got ${e}`); @@ -7396,102 +7475,102 @@ function jq(r, e, t10) { return o.clone(); let n = { x: o }, s = { perm: e }; return o.dtype === "complex64" ? De(() => { - let a = si(o), i = su(o); - return a = T.runKernel(ao, { x: a }, s), i = T.runKernel(ao, { x: i }, s), t10 && (i = pr(i)), $r(a, i); - }) : T.runKernel(ao, n, s); + let a = ui(o), i = pu(o); + return a = T.runKernel(po, { x: a }, s), i = T.runKernel(po, { x: i }, s), t8 && (i = pr(i)), Er(a, i); + }) : T.runKernel(po, n, s); } -var dc = N({ transpose_: jq }); -function Xq(r, e, t10, o, n = true) { - let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t10, "decay", "movingAverage"); - nw(s, a), $(br(s.shape, a.shape), () => "Shape mismatch in v and x"); +var fc = N({ transpose_: C6 }); +function w6(r, e, t8, o, n = true) { + let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t8, "decay", "movingAverage"); + dw(s, a), $(Cr(s.shape, a.shape), () => "Shape mismatch in v and x"); let p = ke(1), u = Te(p, i), c = se(Te(a, s), u); if (n) { $(o != null, () => "When using zeroDebias: true, step is required."); let l = v(o, "step", "movingAverage"); - c = Ke(c, Te(p, ri(i, l))); + c = je(c, Te(p, si(i, l))); } - return be(s, c); + return Ce(s, c); } -var Yq = N({ movingAverage_: Xq }); -function Qq(r, e, t10) { - Ct(t10); +var S6 = N({ movingAverage_: w6 }); +function I6(r, e, t8) { + wt(t8); let o = v(r, "indices", "scatterND", "int32"), n = v(e, "updates", "scatterND"); - mc(n, o, t10); - let s = { indices: o, updates: n }, a = { shape: t10 }; - return T.runKernel(ps, s, a); + dc(n, o, t8); + let s = { indices: o, updates: n }, a = { shape: t8 }; + return T.runKernel(ms, s, a); } -var Zq = N({ scatterND_: Qq }); -function D1(r, e, t10, o) { +var v6 = N({ scatterND_: I6 }); +function W1(r, e, t8, o) { if (r.dtype !== "int32") throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`); if (r.rank > 2) throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`); let n = r.rank > 0 ? r.shape[0] : 1, s = r.rank > 1 ? r.shape[1] : 1; - if (t10.length !== s) - throw new Error(`outputShape has incorrect number of elements:, ${t10.length}, should be: ${s}.`); + if (t8.length !== s) + throw new Error(`outputShape has incorrect number of elements:, ${t8.length}, should be: ${s}.`); let a = e.size; if (!(e.rank === 0 || e.rank === 1 && a === n)) throw new Error(`sparseValues has incorrect shape ${e.shape}, should be [] or [${n}]`); if (e.dtype !== o.dtype) throw new Error("sparseValues.dtype must match defaultValues.dtype"); } -function e6(r, e, t10, o = 0) { - Ct(t10); +function N6(r, e, t8, o = 0) { + wt(t8); let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(e, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype); - D1(n, s, t10, a); - let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t10 }; - return T.runKernel(Cs, i, p); + W1(n, s, t8, a); + let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t8 }; + return T.runKernel(vs, i, p); } -var t6 = N({ sparseToDense_: e6 }); -function r6(r, e) { - let t10 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t10 }; - return T.runKernel(Sn, n); +var T6 = N({ sparseToDense_: N6 }); +function _6(r, e) { + let t8 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t8 }; + return T.runKernel(vn, n); } -var o6 = N({ gatherND_: r6 }); -function A1(r, e) { +var $6 = N({ gatherND_: _6 }); +function U1(r, e) { if (e == null) return r.shape.slice(); - if (br(r.shape, e)) + if (Cr(r.shape, e)) return e; if (r.shape.length === e.length) { - let t10 = []; + let t8 = []; for (let o = 0; o < r.shape.length; o++) - e[o] == null && r.shape[o] != null ? t10.push(r.shape[o]) : t10.push(e[o]); - return t10; + e[o] == null && r.shape[o] != null ? t8.push(r.shape[o]) : t8.push(e[o]); + return t8; } return e; } -function n6(r, e, t10, o) { +function E6(r, e, t8, o) { let n = v(r, "x", "dropout"); if ($(n.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`), $(e >= 0 && e < 1, () => `rate must be a float in the range [0, 1), but got ${e}.`), e === 0) - return r instanceof pt ? n.clone() : n; - let s = A1(n, t10), a = 1 - e, i = Ke(yd(be(uc(s, 0, 1, "float32", o), a)), a); + return r instanceof ut ? n.clone() : n; + let s = U1(n, t8), a = 1 - e, i = je(Cd(Ce(pc(s, 0, 1, "float32", o), a)), a); return se(n, i); } -var s6 = N({ dropout_: n6 }); -function Pw(r) { +var R6 = N({ dropout_: E6 }); +function Gw(r) { return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2)))); } -function _l(r, e, t10) { +function _l(r, e, t8) { let o = 1 - r % 2, n = new Float32Array(r); for (let s = 0; s < r; ++s) { let a = 2 * Math.PI * s / (r + o - 1); - n[s] = e - t10 * Math.cos(a); + n[s] = e - t8 * Math.cos(a); } return xr(n, "float32"); } -async function a6(r, e, t10 = 1) { +async function D6(r, e, t8 = 1) { let o = v(r, "predictions", "inTopK"), n = v(e, "targets", "inTopK"); - $(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), xt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension."); + $(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), yt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension."); let s = o.shape[o.shape.length - 1]; - $(t10 > 0 && t10 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t10}`); - let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = LC("bool", p); + $(t8 > 0 && t8 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t8}`); + let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = qC("bool", p); for (let l = 0; l < p; l++) { let m = l * u, d = a.subarray(m, m + u), f = []; for (let h = 0; h < d.length; h++) f.push({ value: d[h], index: h }); f.sort((h, g) => g.value - h.value), c[l] = 0; - for (let h = 0; h < t10; h++) + for (let h = 0; h < t8; h++) if (f[h].index === i[l]) { c[l] = 1; break; @@ -7499,60 +7578,60 @@ async function a6(r, e, t10 = 1) { } return r !== o && o.dispose(), e !== n && n.dispose(), ir(c, n.shape, "bool"); } -var i6 = a6; -var Ow = {}; -He(Ow, { conv2d: () => P1, depthwiseConv2d: () => L1, matMul: () => B1 }); -function u6(r, e, t10, o, n, s = "NHWC", a) { +var A6 = D6; +var Hw = {}; +Ke(Hw, { conv2d: () => H1, depthwiseConv2d: () => j1, matMul: () => X1 }); +function F6(r, e, t8, o, n, s = "NHWC", a) { let i = r; r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]])); let p = e; - p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])), $(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), $(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), $(t10.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t10}.`); + p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])), $(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), $(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), $(t8.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t8}.`); let u = s === "NHWC" ? i.shape[3] : i.shape[1], c = s === "NHWC" ? p.shape[3] : p.shape[1]; - $(u === t10[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t10[2]}.`), $(c === t10[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t10[3]}).`), Lt("conv2dDerFilter", n, a); - let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t10 }; - return T.runKernel($i, l, m); + $(u === t8[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t8[2]}.`), $(c === t8[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t8[3]}).`), Lt("conv2dDerFilter", n, a); + let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t8 }; + return T.runKernel(Ai, l, m); } -var F1 = N({ conv2DBackpropFilter_: u6 }); -function Ku(r, e, t10) { - if (t10 == null || t10 === "linear") +var G1 = N({ conv2DBackpropFilter_: F6 }); +function Yu(r, e, t8) { + if (t8 == null || t8 === "linear") return r; - if (t10 === "relu") - return se(r, Gd(e)); - throw new Error(`Cannot compute gradient for fused activation ${t10}.`); + if (t8 === "relu") + return se(r, Kd(e)); + throw new Error(`Cannot compute gradient for fused activation ${t8}.`); } -function qu(r, e) { - let t10 = e, o = fd(r.shape, e.shape); - return o.length > 0 && (t10 = ot(t10, o)), W(t10, r.shape); +function Qu(r, e) { + let t8 = e, o = gd(r.shape, e.shape); + return o.length > 0 && (t8 = ot(t8, o)), W(t8, r.shape); } -function ju(r, e, t10, o) { +function Zu(r, e, t8, o) { if (e === "linear") return r; if (e === "relu") - return iu(r); + return lu(r); if (e === "elu") - return gd(r); + return yd(r); if (e === "relu6") - return zd(r); + return Wd(r); if (e === "prelu") - return Ad(r, t10); + return Pd(r, t8); if (e === "leakyrelu") - return wd(r, o); + return Id(r, o); if (e === "sigmoid") - return wa(r); + return Na(r); throw new Error(`Unknown fused activation ${e}.`); } -var Xu = (r, e) => !(r > 0) || e === "linear"; -function p6({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { - if (p = p || "linear", Xu(T.state.gradientDepth, p) === false) { +var Ju = (r, e) => !(r > 0) || e === "linear"; +function P6({ x: r, filter: e, strides: t8, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { + if (p = p || "linear", Ju(T.state.gradientDepth, p) === false) { $(n === "NHWC", () => `Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`); - let _ = ou(r, e, t10, o, n, s, a); - return i != null && (_ = be(_, i)), ju(_, p, u, c); + let _ = iu(r, e, t8, o, n, s, a); + return i != null && (_ = Ce(_, i)), Zu(_, p, u, c); } let l = v(r, "x", "conv2d", "float32"), m = v(e, "filter", "conv2d", "float32"), d = l, f = false; l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Lt("fused conv2d", o, a); let h = n === "NHWC" ? d.shape[3] : d.shape[1]; - $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(gr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`); - let g = Mu(d.shape, m.shape, t10, s, o, a), x; + $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(gr(t8, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`); + let g = Vu(d.shape, m.shape, t8, s, o, a), x; i != null && (x = v(i, "bias", "fused conv2d"), [x] = Oe(x, l), n === "NHWC" ? rt(g.outShape, x.shape) : ($(x.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`), $(x.shape.length === 0 || x.shape[0] === g.outChannels || x.shape[0] === 1, () => `Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`))); let b; if (u != null) { @@ -7568,333 +7647,333 @@ function p6({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dil } b = v(u, "prelu weights", "fused conv2d"); } - let w = (_, E) => { + let C = (_, E) => { $(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`); - let [R, D, F, O] = E, M = Ku(_, F, p); - $(Ou(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`); - let L = dd(D.shape, M, R, t10, o), B = F1(D, M, R.shape, t10, o), z = [L, B]; + let [R, D, P, O] = E, M = Yu(_, P, p); + $(zu(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`); + let L = hd(D.shape, M, R, t8, o), B = G1(D, M, R.shape, t8, o), z = [L, B]; if (O != null) { - let U = qu(O, M); + let U = Qu(O, M); z.push(U); } return z; - }, S = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; - return i == null ? Ir((E, R, D) => { - let F = T.runKernel(Co, S, k); - return D([R, E, F]), f && (F = W(F, [F.shape[1], F.shape[2], F.shape[3]])), { value: F, gradFunc: w }; - })(d, m) : Ir((E, R, D, F) => { - let O = T.runKernel(Co, S, k); - return F([R, E, O, D]), f && (O = W(O, [O.shape[1], O.shape[2], O.shape[3]])), { value: O, gradFunc: w }; + }, S = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; + return i == null ? vr((E, R, D) => { + let P = T.runKernel(Io, S, k); + return D([R, E, P]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: C }; + })(d, m) : vr((E, R, D, P) => { + let O = T.runKernel(Io, S, k); + return P([R, E, O, D]), f && (O = W(O, [O.shape[1], O.shape[2], O.shape[3]])), { value: O, gradFunc: C }; })(d, m, x); } -var P1 = N({ fusedConv2d_: p6 }); -function c6(r, e, t10, o, n, s = [1, 1], a) { +var H1 = N({ fusedConv2d_: P6 }); +function O6(r, e, t8, o, n, s = [1, 1], a) { let i = r; r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]])); let p = e; p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])); - let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: t10 }; - return T.runKernel(Ei, u, c); + let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: t8 }; + return T.runKernel(Fi, u, c); } -var O1 = N({ depthwiseConv2dNativeBackpropFilter_: c6 }); -function l6(r, e, t10, o, n, s = [1, 1], a) { +var K1 = N({ depthwiseConv2dNativeBackpropFilter_: O6 }); +function M6(r, e, t8, o, n, s = [1, 1], a) { let i = e, p = false; e.rank === 3 && (p = true, i = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])); - let u = { dy: i, filter: t10 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(Ri, u, c); + let u = { dy: i, filter: t8 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(Pi, u, c); return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var M1 = N({ depthwiseConv2dNativeBackpropInput_: l6 }); -function m6({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { - if (Xu(T.state.gradientDepth, p) === false) { - let k = ac(r, e, t10, o, n, s, a); - return i != null && (k = be(k, i)), ju(k, p, u, c); +var q1 = N({ depthwiseConv2dNativeBackpropInput_: M6 }); +function L6({ x: r, filter: e, strides: t8, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { + if (Ju(T.state.gradientDepth, p) === false) { + let k = ic(r, e, t8, o, n, s, a); + return i != null && (k = Ce(k, i)), Zu(k, p, u, c); } let l = v(r, "x", "depthwiseConv2d", "float32"), m = v(e, "filter", "depthwiseConv2d", "float32"), d = l, f = false; - l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(gr(t10, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), Lt("fused depthwiseConv2d", o, a); - let h = Mu(d.shape, m.shape, t10, s, o, a, true), g; + l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(gr(t8, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`), Lt("fused depthwiseConv2d", o, a); + let h = Vu(d.shape, m.shape, t8, s, o, a, true), g; i != null && (g = v(i, "bias", "fused conv2d"), [g] = Oe(g, l), rt(h.outShape, g.shape)); let x; u != null && (x = v(u, "prelu weights", "fused depthwiseConv2d")); let b = (k, _) => { - $(Ou(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`); - let [E, R, D, F] = _, O = Ku(k, D, p), M = M1(R.shape, O, E, t10, o, s, a), L = O1(R, O, E.shape, t10, o, s, a); - if (F != null) { - let B = qu(g, O); + $(zu(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`); + let [E, R, D, P] = _, O = Yu(k, D, p), M = q1(R.shape, O, E, t8, o, s, a), L = K1(R, O, E.shape, t8, o, s, a); + if (P != null) { + let B = Qu(g, O); return [M, L, B]; } return [M, L]; - }, w = { x: d, filter: m, bias: g, preluActivationWeights: x }, S = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; - return i == null ? Ir((_, E, R) => { - let D = T.runKernel(wo, w, S); + }, C = { x: d, filter: m, bias: g, preluActivationWeights: x }, S = { strides: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; + return i == null ? vr((_, E, R) => { + let D = T.runKernel(vo, C, S); return R([E, _, D]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b }; - })(d, m) : Ir((_, E, R, D) => { - let F = T.runKernel(wo, w, S); - return D([E, _, F, R]), f && (F = W(F, [F.shape[1], F.shape[2], F.shape[3]])), { value: F, gradFunc: b }; + })(d, m) : vr((_, E, R, D) => { + let P = T.runKernel(vo, C, S); + return D([E, _, P, R]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: b }; })(d, m, g); } -var L1 = N({ fusedDepthwiseConv2d_: m6 }); -function d6({ a: r, b: e, transposeA: t10 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) { - if (Xu(T.state.gradientDepth, s) === false) { - let O = Qe(r, e, t10, o); - return n != null && (O = be(O, n)), ju(O, s, a, i); +var j1 = N({ fusedDepthwiseConv2d_: L6 }); +function B6({ a: r, b: e, transposeA: t8 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) { + if (Ju(T.state.gradientDepth, s) === false) { + let O = Ze(r, e, t8, o); + return n != null && (O = Ce(O, n)), Zu(O, s, a, i); } let p = v(r, "a", "fused matMul"), u = v(e, "b", "fused matMul"); [p, u] = Oe(p, u); - let c = t10 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t10 ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = Ue(f), x = Ue(h); - $(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t10} and transposeB=${o} must match.`); - let w = rt(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), S = t10 ? W(p, [g, c, m]) : W(p, [g, m, c]), k = o ? W(u, [x, d, l]) : W(u, [x, l, d]), _; - n != null && (_ = v(n, "bias", "fused matMul"), [_] = Oe(_, p), rt(w, _.shape)); + let c = t8 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t8 ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = Ge(f), x = Ge(h); + $(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t8} and transposeB=${o} must match.`); + let C = rt(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), S = t8 ? W(p, [g, c, m]) : W(p, [g, m, c]), k = o ? W(u, [x, d, l]) : W(u, [x, l, d]), _; + n != null && (_ = v(n, "bias", "fused matMul"), [_] = Oe(_, p), rt(C, _.shape)); let E; a != null && (E = v(a, "prelu weights", "fused matMul")); let R = (O, M) => { - let [L, B, z, U] = M, j = Ku(W(O, z.shape), z, s), H, X; - if (!t10 && !o ? (H = Qe(j, B, false, true), X = Qe(L, j, true, false)) : !t10 && o ? (H = Qe(j, B, false, false), X = Qe(j, L, true, false)) : t10 && !o ? (H = Qe(B, j, false, true), X = Qe(L, j, false, false)) : (H = Qe(B, j, true, true), X = Qe(j, L, true, true)), n != null) { - let J = qu(U, j); - return [H, X, J]; + let [L, B, z, U] = M, j = Yu(W(O, z.shape), z, s), q, Y; + if (!t8 && !o ? (q = Ze(j, B, false, true), Y = Ze(L, j, true, false)) : !t8 && o ? (q = Ze(j, B, false, false), Y = Ze(j, L, true, false)) : t8 && !o ? (q = Ze(B, j, false, true), Y = Ze(L, j, false, false)) : (q = Ze(B, j, true, true), Y = Ze(j, L, true, true)), n != null) { + let J = Qu(U, j); + return [q, Y, J]; } else - return [H, X]; - }, D = { a: S, b: k, bias: _, preluActivationWeights: E }, F = { transposeA: t10, transposeB: o, activation: s, leakyreluAlpha: i }; - return n == null ? Ir((M, L, B) => { - let z = T.runKernel(bo, D, F); - return B([M, L, z]), { value: W(z, w), gradFunc: R }; - })(S, k) : Ir((M, L, B, z) => { - let U = T.runKernel(bo, D, F); - return z([M, L, U, B]), { value: W(U, w), gradFunc: R }; + return [q, Y]; + }, D = { a: S, b: k, bias: _, preluActivationWeights: E }, P = { transposeA: t8, transposeB: o, activation: s, leakyreluAlpha: i }; + return n == null ? vr((M, L, B) => { + let z = T.runKernel(So, D, P); + return B([M, L, z]), { value: W(z, C), gradFunc: R }; + })(S, k) : vr((M, L, B, z) => { + let U = T.runKernel(So, D, P); + return z([M, L, U, B]), { value: W(U, C), gradFunc: R }; })(S, k, _); } -var B1 = N({ fusedMatMul_: d6 }); -function f6(r) { +var X1 = N({ fusedMatMul_: B6 }); +function z6(r) { return _l(r, 0.54, 0.46); } -var z1 = N({ hammingWindow_: f6 }); -function h6(r) { +var Y1 = N({ hammingWindow_: z6 }); +function V6(r) { return _l(r, 0.5, 0.5); } -var jd = N({ hannWindow_: h6 }); -function g6(r, e, t10, o = false, n = 0) { +var Yd = N({ hannWindow_: V6 }); +function W6(r, e, t8, o = false, n = 0) { let s = 0, a = []; for (; s + e <= r.size; ) - a.push(qe(r, s, e)), s += t10; + a.push(Xe(r, s, e)), s += t8; if (o) for (; s < r.size; ) { - let i = s + e - r.size, p = yt([qe(r, s, e - i), Sa([i], n)]); - a.push(p), s += t10; + let i = s + e - r.size, p = bt([Xe(r, s, e - i), Ta([i], n)]); + a.push(p), s += t8; } - return a.length === 0 ? uu([], [0, e]) : W(yt(a), [a.length, e]); + return a.length === 0 ? mu([], [0, e]) : W(bt(a), [a.length, e]); } -var Xd = N({ frame_: g6 }); -function x6(r, e, t10, o, n = jd) { - o == null && (o = Pw(e)); - let s = Xd(r, e, t10), a = se(s, n(e)); - return cc(a, o); +var Qd = N({ frame_: W6 }); +function U6(r, e, t8, o, n = Yd) { + o == null && (o = Gw(e)); + let s = Qd(r, e, t8), a = se(s, n(e)); + return lc(a, o); } -var V1 = N({ stft_: x6 }); -function y6(r, e, t10, o, n = "bilinear", s = 0) { - let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t10, "boxInd", "cropAndResize", "int32"), u = i.shape[0]; +var Q1 = N({ stft_: U6 }); +function G6(r, e, t8, o, n = "bilinear", s = 0) { + let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t8, "boxInd", "cropAndResize", "int32"), u = i.shape[0]; $(a.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`), $(p.rank === 1 && p.shape[0] === u, () => `Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`), $(o.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`), $(o[0] >= 1 && o[1] >= 1, () => `cropSize must be atleast [1,1], but was ${o}`), $(n === "bilinear" || n === "nearest", () => `method must be bilinear or nearest, but was ${n}`); let c = { image: a, boxes: i, boxInd: p }, l = { method: n, extrapolationValue: s, cropSize: o }; - return T.runKernel(pn, c, l); + return T.runKernel(cn, c, l); } -var W1 = N({ cropAndResize_: y6 }); -function b6(r) { +var Z1 = N({ cropAndResize_: G6 }); +function H6(r) { let e = v(r, "image", "flipLeftRight", "float32"); $(e.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`); - let t10 = { image: e }; - return T.runKernel(yn, t10, {}); + let t8 = { image: e }; + return T.runKernel(Cn, t8, {}); } -var U1 = N({ flipLeftRight_: b6 }); -function C6(r) { - let e = v(r, "image", "grayscaleToRGB"), t10 = e.rank - 1, o = e.shape[t10]; +var J1 = N({ flipLeftRight_: H6 }); +function K6(r) { + let e = v(r, "image", "grayscaleToRGB"), t8 = e.rank - 1, o = e.shape[t8]; $(e.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`), $(o === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`); let n = new Array(e.rank); - return n.fill(1, 0, t10), n[t10] = 3, nu(e, n); + return n.fill(1, 0, t8), n[t8] = 3, uu(e, n); } -var G1 = N({ grayscaleToRGB_: C6 }); -function w6(r, e, t10 = 0, o = 0.5) { +var eN = N({ grayscaleToRGB_: K6 }); +function q6(r, e, t8 = 0, o = 0.5) { let n = v(r, "image", "rotateWithOffset", "float32"); $(n.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`); - let s = { image: n }, a = { radians: e, fillValue: t10, center: o }; - return T.runKernel(_s, s, a); + let s = { image: n }, a = { radians: e, fillValue: t8, center: o }; + return T.runKernel(Ds, s, a); } -var H1 = N({ rotateWithOffset_: w6 }); -function No(r, e, t10, o, n, s) { +var tN = N({ rotateWithOffset_: q6 }); +function $o(r, e, t8, o, n, s) { o == null && (o = 0.5), n == null && (n = Number.NEGATIVE_INFINITY), s == null && (s = 0); let a = r.shape[0]; - return t10 = Math.min(t10, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), $(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), $(e.rank === 1, () => "scores must be a 1D tensor"), $(e.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }; + return t8 = Math.min(t8, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), $(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), $(e.rank === 1, () => "scores must be a 1D tensor"), $(e.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: t8, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }; } -function S6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) { - let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = No(s, a, t10, o, n); - t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; - let p = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n }; - return T.runKernel(jn, { boxes: s, scores: a }, p); +function j6(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY) { + let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = $o(s, a, t8, o, n); + t8 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; + let p = { maxOutputSize: t8, iouThreshold: o, scoreThreshold: n }; + return T.runKernel(Qn, { boxes: s, scores: a }, p); } -var K1 = N({ nonMaxSuppression_: S6 }); -function q1(r, e, t10) { - let o = I6(r, e, t10), n = o < 0 ? -(o + 1) : o; +var rN = N({ nonMaxSuppression_: j6 }); +function oN(r, e, t8) { + let o = X6(r, e, t8), n = o < 0 ? -(o + 1) : o; r.splice(n, 0, e); } -function I6(r, e, t10) { - return k6(r, e, t10 || v6); +function X6(r, e, t8) { + return Q6(r, e, t8 || Y6); } -function v6(r, e) { +function Y6(r, e) { return r > e ? 1 : r < e ? -1 : 0; } -function k6(r, e, t10) { +function Q6(r, e, t8) { let o = 0, n = r.length, s = 0, a = false; for (; o < n; ) { s = o + (n - o >>> 1); - let i = t10(e, r[s]); + let i = t8(e, r[s]); i > 0 ? o = s + 1 : (n = s, a = !i); } return a ? o : -o - 1; } -function Yd(r, e, t10, o, n) { - return Mw(r, e, t10, o, n, 0); +function Zd(r, e, t8, o, n) { + return Kw(r, e, t8, o, n, 0); } -function Qd(r, e, t10, o, n, s) { - return Mw(r, e, t10, o, n, 0, false, s, true); +function Jd(r, e, t8, o, n, s) { + return Kw(r, e, t8, o, n, 0, false, s, true); } -function Zd(r, e, t10, o, n, s) { - return Mw(r, e, t10, o, n, s, true); +function ef(r, e, t8, o, n, s) { + return Kw(r, e, t8, o, n, s, true); } -function Mw(r, e, t10, o, n, s, a = false, i = false, p = false) { +function Kw(r, e, t8, o, n, s, a = false, i = false, p = false) { let u = []; for (let g = 0; g < e.length; g++) e[g] > n && u.push({ score: e[g], boxIndex: g, suppressBeginIndex: 0 }); - u.sort(j1); + u.sort(nN); let c = s > 0 ? -0.5 / s : 0, l = [], m = []; - for (; l.length < t10 && u.length > 0; ) { - let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: w } = g; + for (; l.length < t8 && u.length > 0; ) { + let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: C } = g; if (x < n) break; let S = false; - for (let k = l.length - 1; k >= w; --k) { - let _ = N6(r, b, l[k]); + for (let k = l.length - 1; k >= C; --k) { + let _ = Z6(r, b, l[k]); if (_ >= o) { S = true; break; } - if (g.score = g.score * T6(o, c, _), g.score <= n) + if (g.score = g.score * J6(o, c, _), g.score <= n) break; } - g.suppressBeginIndex = l.length, S || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && q1(u, g, j1)); + g.suppressBeginIndex = l.length, S || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && oN(u, g, nN)); } - let d = l.length, f = t10 - d; + let d = l.length, f = t8 - d; i && f > 0 && (l.push(...new Array(f).fill(0)), m.push(...new Array(f).fill(0))); let h = { selectedIndices: l }; return a && (h.selectedScores = m), p && (h.validOutputs = d), h; } -function N6(r, e, t10) { - let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t10 * 4, t10 * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c); +function Z6(r, e, t8) { + let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t8 * 4, t8 * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c); if (d <= 0 || f <= 0) return 0; - let h = Math.max(s, u), g = Math.max(a, c), x = Math.min(i, l), b = Math.min(p, m), w = Math.max(x - h, 0) * Math.max(b - g, 0); - return w / (d + f - w); + let h = Math.max(s, u), g = Math.max(a, c), x = Math.min(i, l), b = Math.min(p, m), C = Math.max(x - h, 0) * Math.max(b - g, 0); + return C / (d + f - C); } -function T6(r, e, t10) { - let o = Math.exp(e * t10 * t10); - return t10 <= r ? o : 0; +function J6(r, e, t8) { + let o = Math.exp(e * t8 * t8); + return t8 <= r ? o : 0; } -function j1(r, e) { +function nN(r, e) { return r.score - e.score || r.score === e.score && e.boxIndex - r.boxIndex; } -async function _6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) { - let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = No(s, a, t10, o, n); - t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; - let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Yd(u, c, t10, o, n); +async function ej(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY) { + let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = $o(s, a, t8, o, n); + t8 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; + let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Zd(u, c, t8, o, n); return s !== r && s.dispose(), a !== e && a.dispose(), xr(l, "int32"); } -var X1 = _6; -function $6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { - let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = No(a, i, t10, o, n, s); - t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; - let u = { boxes: a, scores: i }, c = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(Xn, u, c); +var sN = ej; +function tj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { + let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = $o(a, i, t8, o, n, s); + t8 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; + let u = { boxes: a, scores: i }, c = { maxOutputSize: t8, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(Zn, u, c); return { selectedIndices: l[0], selectedScores: l[1] }; } -var Y1 = N({ nonMaxSuppressionWithScore_: $6 }); -async function E6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { - let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = No(a, i, t10, o, n, s); - t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; - let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = Zd(c, l, t10, o, n, s); +var aN = N({ nonMaxSuppressionWithScore_: tj }); +async function rj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { + let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = $o(a, i, t8, o, n, s); + t8 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; + let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = ef(c, l, t8, o, n, s); return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: xr(m, "int32"), selectedScores: xr(d) }; } -var Q1 = E6; -function R6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { - let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = No(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(Ha, m, d); +var iN = rj; +function oj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { + let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = $o(a, i, t8, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(Xa, m, d); return { selectedIndices: f[0], validOutputs: f[1] }; } -var Z1 = N({ nonMaxSuppressionPadded_: R6 }); -async function D6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { - let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = No(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = Qd(m, d, u, c, l, s); +var uN = N({ nonMaxSuppressionPadded_: oj }); +async function nj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { + let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = $o(a, i, t8, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = Jd(m, d, u, c, l, s); return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: xr(f, "int32"), validOutputs: ke(h, "int32") }; } -var J1 = D6; -function A6(r, e, t10 = false, o = false) { +var pN = nj; +function sj(r, e, t8 = false, o = false) { let n = v(r, "images", "resizeBilinear"); - $(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${e}.`), $(o === false || t10 === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."); + $(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${e}.`), $(o === false || t8 === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."); let s = n, a = false; n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); - let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(ns, i, p); + let [] = e, i = { images: s }, p = { alignCorners: t8, halfPixelCenters: o, size: e }, u = T.runKernel(is, i, p); return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; } -var eN = N({ resizeBilinear_: A6 }); -function F6(r, e, t10 = false, o = false) { +var cN = N({ resizeBilinear_: sj }); +function aj(r, e, t8 = false, o = false) { let n = v(r, "images", "resizeNearestNeighbor"); - $(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || t10 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."); + $(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || t8 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."); let s = n, a = false; n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); - let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(os, i, p); + let [] = e, i = { images: s }, p = { alignCorners: t8, halfPixelCenters: o, size: e }, u = T.runKernel(as, i, p); return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; } -var tN = N({ resizeNearestNeighbor_: F6 }); -function P6(r, e = "binary", t10 = false, o = 0.5) { +var lN = N({ resizeNearestNeighbor_: aj }); +function ij(r, e = "binary", t8 = false, o = 0.5) { let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = se(xr([o]), 255), c, l, m, d; if ($(n.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${n.rank}.`), $(n.shape[2] === 3 || n.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`), $(n.dtype === "int32" || n.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`), $(e === "otsu" || e === "binary", () => `Method must be binary or otsu, but was ${e}`), n.shape[2] === 3) { - [c, l, m] = ai(n, [1, 1, 1], -1); + [c, l, m] = pi(n, [1, 1, 1], -1); let g = se(c, s), x = se(l, a), b = se(m, i); - d = be(be(g, x), b); + d = Ce(Ce(g, x), b); } else d = r; if (e === "otsu") { - let g = md(Ye(Vd(d), "int32"), ir([]), 256); - u = O6(g, p); + let g = fd(qe(Ud(d), "int32"), ir([]), 256); + u = uj(g, p); } - let f = t10 ? ic(d, u) : Bu(d, u); - return Ye(se(f, 255), "int32"); + let f = t8 ? uc(d, u) : Uu(d, u); + return qe(se(f, 255), "int32"); } -function O6(r, e) { - let t10 = xr([-1]), o = xr([0]), n = xr([0]), s, a, i, p, u, c; +function uj(r, e) { + let t8 = xr([-1]), o = xr([0]), n = xr([0]), s, a, i, p, u, c; for (let l = 0; l < r.size - 1; l++) { - s = qe(r, 0, l + 1), a = qe(r, l + 1), u = Ke(ot(s), e), c = Ke(ot(a), e); - let m = ot(se(s, au(0, s.size))); - i = Ke(m, ot(s)); - let d = Sa(a.shape, s.size), f = be(au(0, a.size), d), h = se(a, f); - p = Ke(ot(h), ot(a)); + s = Xe(r, 0, l + 1), a = Xe(r, l + 1), u = je(ot(s), e), c = je(ot(a), e); + let m = ot(se(s, cu(0, s.size))); + i = je(m, ot(s)); + let d = Ta(a.shape, s.size), f = Ce(cu(0, a.size), d), h = se(a, f); + p = je(ot(h), ot(a)); let g = Te(i, p), x = Te(i, p), b = se(u, c); n = se(se(b, g), x); - let w = Bu(n, o); - o = io(w, n, o), t10 = io(w, xr([l]), t10); + let C = Uu(n, o); + o = co(C, n, o), t8 = co(C, xr([l]), t8); } - return t10; + return t8; } -var rN = N({ threshold_: P6 }); -function M6(r, e, t10 = "nearest", o = "constant", n = 0, s) { +var mN = N({ threshold_: ij }); +function pj(r, e, t8 = "nearest", o = "constant", n = 0, s) { let a = v(r, "image", "transform", "float32"), i = v(e, "transforms", "transform", "float32"); $(a.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && (i.shape[0] === a.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), $(s == null || s.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${s}.`); - let p = { image: a, transforms: i }, u = { interpolation: t10, fillMode: o, fillValue: n, outputShape: s }; - return T.runKernel(Ts, p, u); + let p = { image: a, transforms: i }, u = { interpolation: t8, fillMode: o, fillValue: n, outputShape: s }; + return T.runKernel(Rs, p, u); } -var oN = N({ transform_: M6 }); -function L6(r, e, t10) { +var dN = N({ transform_: pj }); +function cj(r, e, t8) { let o = v(r, "a", "bandPart"); $(o.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${o.rank}.`); let n = o.shape, [s, a] = o.shape.slice(-2), i, p; - typeof e == "number" ? ($(e % 1 === 0, () => `bandPart(): numLower must be an integer, got ${e}.`), $(e <= s, () => `bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`), i = v(e < 0 ? s : e, "numLower", "bandPart")) : ($(e.dtype === "int32", () => "bandPart(): numLower's dtype must be an int32."), i = io(kl(e, 0), s, Wu(e, s))), typeof t10 == "number" ? ($(t10 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t10}.`), $(t10 <= a, () => `bandPart(): numUpper (${t10}) must not be greater than the number of columns (${a}).`), p = v(t10 < 0 ? a : t10, "numUpper", "bandPart")) : ($(t10.dtype === "int32", () => "bandPart(): numUpper's dtype must be an int32."), p = io(kl(t10, 0), a, Wu(t10, a))); - let u = W(au(0, s, 1, "int32"), [-1, 1]), c = au(0, a, 1, "int32"), l = Te(u, c), m = zu(ic(l, i), Cd(l, pr(p))), d = Wr([s, a], o.dtype); - return W(vr(po(W(o, [-1, s, a])).map((f) => io(m, f, d))), n); + typeof e == "number" ? ($(e % 1 === 0, () => `bandPart(): numLower must be an integer, got ${e}.`), $(e <= s, () => `bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`), i = v(e < 0 ? s : e, "numLower", "bandPart")) : ($(e.dtype === "int32", () => "bandPart(): numLower's dtype must be an int32."), i = co(kl(e, 0), s, Ku(e, s))), typeof t8 == "number" ? ($(t8 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t8}.`), $(t8 <= a, () => `bandPart(): numUpper (${t8}) must not be greater than the number of columns (${a}).`), p = v(t8 < 0 ? a : t8, "numUpper", "bandPart")) : ($(t8.dtype === "int32", () => "bandPart(): numUpper's dtype must be an int32."), p = co(kl(t8, 0), a, Ku(t8, a))); + let u = W(cu(0, s, 1, "int32"), [-1, 1]), c = cu(0, a, 1, "int32"), l = Te(u, c), m = Gu(uc(l, i), Sd(l, pr(p))), d = Gr([s, a], o.dtype); + return W(kr(mo(W(o, [-1, s, a])).map((f) => co(m, f, d))), n); } -var nN = N({ bandPart_: L6 }); -function B6(r) { +var fN = N({ bandPart_: cj }); +function lj(r) { let e; if (Array.isArray(r)) { e = false, $(r != null && r.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); @@ -7902,181 +7981,181 @@ function B6(r) { for (let s = 1; s < r.length; ++s) $(r[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`); } else - e = true, r = ai(r, r.shape[0], 0).map((n) => lc(n, [0])); + e = true, r = pi(r, r.shape[0], 0).map((n) => mc(n, [0])); $(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`); - let t10 = [], o = r; + let t8 = [], o = r; for (let n = 0; n < r.length; ++n) - t10.push(T.tidy(() => { + t8.push(T.tidy(() => { let s = o[n]; if (n > 0) for (let a = 0; a < n; ++a) { - let i = se(ot(se(t10[a], s)), t10[a]); + let i = se(ot(se(t8[a], s)), t8[a]); s = Te(s, i); } - return Ke(s, Lu(s, "euclidean")); + return je(s, Wu(s, "euclidean")); })); - return e ? vr(t10, 0) : t10; + return e ? kr(t8, 0) : t8; } -var sN = N({ gramSchmidt_: B6 }); -function z6(r, e = false) { +var hN = N({ gramSchmidt_: lj }); +function mj(r, e = false) { if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2) - return aN(r, e); + return gN(r, e); { - let t10 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = po(W(r, [t10, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = []; + let t8 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = mo(W(r, [t8, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = []; o.forEach((p) => { - let [u, c] = aN(p, e); + let [u, c] = gN(p, e); n.push(u), s.push(c); }); - let a = W(vr(n, 0), r.shape), i = W(vr(s, 0), r.shape); + let a = W(kr(n, 0), r.shape), i = W(kr(s, 0), r.shape); return [a, i]; } } -function aN(r, e = false) { +function gN(r, e = false) { return T.tidy(() => { $(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`); - let t10 = r.shape[0], o = r.shape[1], n = xd(t10), s = Vr(r), a = uu([[1]], [1, 1]), i = Vr(a), p = t10 >= o ? o : t10; + let t8 = r.shape[0], o = r.shape[1], n = bd(t8), s = Ur(r), a = mu([[1]], [1, 1]), i = Ur(a), p = t8 >= o ? o : t8; for (let u = 0; u < p; ++u) { let c = s, l = i, m = n; [i, s, n] = T.tidy(() => { - let d = qe(s, [u, u], [t10 - u, 1]), f = Lu(d), h = qe(s, [u, u], [1, 1]), g = io(Bu(h, 0), uu([[-1]]), uu([[1]])), x = Te(h, se(g, f)), b = Ke(d, x); - b.shape[0] === 1 ? i = Vr(a) : i = yt([a, qe(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0); - let w = pr(Ke(Qe(g, x), f)), S = qe(s, [u, 0], [t10 - u, o]), k = se(w, i), _ = dc(i); + let d = Xe(s, [u, u], [t8 - u, 1]), f = Wu(d), h = Xe(s, [u, u], [1, 1]), g = co(Uu(h, 0), mu([[-1]]), mu([[1]])), x = Te(h, se(g, f)), b = je(d, x); + b.shape[0] === 1 ? i = Ur(a) : i = bt([a, Xe(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0); + let C = pr(je(Ze(g, x), f)), S = Xe(s, [u, 0], [t8 - u, o]), k = se(C, i), _ = fc(i); if (u === 0) - s = Te(S, Qe(k, Qe(_, S))); + s = Te(S, Ze(k, Ze(_, S))); else { - let D = Te(S, Qe(k, Qe(_, S))); - s = yt([qe(s, [0, 0], [u, o]), D], 0); + let D = Te(S, Ze(k, Ze(_, S))); + s = bt([Xe(s, [0, 0], [u, o]), D], 0); } - let E = dc(k), R = qe(n, [0, u], [t10, n.shape[1] - u]); + let E = fc(k), R = Xe(n, [0, u], [t8, n.shape[1] - u]); if (u === 0) - n = Te(R, Qe(Qe(R, i), E)); + n = Te(R, Ze(Ze(R, i), E)); else { - let D = Te(R, Qe(Qe(R, i), E)); - n = yt([qe(n, [0, 0], [t10, u]), D], 1); + let D = Te(R, Ze(Ze(R, i), E)); + n = bt([Xe(n, [0, 0], [t8, u]), D], 1); } return [i, s, n]; }), Ot([c, l, m]); } - return !e && t10 > o && (n = qe(n, [0, 0], [t10, o]), s = qe(s, [0, 0], [o, o])), [n, s]; + return !e && t8 > o && (n = Xe(n, [0, 0], [t8, o]), s = Xe(s, [0, 0], [o, o])), [n, s]; }); } -var iN = N({ qr_: z6 }); +var xN = N({ qr_: mj }); var Et; (function(r) { r[r.NONE = 0] = "NONE", r[r.MEAN = 1] = "MEAN", r[r.SUM = 2] = "SUM", r[r.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS"; })(Et || (Et = {})); -function V6(r, e, t10 = Et.SUM_BY_NONZERO_WEIGHTS) { +function dj(r, e, t8 = Et.SUM_BY_NONZERO_WEIGHTS) { let o = v(r, "losses", "computeWeightedLoss"), n = null; e != null && (n = v(e, "weights", "computeWeightedLoss")); let s = n == null ? o : se(o, n); - if (t10 === Et.NONE) + if (t8 === Et.NONE) return s; - if (t10 === Et.SUM) + if (t8 === Et.SUM) return ot(s); - if (t10 === Et.MEAN) { + if (t8 === Et.MEAN) { if (n == null) - return Vu(s); + return Hu(s); { - let a = o.size / n.size, i = Ke(ot(s), ot(n)); - return a > 1 ? Ke(i, ke(a)) : i; + let a = o.size / n.size, i = je(ot(s), ot(n)); + return a > 1 ? je(i, ke(a)) : i; } } - if (t10 === Et.SUM_BY_NONZERO_WEIGHTS) { + if (t8 === Et.SUM_BY_NONZERO_WEIGHTS) { if (n == null) - return Ke(ot(s), ke(o.size)); + return je(ot(s), ke(o.size)); { - let a = se(n, va(o.shape)), i = Ye(ot(Rd(a, ke(0))), "float32"); - return Ke(ot(s), i); + let a = se(n, $a(o.shape)), i = qe(ot(Ad(a, ke(0))), "float32"); + return je(ot(s), i); } } - throw Error(`Unknown reduction: ${t10}`); + throw Error(`Unknown reduction: ${t8}`); } -var cr = N({ computeWeightedLoss_: V6 }); -function W6(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) { +var cr = N({ computeWeightedLoss_: dj }); +function fj(r, e, t8, o = Et.SUM_BY_NONZERO_WEIGHTS) { let n = v(r, "labels", "absoluteDifference"), s = v(e, "predictions", "absoluteDifference"), a = null; - t10 != null && (a = v(t10, "weights", "absoluteDifference")), xt(n.shape, s.shape, "Error in absoluteDifference: "); - let i = Zt(Te(n, s)); + t8 != null && (a = v(t8, "weights", "absoluteDifference")), yt(n.shape, s.shape, "Error in absoluteDifference: "); + let i = Jt(Te(n, s)); return cr(i, a, o); } -var uN = N({ absoluteDifference_: W6 }); -function U6(r, e, t10, o, n = Et.SUM_BY_NONZERO_WEIGHTS) { +var yN = N({ absoluteDifference_: fj }); +function hj(r, e, t8, o, n = Et.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "labels", "cosineDistance"), a = v(e, "predictions", "cosineDistance"), i = null; - o != null && (i = v(o, "weights", "cosineDistance")), xt(s.shape, a.shape, "Error in cosineDistance: "); - let p = ke(1), u = Te(p, ot(se(s, a), t10, true)); + o != null && (i = v(o, "weights", "cosineDistance")), yt(s.shape, a.shape, "Error in cosineDistance: "); + let p = ke(1), u = Te(p, ot(se(s, a), t8, true)); return cr(u, i, n); } -var pN = N({ cosineDistance_: U6 }); -function G6(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) { +var bN = N({ cosineDistance_: hj }); +function gj(r, e, t8, o = Et.SUM_BY_NONZERO_WEIGHTS) { let n = v(r, "labels", "hingeLoss"), s = v(e, "predictions", "hingeLoss"), a = null; - t10 != null && (a = v(t10, "weights", "hingeLoss")), xt(n.shape, s.shape, "Error in hingeLoss: "); + t8 != null && (a = v(t8, "weights", "hingeLoss")), yt(n.shape, s.shape, "Error in hingeLoss: "); let i = ke(1); n = Te(se(ke(2), n), i); - let p = iu(Te(i, se(n, s))); + let p = lu(Te(i, se(n, s))); return cr(p, a, o); } -var cN = N({ hingeLoss_: G6 }); -function H6(r, e, t10, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) { +var CN = N({ hingeLoss_: gj }); +function xj(r, e, t8, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "labels", "huberLoss"), a = v(e, "predictions", "huberLoss"), i = null; - t10 != null && (i = v(t10, "weights", "huberLoss")), xt(s.shape, a.shape, "Error in huberLoss: "); - let p = ke(o), u = Zt(Te(a, s)), c = Wu(u, p), l = Te(u, c), m = be(se(ke(0.5), Jt(c)), se(p, l)); + t8 != null && (i = v(t8, "weights", "huberLoss")), yt(s.shape, a.shape, "Error in huberLoss: "); + let p = ke(o), u = Jt(Te(a, s)), c = Ku(u, p), l = Te(u, c), m = Ce(se(ke(0.5), er(c)), se(p, l)); return cr(m, i, n); } -var lN = N({ huberLoss_: H6 }); -function K6(r, e, t10, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) { +var wN = N({ huberLoss_: xj }); +function yj(r, e, t8, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "labels", "logLoss"), a = v(e, "predictions", "logLoss"), i = null; - t10 != null && (i = v(t10, "weights", "logLoss")), xt(s.shape, a.shape, "Error in logLoss: "); - let p = ke(1), u = ke(o), c = pr(se(s, ni(be(a, u)))), l = se(Te(p, s), ni(be(Te(p, a), u))), m = Te(c, l); + t8 != null && (i = v(t8, "weights", "logLoss")), yt(s.shape, a.shape, "Error in logLoss: "); + let p = ke(1), u = ke(o), c = pr(se(s, ii(Ce(a, u)))), l = se(Te(p, s), ii(Ce(Te(p, a), u))), m = Te(c, l); return cr(m, i, n); } -var mN = N({ logLoss_: K6 }); -function q6(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) { +var SN = N({ logLoss_: yj }); +function bj(r, e, t8, o = Et.SUM_BY_NONZERO_WEIGHTS) { let n = v(r, "labels", "meanSquaredError"), s = v(e, "predictions", "meanSquaredError"), a = null; - t10 != null && (a = v(t10, "weights", "meanSquaredError")), xt(n.shape, s.shape, "Error in meanSquaredError: "); - let i = Ud(n, s); + t8 != null && (a = v(t8, "weights", "meanSquaredError")), yt(n.shape, s.shape, "Error in meanSquaredError: "); + let i = Hd(n, s); return cr(i, a, o); } -var dN = N({ meanSquaredError_: q6 }); -function j6(r, e) { - let t10 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits"); - xt(t10.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: "); - let n = iu(o), s = se(o, t10), a = Sd(ko(pr(Zt(o)))); - return be(Te(n, s), a); +var IN = N({ meanSquaredError_: bj }); +function Cj(r, e) { + let t8 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits"); + yt(t8.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: "); + let n = lu(o), s = se(o, t8), a = vd(_o(pr(Jt(o)))); + return Ce(Te(n, s), a); } -function X6(r, e, t10, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { +function wj(r, e, t8, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "multiClassLabels", "sigmoidCrossEntropy"), a = v(e, "logits", "sigmoidCrossEntropy"), i = null; - if (t10 != null && (i = v(t10, "weights", "sigmoidCrossEntropy")), xt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) { + if (t8 != null && (i = v(t8, "weights", "sigmoidCrossEntropy")), yt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) { let u = ke(o), c = ke(1), l = ke(0.5); - s = be(se(s, Te(c, u)), se(l, u)); + s = Ce(se(s, Te(c, u)), se(l, u)); } - let p = j6(s, a); + let p = Cj(s, a); return cr(p, i, n); } -var fN = N({ sigmoidCrossEntropy_: X6 }); -function Y6(r, e, t10 = -1) { - if (t10 === -1 && (t10 = e.rank - 1), t10 !== e.rank - 1) - throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t10}`); - return Ir((n, s, a) => { - let p = kd(s, [t10], true), u = Te(Ye(s, "float32"), p); +var vN = N({ sigmoidCrossEntropy_: wj }); +function Sj(r, e, t8 = -1) { + if (t8 === -1 && (t8 = e.rank - 1), t8 !== e.rank - 1) + throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t8}`); + return vr((n, s, a) => { + let p = Td(s, [t8], true), u = Te(qe(s, "float32"), p); a([n, u]); let c = pr(se(u, n)); - return { value: ot(c, [t10]), gradFunc: (d, f) => { - let [h, g] = f, x = ti(d.shape, [t10]); - return [se(W(d, x), Te(Ye(h, "float32"), ko(g))), se(W(d, x), Te(ko(g), Ye(h, "float32")))]; + return { value: ot(c, [t8]), gradFunc: (d, f) => { + let [h, g] = f, x = ni(d.shape, [t8]); + return [se(W(d, x), Te(qe(h, "float32"), _o(g))), se(W(d, x), Te(_o(g), qe(h, "float32")))]; } }; })(r, e); } -function Q6(r, e, t10, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { +function Ij(r, e, t8, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "onehotLabels", "softmaxCrossEntropy"), a = v(e, "logits", "softmaxCrossEntropy"), i = null; - if (t10 != null && (i = v(t10, "weights", "softmaxCrossEntropy")), xt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) { + if (t8 != null && (i = v(t8, "weights", "softmaxCrossEntropy")), yt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) { let u = ke(o), c = ke(1), l = ke(s.shape[1]); - s = be(se(s, Te(c, u)), Ke(u, l)); + s = Ce(se(s, Te(c, u)), je(u, l)); } - let p = Y6(s, a); + let p = Sj(s, a); return cr(p, i, n); } -var hN = N({ softmaxCrossEntropy_: Q6 }); -function Z6(r, e, t10, o) { - let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t10, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype); +var kN = N({ softmaxCrossEntropy_: Ij }); +function vj(r, e, t8, o) { + let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t8, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype); if (n.rank !== 2) throw new Error(`Indices should be Tensor2D but received shape ${n.shape}`); @@ -8086,12 +8165,12 @@ function Z6(r, e, t10, o) { throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`); if (i.rank !== 0) throw new Error(`Default value should be a scalar but received shape ${i.shape}`); - let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(Vi, p); + let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(Hi, p); return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] }; } -var gN = N({ sparseFillEmptyRows_: Z6 }); -function J6(r, e, t10) { - let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t10, "newShape", "sparseReshape", "int32"); +var NN = N({ sparseFillEmptyRows_: vj }); +function kj(r, e, t8) { + let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t8, "newShape", "sparseReshape", "int32"); if (o.rank !== 2) throw new Error(`Input indices should be Tensor2D but received shape ${o.shape}`); @@ -8099,12 +8178,12 @@ function J6(r, e, t10) { throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`); if (s.rank !== 1) throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`); - let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(Xa, a); + let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(Za, a); return { outputIndices: i[0], outputShape: i[1] }; } -var xN = N({ sparseReshape_: J6 }); -function ej(r, e, t10) { - let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t10, "segmentIds", "sparseSegmentMean", "int32"); +var TN = N({ sparseReshape_: kj }); +function Nj(r, e, t8) { + let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t8, "segmentIds", "sparseSegmentMean", "int32"); if (o.rank < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.rank !== 1) @@ -8114,11 +8193,11 @@ function ej(r, e, t10) { throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`); let a = { data: o, indices: n, segmentIds: s }; - return T.runKernel(Wi, a); + return T.runKernel(Ki, a); } -var yN = N({ sparseSegmentMean_: ej }); -function tj(r, e, t10) { - let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t10, "segmentIds", "sparseSegmentSum", "int32"); +var _N = N({ sparseSegmentMean_: Nj }); +function Tj(r, e, t8) { + let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t8, "segmentIds", "sparseSegmentSum", "int32"); if (o.rank < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.rank !== 1) @@ -8128,10 +8207,10 @@ function tj(r, e, t10) { throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`); let a = { data: o, indices: n, segmentIds: s }; - return T.runKernel(Ui, a); + return T.runKernel(qi, a); } -var bN = N({ sparseSegmentSum_: tj }); -function rj(r, e, t10, o, n, s, a, i) { +var $N = N({ sparseSegmentSum_: Tj }); +function _j(r, e, t8, o, n, s, a, i) { let p = v(r, "data", "stringNGrams", "string"); if (p.dtype !== "string") throw new Error("Data must be of datatype string"); @@ -8140,73 +8219,73 @@ function rj(r, e, t10, o, n, s, a, i) { let u = v(e, "dataSplits", "stringNGrams"); if (u.dtype !== "int32") throw new Error("Data splits must be of datatype int32"); - let c = { separator: t10, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(ma, l, c); + let c = { separator: t8, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(xa, l, c); return { nGrams: m[0], nGramsSplits: m[1] }; } -var CN = N({ stringNGrams_: rj }); -function oj(r, e, t10 = true) { +var EN = N({ stringNGrams_: _j }); +function $j(r, e, t8 = true) { let o = v(r, "input", "stringSplit", "string"), n = v(e, "delimiter", "stringSplit", "string"); if (o.rank !== 1) throw new Error(`Input should be Tensor1D but received shape ${o.shape}`); if (n.rank !== 0) throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`); - let s = { skipEmpty: t10 }, a = { input: o, delimiter: n }, i = T.runKernel(Hi, a, s); + let s = { skipEmpty: t8 }, a = { input: o, delimiter: n }, i = T.runKernel(Xi, a, s); return { indices: i[0], values: i[1], shape: i[2] }; } -var wN = N({ stringSplit_: oj }); -function nj(r, e) { - let t10 = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: e }; +var RN = N({ stringSplit_: $j }); +function Ej(r, e) { + let t8 = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: e }; if (e <= 0) throw new Error("Number of buckets must be at least 1"); - let n = { input: t10 }; - return T.runKernel(Ki, n, o); + let n = { input: t8 }; + return T.runKernel(Yi, n, o); } -var SN = N({ stringToHashBucketFast_: nj }); -function sj(r, e, t10, o = true) { - let n = v(r, "input", "staticRegexReplace", "string"), s = { pattern: e, rewrite: t10, replaceGlobal: o }; - return T.runKernel(_u, { x: n }, s); +var DN = N({ stringToHashBucketFast_: Ej }); +function Rj(r, e, t8, o = true) { + let n = v(r, "input", "staticRegexReplace", "string"), s = { pattern: e, rewrite: t8, replaceGlobal: o }; + return T.runKernel(Du, { x: n }, s); } -var IN = N({ staticRegexReplace_: sj }); -var aj = { fft: pc, ifft: Hu, rfft: cc, irfft: Wd }; -var ij = { hammingWindow: z1, hannWindow: jd, frame: Xd, stft: V1 }; -var uj = { flipLeftRight: U1, grayscaleToRGB: G1, resizeNearestNeighbor: tN, resizeBilinear: eN, rotateWithOffset: H1, cropAndResize: W1, nonMaxSuppression: K1, nonMaxSuppressionAsync: X1, nonMaxSuppressionWithScore: Y1, nonMaxSuppressionWithScoreAsync: Q1, nonMaxSuppressionPadded: Z1, nonMaxSuppressionPaddedAsync: J1, threshold: rN, transform: oN }; -var pj = { bandPart: nN, gramSchmidt: sN, qr: iN }; -var cj = { absoluteDifference: uN, computeWeightedLoss: cr, cosineDistance: pN, hingeLoss: cN, huberLoss: lN, logLoss: mN, meanSquaredError: dN, sigmoidCrossEntropy: fN, softmaxCrossEntropy: hN }; -var lj = { sparseFillEmptyRows: gN, sparseReshape: xN, sparseSegmentMean: yN, sparseSegmentSum: bN }; -var mj = { stringNGrams: CN, stringSplit: wN, stringToHashBucketFast: SN, staticRegexReplace: IN }; -var vN = {}; -He(vN, { Serializable: () => $l, SerializationMap: () => Na, registerClass: () => Lw }); +var AN = N({ staticRegexReplace_: Rj }); +var Dj = { fft: cc, ifft: Xu, rfft: lc, irfft: Gd }; +var Aj = { hammingWindow: Y1, hannWindow: Yd, frame: Qd, stft: Q1 }; +var Fj = { flipLeftRight: J1, grayscaleToRGB: eN, resizeNearestNeighbor: lN, resizeBilinear: cN, rotateWithOffset: tN, cropAndResize: Z1, nonMaxSuppression: rN, nonMaxSuppressionAsync: sN, nonMaxSuppressionWithScore: aN, nonMaxSuppressionWithScoreAsync: iN, nonMaxSuppressionPadded: uN, nonMaxSuppressionPaddedAsync: pN, threshold: mN, transform: dN }; +var Pj = { bandPart: fN, gramSchmidt: hN, qr: xN }; +var Oj = { absoluteDifference: yN, computeWeightedLoss: cr, cosineDistance: bN, hingeLoss: CN, huberLoss: wN, logLoss: SN, meanSquaredError: IN, sigmoidCrossEntropy: vN, softmaxCrossEntropy: kN }; +var Mj = { sparseFillEmptyRows: NN, sparseReshape: TN, sparseSegmentMean: _N, sparseSegmentSum: $N }; +var Lj = { stringNGrams: EN, stringSplit: RN, stringToHashBucketFast: DN, staticRegexReplace: AN }; +var FN = {}; +Ke(FN, { Serializable: () => $l, SerializationMap: () => Ra, registerClass: () => qw }); var $l = class { getClassName() { return this.constructor.className; } - static fromConfig(e, t10) { - return new e(t10); + static fromConfig(e, t8) { + return new e(t8); } }; -var Na = class { +var Ra = class { constructor() { this.classNameMap = {}; } static getMap() { - return Na.instance == null && (Na.instance = new Na()), Na.instance; + return Ra.instance == null && (Ra.instance = new Ra()), Ra.instance; } static register(e) { - Na.getMap().classNameMap[e.className] = [e, e.fromConfig]; + Ra.getMap().classNameMap[e.className] = [e, e.fromConfig]; } }; -function Lw(r) { - $(r.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), $(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Na.register(r); +function qw(r) { + $(r.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), $(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Ra.register(r); } -var kr = class extends $l { - minimize(e, t10 = false, o) { +var Nr = class extends $l { + minimize(e, t8 = false, o) { let { value: n, grads: s } = this.computeGradients(e, o); if (o != null) { let a = o.map((i) => ({ name: i.name, tensor: s[i.name] })); this.applyGradients(a); } else this.applyGradients(s); - return Ot(s), t10 ? n : (n.dispose(), null); + return Ot(s), t8 ? n : (n.dispose(), null); } get iterations() { return this.iterations_ == null && (this.iterations_ = 0), this.iterations_; @@ -8214,8 +8293,8 @@ var kr = class extends $l { incrementIterations() { this.iterations_ = this.iterations + 1; } - computeGradients(e, t10) { - return vw(e, t10); + computeGradients(e, t8) { + return Aw(e, t8); } dispose() { this.iterations_ != null && Ot(this.iterations_); @@ -8233,13 +8312,13 @@ var kr = class extends $l { return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1); } }; -Object.defineProperty(kr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null }); -var Yu = class extends kr { +Object.defineProperty(Nr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null }); +var ep = class extends Nr { static get className() { return "Adadelta"; } - constructor(e, t10, o = null) { - super(), this.learningRate = e, this.rho = t10, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon()); + constructor(e, t8, o = null) { + super(), this.learningRate = e, this.rho = t8, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon()); } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { @@ -8250,9 +8329,9 @@ var Yu = class extends kr { return; let p = this.accumulatedGrads[n].variable, u = this.accumulatedUpdates[n].variable; De(() => { - let c = be(se(p, this.rho), se(Jt(i), 1 - this.rho)), l = se(Ke(Rr(be(u, this.epsilon)), Rr(be(p, this.epsilon))), i), m = be(se(u, this.rho), se(Jt(l), 1 - this.rho)); + let c = Ce(se(p, this.rho), se(er(i), 1 - this.rho)), l = se(je(Dr(Ce(u, this.epsilon)), Dr(Ce(p, this.epsilon))), i), m = Ce(se(u, this.rho), se(er(l), 1 - this.rho)); p.assign(c), u.assign(m); - let d = be(se(l, -this.learningRate), s); + let d = Ce(se(l, -this.learningRate), s); s.assign(d); }); }), this.incrementIterations(); @@ -8262,39 +8341,39 @@ var Yu = class extends kr { } async getWeights() { let e = [...this.accumulatedGrads, ...this.accumulatedUpdates]; - return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable }))); + return [await this.saveIterations()].concat(e.map((t8) => ({ name: t8.originalName, tensor: t8.variable }))); } async setWeights(e) { e = await this.extractIterations(e); - let t10 = e.length / 2, o = false; - this.accumulatedGrads = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); + let t8 = e.length / 2, o = false; + this.accumulatedGrads = e.slice(0, t8).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t8, t8 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); } getConfig() { return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon }; } - static fromConfig(e, t10) { - return new e(t10.learningRate, t10.rho, t10.epsilon); + static fromConfig(e, t8) { + return new e(t8.learningRate, t8.rho, t8.epsilon); } }; -var Qu = class extends kr { +var tp = class extends Nr { static get className() { return "Adagrad"; } - constructor(e, t10 = 0.1) { - super(), this.learningRate = e, this.initialAccumulatorValue = t10, this.accumulatedGrads = []; + constructor(e, t8 = 0.1) { + super(), this.learningRate = e, this.initialAccumulatorValue = t8, this.accumulatedGrads = []; } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { let s = T.registeredVariables[o]; - this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: De(() => Sa(s.shape, this.initialAccumulatorValue).variable(false)) }); + this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: De(() => Ta(s.shape, this.initialAccumulatorValue).variable(false)) }); let a = Array.isArray(e) ? e[n].tensor : e[o]; if (a == null) return; let i = this.accumulatedGrads[n].variable; De(() => { - let p = be(i, Jt(a)); + let p = Ce(i, er(a)); i.assign(p); - let u = be(se(Ke(a, Rr(be(p, T.backend.epsilon()))), -this.learningRate), s); + let u = Ce(se(je(a, Dr(Ce(p, T.backend.epsilon()))), -this.learningRate), s); s.assign(u); }); }), this.incrementIterations(); @@ -8307,38 +8386,38 @@ var Qu = class extends kr { } async setWeights(e) { e = await this.extractIterations(e); - let t10 = false; - this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) })); + let t8 = false; + this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t8) })); } getConfig() { return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue }; } - static fromConfig(e, t10) { - return new e(t10.learningRate, t10.initialAccumulatorValue); + static fromConfig(e, t8) { + return new e(t8.learningRate, t8.initialAccumulatorValue); } }; -var Zu = class extends kr { +var rp = class extends Nr { static get className() { return "Adam"; } - constructor(e, t10, o, n = null) { - super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], De(() => { - this.accBeta1 = ke(t10).variable(), this.accBeta2 = ke(o).variable(); + constructor(e, t8, o, n = null) { + super(), this.learningRate = e, this.beta1 = t8, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], De(() => { + this.accBeta1 = ke(t8).variable(), this.accBeta2 = ke(o).variable(); }), n == null && (this.epsilon = T.backend.epsilon()); } applyGradients(e) { - let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); + let t8 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); De(() => { let o = Te(1, this.accBeta1), n = Te(1, this.accBeta2); - t10.forEach((s, a) => { + t8.forEach((s, a) => { let i = T.registeredVariables[s], p = false; this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: De(() => Ht(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: De(() => Ht(i).variable(p)) }); let u = Array.isArray(e) ? e[a].tensor : e[s]; if (u == null) return; - let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = be(se(c, this.beta1), se(u, 1 - this.beta1)), d = be(se(l, this.beta2), se(Jt(u), 1 - this.beta2)), f = Ke(m, o), h = Ke(d, n); + let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = Ce(se(l, this.beta2), se(er(u), 1 - this.beta2)), f = je(m, o), h = je(d, n); c.assign(m), l.assign(d); - let g = be(se(Ke(f, be(Rr(h), this.epsilon)), -this.learningRate), i); + let g = Ce(se(je(f, Ce(Dr(h), this.epsilon)), -this.learningRate), i); i.assign(g); }), this.accBeta1.assign(se(this.accBeta1, this.beta1)), this.accBeta2.assign(se(this.accBeta2, this.beta2)); }), this.incrementIterations(); @@ -8348,46 +8427,46 @@ var Zu = class extends kr { } async getWeights() { let e = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment]; - return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable }))); + return [await this.saveIterations()].concat(e.map((t8) => ({ name: t8.originalName, tensor: t8.variable }))); } async setWeights(e) { e = await this.extractIterations(e), De(() => { - this.accBeta1.assign(ri(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ri(this.beta2, this.iterations_ + 1)); + this.accBeta1.assign(si(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(si(this.beta2, this.iterations_ + 1)); }); - let t10 = e.length / 2, o = false; - this.accumulatedFirstMoment = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); + let t8 = e.length / 2, o = false; + this.accumulatedFirstMoment = e.slice(0, t8).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t8, t8 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); } getConfig() { return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon }; } - static fromConfig(e, t10) { - return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon); + static fromConfig(e, t8) { + return new e(t8.learningRate, t8.beta1, t8.beta2, t8.epsilon); } }; -var Ju = class extends kr { +var op = class extends Nr { static get className() { return "Adamax"; } - constructor(e, t10, o, n = null, s = 0) { - super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], De(() => { - this.iteration = ke(0).variable(), this.accBeta1 = ke(t10).variable(); + constructor(e, t8, o, n = null, s = 0) { + super(), this.learningRate = e, this.beta1 = t8, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], De(() => { + this.iteration = ke(0).variable(), this.accBeta1 = ke(t8).variable(); }), n == null && (this.epsilon = T.backend.epsilon()); } applyGradients(e) { - let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); + let t8 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); De(() => { - let o = Te(1, this.accBeta1), n = Ke(-this.learningRate, be(se(this.iteration, this.decay), 1)); - t10.forEach((s, a) => { + let o = Te(1, this.accBeta1), n = je(-this.learningRate, Ce(se(this.iteration, this.decay), 1)); + t8.forEach((s, a) => { let i = T.registeredVariables[s], p = false; this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ht(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Ht(i).variable(p) }); let u = Array.isArray(e) ? e[a].tensor : e[s]; if (u == null) return; - let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = be(se(c, this.beta1), se(u, 1 - this.beta1)), d = se(l, this.beta2), f = Zt(u), h = Ed(d, f); + let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = se(l, this.beta2), f = Jt(u), h = Dd(d, f); c.assign(m), l.assign(h); - let g = be(se(Ke(n, o), Ke(m, be(h, this.epsilon))), i); + let g = Ce(se(je(n, o), je(m, Ce(h, this.epsilon))), i); i.assign(g); - }), this.iteration.assign(be(this.iteration, 1)), this.accBeta1.assign(se(this.accBeta1, this.beta1)); + }), this.iteration.assign(Ce(this.iteration, 1)), this.accBeta1.assign(se(this.accBeta1, this.beta1)); }), this.incrementIterations(); } dispose() { @@ -8402,11 +8481,11 @@ var Ju = class extends kr { getConfig() { return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon, decay: this.decay }; } - static fromConfig(e, t10) { - return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon, t10.decay); + static fromConfig(e, t8) { + return new e(t8.learningRate, t8.beta1, t8.beta2, t8.epsilon, t8.decay); } }; -var ii = class extends kr { +var ci = class extends Nr { static get className() { return "SGD"; } @@ -8420,13 +8499,13 @@ var ii = class extends kr { return; let a = T.registeredVariables[o]; De(() => { - let i = be(se(this.c, s), a); + let i = Ce(se(this.c, s), a); a.assign(i); }); }), this.incrementIterations(); } setLearningRate(e) { - this.learningRate = e, this.c != null && this.c.dispose(), this.c = Er(ke(-e)); + this.learningRate = e, this.c != null && this.c.dispose(), this.c = Rr(ke(-e)); } dispose() { this.c.dispose(); @@ -8441,16 +8520,16 @@ var ii = class extends kr { getConfig() { return { learningRate: this.learningRate }; } - static fromConfig(e, t10) { - return new e(t10.learningRate); + static fromConfig(e, t8) { + return new e(t8.learningRate); } }; -var ep = class extends ii { +var np = class extends ci { static get className() { return "Momentum"; } - constructor(e, t10, o = false) { - super(e), this.learningRate = e, this.momentum = t10, this.useNesterov = o, this.accumulations = [], this.m = ke(this.momentum); + constructor(e, t8, o = false) { + super(e), this.learningRate = e, this.momentum = t8, this.useNesterov = o, this.accumulations = [], this.m = ke(this.momentum); } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { @@ -8458,8 +8537,8 @@ var ep = class extends ii { this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: De(() => Ht(s).variable(false)) }); let a = this.accumulations[n].variable, i = Array.isArray(e) ? e[n].tensor : e[o]; i != null && De(() => { - let p, u = be(se(this.m, a), i); - this.useNesterov ? p = be(se(this.c, be(i, se(u, this.m))), s) : p = be(se(this.c, u), s), a.assign(u), s.assign(p); + let p, u = Ce(se(this.m, a), i); + this.useNesterov ? p = Ce(se(this.c, Ce(i, se(u, this.m))), s) : p = Ce(se(this.c, u), s), a.assign(u), s.assign(p); }); }), this.incrementIterations(); } @@ -8474,22 +8553,22 @@ var ep = class extends ii { } async setWeights(e) { e = await this.extractIterations(e); - let t10 = false; - this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) })); + let t8 = false; + this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t8) })); } getConfig() { return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov }; } - static fromConfig(e, t10) { - return new e(t10.learningRate, t10.momentum, t10.useNesterov); + static fromConfig(e, t8) { + return new e(t8.learningRate, t8.momentum, t8.useNesterov); } }; -var tp = class extends kr { +var sp = class extends Nr { static get className() { return "RMSProp"; } - constructor(e, t10 = 0.9, o = 0, n = null, s = false) { - if (super(), this.learningRate = e, this.decay = t10, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null) + constructor(e, t8 = 0.9, o = 0, n = null, s = false) { + if (super(), this.learningRate = e, this.decay = t8, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null) throw new Error("learningRate for RMSPropOptimizer must be defined."); } applyGradients(e) { @@ -8501,14 +8580,14 @@ var tp = class extends kr { return; let p = this.accumulatedMeanSquares[n].variable, u = this.accumulatedMoments[n].variable; De(() => { - let c = be(se(p, this.decay), se(Jt(i), 1 - this.decay)); + let c = Ce(se(p, this.decay), se(er(i), 1 - this.decay)); if (this.centered) { - let l = this.accumulatedMeanGrads[n].variable, m = be(se(l, this.decay), se(i, 1 - this.decay)), d = Ke(se(i, this.learningRate), Rr(Te(c, be(Jt(m), this.epsilon)))), f = be(se(u, this.momentum), d); + let l = this.accumulatedMeanGrads[n].variable, m = Ce(se(l, this.decay), se(i, 1 - this.decay)), d = je(se(i, this.learningRate), Dr(Te(c, Ce(er(m), this.epsilon)))), f = Ce(se(u, this.momentum), d); p.assign(c), l.assign(m), u.assign(f); let h = Te(s, f); s.assign(h); } else { - let l = be(se(p, this.decay), se(Jt(i), 1 - this.decay)), m = be(se(u, this.momentum), Ke(se(i, this.learningRate), Rr(be(l, this.epsilon)))); + let l = Ce(se(p, this.decay), se(er(i), 1 - this.decay)), m = Ce(se(u, this.momentum), je(se(i, this.learningRate), Dr(Ce(l, this.epsilon)))); p.assign(l), u.assign(m); let d = Te(s, m); s.assign(d); @@ -8521,180 +8600,128 @@ var tp = class extends kr { } async getWeights() { let e = [...this.accumulatedMeanSquares, ...this.accumulatedMoments]; - return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable }))); + return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t8) => ({ name: t8.originalName, tensor: t8.variable }))); } async setWeights(e) { e = await this.extractIterations(e); - let t10 = this.centered ? e.length / 3 : e.length / 2, o = false; - this.accumulatedMeanSquares = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t10 * 2, t10 * 3).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }))); + let t8 = this.centered ? e.length / 3 : e.length / 2, o = false; + this.accumulatedMeanSquares = e.slice(0, t8).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t8, t8 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t8 * 2, t8 * 3).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }))); } getConfig() { return { learningRate: this.learningRate, decay: this.decay, momentum: this.momentum, epsilon: this.epsilon, centered: this.centered }; } - static fromConfig(e, t10) { - return new e(t10.learningRate, t10.decay, t10.momentum, t10.epsilon, t10.centered); + static fromConfig(e, t8) { + return new e(t8.learningRate, t8.decay, t8.momentum, t8.epsilon, t8.centered); } }; -var dj = [Yu, Qu, Zu, Ju, ep, tp, ii]; -function kN() { - for (let r of dj) - Lw(r); +var Bj = [ep, tp, rp, op, np, sp, ci]; +function PN() { + for (let r of Bj) + qw(r); } -var pi = {}; -He(pi, { browserFiles: () => TN, browserHTTPRequest: () => EN, concatenateArrayBuffers: () => tc, copyModel: () => ok, decodeWeights: () => rd, encodeWeights: () => V0, fromMemory: () => RN, fromMemorySync: () => Gw, getLoadHandlers: () => q0, getModelArtifactsForJSON: () => rc, getModelArtifactsForJSONSync: () => mw, getModelArtifactsInfoForJSON: () => ga, getSaveHandlers: () => K0, getWeightSpecs: () => nd, http: () => tf, isHTTPScheme: () => ef, listModels: () => tk, loadWeights: () => _N, moveModel: () => nk, registerLoadRouter: () => H0, registerSaveRouter: () => G0, removeModel: () => rk, weightsLoaderFactory: () => Ww, withSaveHandler: () => DN, withSaveHandlerSync: () => AN }); -var fj = "model"; -var hj = ".json"; -var gj = ".weights.bin"; -function NN(r) { +var mi = {}; +Ke(mi, { CompositeArrayBuffer: () => jt, browserFiles: () => MN, browserHTTPRequest: () => zN, concatenateArrayBuffers: () => J0, copyModel: () => dk, decodeWeights: () => nd, encodeWeights: () => Y0, fromMemory: () => VN, fromMemorySync: () => Jw, getLoadHandlers: () => ok, getModelArtifactsForJSON: () => oc, getModelArtifactsForJSONSync: () => ww, getModelArtifactsInfoForJSON: () => wa, getSaveHandlers: () => rk, getWeightSpecs: () => ad, http: () => rf, isHTTPScheme: () => tf, listModels: () => lk, loadWeights: () => LN, moveModel: () => fk, registerLoadRouter: () => tk, registerSaveRouter: () => ek, removeModel: () => mk, weightsLoaderFactory: () => Qw, withSaveHandler: () => WN, withSaveHandlerSync: () => UN }); +var zj = "model"; +var Vj = ".json"; +var Wj = ".weights.bin"; +function ON(r) { return new Promise((e) => setTimeout(e)).then(r); } -var ui = class { +var li = class { constructor(e) { - if (!P().getBool("IS_BROWSER")) + if (!A().getBool("IS_BROWSER")) throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); - e.startsWith(ui.URL_SCHEME) && (e = e.slice(ui.URL_SCHEME.length)), (e == null || e.length === 0) && (e = fj), this.modelJsonFileName = e + hj, this.weightDataFileName = e + gj; + e.startsWith(li.URL_SCHEME) && (e = e.slice(li.URL_SCHEME.length)), (e == null || e.length === 0) && (e = zj), this.modelJsonFileName = e + Vj, this.weightDataFileName = e + Wj; } async save(e) { if (typeof document == "undefined") throw new Error("Browser downloads are not supported in this environment since `document` is not present"); - let t10 = window.URL.createObjectURL(new Blob([e.weightData], { type: "application/octet-stream" })); + let t8 = jt.join(e.weightData), o = window.URL.createObjectURL(new Blob([t8], { type: "application/octet-stream" })); if (e.modelTopology instanceof ArrayBuffer) throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); { - let o = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], n = od(e, o), s = window.URL.createObjectURL(new Blob([JSON.stringify(n)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; - if (a.download = this.modelJsonFileName, a.href = s, await NN(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { - let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; - i.download = this.weightDataFileName, i.href = t10, await NN(() => i.dispatchEvent(new MouseEvent("click"))); + let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = sd(e, n), a = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), i = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + if (i.download = this.modelJsonFileName, i.href = a, await ON(() => i.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { + let p = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + p.download = this.weightDataFileName, p.href = o, await ON(() => p.dispatchEvent(new MouseEvent("click"))); } - return { modelArtifactsInfo: ga(e) }; + return { modelArtifactsInfo: wa(e) }; } } }; -ui.URL_SCHEME = "downloads://"; -var Bw = class { +li.URL_SCHEME = "downloads://"; +var jw = class { constructor(e) { if (e == null || e.length < 1) throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`); this.jsonFile = e[0], this.weightsFiles = e.slice(1); } async load() { - return new Promise((e, t10) => { + return new Promise((e, t8) => { let o = new FileReader(); o.onload = (n) => { let s = JSON.parse(n.target.result), a = s.modelTopology; if (a == null) { - t10(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); + t8(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); return; } if (s.weightsManifest == null) { - t10(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); + t8(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); return; } if (this.weightsFiles.length === 0) { e({ modelTopology: a }); return; } - let p = rc(s, (u) => this.loadWeights(u)); + let p = oc(s, (u) => this.loadWeights(u)); e(p); - }, o.onerror = (n) => t10(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile); + }, o.onerror = (n) => t8(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile); }); } loadWeights(e) { - let t10 = [], o = []; + let t8 = [], o = []; for (let a of e) - t10.push(...a.weights), o.push(...a.paths); + t8.push(...a.weights), o.push(...a.paths); let n = this.checkManifestAndWeightFiles(e), s = o.map((a) => this.loadWeightsFile(a, n[a])); - return Promise.all(s).then((a) => [t10, tc(a)]); + return Promise.all(s).then((a) => [t8, a]); } - loadWeightsFile(e, t10) { + loadWeightsFile(e, t8) { return new Promise((o, n) => { let s = new FileReader(); s.onload = (a) => { let i = a.target.result; o(i); - }, s.onerror = (a) => n(`Failed to weights data from file of path '${e}'.`), s.readAsArrayBuffer(t10); + }, s.onerror = (a) => n(`Failed to weights data from file of path '${e}'.`), s.readAsArrayBuffer(t8); }); } checkManifestAndWeightFiles(e) { - let t10 = [], o = this.weightsFiles.map((s) => lw(s.name)), n = {}; + let t8 = [], o = this.weightsFiles.map((s) => Cw(s.name)), n = {}; for (let s of e) s.paths.forEach((a) => { - let i = lw(a); - if (t10.indexOf(i) !== -1) + let i = Cw(a); + if (t8.indexOf(i) !== -1) throw new Error(`Duplicate file basename found in weights manifest: '${i}'`); - if (t10.push(i), o.indexOf(i) === -1) + if (t8.push(i), o.indexOf(i) === -1) throw new Error(`Weight file with basename '${i}' is not provided.`); n[a] = this.weightsFiles[o.indexOf(i)]; }); - if (t10.length !== this.weightsFiles.length) - throw new Error(`Mismatch in the number of files in weights manifest (${t10.length}) and the number of weight files provided (${this.weightsFiles.length}).`); + if (t8.length !== this.weightsFiles.length) + throw new Error(`Mismatch in the number of files in weights manifest (${t8.length}) and the number of weight files provided (${this.weightsFiles.length}).`); return n; } }; -var xj = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(ui.URL_SCHEME) ? yj(r.slice(ui.URL_SCHEME.length)) : null; -ft.registerSaveRouter(xj); -function yj(r = "model") { - return new ui(r); +var Uj = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(li.URL_SCHEME) ? Gj(r.slice(li.URL_SCHEME.length)) : null; +ft.registerSaveRouter(Uj); +function Gj(r = "model") { + return new li(r); } -function TN(r) { - return new Bw(r); +function MN(r) { + return new jw(r); } -var Jd = class { - constructor(e) { - if (this.shards = [], this.previousShardIndex = 0, e instanceof Array || (e = [e]), e = e.map((o) => Pt(o) ? o.buffer : o), e.length === 0) - return; - this.bufferUniformSize = e[0].byteLength; - let t10 = 0; - for (let o = 0; o < e.length; o++) { - let n = e[o]; - o !== e.length - 1 && n.byteLength !== this.bufferUniformSize && (this.bufferUniformSize = void 0); - let s = t10 + n.byteLength; - this.shards.push({ buffer: n, start: t10, end: s }), t10 = s; - } - this.shards.length === 0 && (this.byteLength = 0), this.byteLength = this.shards[this.shards.length - 1].end; - } - slice(e = 0, t10 = this.byteLength) { - if (e = isNaN(Number(e)) ? 0 : e, t10 = isNaN(Number(t10)) ? 0 : t10, e = Math.max(0, e), t10 = Math.min(this.byteLength, t10), t10 <= e) - return new ArrayBuffer(0); - let o = this.findShardForByte(e); - if (o === -1) - throw new Error(`Could not find start shard for byte ${e}`); - let n = t10 - e, s = new ArrayBuffer(n), a = new Uint8Array(s), i = 0; - for (let p = o; p < this.shards.length; p++) { - let u = this.shards[p], l = e + i - u.start, m = i, f = Math.min(t10, u.end) - u.start, h = new Uint8Array(u.buffer.slice(l, f)); - if (a.set(h, m), i += h.length, t10 < u.end) - break; - } - return s; - } - findShardForByte(e) { - if (this.shards.length === 0 || e < 0 || e >= this.byteLength) - return -1; - if (this.bufferUniformSize != null) - return this.previousShardIndex = Math.floor(e / this.bufferUniformSize), this.previousShardIndex; - function t10(n) { - return e < n.start ? -1 : e >= n.end ? 1 : 0; - } - if (t10(this.shards[this.previousShardIndex]) === 0) - return this.previousShardIndex; - let o = bj(this.shards, t10); - return o === -1 ? -1 : (this.previousShardIndex = o, this.previousShardIndex); - } -}; -function bj(r, e) { - let t10 = 0, o = r.length; - for (; t10 <= o; ) { - let n = Math.floor((o - t10) / 2) + t10, s = e(r[n]); - if (s === 0) - return n; - s < 0 ? o = n : t10 = n + 1; - } - return -1; -} -function zw(r, e, t10, o) { - a(r), t10 = t10 == null ? 0 : t10, o = o == null ? 1 : o, i(t10, o); +function Xw(r, e, t8, o) { + a(r), t8 = t8 == null ? 0 : t8, o = o == null ? 1 : o, i(t8, o); let n = 0, s = (p) => (p.then((u) => { - let c = t10 + ++n / r.length * (o - t10); + let c = t8 + ++n / r.length * (o - t8); return e(c), u; }), p); function a(p) { @@ -8705,26 +8732,26 @@ function zw(r, e, t10, o) { } return Promise.all(r.map(s)); } -async function Vw(r, e) { +async function Yw(r, e) { e == null && (e = {}); - let t10 = e.fetchFunc == null ? P().platform.fetch : e.fetchFunc, o = r.map((l) => t10(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await zw(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; - return e.onProgress == null ? await Promise.all(i) : await zw(i, e.onProgress, p, u); + let t8 = e.fetchFunc == null ? A().platform.fetch : e.fetchFunc, o = r.map((l) => t8(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await Xw(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; + return e.onProgress == null ? await Promise.all(i) : await Xw(i, e.onProgress, p, u); } -async function _N(r, e = "", t10, o) { - return Ww((a) => Vw(a, { requestInit: o }))(r, e, t10); +async function LN(r, e = "", t8, o) { + return Qw((a) => Yw(a, { requestInit: o }))(r, e, t8); } -function Ww(r) { - return async (e, t10 = "", o) => { +function Qw(r) { + return async (e, t8 = "", o) => { let n = e.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = []; if (e.forEach((d, f) => { let h = 0; d.weights.forEach((g) => { - let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Cl[x] * Ue(g.shape), w = () => { + let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Cl[x] * Ge(g.shape), C = () => { n[f] = true, s[f] == null && (s[f] = []), s[f].push({ manifestEntry: g, groupOffset: h, sizeBytes: b }); }; o != null ? o.forEach((S, k) => { - S === g.name && (w(), a[k] = true); - }) : w(), i.push(g.name), h += b; + S === g.name && (C(), a[k] = true); + }) : C(), i.push(g.name), h += b; }); }), !a.every((d) => d)) { let d = o.filter((f, h) => !a[h]); @@ -8734,92 +8761,95 @@ Manifest JSON has weights with names: ${i.join(", ")}.`); let p = n.reduce((d, f, h) => (f && d.push(h), d), []), u = []; p.forEach((d) => { e[d].paths.forEach((f) => { - let h = t10 + (t10.endsWith("/") ? "" : "/") + f; + let h = t8 + (t8.endsWith("/") ? "" : "/") + f; u.push(h); }); }); let c = await r(u), l = {}, m = 0; return p.forEach((d) => { - let f = e[d].paths.length, h = new Jd(c.slice(m, m + f)); + let f = e[d].paths.length, h = new jt(c.slice(m, m + f)); s[d].forEach((x) => { - let b = h.slice(x.groupOffset, x.groupOffset + x.sizeBytes), w = rd(b, [x.manifestEntry]); - for (let S in w) - l[S] = w[S]; + let b = h.slice(x.groupOffset, x.groupOffset + x.sizeBytes), C = nd(b, [x.manifestEntry]); + for (let S in C) + l[S] = C[S]; }), m += f; }), l; }; } -var Cj = "application/octet-stream"; -var wj = "application/json"; +var Hj = "application/octet-stream"; +var Kj = "application/json"; var El = class { - constructor(e, t10) { - if (this.DEFAULT_METHOD = "POST", t10 == null && (t10 = {}), this.weightPathPrefix = t10.weightPathPrefix, this.onProgress = t10.onProgress, this.weightUrlConverter = t10.weightUrlConverter, t10.fetchFunc != null ? ($(typeof t10.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t10.fetchFunc) : this.fetch = P().platform.fetch, $(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && $(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t10.requestInit != null && t10.requestInit.body != null) + constructor(e, t8) { + if (this.DEFAULT_METHOD = "POST", t8 == null && (t8 = {}), this.weightPathPrefix = t8.weightPathPrefix, this.onProgress = t8.onProgress, this.weightUrlConverter = t8.weightUrlConverter, t8.fetchFunc != null ? ($(typeof t8.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t8.fetchFunc) : this.fetch = A().platform.fetch, $(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && $(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t8.requestInit != null && t8.requestInit.body != null) throw new Error("requestInit is expected to have no pre-existing body, but has one."); - this.requestInit = t10.requestInit || {}; + this.requestInit = t8.requestInit || {}; } async save(e) { if (e.modelTopology instanceof ArrayBuffer) throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); - let t10 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); - t10.body = new FormData(); - let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = od(e, o); - t10.body.append("model.json", new Blob([JSON.stringify(n)], { type: wj }), "model.json"), e.weightData != null && t10.body.append("model.weights.bin", new Blob([e.weightData], { type: Cj }), "model.weights.bin"); - let s = await this.fetch(this.path, t10); + let t8 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + t8.body = new FormData(); + let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = sd(e, o); + if (t8.body.append("model.json", new Blob([JSON.stringify(n)], { type: Kj }), "model.json"), e.weightData != null) { + let a = jt.join(e.weightData); + t8.body.append("model.weights.bin", new Blob([a], { type: Hj }), "model.weights.bin"); + } + let s = await this.fetch(this.path, t8); if (s.ok) - return { modelArtifactsInfo: ga(e), responses: [s] }; + return { modelArtifactsInfo: wa(e), responses: [s] }; throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.status}.`); } async load() { let e = await this.fetch(this.path, this.requestInit); if (!e.ok) throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`); - let t10; + let t8; try { - t10 = await e.json(); + t8 = await e.json(); } catch (s) { let a = `Failed to parse model JSON of response from ${this.path}.`; throw this.path.endsWith(".pb") ? a += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository." : a += " Please make sure the server is serving valid JSON for this request.", new Error(a); } - let o = t10.modelTopology, n = t10.weightsManifest; + let o = t8.modelTopology, n = t8.weightsManifest; if (o == null && n == null) throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); - return rc(t10, (s) => this.loadWeights(s)); + return oc(t8, (s) => this.loadWeights(s)); } async loadWeights(e) { - let t10 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = Sj(t10), s = this.weightPathPrefix || o, a = nd(e), i = [], p = []; + let t8 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = qj(t8), s = this.weightPathPrefix || o, a = ad(e), i = [], p = []; for (let c of e) for (let l of c.paths) this.weightUrlConverter != null ? p.push(this.weightUrlConverter(l)) : i.push(s + l + n); this.weightUrlConverter && i.push(...await Promise.all(p)); - let u = await Vw(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress }); - return [a, tc(u)]; + let u = await Yw(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress }); + return [a, u]; } }; El.URL_SCHEME_REGEX = /^https?:\/\//; -function Sj(r) { - let e = r.lastIndexOf("/"), t10 = r.lastIndexOf("?"), o = r.substring(0, e), n = t10 > e ? r.substring(t10) : ""; +function qj(r) { + let e = r.lastIndexOf("/"), t8 = r.lastIndexOf("?"), o = r.substring(0, e), n = t8 > e ? r.substring(t8) : ""; return [o + "/", n]; } -function ef(r) { +function tf(r) { return r.match(El.URL_SCHEME_REGEX) != null; } -var $N = (r, e) => { +var BN = (r, e) => { if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null)) return null; { - let t10 = true; - if (Array.isArray(r) ? t10 = r.every((o) => ef(o)) : t10 = ef(r), t10) - return tf(r, e); + let t8 = true; + if (Array.isArray(r) ? t8 = r.every((o) => tf(o)) : t8 = tf(r), t8) + return rf(r, e); } return null; }; -ft.registerSaveRouter($N); -ft.registerLoadRouter($N); -function tf(r, e) { +ft.registerSaveRouter(BN); +ft.registerLoadRouter(BN); +function rf(r, e) { return new El(r, e); } -function EN(r, e) { - return tf(r, e); +function zN(r, e) { + return rf(r, e); } var Rl = class { constructor(e) { @@ -8829,7 +8859,7 @@ var Rl = class { return this.modelArtifacts; } }; -var rf = class { +var of = class { constructor(e) { this.saveHandler = e; } @@ -8837,44 +8867,45 @@ var rf = class { return this.saveHandler(e); } }; -var Uw = class { +var Zw = class { constructor(e) { - e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t10) => Promise.resolve(e.save(t10))); + e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t8) => Promise.resolve(e.save(t8))); } }; -function RN(r, e, t10, o) { +function VN(r, e, t8, o) { let n = arguments; - return new Uw(Gw(...n)); + return new Zw(Jw(...n)); } -function Gw(r, e, t10, o) { - return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new Rl(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r, weightSpecs: e, weightData: t10, trainingConfig: o })); +function Jw(r, e, t8, o) { + return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new Rl(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r, weightSpecs: e, weightData: t8, trainingConfig: o })); } -function DN(r) { - return new rf(r); +function WN(r) { + return new of(r); } -function AN(r) { - return new rf(r); +function UN(r) { + return new of(r); } -var PN = {}; -He(PN, { confusionMatrix: () => FN }); -function Ij(r, e, t10) { +var HN = {}; +Ke(HN, { confusionMatrix: () => GN }); +function jj(r, e, t8) { let o = v(r, "labels", "confusionMatrix"), n = v(e, "predictions", "confusionMatrix"); - $(t10 == null || t10 > 0 && Number.isInteger(t10), () => `If provided, numClasses must be a positive integer, but got ${t10}`), $(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), $(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), $(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`), $(t10 > 0 && Number.isInteger(t10), () => `numClasses is required to be a positive integer, but got ${t10}`); - let s = Tl(Ye(o, "int32"), t10), a = Tl(Ye(n, "int32"), t10), i = dc(s), p = Qe(i, a); - return Ye(p, "int32"); + $(t8 == null || t8 > 0 && Number.isInteger(t8), () => `If provided, numClasses must be a positive integer, but got ${t8}`), $(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), $(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), $(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`), $(t8 > 0 && Number.isInteger(t8), () => `numClasses is required to be a positive integer, but got ${t8}`); + let s = Tl(qe(o, "int32"), t8), a = Tl(qe(n, "int32"), t8), i = fc(s), p = Ze(i, a); + return qe(p, "int32"); } -var FN = N({ confusionMatrix_: Ij }); -var MN = {}; -He(MN, { fromPixels: () => Ej, fromPixelsAsync: () => _j, toPixels: () => $j }); -var rp; -function ON(r, e = 3) { +var GN = N({ confusionMatrix_: jj }); +var XN = {}; +Ke(XN, { draw: () => rX, fromPixels: () => oX, fromPixelsAsync: () => Jj, toPixels: () => tX }); +var ap; +var KN = false; +function qN(r, e = 3) { if (e > 4) throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); if (r == null) throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); - let t10 = false, o = false, n = false, s = false, a = false, i = false; + let t8 = false, o = false, n = false, s = false, a = false, i = false; if (r.data instanceof Uint8Array) - t10 = true; + t8 = true; else if (typeof ImageData != "undefined" && r instanceof ImageData) o = true; else if (typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement) @@ -8887,25 +8918,25 @@ function ON(r, e = 3) { i = true; else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r.constructor.name}`); - if (fl($u, T.backendName) != null) { + if (fl(Au, T.backendName) != null) { let f = { pixels: r }, h = { numChannels: e }; - return T.runKernel($u, f, h); + return T.runKernel(Au, f, h); } let [u, c] = n ? [r.videoWidth, r.videoHeight] : [r.width, r.height], l; if (a) l = r.getContext("2d").getImageData(0, 0, u, c).data; - else if (o || t10) + else if (o || t8) l = r.data; else if (s || n || i) { - if (rp == null) + if (ap == null) if (typeof document == "undefined") if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined") - rp = new OffscreenCanvas(1, 1).getContext("2d"); + ap = new OffscreenCanvas(1, 1).getContext("2d"); else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported."); else - rp = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); - rp.canvas.width = u, rp.canvas.height = c, rp.drawImage(r, 0, 0, u, c), l = rp.getImageData(0, 0, u, c).data; + ap = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); + ap.canvas.width = u, ap.canvas.height = c, ap.drawImage(r, 0, 0, u, c), l = ap.getImageData(0, 0, u, c).data; } let m; if (e === 4) @@ -8917,56 +8948,64 @@ function ON(r, e = 3) { for (let g = 0; g < e; ++g) m[h * e + g] = l[h * 4 + g]; } - return Hd(m, [c, u, e], "int32"); + return qd(m, [c, u, e], "int32"); } -function vj(r) { +function Xj(r) { return r != null && r.data instanceof Uint8Array; } -function kj() { +function Yj() { return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap"); } -function Nj(r) { +function Qj(r) { return r != null && r.width !== 0 && r.height !== 0; } -function Tj(r) { - return kj() && !(r instanceof ImageBitmap) && Nj(r) && !vj(r); +function Zj(r) { + return Yj() && !(r instanceof ImageBitmap) && Qj(r) && !Xj(r); } -async function _j(r, e = 3) { - let t10 = null; - if (P().getBool("WRAP_TO_IMAGEBITMAP") && Tj(r)) { +async function Jj(r, e = 3) { + let t8 = null; + if (A().getBool("WRAP_TO_IMAGEBITMAP") && Zj(r)) { let o; try { o = await createImageBitmap(r, { premultiplyAlpha: "none" }); } catch (n) { o = null; } - o != null && o.width === r.width && o.height === r.height ? t10 = o : t10 = r; + o != null && o.width === r.width && o.height === r.height ? t8 = o : t8 = r; } else - t10 = r; - return ON(t10, e); + t8 = r; + return qN(t8, e); } -async function $j(r, e) { - let t10 = v(r, "img", "toPixels"); - if (!(r instanceof pt)) { - let u = t10; - t10 = Ye(u, "int32"), u.dispose(); +function jN(r) { + if (r.rank !== 2 && r.rank !== 3) + throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r.rank}.`); + let e = r.rank === 2 ? 1 : r.shape[2]; + if (e > 4 || e === 2) + throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${e}`); + if (r.dtype !== "float32" && r.dtype !== "int32") + throw new Error(`Unsupported type for toPixels: ${r.dtype}. Please use float32 or int32 tensors.`); +} +function eX(r) { + let e = (r == null ? void 0 : r.alpha) || 1; + if (e > 1 || e < 0) + throw new Error(`Alpha value ${e} is suppoed to be in range [0 - 1].`); +} +async function tX(r, e) { + let t8 = v(r, "img", "toPixels"); + if (!(r instanceof ut)) { + let u = t8; + t8 = qe(u, "int32"), u.dispose(); } - if (t10.rank !== 2 && t10.rank !== 3) - throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${t10.rank}.`); - let [o, n] = t10.shape.slice(0, 2), s = t10.rank === 2 ? 1 : t10.shape[2]; - if (s > 4 || s === 2) - throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`); - if (t10.dtype !== "float32" && t10.dtype !== "int32") - throw new Error(`Unsupported type for toPixels: ${t10.dtype}. Please use float32 or int32 tensors.`); - let a = await t10.data(), i = t10.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4); + jN(t8); + let [o, n] = t8.shape.slice(0, 2), s = t8.rank === 2 ? 1 : t8.shape[2], a = await t8.data(), i = t8.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4); for (let u = 0; u < o * n; ++u) { let c = [0, 0, 0, 255]; for (let m = 0; m < s; m++) { let d = a[u * s + m]; - if (t10.dtype === "float32") { + if (t8.dtype === "float32") { if (d < 0 || d > 1) throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${d}.`); - } else if (t10.dtype === "int32" && (d < 0 || d > 255)) + } else if (t8.dtype === "int32" && (d < 0 || d > 255)) throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`); s === 1 ? (c[0] = d * i, c[1] = d * i, c[2] = d * i) : c[m] = d * i; } @@ -8974,26 +9013,36 @@ async function $j(r, e) { p[l + 0] = Math.round(c[0]), p[l + 1] = Math.round(c[1]), p[l + 2] = Math.round(c[2]), p[l + 3] = Math.round(c[3]); } if (e != null) { - e.width = n, e.height = o; + KN || (console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."), KN = true), e.width = n, e.height = o; let u = e.getContext("2d"), c = new ImageData(p, n, o); u.putImageData(c, 0, 0); } - return t10 !== r && t10.dispose(), p; + return t8 !== r && t8.dispose(), p; } -var Ej = N({ fromPixels_: ON }); -var of = {}; -He(of, { prepareAndValidate: () => LN }); -function LN(r, e) { - let t10 = r.shape.length, o = e.shape.length; - if (t10 < 1) - throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t10}.`); +function rX(r, e, t8) { + let o = v(r, "img", "draw"); + if (!(r instanceof ut)) { + let a = o; + o = qe(a, "int32"), a.dispose(); + } + jN(o), eX(t8 == null ? void 0 : t8.imageOptions); + let n = { image: o }, s = { canvas: e, options: t8 }; + T.runKernel(qm, n, s); +} +var oX = N({ fromPixels_: qN }); +var nf = {}; +Ke(nf, { prepareAndValidate: () => YN }); +function YN(r, e) { + let t8 = r.shape.length, o = e.shape.length; + if (t8 < 1) + throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t8}.`); if (o < 1) throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${o}.`); if (e.dtype !== "int32") throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`); - if (e.shape[o - 1] > t10) - throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t10}`); - if (Ue(r.shape) === 0) + if (e.shape[o - 1] > t8) + throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t8}`); + if (Ge(r.shape) === 0) throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`); let n = e.shape, s = n[n.length - 1], a = 1; for (let l = 0; l < n.length - 1; ++l) @@ -9001,268 +9050,268 @@ function LN(r, e) { let i = r.shape, p = n.slice(); p.pop(); let u = 1; - for (let l = s; l < t10; ++l) + for (let l = s; l < t8; ++l) u *= i[l], p.push(i[l]); - let c = [...Us(r.shape).map((l) => l / u), 1].slice(0, s); + let c = [...qs(r.shape).map((l) => l / u), 1].slice(0, s); return [p, a, u, c]; } var ct = {}; -He(ct, { assertParamsValid: () => Dj, computeFlatOffset: () => Mj, computeOutShape: () => Fj, getNormalizedAxes: () => Pj, isSliceContinous: () => Oj, maskToAxes: () => Aj, parseSliceParams: () => Lj, sliceInfo: () => Bj, startForAxis: () => KN, startIndicesWithElidedDims: () => UN, stopForAxis: () => qN, stopIndicesWithElidedDims: () => GN, stridesForAxis: () => HN, stridesWithElidedDims: () => zN }); -var Hw = -2; -var Rj = -1; -function Dj(r, e, t10) { +Ke(ct, { assertParamsValid: () => sX, computeFlatOffset: () => cX, computeOutShape: () => iX, getNormalizedAxes: () => uX, isSliceContinous: () => pX, maskToAxes: () => aX, parseSliceParams: () => lX, sliceInfo: () => mX, startForAxis: () => nT, startIndicesWithElidedDims: () => tT, stopForAxis: () => sT, stopIndicesWithElidedDims: () => rT, stridesForAxis: () => oT, stridesWithElidedDims: () => ZN }); +var eS = -2; +var nX = -1; +function sX(r, e, t8) { let o = r.shape.length; - $(o === e.length, () => `Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`), $(o === t10.length, () => `Error in slice${o}D: Length of size ${t10} must match the rank of the array (${o}).`); + $(o === e.length, () => `Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`), $(o === t8.length, () => `Error in slice${o}D: Length of size ${t8} must match the rank of the array (${o}).`); for (let n = 0; n < o; ++n) - $(e[n] + t10[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t10[n]}) would overflow input.shape[${n}] (${r.shape[n]})`); + $(e[n] + t8[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t8[n]}) would overflow input.shape[${n}] (${r.shape[n]})`); } -function Aj(r) { - let e = [], t10 = 0; +function aX(r) { + let e = [], t8 = 0; for (; r > 0; ) - r & 1 && e.push(t10), r /= 2, t10++; + r & 1 && e.push(t8), r /= 2, t8++; return e; } -function Fj(r, e, t10) { +function iX(r, e, t8) { let o = []; for (let n = 0; n < r.length; n++) - o[n] = Math.ceil((e[n] - r[n]) / t10[n]); + o[n] = Math.ceil((e[n] - r[n]) / t8[n]); return o; } -function zN(r, e, t10, o) { +function ZN(r, e, t8, o) { let n = [...r]; for (let s = n.length; s < o.length; s++) n.push(1); - for (let s = 0; s < t10; s++) + for (let s = 0; s < t8; s++) s === 0 ? n[e] = 1 : (n.splice(e, 0, 1), n.pop()); return n; } -function VN(r, e, t10) { - return t10 <= r ? t10 : t10 - (e - 1); +function JN(r, e, t8) { + return t8 <= r ? t8 : t8 - (e - 1); } -function WN(r, e) { - let t10 = []; +function eT(r, e) { + let t8 = []; for (let o = 0; o < r; o++) - t10.push(e + o); - return t10; + t8.push(e + o); + return t8; } -function Pj(r, e, t10, o, n, s, a, i, p) { +function uX(r, e, t8, o, n, s, a, i, p) { let u = r.length, c = new Array(u), l = new Array(u), m = new Array(u); - if (e.length && t10 > 0) { - let d = e[0], f = t10 + 1; - c = UN(a, d, f, o, r), l = GN(i, d, f, n, r), m = zN(s, d, f, r); + if (e.length && t8 > 0) { + let d = e[0], f = t8 + 1; + c = tT(a, d, f, o, r), l = rT(i, d, f, n, r), m = ZN(s, d, f, r); } else for (let d = 0; d < u; d++) - c[d] = KN(a, o, s, r, d, p), l[d] = qN(i, n, s, r, d, p), m[d] = HN(s, d, p); + c[d] = nT(a, o, s, r, d, p), l[d] = sT(i, n, s, r, d, p), m[d] = oT(s, d, p); return { begin: c, end: l, strides: m }; } -function UN(r, e, t10, o, n) { - let s = [...n], a = WN(t10, e); +function tT(r, e, t8, o, n) { + let s = [...n], a = eT(t8, e); for (let i = 0; i < s.length; i++) if (a.indexOf(i) > -1) s[i] = 0; else { - let p = VN(e, t10, i), u = o[p]; + let p = JN(e, t8, i), u = o[p]; r & 1 << p && (u = 0), s[i] = u; } return s; } -function GN(r, e, t10, o, n) { - let s = [...n], a = WN(t10, e); +function rT(r, e, t8, o, n) { + let s = [...n], a = eT(t8, e); for (let i = 0; i < s.length; i++) if (a.indexOf(i) > -1) s[i] = Number.MAX_SAFE_INTEGER; else { - let p = VN(e, t10, i), u = o[p]; + let p = JN(e, t8, i), u = o[p]; r & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u; } for (let i = 0; i < s.length; i++) { let p = n[i]; - s[i] < 0 && (s[i] += p), s[i] = zp(0, s[i], n[i]); + s[i] < 0 && (s[i] += p), s[i] = Gp(0, s[i], n[i]); } return s; } -function HN(r, e, t10) { +function oT(r, e, t8) { let o = r[e]; - return (t10 & 1 << e || o == null) && (o = 1), o; + return (t8 & 1 << e || o == null) && (o = 1), o; } -function KN(r, e, t10, o, n, s) { - let a = e[n], i = t10[n] || 1; +function nT(r, e, t8, o, n, s) { + let a = e[n], i = t8[n] || 1; (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER); let p = o[n]; - return a < 0 && (a += p), a = zp(0, a, p - 1), a; + return a < 0 && (a += p), a = Gp(0, a, p - 1), a; } -function qN(r, e, t10, o, n, s) { - let a = e[n], i = t10[n] || 1; +function sT(r, e, t8, o, n, s) { + let a = e[n], i = t8[n] || 1; (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER); let p = o[n]; - return a < 0 && (a += p), i > 0 ? a = zp(0, a, p) : a = zp(-1, a, p - 1), a; + return a < 0 && (a += p), i > 0 ? a = Gp(0, a, p) : a = Gp(-1, a, p - 1), a; } -function Oj(r, e, t10) { - let o = t10.length; - for (let n = 0; n < t10.length; n++) - if (t10[n] > 1) { +function pX(r, e, t8) { + let o = t8.length; + for (let n = 0; n < t8.length; n++) + if (t8[n] > 1) { o = n; break; } - for (let n = o + 1; n < t10.length; n++) - if (e[n] > 0 || t10[n] !== r[n]) + for (let n = o + 1; n < t8.length; n++) + if (e[n] > 0 || t8[n] !== r[n]) return false; return true; } -function Mj(r, e) { - let t10 = r.length > 0 ? r[r.length - 1] : 1; +function cX(r, e) { + let t8 = r.length > 0 ? r[r.length - 1] : 1; for (let o = 0; o < r.length - 1; o++) - t10 += r[o] * e[o]; - return t10; + t8 += r[o] * e[o]; + return t8; } -function Lj(r, e, t10) { +function lX(r, e, t8) { let o, n = r.shape.length; typeof e == "number" ? o = [e, ...new Array(n - 1).fill(0)] : e.length < n ? o = e.concat(new Array(n - e.length).fill(0)) : o = e.slice(), o.forEach((a) => { $(a !== -1, () => "slice() does not support negative begin indexing."); }); let s; - return t10 == null ? s = new Array(n).fill(-1) : typeof t10 == "number" ? s = [t10, ...new Array(n - 1).fill(-1)] : t10.length < n ? s = t10.concat(new Array(n - t10.length).fill(-1)) : s = t10, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s]; + return t8 == null ? s = new Array(n).fill(-1) : typeof t8 == "number" ? s = [t8, ...new Array(n - 1).fill(-1)] : t8.length < n ? s = t8.concat(new Array(n - t8.length).fill(-1)) : s = t8, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s]; } -function Bj(r, e, t10, o, n, s, a, i, p) { +function mX(r, e, t8, o, n, s, a, i, p) { let u; if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && a & a - 1) throw new Error("Multiple ellipses in slice is not allowed."); - let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t10.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; - for (let w = 0; w < l.dims; w++) - c && 1 << w & i && l.numAddAxisAfterEllipsis++, 1 << w & a && (c = true); + let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t8.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; + for (let C = 0; C < l.dims; C++) + c && 1 << C & i && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true); c || (l.ellipsisMask |= 1 << l.dims, l.dims++); let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false }; - zj(l, m); + dX(l, m); let d = true, f = true, h = true, g = [], x = []; - for (let w = 0; w < r.length; ++w) { - if (m.strides[w] === 0) - throw Error(`strides[${w}] must be non-zero`); - let S = !!(m.shrinkAxisMask & 1 << w), k = r[w]; + for (let C = 0; C < r.length; ++C) { + if (m.strides[C] === 0) + throw Error(`strides[${C}] must be non-zero`); + let S = !!(m.shrinkAxisMask & 1 << C), k = r[C]; if (k === -1) { g.push(S ? 1 : -1); continue; } - let _ = [m.beginMask & 1 << w, m.endMask & 1 << w], E = [m.strides[w] > 0 ? 0 : -1, m.strides[w] > 0 ? k : k - 1]; - if (S && m.strides[w] <= 0) + let _ = [m.beginMask & 1 << C, m.endMask & 1 << C], E = [m.strides[C] > 0 ? 0 : -1, m.strides[C] > 0 ? k : k - 1]; + if (S && m.strides[C] <= 0) throw Error("only stride 1 allowed on non-range indexing."); - h = h && m.strides[w] === 1; - let R = !!(m.beginMask & 1 << w && m.endMask & 1 << w); + h = h && m.strides[C] === 1; + let R = !!(m.beginMask & 1 << C && m.endMask & 1 << C); if (m.beginValid && m.endValid) { if (S) { - let M = m.begin[w] < 0 ? k + m.begin[w] : m.begin[w]; - if (m.begin[w] = M, m.end[w] = m.begin[w] + 1, M < 0 || M >= k) - throw Error(`slice index ${m.begin[w]} of dimension ${w} out of bounds.`); + let M = m.begin[C] < 0 ? k + m.begin[C] : m.begin[C]; + if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k) + throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`); } else - m.begin[w] = BN(m.begin[w], 0, m.strides[w], k, _, E), m.end[w] = BN(m.end[w], 1, m.strides[w], k, _, E); - let O = m.strides[w] === 1 && m.begin[w] === 0 && m.end[w] === k; - d = d && O, f = f && (w === 0 && m.strides[w] === 1 || O); + m.begin[C] = QN(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = QN(m.end[C], 1, m.strides[C], k, _, E); + let O = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k; + d = d && O, f = f && (C === 0 && m.strides[C] === 1 || O); } else - d = d && m.strides[w] === 1 && R, f = f && (w === 0 && m.strides[w] === 1 || R); - let D, F = false; - if (m.beginValid && m.endValid ? (D = m.end[w] - m.begin[w], F = true) : S ? (D = 1, F = true) : R && k >= 0 && (m.strides[w] < 0 ? D = -k : D = k, F = true), F) { + d = d && m.strides[C] === 1 && R, f = f && (C === 0 && m.strides[C] === 1 || R); + let D, P = false; + if (m.beginValid && m.endValid ? (D = m.end[C] - m.begin[C], P = true) : S ? (D = 1, P = true) : R && k >= 0 && (m.strides[C] < 0 ? D = -k : D = k, P = true), P) { let O; - D === 0 || D < 0 != m.strides[w] < 0 ? O = 0 : O = Math.trunc(D / m.strides[w]) + (D % m.strides[w] !== 0 ? 1 : 0), g.push(O); + D === 0 || D < 0 != m.strides[C] < 0 ? O = 0 : O = Math.trunc(D / m.strides[C]) + (D % m.strides[C] !== 0 ? 1 : 0), g.push(O); } else g.push(-1); } - for (let w = 0; w < m.finalShapeGatherIndices.length; ++w) { - let S = m.finalShapeGatherIndices[w]; - S >= 0 ? x.push(g[S]) : S === Hw && x.push(1); + for (let C = 0; C < m.finalShapeGatherIndices.length; ++C) { + let S = m.finalShapeGatherIndices[C]; + S >= 0 ? x.push(g[S]) : S === eS && x.push(1); } - return { finalShapeSparse: x.filter((w, S) => m.finalShapeGatherIndices[S] !== Hw), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides }; + return { finalShapeSparse: x.filter((C, S) => m.finalShapeGatherIndices[S] !== eS), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides }; } -function zj(r, e) { +function dX(r, e) { e.beginMask = 0, e.endMask = 0, e.shrinkAxisMask = 0; - let t10 = 0; + let t8 = 0; e.beginValid = r.begin != null, e.endValid = r.end != null, e.begin = new Array(e.dims), e.end = new Array(e.dims), e.strides = new Array(e.dims), e.finalShapeGatherIndices = [], e.finalShapeGatherIndicesSparse = [], e.inputShapeGatherIndicesSparse = new Array(e.dims); for (let o = 0; o < r.dims; o++) if (1 << o & r.ellipsisMask) { let n = Math.min(e.dims - (r.dims - o) + 1 + r.numAddAxisAfterEllipsis, e.dims); - for (; t10 < n; t10++) - e.begin[t10] = 0, e.end[t10] = 0, e.strides[t10] = 1, e.beginMask |= 1 << t10, e.endMask |= 1 << t10, e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t10] = o; + for (; t8 < n; t8++) + e.begin[t8] = 0, e.end[t8] = 0, e.strides[t8] = 1, e.beginMask |= 1 << t8, e.endMask |= 1 << t8, e.finalShapeGatherIndices.push(t8), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t8] = o; } else if (1 << o & r.newAxisMask) - e.finalShapeGatherIndices.push(Hw), e.finalShapeGatherIndicesSparse.push(-1); + e.finalShapeGatherIndices.push(eS), e.finalShapeGatherIndicesSparse.push(-1); else { - if (t10 === e.begin.length) - throw Error(`Index out of range using input dim ${t10}; input has only ${e.dims} dims, ${e.begin.length}.`); - r.begin != null && (e.begin[t10] = r.begin[o]), r.end != null && (e.end[t10] = r.end[o]), e.strides[t10] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t10), r.endMask & 1 << o && (e.endMask |= 1 << t10), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(Rj), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t10) : (e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t10] = o, t10++; + if (t8 === e.begin.length) + throw Error(`Index out of range using input dim ${t8}; input has only ${e.dims} dims, ${e.begin.length}.`); + r.begin != null && (e.begin[t8] = r.begin[o]), r.end != null && (e.end[t8] = r.end[o]), e.strides[t8] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t8), r.endMask & 1 << o && (e.endMask |= 1 << t8), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(nX), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t8) : (e.finalShapeGatherIndices.push(t8), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t8] = o, t8++; } } -function BN(r, e, t10, o, n, s) { +function QN(r, e, t8, o, n, s) { if (n[e]) - return t10 > 0 ? s[e] : s[e + 1 & 1]; + return t8 > 0 ? s[e] : s[e + 1 & 1]; { let a = r < 0 ? o + r : r; return a < s[0] ? s[0] : a > s[1] ? s[1] : a; } } -var Vj = "4.5.0"; +var fX = "4.7.0"; var Dl = class { static sgd(e) { - return new ii(e); + return new ci(e); } - static momentum(e, t10, o = false) { - return new ep(e, t10, o); + static momentum(e, t8, o = false) { + return new np(e, t8, o); } - static rmsprop(e, t10 = 0.9, o = 0, n = null, s = false) { - return new tp(e, t10, o, n, s); + static rmsprop(e, t8 = 0.9, o = 0, n = null, s = false) { + return new sp(e, t8, o, n, s); } - static adam(e = 1e-3, t10 = 0.9, o = 0.999, n = null) { - return new Zu(e, t10, o, n); + static adam(e = 1e-3, t8 = 0.9, o = 0.999, n = null) { + return new rp(e, t8, o, n); } - static adadelta(e = 1e-3, t10 = 0.95, o = null) { - return new Yu(e, t10, o); + static adadelta(e = 1e-3, t8 = 0.95, o = null) { + return new ep(e, t8, o); } - static adamax(e = 2e-3, t10 = 0.9, o = 0.999, n = null, s = 0) { - return new Ju(e, t10, o, n, s); + static adamax(e = 2e-3, t8 = 0.9, o = 0.999, n = null, s = 0) { + return new op(e, t8, o, n, s); } - static adagrad(e, t10 = 0.1) { - return new Qu(e, t10); + static adagrad(e, t8 = 0.1) { + return new tp(e, t8); } }; -var CUe = Dl; -var Wj = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())(); -function Kw() { - return new Promise((r) => Wj(() => r())); +var uGe = Dl; +var hX = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())(); +function tS() { + return new Promise((r) => hX(() => r())); } -var C = {}; -He(C, { ERF_A1: () => sX, ERF_A2: () => aX, ERF_A3: () => iX, ERF_A4: () => uX, ERF_A5: () => pX, ERF_P: () => nX, PARALLELIZE_THRESHOLD: () => nf, RowPartitionType: () => Ta, SELU_SCALE: () => oX, SELU_SCALEALPHA: () => rX, applyActivation: () => ju, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => yH, assertParamsConsistent: () => Uj, assignToTypedArray: () => hX, axesAreInnerMostDims: () => Iw, calculateShapes: () => v1, checkEinsumDimSizes: () => wX, checkPadOnDimRoundingMode: () => Lt, combineLocations: () => e2, combineRaggedTensorToTensorShapes: () => Hj, complexWithEvenIndex: () => mX, complexWithOddIndex: () => dX, computeConv2DInfo: () => Mu, computeConv3DInfo: () => bk, computeDefaultPad: () => Sw, computeDilation2DInfo: () => g4, computeOptimalWindowSize: () => Xj, computeOutAndReduceShapes: () => xH, computeOutShape: () => Gj, computePool2DInfo: () => ww, computePool3DInfo: () => x4, convertConv2DDataFormat: () => Ck, decodeEinsumEquation: () => bX, eitherStridesOrDilationsAreOne: () => gr, expandShapeToKeepDim: () => ti, exponent: () => xX, exponents: () => gX, fromStringArrayToUint8: () => WX, fromUint8ToStringArray: () => VX, getAxesPermutation: () => bH, getBroadcastDims: () => jk, getComplexWithIndex: () => fX, getEinsumComputePath: () => SX, getEinsumPermutation: () => CX, getFusedBiasGradient: () => qu, getFusedDyActivation: () => Ku, getImageCenter: () => Yj, getInnerMostAxes: () => wH, getPermuted: () => Zj, getRaggedRank: () => qj, getReductionAxes: () => fd, getReshaped: () => Qj, getReshapedPermuted: () => Jj, getRowPartitionTypesHelper: () => Kj, getSliceBeginCoords: () => eX, getSliceSize: () => tX, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => NX, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => TX, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => _X, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => RX, getSparseReshapeInputOutputMismatchErrorMessage: () => AX, getSparseReshapeInputOutputMultipleErrorMessage: () => DX, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => $X, getSparseReshapeNegativeOutputDimErrorMessage: () => EX, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => MX, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => FX, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => PX, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => OX, getUndoAxesPermutation: () => CH, isIdentityPermutation: () => IX, log: () => mG, mergeRealAndImagArrays: () => cX, prepareAndValidate: () => LN, prepareSplitSize: () => kX, segment_util: () => jw, shouldFuse: () => Xu, slice_util: () => ct, splitRealAndImagArrays: () => lX, stridesOrDilationsArePositive: () => ba, tupleValuesAreOne: () => Ou, upcastType: () => dt, validateDefaultValueShape: () => jj, validateInput: () => mc, validateUpdateShape: () => Fw, warn: () => ha }); -function Uj(r, e) { - let t10 = r[0].length; +var w = {}; +Ke(w, { ERF_A1: () => DX, ERF_A2: () => AX, ERF_A3: () => FX, ERF_A4: () => PX, ERF_A5: () => OX, ERF_P: () => RX, PARALLELIZE_THRESHOLD: () => sf, RowPartitionType: () => Da, SELU_SCALE: () => EX, SELU_SCALEALPHA: () => $X, applyActivation: () => Zu, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => GH, assertParamsConsistent: () => gX, assignToTypedArray: () => WX, axesAreInnerMostDims: () => Dw, calculateShapes: () => F1, checkEinsumDimSizes: () => jX, checkPadOnDimRoundingMode: () => Lt, combineLocations: () => c2, combineRaggedTensorToTensorShapes: () => yX, complexWithEvenIndex: () => BX, complexWithOddIndex: () => zX, computeConv2DInfo: () => Vu, computeConv3DInfo: () => $k, computeDefaultPad: () => Rw, computeDilation2DInfo: () => W4, computeOptimalWindowSize: () => SX, computeOutAndReduceShapes: () => UH, computeOutShape: () => xX, computePool2DInfo: () => Ew, computePool3DInfo: () => U4, convertConv2DDataFormat: () => Ek, decodeEinsumEquation: () => KX, eitherStridesOrDilationsAreOne: () => gr, expandShapeToKeepDim: () => ni, exponent: () => GX, exponents: () => UX, fromStringArrayToUint8: () => h5, fromUint8ToStringArray: () => f5, getAxesPermutation: () => HH, getBroadcastDims: () => n2, getComplexWithIndex: () => VX, getEinsumComputePath: () => XX, getEinsumPermutation: () => qX, getFusedBiasGradient: () => Qu, getFusedDyActivation: () => Yu, getImageCenter: () => IX, getInnerMostAxes: () => qH, getPermuted: () => kX, getRaggedRank: () => CX, getReductionAxes: () => gd, getReshaped: () => vX, getReshapedPermuted: () => NX, getRowPartitionTypesHelper: () => bX, getSliceBeginCoords: () => TX, getSliceSize: () => _X, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => JX, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => e5, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => t5, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => n5, getSparseReshapeInputOutputMismatchErrorMessage: () => a5, getSparseReshapeInputOutputMultipleErrorMessage: () => s5, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => r5, getSparseReshapeNegativeOutputDimErrorMessage: () => o5, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => c5, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => i5, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => u5, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => p5, getUndoAxesPermutation: () => KH, isIdentityPermutation: () => YX, log: () => MG, mergeRealAndImagArrays: () => MX, prepareAndValidate: () => YN, prepareSplitSize: () => ZX, segment_util: () => oS, shouldFuse: () => Ju, slice_util: () => ct, splitRealAndImagArrays: () => LX, stridesOrDilationsArePositive: () => va, tupleValuesAreOne: () => zu, upcastType: () => dt, validateDefaultValueShape: () => wX, validateInput: () => dc, validateUpdateShape: () => Uw, warn: () => Ca }); +function gX(r, e) { + let t8 = r[0].length; r.forEach((n, s) => { - $(n.length === t10, () => `Error in concat${t10}D: rank of tensors[${s}] must be the same as the rank of the rest (${t10})`); - }), $(e >= 0 && e < t10, () => `Error in concat${t10}D: axis must be between 0 and ${t10 - 1}.`); + $(n.length === t8, () => `Error in concat${t8}D: rank of tensors[${s}] must be the same as the rank of the rest (${t8})`); + }), $(e >= 0 && e < t8, () => `Error in concat${t8}D: axis must be between 0 and ${t8 - 1}.`); let o = r[0]; r.forEach((n, s) => { - for (let a = 0; a < t10; a++) - $(a === e || n[a] === o[a], () => `Error in concat${t10}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`); + for (let a = 0; a < t8; a++) + $(a === e || n[a] === o[a], () => `Error in concat${t8}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`); }); } -function Gj(r, e) { - let t10 = r[0].slice(); +function xX(r, e) { + let t8 = r[0].slice(); for (let o = 1; o < r.length; o++) - t10[e] += r[o][e]; - return t10; + t8[e] += r[o][e]; + return t8; } -var Ta; +var Da; (function(r) { r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS"; -})(Ta || (Ta = {})); -function Hj(r, e, t10) { +})(Da || (Da = {})); +function yX(r, e, t8) { let o = new Array(); - if (t10 == null && e == null) + if (t8 == null && e == null) return o; if (e == null) - for (; o.length < r + t10.length; ) + for (; o.length < r + t8.length; ) o.push(-1); else o = e.slice(); - if (t10 == null) + if (t8 == null) return o; - if (r + t10.length !== o.length) - throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t10.length}, but shape.rank = ${o.length}`); - for (let n = 1; n < t10.length; ++n) { - let s = t10[n], a = o[o.length - t10.length + n], i = o[a]; + if (r + t8.length !== o.length) + throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t8.length}, but shape.rank = ${o.length}`); + for (let n = 1; n < t8.length; ++n) { + let s = t8[n], a = o[o.length - t8.length + n], i = o[a]; if (s >= 0) if (i >= 0) { if (i !== s) @@ -9272,42 +9321,42 @@ function Hj(r, e, t10) { } return o; } -function Kj(r) { - let e = { FIRST_DIM_SIZE: Ta.FIRST_DIM_SIZE, VALUE_ROWIDS: Ta.VALUE_ROWIDS, ROW_LENGTHS: Ta.ROW_LENGTHS, ROW_SPLITS: Ta.ROW_SPLITS, ROW_LIMITS: Ta.ROW_LIMITS, ROW_STARTS: Ta.ROW_STARTS }, t10 = []; +function bX(r) { + let e = { FIRST_DIM_SIZE: Da.FIRST_DIM_SIZE, VALUE_ROWIDS: Da.VALUE_ROWIDS, ROW_LENGTHS: Da.ROW_LENGTHS, ROW_SPLITS: Da.ROW_SPLITS, ROW_LIMITS: Da.ROW_LIMITS, ROW_STARTS: Da.ROW_STARTS }, t8 = []; for (let o of r) if (o in e) - t10.push(e[o]); + t8.push(e[o]); else break; - return t10; + return t8; } -function qj(r) { - return r.length === 0 ? 0 : r[0] === Ta.FIRST_DIM_SIZE ? r.length - 1 : r.length; +function CX(r) { + return r.length === 0 ? 0 : r[0] === Da.FIRST_DIM_SIZE ? r.length - 1 : r.length; } -function jj(r, e) { +function wX(r, e) { if (r == null || e == null) return; - let t10 = r.length, o = e.length; - if (t10 >= o) - throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t10} must be less than ragged tensor input flatValues.rank = ${o})`); - for (let n = 0; n < Math.min(t10, o - 1); ++n) { + let t8 = r.length, o = e.length; + if (t8 >= o) + throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t8} must be less than ragged tensor input flatValues.rank = ${o})`); + for (let n = 0; n < Math.min(t8, o - 1); ++n) { let s = r[n], a = e[n + 1]; if (s >= 0 && a >= 0 && s !== 1 && s !== a) throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n - r.length}] = ${s} but ragged tensor input.flatValues.shape[${n - r.length}] = ${a}`); } } -var nf = 30; -function Xj(r) { - return r <= nf ? r : Wp(r, Math.floor(Math.sqrt(r))); +var sf = 30; +function SX(r) { + return r <= sf ? r : Kp(r, Math.floor(Math.sqrt(r))); } -function Yj(r, e, t10) { - let o = t10 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]); +function IX(r, e, t8) { + let o = t8 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]); return [o, n]; } -function Qj(r, e, t10, o = true) { +function vX(r, e, t8, o = true) { let n = []; if (o) - n = n.concat(e.slice(0)), n.push(r[0] / t10), n = n.concat(r.slice(1)); + n = n.concat(e.slice(0)), n.push(r[0] / t8), n = n.concat(r.slice(1)); else { n = n.concat(r[0]); let s = e.length; @@ -9317,9 +9366,9 @@ function Qj(r, e, t10, o = true) { } return n; } -function Zj(r, e, t10 = true) { +function kX(r, e, t8 = true) { let o = []; - if (t10) { + if (t8) { o.push(e); for (let n = e + 1; n < r; ++n) n <= 2 * e ? (o.push(n), o.push(n - (e + 1))) : o.push(n); @@ -9331,92 +9380,92 @@ function Zj(r, e, t10 = true) { } return o; } -function Jj(r, e, t10, o = true) { +function NX(r, e, t8, o = true) { let n = []; - o ? n.push(r[0] / t10) : n.push(r[0] * t10); + o ? n.push(r[0] / t8) : n.push(r[0] * t8); for (let s = 1; s < r.length; ++s) s <= e.length ? o ? n.push(e[s - 1] * r[s]) : n.push(r[s] / e[s - 1]) : n.push(r[s]); return n; } -function eX(r, e) { - let t10 = [0]; +function TX(r, e) { + let t8 = [0]; for (let o = 0; o < e; ++o) - t10.push(r[o][0]); - return t10; + t8.push(r[o][0]); + return t8; } -function tX(r, e, t10) { +function _X(r, e, t8) { let o = r.slice(0, 1); - for (let n = 0; n < t10; ++n) + for (let n = 0; n < t8; ++n) o.push(r[n + 1] - e[n][0] - e[n][1]); return o; } -var rX = 1.7580993408473768; -var oX = 1.0507009873554805; -var nX = 0.3275911; -var sX = 0.254829592; -var aX = -0.284496736; -var iX = 1.421413741; -var uX = -1.453152027; -var pX = 1.061405429; -function cX(r, e) { +var $X = 1.7580993408473768; +var EX = 1.0507009873554805; +var RX = 0.3275911; +var DX = 0.254829592; +var AX = -0.284496736; +var FX = 1.421413741; +var PX = -1.453152027; +var OX = 1.061405429; +function MX(r, e) { if (r.length !== e.length) throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${e.length}.`); - let t10 = new Float32Array(r.length * 2); - for (let o = 0; o < t10.length; o += 2) - t10[o] = r[o / 2], t10[o + 1] = e[o / 2]; - return t10; + let t8 = new Float32Array(r.length * 2); + for (let o = 0; o < t8.length; o += 2) + t8[o] = r[o / 2], t8[o + 1] = e[o / 2]; + return t8; } -function lX(r) { - let e = new Float32Array(r.length / 2), t10 = new Float32Array(r.length / 2); +function LX(r) { + let e = new Float32Array(r.length / 2), t8 = new Float32Array(r.length / 2); for (let o = 0; o < r.length; o += 2) - e[o / 2] = r[o], t10[o / 2] = r[o + 1]; - return { real: e, imag: t10 }; + e[o / 2] = r[o], t8[o / 2] = r[o + 1]; + return { real: e, imag: t8 }; } -function mX(r) { - let e = Math.ceil(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e); +function BX(r) { + let e = Math.ceil(r.length / 4), t8 = new Float32Array(e), o = new Float32Array(e); for (let n = 0; n < r.length; n += 4) - t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; - return { real: t10, imag: o }; + t8[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; + return { real: t8, imag: o }; } -function dX(r) { - let e = Math.floor(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e); +function zX(r) { + let e = Math.floor(r.length / 4), t8 = new Float32Array(e), o = new Float32Array(e); for (let n = 2; n < r.length; n += 4) - t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; - return { real: t10, imag: o }; + t8[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; + return { real: t8, imag: o }; } -function fX(r, e) { - let t10 = r[e * 2], o = r[e * 2 + 1]; - return { real: t10, imag: o }; +function VX(r, e) { + let t8 = r[e * 2], o = r[e * 2 + 1]; + return { real: t8, imag: o }; } -function hX(r, e, t10, o) { - r[o * 2] = e, r[o * 2 + 1] = t10; +function WX(r, e, t8, o) { + r[o * 2] = e, r[o * 2 + 1] = t8; } -function gX(r, e) { - let t10 = new Float32Array(r / 2), o = new Float32Array(r / 2); +function UX(r, e) { + let t8 = new Float32Array(r / 2), o = new Float32Array(r / 2); for (let n = 0; n < Math.ceil(r / 2); n++) { let s = (e ? 2 : -2) * Math.PI * (n / r); - t10[n] = Math.cos(s), o[n] = Math.sin(s); + t8[n] = Math.cos(s), o[n] = Math.sin(s); } - return { real: t10, imag: o }; + return { real: t8, imag: o }; } -function xX(r, e, t10) { - let o = (t10 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o); +function GX(r, e, t8) { + let o = (t8 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o); return { real: n, imag: s }; } -var qw = "->"; -var yX = /->/g; -var jN = ","; -var XN = "..."; -function bX(r, e) { +var rS = "->"; +var HX = /->/g; +var aT = ","; +var iT = "..."; +function KX(r, e) { r = r.replace(/\s/g, ""); - let t10 = (r.length - r.replace(yX, "").length) / qw.length; - if (t10 < 1) + let t8 = (r.length - r.replace(HX, "").length) / rS.length; + if (t8 < 1) throw new Error("Equations without an arrow are not supported."); - if (t10 > 1) - throw new Error(`Equation must contain exactly one arrow ("${qw}").`); - let [o, n] = r.split(qw); - $(o.indexOf(XN) === -1, () => `The ellipsis notation ("${XN}") is not supported yet.`); - let s = o.split(jN), a = s.length; + if (t8 > 1) + throw new Error(`Equation must contain exactly one arrow ("${rS}").`); + let [o, n] = r.split(rS); + $(o.indexOf(iT) === -1, () => `The ellipsis notation ("${iT}") is not supported yet.`); + let s = o.split(aT), a = s.length; if (e !== a) throw new Error(`Expected ${a} input tensors, received ${e}`); if (a > 2) @@ -9430,7 +9479,7 @@ function bX(r, e) { } for (let m = 0; m < o.length; ++m) { let d = o[m]; - i.indexOf(d) === -1 && d !== jN && i.push(d); + i.indexOf(d) === -1 && d !== aT && i.push(d); } let p = new Array(s.length); for (let m = 0; m < a; ++m) { @@ -9445,222 +9494,222 @@ function bX(r, e) { l.push(m); return { allDims: i, summedDims: l, idDims: p }; } -function CX(r, e) { - let t10 = new Array(r); - t10.fill(-1); +function qX(r, e) { + let t8 = new Array(r); + t8.fill(-1); for (let n = 0; n < e.length; ++n) - t10[e[n]] = n; + t8[e[n]] = n; let o = []; for (let n = 0; n < r; ++n) - t10[n] === -1 && o.push(n); - return t10 = t10.filter((n) => n !== -1), { permutationIndices: t10, expandDims: o }; + t8[n] === -1 && o.push(n); + return t8 = t8.filter((n) => n !== -1), { permutationIndices: t8, expandDims: o }; } -function wX(r, e, t10) { +function jX(r, e, t8) { let o = new Array(r); - for (let n = 0; n < t10.length; ++n) { - let s = t10[n].shape; + for (let n = 0; n < t8.length; ++n) { + let s = t8[n].shape; for (let a = 0; a < e[n].length; ++a) o[e[n][a]] === void 0 ? o[e[n][a]] = s[a] : $(o[e[n][a]] === s[a], () => `Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`); } } -function SX(r, e) { - let t10 = r, o = [], n = 0; - r.length === 0 && t10.push(-1), n = r.length + 1; +function XX(r, e) { + let t8 = r, o = [], n = 0; + r.length === 0 && t8.push(-1), n = r.length + 1; for (let a = 0; a < n; ++a) o.push([]); let s = []; - for (let a = 0; a < t10.length; ++a) { - let i = t10[a], p = vX(e, i); + for (let a = 0; a < t8.length; ++a) { + let i = t8[a], p = QX(e, i); for (let u of p) s.indexOf(u) === -1 && (o[a].push(u), s.push(u)); } - return { path: t10, steps: o }; + return { path: t8, steps: o }; } -function IX(r) { - return r.every((e, t10) => e === t10); +function YX(r) { + return r.every((e, t8) => e === t8); } -function vX(r, e) { - let t10 = []; +function QX(r, e) { + let t8 = []; for (let o = 0; o < r.length; ++o) - (r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t10.push(o); - return t10; + (r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t8.push(o); + return t8; } -function kX(r, e, t10 = 0) { +function ZX(r, e, t8 = 0) { let o = []; if (typeof e == "number") - $(r.shape[t10] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t10] / e); + $(r.shape[t8] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t8] / e); else { let n = e.reduce((a, i) => (i === -1 && (a += 1), a), 0); $(n <= 1, () => "There should be only one negative value in split array."); let s = e.indexOf(-1); if (s !== -1) { let a = e.reduce((i, p) => p > 0 ? i + p : i); - e[s] = r.shape[t10] - a; + e[s] = r.shape[t8] - a; } - $(r.shape[t10] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e; + $(r.shape[t8] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e; } return o; } -function NX(r) { +function JX(r) { return `Received SparseTensor with denseShape[0] = 0 but indices.shape[0] = ${r}`; } -function TX(r, e) { +function e5(r, e) { return `indices(${r}, 0) is invalid: ${e} < 0`; } -function _X(r, e, t10) { - return `indices(${r}, 0) is invalid: ${e} >= ${t10}`; +function t5(r, e, t8) { + return `indices(${r}, 0) is invalid: ${e} >= ${t8}`; } -function $X(r, e) { +function r5(r, e) { return `only one output dimension may be -1, not both ${r} and ${e}`; } -function EX(r, e) { +function o5(r, e) { return `size ${r} must be non-negative, not ${e}`; } -function RX() { +function n5() { return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"; } -function DX(r, e) { - let t10 = Ue(r), o = Ue(e); - return `Input to reshape is a SparseTensor with ${t10} +function s5(r, e) { + let t8 = Ge(r), o = Ge(e); + return `Input to reshape is a SparseTensor with ${t8} dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`; } -function AX(r, e) { - let t10 = Ue(r), o = Ue(e); - return `Input to reshape is a tensor with ${t10} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`; +function a5(r, e) { + let t8 = Ge(r), o = Ge(e); + return `Input to reshape is a tensor with ${t8} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`; } -function FX() { +function i5() { return "segment ids must be >= 0"; } -function PX() { +function u5() { return "segment ids are not increasing"; } -function OX(r, e) { +function p5(r, e) { return `Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`; } -function MX(r, e, t10) { - return `Bad: indices[${r}] == ${e} out of range [0, ${t10})`; +function c5(r, e, t8) { + return `Bad: indices[${r}] == ${e} out of range [0, ${t8})`; } -var jw = {}; -He(jw, { collectGatherOpShapeInfo: () => zX, computeOutShape: () => BX, segOpComputeOptimalWindowSize: () => LX }); -function LX(r, e) { - let t10 = false, o; - for (r <= nf ? (o = r, t10 = true) : o = Wp(r, Math.floor(Math.sqrt(r))); !t10; ) - o > e || o === r ? t10 = true : o = Wp(r, o + 1); +var oS = {}; +Ke(oS, { collectGatherOpShapeInfo: () => d5, computeOutShape: () => m5, segOpComputeOptimalWindowSize: () => l5 }); +function l5(r, e) { + let t8 = false, o; + for (r <= sf ? (o = r, t8 = true) : o = Kp(r, Math.floor(Math.sqrt(r))); !t8; ) + o > e || o === r ? t8 = true : o = Kp(r, o + 1); return o; } -function BX(r, e, t10) { +function m5(r, e, t8) { let o = [], n = r.length; for (let s = 0; s < n; s++) - s !== e ? o.push(r[s]) : o.push(t10); + s !== e ? o.push(r[s]) : o.push(t8); return o; } -function zX(r, e, t10, o) { +function d5(r, e, t8, o) { let n = e.shape.length, s = r.shape.length; if (o !== 0 && (o < -n || o > n)) throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`); if (o < 0 && (o += n), o > s) throw new Error(`batchDims (${o}) must be less than rank(x) ( ${s}).`); - if (t10 < o) - throw new Error(`batchDims (${o}) must be less than or equal to axis (${t10}).`); + if (t8 < o) + throw new Error(`batchDims (${o}) must be less than or equal to axis (${t8}).`); for (let l = 0; l < o; ++l) if (r.shape[l] !== e.shape[l]) throw new Error(`x.shape[${l}]: ${r.shape[l]} should be equal to indices.shape[${l}]: ${e.shape[l]}.`); - let a = r.shape[t10], i = [], p = 1, u = 1, c = 1; + let a = r.shape[t8], i = [], p = 1, u = 1, c = 1; for (let l = 0; l < o; ++l) i.push(r.shape[l]), p *= r.shape[l]; - for (let l = o; l < t10; l++) + for (let l = o; l < t8; l++) i.push(r.shape[l]), u *= r.shape[l]; for (let l = o; l < n; l++) i.push(e.shape[l]); - for (let l = t10 + 1; l < s; l++) + for (let l = t8 + 1; l < s; l++) i.push(r.shape[l]), c *= r.shape[l]; return { batchSize: p, sliceSize: c, outerSize: u, dimSize: a, outputShape: i }; } -function VX(r) { +function f5(r) { try { - return r.map((e) => Jp(e)); + return r.map((e) => tc(e)); } catch (e) { throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`); } } -function WX(r) { - return r.map((e) => Yi(e)); +function h5(r) { + return r.map((e) => eu(e)); } var Wt = {}; -He(Wt, { nonMaxSuppressionV3Impl: () => Yd, nonMaxSuppressionV4Impl: () => Qd, nonMaxSuppressionV5Impl: () => Zd, whereImpl: () => Kd }); -kN(); -var UX = P(); -UX.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => { +Ke(Wt, { nonMaxSuppressionV3Impl: () => Zd, nonMaxSuppressionV4Impl: () => Jd, nonMaxSuppressionV5Impl: () => ef, whereImpl: () => jd }); +PN(); +var g5 = A(); +g5.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => { r && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); }); -var co; +var fo; (function(r) { r[r.DT_INVALID = 0] = "DT_INVALID", r[r.DT_FLOAT = 1] = "DT_FLOAT", r[r.DT_DOUBLE = 2] = "DT_DOUBLE", r[r.DT_INT32 = 3] = "DT_INT32", r[r.DT_UINT8 = 4] = "DT_UINT8", r[r.DT_INT16 = 5] = "DT_INT16", r[r.DT_INT8 = 6] = "DT_INT8", r[r.DT_STRING = 7] = "DT_STRING", r[r.DT_COMPLEX64 = 8] = "DT_COMPLEX64", r[r.DT_INT64 = 9] = "DT_INT64", r[r.DT_BOOL = 10] = "DT_BOOL", r[r.DT_QINT8 = 11] = "DT_QINT8", r[r.DT_QUINT8 = 12] = "DT_QUINT8", r[r.DT_QINT32 = 13] = "DT_QINT32", r[r.DT_BFLOAT16 = 14] = "DT_BFLOAT16", r[r.DT_QINT16 = 15] = "DT_QINT16", r[r.DT_QUINT16 = 16] = "DT_QUINT16", r[r.DT_UINT16 = 17] = "DT_UINT16", r[r.DT_COMPLEX128 = 18] = "DT_COMPLEX128", r[r.DT_HALF = 19] = "DT_HALF", r[r.DT_RESOURCE = 20] = "DT_RESOURCE", r[r.DT_VARIANT = 21] = "DT_VARIANT", r[r.DT_UINT32 = 22] = "DT_UINT32", r[r.DT_UINT64 = 23] = "DT_UINT64", r[r.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", r[r.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", r[r.DT_INT32_REF = 103] = "DT_INT32_REF", r[r.DT_UINT8_REF = 104] = "DT_UINT8_REF", r[r.DT_INT16_REF = 105] = "DT_INT16_REF", r[r.DT_INT8_REF = 106] = "DT_INT8_REF", r[r.DT_STRING_REF = 107] = "DT_STRING_REF", r[r.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", r[r.DT_INT64_REF = 109] = "DT_INT64_REF", r[r.DT_BOOL_REF = 110] = "DT_BOOL_REF", r[r.DT_QINT8_REF = 111] = "DT_QINT8_REF", r[r.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", r[r.DT_QINT32_REF = 113] = "DT_QINT32_REF", r[r.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", r[r.DT_QINT16_REF = 115] = "DT_QINT16_REF", r[r.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", r[r.DT_UINT16_REF = 117] = "DT_UINT16_REF", r[r.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", r[r.DT_HALF_REF = 119] = "DT_HALF_REF", r[r.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", r[r.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", r[r.DT_UINT32_REF = 122] = "DT_UINT32_REF", r[r.DT_UINT64_REF = 123] = "DT_UINT64_REF"; -})(co || (co = {})); -var YN; +})(fo || (fo = {})); +var uT; (function(r) { let e; - (function(t10) { - t10[t10.LEGACY = 0] = "LEGACY", t10[t10.V1 = 1] = "V1", t10[t10.V2 = 2] = "V2"; + (function(t8) { + t8[t8.LEGACY = 0] = "LEGACY", t8[t8.V1 = 1] = "V1", t8[t8.V2 = 2] = "V2"; })(e = r.CheckpointFormatVersion || (r.CheckpointFormatVersion = {})); -})(YN || (YN = {})); -var Yw = {}; -function HX(r, e) { - let t10 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e }; - Yw[r] = t10; +})(uT || (uT = {})); +var sS = {}; +function y5(r, e) { + let t8 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e }; + sS[r] = t8; } -function sf(r) { - return Yw[r]; +function af(r) { + return sS[r]; } -function KX(r) { - delete Yw[r]; +function b5(r) { + delete sS[r]; } -function I(r, e, t10, o, n) { +function I(r, e, t8, o, n) { let s = e.inputParams[r]; if (s && s.inputIndexStart !== void 0) { let i = s.inputIndexStart, p = s.inputIndexEnd === 0 ? void 0 : s.inputIndexEnd === void 0 ? i + 1 : s.inputIndexEnd, u = i < 0 ? e.inputNames.length + i : i; if (s.type === "tensor") - return Bt(e.inputNames[u], t10, o, n); + return Bt(e.inputNames[u], t8, o, n); if (s.type === "tensors") { let m = e.inputs.slice(i, p); return e.inputNames.slice(i, p).filter((f, h) => { var g; return ((g = m[h]) === null || g === void 0 ? void 0 : g.op) !== "NoOp"; - }).map((f) => Bt(f, t10, o, n)); + }).map((f) => Bt(f, t8, o, n)); } - let c = Bt(e.inputNames[u], t10, o, n), l = c.dataSync(); + let c = Bt(e.inputNames[u], t8, o, n), l = c.dataSync(); return s.type === "number" ? l[0] : y.toNestedArray(c.shape, l); } let a = e.attrParams[r]; return a && a.value; } -function Bt(r, e, t10, o) { - let [n, s] = Nr(r, t10); +function Bt(r, e, t8, o) { + let [n, s] = Tr(r, t8); if (o != null) { let i = o.getHashTableHandleByName(n); if (i != null) return i; } - let a = t10.currentContextIds.find((i) => !!e[af(n, i)]); - return a !== void 0 ? e[af(n, a)][s] : void 0; + let a = t8.currentContextIds.find((i) => !!e[uf(n, i)]); + return a !== void 0 ? e[uf(n, a)][s] : void 0; } -function Qw(r, e, t10) { - return e[af(r, t10.currentContextId)]; +function aS(r, e, t8) { + return e[uf(r, t8.currentContextId)]; } -function Ds(r, e) { - let [t10, o, n] = Nr(r, e); - return [af(t10, e && e.currentContextId), o, n]; +function Os(r, e) { + let [t8, o, n] = Tr(r, e); + return [uf(t8, e && e.currentContextId), o, n]; } -function af(r, e) { +function uf(r, e) { return e ? `${r}-${e}` : r; } -function Nr(r, e) { +function Tr(r, e) { if (r === "") return ["", 0, void 0]; - let t10 = e != null && e.parseNodeNameCache != null; - if (t10) { + let t8 = e != null && e.parseNodeNameCache != null; + if (t8) { let s = e.parseNodeNameCache.get(r); if (s != null) return s; @@ -9672,12 +9721,12 @@ function Nr(r, e) { let s = o[0], a = o.length === 3 ? o[1] : void 0, i = Number(o[o.length - 1]); n = [s, i, a]; } - return t10 && e.parseNodeNameCache.set(r, n), n; + return t8 && e.parseNodeNameCache.set(r, n), n; } -function Al(r, e, t10) { - let o = I("pad", r, e, t10); +function Al(r, e, t8) { + let o = I("pad", r, e, t8); if (o === "explicit") { - o = I("explicitPaddings", r, e, t10); + o = I("explicitPaddings", r, e, t8); let n = [[0, 0], [0, 0], [0, 0], [0, 0]]; for (let s = 0; s < 4; s++) n[s][0] = o[s * 2], n[s][1] = o[s * 2 + 1]; @@ -9685,86 +9734,86 @@ function Al(r, e, t10) { } return o; } -function As(r) { - return r.kept ? r : Vr(r); +function Ms(r) { + return r.kept ? r : Ur(r); } -var Zw = {}; -He(Zw, { json: () => qX }); -var qX = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; -var Jw = {}; -He(Jw, { json: () => jX }); -var jX = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: 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tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsFinite", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", 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}]; +Ke(iS, { json: () => C5 }); +var C5 = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", 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category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BitwiseAnd", category: "logical", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }] }]; +Ke(uS, { json: () => w5 }); +var w5 = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsFinite", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsInf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; var pS = {}; -He(pS, { json: () => n5 }); -var n5 = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "MatrixBandPart", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "numLower", type: "tensor" }, { start: 1, name: "numUpper", type: "tensor" }] }]; +Ke(pS, { json: () => S5 }); +var S5 = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayReadV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayGatherV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }] }, { tfOpName: "TensorArrayScatterV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArrayConcatV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape_except0", name: "elementShapeExcept0", type: "shape", notSupported: true }] }, { tfOpName: "TensorArraySplitV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }, { start: 2, name: "lengths", type: "number[]" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArraySizeV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }] }, { tfOpName: "TensorArrayCloseV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }] }, { tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcatV2", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }, { tfOpName: "TensorListResize", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "size", type: "number" }] }]; var cS = {}; -He(cS, { json: () => s5 }); -var s5 = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }]; +Ke(cS, { json: () => I5 }); +var I5 = [{ tfOpName: "AvgPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Conv1D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "stride", name: "stride", type: "number" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NWC" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "dilation", name: "dilation", type: "number", defaultValue: 1 }] }, { tfOpName: "Conv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { tfOpName: "DepthwiseConv2d", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "DepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "Dilation2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "rates", name: "dilations", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }] }]; var lS = {}; -He(lS, { json: () => a5 }); -var a5 = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ 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category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV3", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV4", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; var dS = {}; -He(dS, { json: () => u5 }); -var u5 = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }]; +Ke(dS, { json: () => N5 }); +var N5 = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }]; var fS = {}; -He(fS, { json: () => p5 }); -var p5 = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }]; +Ke(fS, { json: () => T5 }); +var T5 = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }]; var hS = {}; -He(hS, { json: () => c5 }); -var c5 = [{ tfOpName: "StaticRegexReplace", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "pattern", name: "pattern", type: "string" }, { tfName: "rewrite", name: "rewrite", type: "string" }, { tfName: "replace_global", name: "replaceGlobal", type: "bool" }] }, { tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }]; +Ke(hS, { json: () => _5 }); +var _5 = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: 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category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "InitializeTable", category: "hash_table", inputs: [{ start: 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tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }]; +var CS = {}; +Ke(CS, { json: () => A5 }); +var A5 = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", 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F5 = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: 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"pattern", type: "string" }, { tfName: "rewrite", name: "rewrite", type: "string" }, { tfName: "replace_global", name: "replaceGlobal", type: "bool" }] }, { tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }]; +var kS = {}; +Ke(kS, { json: () => L5 }); +var L5 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "EnsureShape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }]; var Fl = class { static get Instance() { return this._instance || (this._instance = new this()); } constructor() { - let e = [Zw, Jw, eS, tS, rS, oS, nS, sS, aS, iS, uS, pS, cS, lS, mS, dS, fS, hS, gS], t10 = [].concat(...e.map((o) => o.json)); - this.opMappers = t10.reduce((o, n) => (o[n.tfOpName] = n, o), {}); + let e = [iS, uS, pS, cS, lS, mS, dS, fS, hS, gS, xS, yS, bS, CS, wS, SS, IS, vS, kS], t8 = [].concat(...e.map((o) => o.json)); + this.opMappers = t8.reduce((o, n) => (o[n.tfOpName] = n, o), {}); } - transformGraph(e, t10 = {}) { + transformGraph(e, t8 = {}) { let o = e.node, n = [], s = [], a = [], i = o.reduce((h, g) => (h[g.name] = this.mapNode(g), g.op.startsWith("Placeholder") ? n.push(h[g.name]) : g.op === "Const" ? s.push(h[g.name]) : (g.input == null || g.input.length === 0) && a.push(h[g.name]), h), {}), p = [], u = [], c = {}, l = {}; - t10 != null && (c = this.mapSignatureEntries(t10.inputs), l = this.mapSignatureEntries(t10.outputs)); + t8 != null && (c = this.mapSignatureEntries(t8.inputs), l = this.mapSignatureEntries(t8.outputs)); let m = Object.keys(i); m.forEach((h) => { let g = i[h]; g.inputNames.forEach((x, b) => { - let [w, , S] = Ds(x), k = i[w]; + let [C, , S] = Os(x), k = i[C]; if (k.outputs != null) { let _ = k.outputs.indexOf(S); if (_ !== -1) { - let E = `${w}:${_}`; + let E = `${C}:${_}`; g.inputNames[b] = E; } } @@ -9774,59 +9823,59 @@ var Fl = class { let g = i[h]; g.children.length === 0 && u.push(g); }) : Object.keys(l).forEach((h) => { - let [g] = Ds(h), x = i[g]; + let [g] = Os(h), x = i[g]; x != null && (x.signatureKey = l[h], u.push(x)); }), Object.keys(c).length > 0 ? Object.keys(c).forEach((h) => { - let [g] = Ds(h), x = i[g]; + let [g] = Os(h), x = i[g]; x && (x.signatureKey = c[h], p.push(x)); }) : p = n; let d = {}; e.library != null && e.library.function != null && (d = e.library.function.reduce((h, g) => (h[g.signature.name] = this.mapFunction(g), h), {})); - let f = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t10, functions: d }; + let f = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t8, functions: d }; return a.length > 0 && (f.initNodes = a), f; } mapSignatureEntries(e) { - return Object.keys(e || {}).reduce((t10, o) => (t10[e[o].name] = o, t10), {}); + return Object.keys(e || {}).reduce((t8, o) => (t8[e[o].name] = o, t8), {}); } mapNode(e) { - let t10 = sf(e.op) || this.opMappers[e.op] || {}; + let t8 = af(e.op) || this.opMappers[e.op] || {}; e.attr == null && (e.attr = {}); - let o = { name: e.name, op: e.op, category: t10.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t10.outputs }; - return t10.inputs != null && (o.inputParams = t10.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t10.attrs != null && (o.attrParams = t10.attrs.reduce((n, s) => { + let o = { name: e.name, op: e.op, category: t8.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t8.outputs }; + return t8.inputs != null && (o.inputParams = t8.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t8.attrs != null && (o.attrParams = t8.attrs.reduce((n, s) => { let a = s.type, i; switch (s.type) { case "string": - i = uf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = uf(e.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "string[]": - i = hf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = hf(e.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "number": - i = cf(e.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = cf(e.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "number[]": - i = ff(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ff(e.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "bool": i = pf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = pf(e.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "bool[]": - i = xf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = xf(e.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "shape": - i = df(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = df(e.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "shape[]": + case "string[]": i = gf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = gf(e.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "dtype": - i = lf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = lf(e.attr, s.tfDeprecatedName, s.defaultValue)); + case "number": + i = lf(e.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = lf(e.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "dtype[]": + case "number[]": + i = hf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = hf(e.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "bool": + i = cf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = cf(e.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "bool[]": + i = yf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = yf(e.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "shape": + i = ff(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ff(e.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "shape[]": + i = xf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = xf(e.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "dtype": i = mf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = mf(e.attr, s.tfDeprecatedName, s.defaultValue)); break; + case "dtype[]": + i = df(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = df(e.attr, s.tfDeprecatedName, s.defaultValue)); + break; case "func": - i = QN(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = QN(e.attr, s.tfDeprecatedName, s.defaultValue)); + i = pT(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = pT(e.attr, s.tfDeprecatedName, s.defaultValue)); break; case "tensor": case "tensors": @@ -9838,21 +9887,21 @@ var Fl = class { }, {})), o; } mapFunction(e) { - let t10 = e.nodeDef, o = [], n = [], s = {}; - t10 != null && (s = t10.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {})); + let t8 = e.nodeDef, o = [], n = [], s = {}; + t8 != null && (s = t8.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {})); let a = [], i = []; e.signature.inputArg.forEach((l) => { - let [m] = Ds(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: xS(l.type), type: "dtype" } }, children: [] }; + let [m] = Os(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: NS(l.type), type: "dtype" } }, children: [] }; d.signatureKey = l.name, a.push(d), s[m] = d; }), Object.keys(s).forEach((l) => { let m = s[l]; m.inputNames.forEach((d, f) => { - let [h, , g] = Ds(d), x = s[h]; + let [h, , g] = Os(d), x = s[h]; if (x.outputs != null) { let b = x.outputs.indexOf(g); if (b !== -1) { - let w = `${h}:${b}`; - m.inputNames[f] = w; + let C = `${h}:${b}`; + m.inputNames[f] = C; } } m.inputs.push(x), x.children.push(m); @@ -9860,306 +9909,306 @@ var Fl = class { }); let u = e.ret; e.signature.outputArg.forEach((l) => { - let [m, d] = Ds(u[l.name]), f = s[m]; + let [m, d] = Os(u[l.name]), f = s[m]; f != null && (f.defaultOutput = d, i.push(f)); }); let c = this.mapArgsToSignature(e); return { nodes: s, inputs: a, outputs: i, weights: n, placeholders: o, signature: c }; } mapArgsToSignature(e) { - return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o), t10), {}), outputs: e.signature.outputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o, e.ret), t10), {}) }; + return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t8, o) => (t8[o.name] = this.mapArgToTensorInfo(o), t8), {}), outputs: e.signature.outputArg.reduce((t8, o) => (t8[o.name] = this.mapArgToTensorInfo(o, e.ret), t8), {}) }; } - mapArgToTensorInfo(e, t10) { + mapArgToTensorInfo(e, t8) { let o = e.name; - return t10 != null && (o = t10[o]), { name: o, dtype: e.type }; + return t8 != null && (o = t8[o]), { name: o, dtype: e.type }; } }; -function m5(r) { - let e = P().global; +function B5(r) { + let e = A().global; if (typeof e.atob != "undefined") return e.atob(r); if (typeof Buffer != "undefined") return new Buffer(r, "base64").toString(); throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()"); } -function ZN(r, e) { - let t10 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : m5(r); - return e ? t10 : t10.toLowerCase(); +function cT(r, e) { + let t8 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : B5(r); + return e ? t8 : t8.toLowerCase(); } -function uf(r, e, t10, o = false) { +function pf(r, e, t8, o = false) { let n = r[e]; - return n != null ? ZN(n.s, o) : t10; + return n != null ? cT(n.s, o) : t8; } -function pf(r, e, t10) { +function cf(r, e, t8) { let o = r[e]; - return o ? o.b : t10; + return o ? o.b : t8; } -function cf(r, e, t10) { - let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t10; +function lf(r, e, t8) { + let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t8; return typeof n == "number" ? n : parseInt(n, 10); } -function xS(r) { - switch (typeof r == "string" && (r = co[r]), r) { - case co.DT_FLOAT: - case co.DT_HALF: +function NS(r) { + switch (typeof r == "string" && (r = fo[r]), r) { + case fo.DT_FLOAT: + case fo.DT_HALF: return "float32"; - case co.DT_INT32: - case co.DT_INT64: - case co.DT_INT8: - case co.DT_UINT8: + case fo.DT_INT32: + case fo.DT_INT64: + case fo.DT_INT8: + case fo.DT_UINT8: return "int32"; - case co.DT_BOOL: + case fo.DT_BOOL: return "bool"; - case co.DT_DOUBLE: + case fo.DT_DOUBLE: return "float32"; - case co.DT_STRING: + case fo.DT_STRING: return "string"; default: return null; } } -function QN(r, e, t10) { +function pT(r, e, t8) { let o = r[e]; - return o && o.func ? o.func.name : t10; + return o && o.func ? o.func.name : t8; } -function lf(r, e, t10) { +function mf(r, e, t8) { let o = r[e]; - return o && o.type ? xS(o.type) : t10; + return o && o.type ? NS(o.type) : t8; } -function mf(r, e, t10) { +function df(r, e, t8) { let o = r[e]; - return o && o.list && o.list.type ? o.list.type.map((n) => xS(n)) : t10; + return o && o.list && o.list.type ? o.list.type.map((n) => NS(n)) : t8; } -function JN(r) { +function lT(r) { if (!r.unknownRank) return r.dim != null ? r.dim.map((e) => typeof e.size == "number" ? e.size : parseInt(e.size, 10)) : []; } -function df(r, e, t10) { +function ff(r, e, t8) { let o = r[e]; - return o && o.shape ? JN(o.shape) : t10; + return o && o.shape ? lT(o.shape) : t8; } -function ff(r, e, t10) { +function hf(r, e, t8) { let o = r[e]; - return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : t10; + return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : t8; } -function hf(r, e, t10, o = false) { +function gf(r, e, t8, o = false) { let n = r[e]; - return n && n.list && n.list.s ? n.list.s.map((s) => ZN(s, o)) : t10; + return n && n.list && n.list.s ? n.list.s.map((s) => cT(s, o)) : t8; } -function gf(r, e, t10) { +function xf(r, e, t8) { let o = r[e]; - return o && o.list && o.list.shape ? o.list.shape.map((n) => JN(n)) : t10; + return o && o.list && o.list.shape ? o.list.shape.map((n) => lT(n)) : t8; } -function xf(r, e, t10) { +function yf(r, e, t8) { let o = r[e]; - return o && o.list && o.list.b ? o.list.b : t10; + return o && o.list && o.list.b ? o.list.b : t8; } -var yf = class { - constructor(e, t10, o) { - this.node = e, this.tensorMap = t10, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((n) => this.getInput(n)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {})); +var bf = class { + constructor(e, t8, o) { + this.node = e, this.tensorMap = t8, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((n) => this.getInput(n)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {})); } getInput(e) { return Bt(e, this.tensorMap, this.context); } - getAttr(e, t10) { + getAttr(e, t8) { let o = this.node.rawAttrs[e]; if (o.tensor != null) return Bt(e, this.tensorMap, this.context); if (o.i != null || o.f != null) - return cf(this.node.rawAttrs, e, t10); + return lf(this.node.rawAttrs, e, t8); if (o.s != null) - return uf(this.node.rawAttrs, e, t10); + return pf(this.node.rawAttrs, e, t8); if (o.b != null) - return pf(this.node.rawAttrs, e, t10); + return cf(this.node.rawAttrs, e, t8); if (o.shape != null) - return df(this.node.rawAttrs, e, t10); + return ff(this.node.rawAttrs, e, t8); if (o.type != null) - return lf(this.node.rawAttrs, e, t10); + return mf(this.node.rawAttrs, e, t8); if (o.list != null) { if (o.list.i != null || o.list.f != null) - return ff(this.node.rawAttrs, e, t10); + return hf(this.node.rawAttrs, e, t8); if (o.list.s != null) - return hf(this.node.rawAttrs, e, t10); + return gf(this.node.rawAttrs, e, t8); if (o.list.shape != null) - return gf(this.node.rawAttrs, e, t10); + return xf(this.node.rawAttrs, e, t8); if (o.list.b != null) - return xf(this.node.rawAttrs, e, t10); + return yf(this.node.rawAttrs, e, t8); if (o.list.type != null) - return mf(this.node.rawAttrs, e, t10); + return df(this.node.rawAttrs, e, t8); } - return t10; + return t8; } }; -var Ze = {}; -He(Ze, { OP_SCOPE_SUFFIX: () => pw, abs: () => Zt, acos: () => ik, acosh: () => uk, add: () => be, addN: () => pk, all: () => ck, any: () => lk, argMax: () => mk, argMin: () => dk, asin: () => fk, asinh: () => hk, atan: () => gk, atan2: () => xk, atanh: () => yk, avgPool: () => cd, avgPool3d: () => wk, basicLSTMCell: () => Sk, batchNorm: () => tu, batchNorm2d: () => vk, batchNorm3d: () => kk, batchNorm4d: () => Nk, batchToSpaceND: () => ld, bincount: () => md, bitwiseAnd: () => Tk, booleanMaskAsync: () => qq, broadcastArgs: () => _k, broadcastTo: () => ru, buffer: () => me, cast: () => Ye, ceil: () => $k, clipByValue: () => Ek, clone: () => Vr, complex: () => $r, concat: () => yt, concat1d: () => Rk, concat2d: () => Dk, concat3d: () => Ak, concat4d: () => Fk, conv1d: () => Pk, conv2d: () => ou, conv2dTranspose: () => Ok, conv3d: () => Mk, conv3dTranspose: () => Bk, cos: () => zk, cosh: () => Vk, cosineWindow: () => _l, cumprod: () => Wk, cumsum: () => Uk, denseBincount: () => Gk, depthToSpace: () => Hk, depthwiseConv2d: () => ac, diag: () => Kk, dilation2d: () => qk, div: () => Ke, divNoNan: () => Xk, dot: () => Yk, dropout: () => s6, einsum: () => Qk, elu: () => gd, enclosingPowerOfTwo: () => Pw, ensureShape: () => Zk, equal: () => hd, erf: () => Jk, euclideanNorm: () => r2, exp: () => ko, expandDims: () => oi, expm1: () => o2, eye: () => xd, fft: () => pc, fill: () => Sa, floor: () => yd, floorDiv: () => pd, fused: () => Ow, gather: () => bd, gatherND: () => o6, greater: () => Bu, greaterEqual: () => Cd, ifft: () => Hu, imag: () => su, image: () => uj, inTopKAsync: () => i6, irfft: () => Wd, isFinite: () => n2, isInf: () => s2, isNaN: () => a2, leakyRelu: () => wd, less: () => kl, lessEqual: () => ic, linalg: () => pj, linspace: () => i2, localResponseNormalization: () => u2, log: () => ni, log1p: () => Sd, logSigmoid: () => p2, logSoftmax: () => c2, logSumExp: () => kd, logicalAnd: () => zu, logicalNot: () => Nd, logicalOr: () => Td, logicalXor: () => l2, losses: () => cj, lowerBound: () => m2, matMul: () => Qe, max: () => Ia, maxPool: () => $d, maxPool3d: () => d2, maxPoolWithArgmax: () => f2, maximum: () => Ed, mean: () => Vu, meshgrid: () => h2, min: () => vl, minimum: () => Wu, mirrorPad: () => g2, mod: () => x2, moments: () => y2, movingAverage: () => Yq, mul: () => se, multiRNNCell: () => b2, multinomial: () => C2, neg: () => pr, norm: () => Lu, notEqual: () => Rd, oneHot: () => Tl, ones: () => va, onesLike: () => w2, op: () => N, outerProduct: () => S2, pad: () => ka, pad1d: () => I2, pad2d: () => v2, pad3d: () => k2, pad4d: () => N2, pool: () => T2, pow: () => ri, prelu: () => Ad, print: () => ud, prod: () => _2, raggedGather: () => $2, raggedRange: () => E2, raggedTensorToTensor: () => R2, rand: () => D2, randomGamma: () => J2, randomNormal: () => Bd, randomStandardNormal: () => e1, randomUniform: () => uc, randomUniformInt: () => t1, range: () => au, real: () => si, reciprocal: () => r1, relu: () => iu, relu6: () => zd, reshape: () => W, reverse: () => uo, reverse1d: () => o1, reverse2d: () => n1, reverse3d: () => s1, reverse4d: () => a1, rfft: () => cc, round: () => Vd, rsqrt: () => i1, scalar: () => ke, scatterND: () => Zq, searchSorted: () => Nl, selu: () => u1, separableConv2d: () => p1, setdiff1dAsync: () => c1, sigmoid: () => wa, sign: () => l1, signal: () => ij, sin: () => m1, sinh: () => d1, slice: () => qe, slice1d: () => f1, slice2d: () => h1, slice3d: () => g1, slice4d: () => x1, softmax: () => y1, softplus: () => vd, spaceToBatchND: () => Dd, sparse: () => lj, sparseToDense: () => t6, spectral: () => aj, split: () => ai, sqrt: () => Rr, square: () => Jt, squaredDifference: () => Ud, squeeze: () => lc, stack: () => vr, step: () => Gd, stridedSlice: () => b1, string: () => mj, sub: () => Te, sum: () => ot, tan: () => C1, tanh: () => Il, tensor: () => ir, tensor1d: () => xr, tensor2d: () => uu, tensor3d: () => Hd, tensor4d: () => w1, tensor5d: () => S1, tensor6d: () => I1, tensorScatterUpdate: () => k1, tile: () => nu, topk: () => N1, transpose: () => dc, truncatedNormal: () => T1, unique: () => _1, unsortedSegmentSum: () => $1, unstack: () => po, upperBound: () => E1, variable: () => R1, where: () => io, whereAsync: () => qd, zeros: () => Wr, zerosLike: () => Ht }); -var eT = (r, e, t10, o = Ze) => { +var Je = {}; +Ke(Je, { OP_SCOPE_SUFFIX: () => yw, abs: () => Jt, acos: () => xk, acosh: () => yk, add: () => Ce, addN: () => bk, all: () => Ck, any: () => wk, argMax: () => Sk, argMin: () => Ik, asin: () => vk, asinh: () => kk, atan: () => Nk, atan2: () => Tk, atanh: () => _k, avgPool: () => md, avgPool3d: () => Rk, basicLSTMCell: () => Dk, batchNorm: () => su, batchNorm2d: () => Fk, batchNorm3d: () => Pk, batchNorm4d: () => Ok, batchToSpaceND: () => dd, bincount: () => fd, bitwiseAnd: () => Mk, booleanMaskAsync: () => b6, broadcastArgs: () => Lk, broadcastTo: () => au, buffer: () => me, cast: () => qe, ceil: () => Bk, clipByValue: () => zk, clone: () => Ur, complex: () => Er, concat: () => bt, concat1d: () => Vk, concat2d: () => Wk, concat3d: () => Uk, concat4d: () => Gk, conv1d: () => Hk, conv2d: () => iu, conv2dTranspose: () => Kk, conv3d: () => qk, conv3dTranspose: () => Xk, cos: () => Yk, cosh: () => Qk, cosineWindow: () => _l, cumprod: () => Zk, cumsum: () => Jk, denseBincount: () => e2, depthToSpace: () => t2, depthwiseConv2d: () => ic, diag: () => r2, dilation2d: () => o2, div: () => je, divNoNan: () => s2, dot: () => a2, dropout: () => R6, einsum: () => i2, elu: () => yd, enclosingPowerOfTwo: () => Gw, ensureShape: () => u2, equal: () => xd, erf: () => p2, euclideanNorm: () => m2, exp: () => _o, expandDims: () => ai, expm1: () => d2, eye: () => bd, fft: () => cc, fill: () => Ta, floor: () => Cd, floorDiv: () => ld, fused: () => Hw, gather: () => wd, gatherND: () => $6, greater: () => Uu, greaterEqual: () => Sd, ifft: () => Xu, imag: () => pu, image: () => Fj, inTopKAsync: () => A6, irfft: () => Gd, isFinite: () => f2, isInf: () => h2, isNaN: () => g2, leakyRelu: () => Id, less: () => kl, lessEqual: () => uc, linalg: () => Pj, linspace: () => x2, localResponseNormalization: () => y2, log: () => ii, log1p: () => vd, logSigmoid: () => b2, logSoftmax: () => C2, logSumExp: () => Td, logicalAnd: () => Gu, logicalNot: () => _d, logicalOr: () => $d, logicalXor: () => w2, losses: () => Oj, lowerBound: () => S2, matMul: () => Ze, max: () => _a, maxPool: () => Rd, maxPool3d: () => I2, maxPoolWithArgmax: () => v2, maximum: () => Dd, mean: () => Hu, meshgrid: () => k2, min: () => vl, minimum: () => Ku, mirrorPad: () => N2, mod: () => T2, moments: () => _2, movingAverage: () => S6, mul: () => se, multiRNNCell: () => $2, multinomial: () => E2, neg: () => pr, norm: () => Wu, notEqual: () => Ad, oneHot: () => Tl, ones: () => $a, onesLike: () => R2, op: () => N, outerProduct: () => D2, pad: () => Ea, pad1d: () => A2, pad2d: () => F2, pad3d: () => P2, pad4d: () => O2, pool: () => M2, pow: () => si, prelu: () => Pd, print: () => cd, prod: () => L2, raggedGather: () => B2, raggedRange: () => z2, raggedTensorToTensor: () => V2, rand: () => W2, randomGamma: () => p1, randomNormal: () => Vd, randomStandardNormal: () => c1, randomUniform: () => pc, randomUniformInt: () => l1, range: () => cu, real: () => ui, reciprocal: () => m1, relu: () => lu, relu6: () => Wd, reshape: () => W, reverse: () => lo, reverse1d: () => d1, reverse2d: () => f1, reverse3d: () => h1, reverse4d: () => g1, rfft: () => lc, round: () => Ud, rsqrt: () => x1, scalar: () => ke, scatterND: () => v6, searchSorted: () => Nl, selu: () => y1, separableConv2d: () => b1, setdiff1dAsync: () => C1, sigmoid: () => Na, sign: () => w1, signal: () => Aj, sin: () => S1, sinh: () => I1, slice: () => Xe, slice1d: () => v1, slice2d: () => k1, slice3d: () => N1, slice4d: () => T1, softmax: () => _1, softplus: () => Nd, spaceToBatchND: () => Fd, sparse: () => Mj, sparseToDense: () => T6, spectral: () => Dj, split: () => pi, sqrt: () => Dr, square: () => er, squaredDifference: () => Hd, squeeze: () => mc, stack: () => kr, step: () => Kd, stridedSlice: () => $1, string: () => Lj, sub: () => Te, sum: () => ot, tan: () => E1, tanh: () => Il, tensor: () => ir, tensor1d: () => xr, tensor2d: () => mu, tensor3d: () => qd, tensor4d: () => R1, tensor5d: () => D1, tensor6d: () => A1, tensorScatterUpdate: () => P1, tile: () => uu, topk: () => O1, transpose: () => fc, truncatedNormal: () => M1, unique: () => L1, unsortedSegmentSum: () => B1, unstack: () => mo, upperBound: () => z1, variable: () => V1, where: () => co, whereAsync: () => Xd, zeros: () => Gr, zerosLike: () => Ht }); +var mT = (r, e, t8, o = Je) => { switch (r.op) { case "BiasAdd": case "AddV2": case "Add": - return [o.add(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.add(I("a", r, e, t8), I("b", r, e, t8))]; case "AddN": - return [o.addN(I("tensors", r, e, t10))]; + return [o.addN(I("tensors", r, e, t8))]; case "FloorMod": case "Mod": - return [o.mod(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.mod(I("a", r, e, t8), I("b", r, e, t8))]; case "Mul": - return [o.mul(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.mul(I("a", r, e, t8), I("b", r, e, t8))]; case "RealDiv": case "Div": - return [o.div(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.div(I("a", r, e, t8), I("b", r, e, t8))]; case "DivNoNan": - return [o.divNoNan(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.divNoNan(I("a", r, e, t8), I("b", r, e, t8))]; case "FloorDiv": - return [o.floorDiv(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.floorDiv(I("a", r, e, t8), I("b", r, e, t8))]; case "Sub": - return [o.sub(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.sub(I("a", r, e, t8), I("b", r, e, t8))]; case "Minimum": - return [o.minimum(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.minimum(I("a", r, e, t8), I("b", r, e, t8))]; case "Maximum": - return [o.maximum(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.maximum(I("a", r, e, t8), I("b", r, e, t8))]; case "Pow": - return [o.pow(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.pow(I("a", r, e, t8), I("b", r, e, t8))]; case "SquaredDifference": - return [o.squaredDifference(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.squaredDifference(I("a", r, e, t8), I("b", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var tT = (r, e, t10, o = Ze) => { +var dT = (r, e, t8, o = Je) => { switch (r.op) { case "Abs": case "ComplexAbs": - return [o.abs(I("x", r, e, t10))]; + return [o.abs(I("x", r, e, t8))]; case "Acos": - return [o.acos(I("x", r, e, t10))]; + return [o.acos(I("x", r, e, t8))]; case "Acosh": - return [o.acosh(I("x", r, e, t10))]; + return [o.acosh(I("x", r, e, t8))]; case "Asin": - return [o.asin(I("x", r, e, t10))]; + return [o.asin(I("x", r, e, t8))]; case "Asinh": - return [o.asinh(I("x", r, e, t10))]; + return [o.asinh(I("x", r, e, t8))]; case "Atan": - return [o.atan(I("x", r, e, t10))]; + return [o.atan(I("x", r, e, t8))]; case "Atan2": - return [o.atan2(I("x", r, e, t10), I("y", r, e, t10))]; + return [o.atan2(I("x", r, e, t8), I("y", r, e, t8))]; case "Atanh": - return [o.atanh(I("x", r, e, t10))]; + return [o.atanh(I("x", r, e, t8))]; case "Ceil": - return [o.ceil(I("x", r, e, t10))]; + return [o.ceil(I("x", r, e, t8))]; case "Complex": - return [o.complex(I("real", r, e, t10), I("imag", r, e, t10))]; + return [o.complex(I("real", r, e, t8), I("imag", r, e, t8))]; case "Cos": - return [o.cos(I("x", r, e, t10))]; + return [o.cos(I("x", r, e, t8))]; case "Cosh": - return [o.cosh(I("x", r, e, t10))]; + return [o.cosh(I("x", r, e, t8))]; case "Elu": - return [o.elu(I("x", r, e, t10))]; + return [o.elu(I("x", r, e, t8))]; case "Erf": - return [o.erf(I("x", r, e, t10))]; + return [o.erf(I("x", r, e, t8))]; case "Exp": - return [o.exp(I("x", r, e, t10))]; + return [o.exp(I("x", r, e, t8))]; case "Expm1": - return [o.expm1(I("x", r, e, t10))]; + return [o.expm1(I("x", r, e, t8))]; case "Floor": - return [o.floor(I("x", r, e, t10))]; + return [o.floor(I("x", r, e, t8))]; case "Log": - return [o.log(I("x", r, e, t10))]; + return [o.log(I("x", r, e, t8))]; case "Log1p": - return [o.log1p(I("x", r, e, t10))]; + return [o.log1p(I("x", r, e, t8))]; case "Imag": - return [o.imag(I("x", r, e, t10))]; + return [o.imag(I("x", r, e, t8))]; case "Neg": - return [o.neg(I("x", r, e, t10))]; + return [o.neg(I("x", r, e, t8))]; case "Reciprocal": - return [o.reciprocal(I("x", r, e, t10))]; + return [o.reciprocal(I("x", r, e, t8))]; case "Real": - return [o.real(I("x", r, e, t10))]; + return [o.real(I("x", r, e, t8))]; case "Relu": - return [o.relu(I("x", r, e, t10))]; + return [o.relu(I("x", r, e, t8))]; case "Round": - return [o.round(I("x", r, e, t10))]; + return [o.round(I("x", r, e, t8))]; case "Selu": - return [o.selu(I("x", r, e, t10))]; + return [o.selu(I("x", r, e, t8))]; case "Sigmoid": - return [o.sigmoid(I("x", r, e, t10))]; + return [o.sigmoid(I("x", r, e, t8))]; case "Sin": - return [o.sin(I("x", r, e, t10))]; + return [o.sin(I("x", r, e, t8))]; case "Sign": - return [o.sign(I("x", r, e, t10))]; + return [o.sign(I("x", r, e, t8))]; case "Sinh": - return [o.sinh(I("x", r, e, t10))]; + return [o.sinh(I("x", r, e, t8))]; case "Softplus": - return [o.softplus(I("x", r, e, t10))]; + return [o.softplus(I("x", r, e, t8))]; case "Sqrt": - return [o.sqrt(I("x", r, e, t10))]; + return [o.sqrt(I("x", r, e, t8))]; case "Square": - return [o.square(I("x", r, e, t10))]; + return [o.square(I("x", r, e, t8))]; case "Tanh": - return [o.tanh(I("x", r, e, t10))]; + return [o.tanh(I("x", r, e, t8))]; case "Tan": - return [o.tan(I("x", r, e, t10))]; + return [o.tan(I("x", r, e, t8))]; case "ClipByValue": - return [o.clipByValue(I("x", r, e, t10), I("clipValueMin", r, e, t10), I("clipValueMax", r, e, t10))]; + return [o.clipByValue(I("x", r, e, t8), I("clipValueMin", r, e, t8), I("clipValueMax", r, e, t8))]; case "Relu6": - return [o.relu6(I("x", r, e, t10))]; + return [o.relu6(I("x", r, e, t8))]; case "Rsqrt": - return [o.rsqrt(Bt(r.inputNames[0], e, t10))]; + return [o.rsqrt(Bt(r.inputNames[0], e, t8))]; case "LeakyRelu": - return [o.leakyRelu(I("x", r, e, t10), I("alpha", r, e, t10))]; + return [o.leakyRelu(I("x", r, e, t8), I("alpha", r, e, t8))]; case "Prelu": - return [o.prelu(I("x", r, e, t10), I("alpha", r, e, t10))]; + return [o.prelu(I("x", r, e, t8), I("alpha", r, e, t8))]; case "IsNan": - return [o.isNaN(Bt(r.inputNames[0], e, t10))]; + return [o.isNaN(Bt(r.inputNames[0], e, t8))]; case "IsInf": - return [o.isInf(Bt(r.inputNames[0], e, t10))]; + return [o.isInf(Bt(r.inputNames[0], e, t8))]; case "IsFinite": - return [o.isFinite(Bt(r.inputNames[0], e, t10))]; + return [o.isFinite(Bt(r.inputNames[0], e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function Ur(r, e, t10 = "") { +function Hr(r, e, t8 = "") { if (!(typeof r == "number" || typeof e == "number")) { - y.assert(r.length === e.length, () => t10 + ` Shapes ${r} and ${e} must match`); + y.assert(r.length === e.length, () => t8 + ` Shapes ${r} and ${e} must match`); for (let o = 0; o < r.length; o++) { let n = r[o], s = e[o]; - y.assert(n < 0 || s < 0 || n === s, () => t10 + ` Shapes ${r} and ${e} must match`); + y.assert(n < 0 || s < 0 || n === s, () => t8 + ` Shapes ${r} and ${e} must match`); } } } -function rT(r) { +function fT(r) { return !(typeof r == "number" || r.some((e) => e < 0)); } -function fc(r, e, t10) { - let o = bf(r, t10), n = !rT(o); +function hc(r, e, t8) { + let o = Cf(r, t8), n = !fT(o); if (n && e.length === 0) throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`); if (n && e.forEach((s) => { - o = bf(s.shape, o); - }), !rT(o)) + o = Cf(s.shape, o); + }), !fT(o)) throw new Error(`Non-fully-defined elementShape: ${o}`); return o; } -function bf(r, e) { +function Cf(r, e) { if (typeof r == "number") return e; if (typeof e == "number") return r; if (r.length !== e.length) throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`); - let t10 = []; + let t8 = []; for (let o = 0; o < r.length; ++o) { let n = r[o], s = e[o]; if (n >= 0 && s >= 0 && n !== s) throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`); - t10[o] = n >= 0 ? n : s; + t8[o] = n >= 0 ? n : s; } - return t10; + return t8; } -var Cf = class { - constructor(e, t10, o, n, s, a, i) { - this.name = e, this.dtype = t10, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = ke(0), Er(this.idTensor); +var wf = class { + constructor(e, t8, o, n, s, a, i) { + this.name = e, this.dtype = t8, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = ke(0), Rr(this.idTensor); } get id() { return this.idTensor.id; @@ -10168,8 +10217,8 @@ var Cf = class { return this.closed_; } clearAndClose(e) { - this.tensors.forEach((t10) => { - (e == null || !e.has(t10.tensor.id)) && t10.tensor.dispose(); + this.tensors.forEach((t8) => { + (e == null || !e.has(t8.tensor.id)) && t8.tensor.dispose(); }), this.tensors = [], this.closed_ = true, this.idTensor.dispose(); } size() { @@ -10180,37 +10229,37 @@ var Cf = class { throw new Error(`TensorArray ${this.name} has already been closed.`); if (e < 0 || e >= this.size()) throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`); - let t10 = this.tensors[e]; - if (t10.cleared) + let t8 = this.tensors[e]; + if (t8.cleared) throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`); - return this.clearAfterRead && (t10.cleared = true), t10.read = true, t10.tensor; + return this.clearAfterRead && (t8.cleared = true), t8.read = true, t8.tensor; } readMany(e) { - return e.map((t10) => this.read(t10)); + return e.map((t8) => this.read(t8)); } - write(e, t10) { + write(e, t8) { if (this.closed_) throw new Error(`TensorArray ${this.name} has already been closed.`); if (e < 0 || !this.dynamicSize && e >= this.maxSize) throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`); let o = this.tensors[e] || {}; - if (t10.dtype !== this.dtype) + if (t8.dtype !== this.dtype) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, - because the value dtype is ${t10.dtype}, but TensorArray dtype is ${this.dtype}.`); - if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t10.shape), Ur(this.elementShape, t10.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), o.read) + because the value dtype is ${t8.dtype}, but TensorArray dtype is ${this.dtype}.`); + if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t8.shape), Hr(this.elementShape, t8.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), o.read) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`); if (o.written) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`); - o.tensor = t10, Er(t10), o.written = true, this.tensors[e] = o; + o.tensor = t8, Rr(t8), o.written = true, this.tensors[e] = o; } - writeMany(e, t10) { - if (e.length !== t10.length) - throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t10.length}.`); - e.forEach((o, n) => this.write(o, t10[n])); + writeMany(e, t8) { + if (e.length !== t8.length) + throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t8.length}.`); + e.forEach((o, n) => this.write(o, t8[n])); } - gather(e, t10) { - if (t10 && t10 !== this.dtype) - throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t10}`); + gather(e, t8) { + if (t8 && t8 !== this.dtype) + throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t8}`); if (e) e = e.slice(0, this.size()); else { @@ -10221,45 +10270,45 @@ var Cf = class { if (e.length === 0) return ir([], [0].concat(this.elementShape)); let o = this.readMany(e); - return Ur(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), vr(o, 0); + return Hr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), kr(o, 0); } concat(e) { if (e && e !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`); if (this.size() === 0) return ir([], [0].concat(this.elementShape)); - let t10 = []; + let t8 = []; for (let n = 0; n < this.size(); n++) - t10.push(n); - let o = this.readMany(t10); - return Ur(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), yt(o, 0); + t8.push(n); + let o = this.readMany(t8); + return Hr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), bt(o, 0); } - scatter(e, t10) { - if (t10.dtype !== this.dtype) - throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`); - if (e.length !== t10.shape[0]) - throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t10.shape[0]}`); + scatter(e, t8) { + if (t8.dtype !== this.dtype) + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t8.dtype}`); + if (e.length !== t8.shape[0]) + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t8.shape[0]}`); let o = Math.max(...e); if (!this.dynamicSize && o >= this.maxSize) throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`); - this.writeMany(e, po(t10, 0)); + this.writeMany(e, mo(t8, 0)); } - split(e, t10) { - if (t10.dtype !== this.dtype) - throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`); + split(e, t8) { + if (t8.dtype !== this.dtype) + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t8.dtype}`); let o = 0, n = e.map((p) => (o += p, o)); - if (o !== t10.shape[0]) + if (o !== t8.shape[0]) throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${o}, and tensor's shape is: ${t10.shape}`); + ${o}, and tensor's shape is: ${t8.shape}`); if (!this.dynamicSize && e.length !== this.maxSize) throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`); - let s = o === 0 ? 0 : t10.size / o, a = []; + let s = o === 0 ? 0 : t8.size / o, a = []; De(() => { - t10 = W(t10, [1, o, s]); + t8 = W(t8, [1, o, s]); for (let p = 0; p < e.length; ++p) { let c = [0, p === 0 ? 0 : n[p - 1], 0], l = [1, e[p], s]; - a[p] = W(qe(t10, c, l), this.elementShape); + a[p] = W(Xe(t8, c, l), this.elementShape); } return a; }); @@ -10269,171 +10318,171 @@ var Cf = class { this.writeMany(i, a); } }; -var ci = class { +var di = class { get id() { return this.idTensor.id; } - constructor(e, t10, o, n = -1) { - this.tensors = e, this.elementShape = t10, this.elementDtype = o, e != null && e.forEach((s) => { + constructor(e, t8, o, n = -1) { + this.tensors = e, this.elementShape = t8, this.elementDtype = o, e != null && e.forEach((s) => { if (o !== s.dtype) throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`); - Ur(t10, s.shape, "TensorList shape mismatch: "), Er(s); - }), this.idTensor = ke(0), this.maxNumElements = n, Er(this.idTensor); + Hr(t8, s.shape, "TensorList shape mismatch: "), Rr(s); + }), this.idTensor = ke(0), this.maxNumElements = n, Rr(this.idTensor); } copy() { - return new ci([...this.tensors], this.elementShape, this.elementDtype); + return new di([...this.tensors], this.elementShape, this.elementDtype); } clearAndClose(e) { - this.tensors.forEach((t10) => { - (e == null || !e.has(t10.id)) && t10.dispose(); + this.tensors.forEach((t8) => { + (e == null || !e.has(t8.id)) && t8.dispose(); }), this.tensors.length = 0, this.idTensor.dispose(); } size() { return this.tensors.length; } - stack(e, t10, o = -1) { - if (t10 !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`); + stack(e, t8, o = -1) { + if (t8 !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t8}, but list elements ${this.elementDtype}`); if (o !== -1 && this.tensors.length !== o) throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`); - Ur(e, this.elementShape, "TensorList shape mismatch: "); - let n = fc(this.elementShape, this.tensors, e); + Hr(e, this.elementShape, "TensorList shape mismatch: "); + let n = hc(this.elementShape, this.tensors, e); return De(() => { let s = this.tensors.map((a) => W(a, n)); - return vr(s, 0); + return kr(s, 0); }); } - popBack(e, t10) { - if (t10 !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`); + popBack(e, t8) { + if (t8 !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t8}, but list elements ${this.elementDtype}`); if (this.size() === 0) throw new Error("Trying to pop from an empty list."); - let o = fc(this.elementShape, this.tensors, e), n = this.tensors.pop(); - return n.kept = false, Ur(n.shape, e, "TensorList shape mismatch: "), W(n, o); + let o = hc(this.elementShape, this.tensors, e), n = this.tensors.pop(); + return n.kept = false, Hr(n.shape, e, "TensorList shape mismatch: "), W(n, o); } pushBack(e) { if (e.dtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`); - if (Ur(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size()) + if (Hr(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size()) throw new Error("Trying to push element into a full list."); - Er(e), this.tensors.push(e); + Rr(e), this.tensors.push(e); } resize(e) { if (e < 0) throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`); if (this.maxNumElements !== -1 && e > this.maxNumElements) throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`); - let t10 = new ci([], this.elementShape, this.elementDtype, this.maxNumElements); - t10.tensors.length = e; + let t8 = new di([], this.elementShape, this.elementDtype, this.maxNumElements); + t8.tensors.length = e; for (let o = 0; o < Math.min(this.tensors.length, e); ++o) - t10.tensors[o] = this.tensors[o]; - return t10; + t8.tensors[o] = this.tensors[o]; + return t8; } - getItem(e, t10, o) { + getItem(e, t8, o) { if (o !== this.elementDtype) throw new Error(`Invalid data types; op elements ${o}, but list elements ${this.elementDtype}`); if (e < 0 || e > this.tensors.length) throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`); if (this.tensors[e] == null) throw new Error(`element at index ${e} is null.`); - Ur(this.tensors[e].shape, t10, "TensorList shape mismatch: "); - let n = fc(this.elementShape, this.tensors, t10); + Hr(this.tensors[e].shape, t8, "TensorList shape mismatch: "); + let n = hc(this.elementShape, this.tensors, t8); return W(this.tensors[e], n); } - setItem(e, t10) { - if (t10.dtype !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t10.dtype}, but list elements ${this.elementDtype}`); + setItem(e, t8) { + if (t8.dtype !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t8.dtype}, but list elements ${this.elementDtype}`); if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements) throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`); - Ur(this.elementShape, t10.shape, "TensorList shape mismatch: "), Er(t10), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t10; + Hr(this.elementShape, t8.shape, "TensorList shape mismatch: "), Rr(t8), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t8; } - gather(e, t10, o) { - if (t10 !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`); - Ur(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size()); - let n = fc(this.elementShape, this.tensors, o); + gather(e, t8, o) { + if (t8 !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t8}, but list elements ${this.elementDtype}`); + Hr(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size()); + let n = hc(this.elementShape, this.tensors, o); return e.length === 0 ? ir([], [0].concat(n)) : De(() => { let s = e.map((a) => W(this.tensors[a], n)); - return vr(s, 0); + return kr(s, 0); }); } - concat(e, t10) { + concat(e, t8) { if (e && e !== this.elementDtype) throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`); - Ur(this.elementShape, t10, "TensorList shape mismatch: "); - let o = fc(this.elementShape, this.tensors, t10); + Hr(this.elementShape, t8, "TensorList shape mismatch: "); + let o = hc(this.elementShape, this.tensors, t8); return this.size() === 0 ? ir([], [0].concat(o)) : De(() => { let n = this.tensors.map((s) => W(s, o)); - return yt(n, 0); + return bt(n, 0); }); } }; -function oT(r, e, t10) { +function hT(r, e, t8) { let o = r.dtype; if (r.shape.length < 1) throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`); - if (r.dtype !== t10) - throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t10}`); + if (r.dtype !== t8) + throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t8}`); let n = r.shape.slice(1); - Ur(n, e, "TensorList shape mismatch: "); - let s = po(r); - return new ci(s, e, o); + Hr(n, e, "TensorList shape mismatch: "); + let s = mo(r); + return new di(s, e, o); } -function nT(r, e, t10, o) { - return new ci([], r, e, o); +function gT(r, e, t8, o) { + return new di([], r, e, o); } -function sT(r, e, t10, o) { +function xT(r, e, t8, o) { if (e.length !== r.shape[0]) throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`); let n = Math.max(...e); if (o != null && o !== -1 && n >= o) throw new Error(`Max index must be < array size (${n} vs. ${o})`); - let s = new ci([], t10, r.dtype, o), a = po(r, 0); + let s = new di([], t8, r.dtype, o), a = mo(r, 0); return e.forEach((i, p) => { s.setItem(i, a[p]); }), s; } -function aT(r, e, t10) { +function yT(r, e, t8) { let o = 0, n = e.map((c) => (o += c, o)); if (o !== r.shape[0]) throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${o}, and tensor's shape is: ${r.shape}`); - let s = r.shape.slice(1), a = bf(s, t10), i = o === 0 ? 0 : r.size / o, p = De(() => { + let s = r.shape.slice(1), a = Cf(s, t8), i = o === 0 ? 0 : r.size / o, p = De(() => { let c = []; r = W(r, [1, o, i]); for (let l = 0; l < e.length; ++l) { let d = [0, l === 0 ? 0 : n[l - 1], 0], f = [1, e[l], i]; - c[l] = W(qe(r, d, f), a); + c[l] = W(Xe(r, d, f), a); } return r.dispose(), c; - }), u = new ci([], t10, r.dtype, e.length); + }), u = new di([], t8, r.dtype, e.length); for (let c = 0; c < p.length; c++) u.setItem(c, p[c]); return u; } -var iT = async (r, e, t10) => { +var bT = async (r, e, t8) => { switch (r.op) { case "If": case "StatelessIf": { - let o = I("thenBranch", r, e, t10), n = I("elseBranch", r, e, t10), s = I("cond", r, e, t10), a = I("args", r, e, t10); - return (await s.data())[0] ? t10.functionMap[o].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap) : t10.functionMap[n].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap); + let o = I("thenBranch", r, e, t8), n = I("elseBranch", r, e, t8), s = I("cond", r, e, t8), a = I("args", r, e, t8); + return (await s.data())[0] ? t8.functionMap[o].executeFunctionAsync(a, t8.tensorArrayMap, t8.tensorListMap) : t8.functionMap[n].executeFunctionAsync(a, t8.tensorArrayMap, t8.tensorListMap); } case "While": case "StatelessWhile": { - let o = I("body", r, e, t10), n = I("cond", r, e, t10), s = I("args", r, e, t10), a = await t10.functionMap[n].executeFunctionAsync(s, t10.tensorArrayMap, t10.tensorListMap), i = s.map((c) => c.id), p = await a[0].data(); + let o = I("body", r, e, t8), n = I("cond", r, e, t8), s = I("args", r, e, t8), a = await t8.functionMap[n].executeFunctionAsync(s, t8.tensorArrayMap, t8.tensorListMap), i = s.map((c) => c.id), p = await a[0].data(); a.forEach((c) => { !c.kept && i.indexOf(c.id) === -1 && c.dispose(); }); let u = s; for (; p[0]; ) { let c = u; - u = await t10.functionMap[o].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap); + u = await t8.functionMap[o].executeFunctionAsync(u, t8.tensorArrayMap, t8.tensorListMap); let l = u.map((d) => d.id); c.forEach((d) => { !d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose(); }); - let m = await t10.functionMap[n].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap); + let m = await t8.functionMap[n].executeFunctionAsync(u, t8.tensorArrayMap, t8.tensorListMap); p = await m[0].data(), m.forEach((d) => { !d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose(); }); @@ -10441,132 +10490,132 @@ var iT = async (r, e, t10) => { return u; } case "LoopCond": { - let o = I("pred", r, e, t10); - return [As(o)]; + let o = I("pred", r, e, t8); + return [Ms(o)]; } case "Switch": { - let o = I("pred", r, e, t10), n = I("data", r, e, t10); - return n.kept || (n = As(n)), (await o.data())[0] ? [void 0, n] : [n, void 0]; + let o = I("pred", r, e, t8), n = I("data", r, e, t8); + return n.kept || (n = Ms(n)), (await o.data())[0] ? [void 0, n] : [n, void 0]; } case "Merge": { - let o = r.inputNames.find((n) => Bt(n, e, t10) !== void 0); + let o = r.inputNames.find((n) => Bt(n, e, t8) !== void 0); if (o) { - let n = Bt(o, e, t10); - return [As(n)]; + let n = Bt(o, e, t8); + return [Ms(n)]; } return; } case "Enter": { - let o = I("frameName", r, e, t10), n = I("tensor", r, e, t10); - return t10.enterFrame(o), [As(n)]; + let o = I("frameName", r, e, t8), n = I("tensor", r, e, t8); + return t8.enterFrame(o), [Ms(n)]; } case "Exit": { - let o = I("tensor", r, e, t10); - return t10.exitFrame(), [As(o)]; + let o = I("tensor", r, e, t8); + return t8.exitFrame(), [Ms(o)]; } case "NextIteration": { - let o = I("tensor", r, e, t10); - return t10.nextIteration(), [As(o)]; + let o = I("tensor", r, e, t8); + return t8.nextIteration(), [Ms(o)]; } case "TensorArrayV3": { - let o = I("size", r, e, t10), n = I("dtype", r, e, t10), s = I("elementShape", r, e, t10), a = I("dynamicSize", r, e, t10), i = I("clearAfterRead", r, e, t10), p = I("identicalElementShapes", r, e, t10), u = I("name", r, e, t10), c = new Cf(u, n, o, s, p, a, i); - return t10.addTensorArray(c), [c.idTensor, ke(1)]; + let o = I("size", r, e, t8), n = I("dtype", r, e, t8), s = I("elementShape", r, e, t8), a = I("dynamicSize", r, e, t8), i = I("clearAfterRead", r, e, t8), p = I("identicalElementShapes", r, e, t8), u = I("name", r, e, t8), c = new wf(u, n, o, s, p, a, i); + return t8.addTensorArray(c), [c.idTensor, ke(1)]; } case "TensorArrayWriteV3": { - let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t8), n = I("index", r, e, t8), s = I("tensor", r, e, t8), a = t8.getTensorArray(o.id); return a.write(n, s), [a.idTensor]; } case "TensorArrayReadV3": { - let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10); - return [t10.getTensorArray(o.id).read(n)]; + let o = I("tensorArrayId", r, e, t8), n = I("index", r, e, t8); + return [t8.getTensorArray(o.id).read(n)]; } case "TensorArrayGatherV3": { - let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("dtype", r, e, t10); - return [t10.getTensorArray(o.id).gather(n, s)]; + let o = I("tensorArrayId", r, e, t8), n = I("indices", r, e, t8), s = I("dtype", r, e, t8); + return [t8.getTensorArray(o.id).gather(n, s)]; } case "TensorArrayScatterV3": { - let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t8), n = I("indices", r, e, t8), s = I("tensor", r, e, t8), a = t8.getTensorArray(o.id); return a.scatter(n, s), [a.idTensor]; } case "TensorArrayConcatV3": { - let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id), s = I("dtype", r, e, t10); + let o = I("tensorArrayId", r, e, t8), n = t8.getTensorArray(o.id), s = I("dtype", r, e, t8); return [n.concat(s)]; } case "TensorArraySplitV3": { - let o = I("tensorArrayId", r, e, t10), n = I("tensor", r, e, t10), s = I("lengths", r, e, t10), a = t10.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t8), n = I("tensor", r, e, t8), s = I("lengths", r, e, t8), a = t8.getTensorArray(o.id); return a.split(s, n), [a.idTensor]; } case "TensorArraySizeV3": { - let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t8), n = t8.getTensorArray(o.id); return [ke(n.size(), "int32")]; } case "TensorArrayCloseV3": { - let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t8), n = t8.getTensorArray(o.id); return n.clearAndClose(), [n.idTensor]; } case "TensorListSetItem": { - let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorList(o.id); + let o = I("tensorListId", r, e, t8), n = I("index", r, e, t8), s = I("tensor", r, e, t8), a = t8.getTensorList(o.id); return a.setItem(n, s), [a.idTensor]; } case "TensorListGetItem": { - let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10); - return [t10.getTensorList(o.id).getItem(n, s, a)]; + let o = I("tensorListId", r, e, t8), n = I("index", r, e, t8), s = I("elementShape", r, e, t8), a = I("elementDType", r, e, t8); + return [t8.getTensorList(o.id).getItem(n, s, a)]; } case "TensorListScatterV2": case "TensorListScatter": { - let o = I("indices", r, e, t10), n = I("tensor", r, e, t10), s = I("elementShape", r, e, t10), a = I("numElements", r, e, t10), i = sT(n, o, s, a); - return t10.addTensorList(i), [i.idTensor]; + let o = I("indices", r, e, t8), n = I("tensor", r, e, t8), s = I("elementShape", r, e, t8), a = I("numElements", r, e, t8), i = xT(n, o, s, a); + return t8.addTensorList(i), [i.idTensor]; } case "TensorListReserve": case "EmptyTensorList": { - let o = I("elementShape", r, e, t10), n = I("elementDType", r, e, t10), s; + let o = I("elementShape", r, e, t8), n = I("elementDType", r, e, t8), s; r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements"; - let a = I(s, r, e, t10), i = r.op === "TensorListReserve" ? -1 : a, p = nT(o, n, a, i); - return t10.addTensorList(p), [p.idTensor]; + let a = I(s, r, e, t8), i = r.op === "TensorListReserve" ? -1 : a, p = gT(o, n, a, i); + return t8.addTensorList(p), [p.idTensor]; } case "TensorListGather": { - let o = I("tensorListId", r, e, t10), n = I("indices", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10); - return [t10.getTensorList(o.id).gather(n, a, s)]; + let o = I("tensorListId", r, e, t8), n = I("indices", r, e, t8), s = I("elementShape", r, e, t8), a = I("elementDType", r, e, t8); + return [t8.getTensorList(o.id).gather(n, a, s)]; } case "TensorListStack": { - let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = I("numElements", r, e, t10); - return [t10.getTensorList(o.id).stack(n, s, a)]; + let o = I("tensorListId", r, e, t8), n = I("elementShape", r, e, t8), s = I("elementDType", r, e, t8), a = I("numElements", r, e, t8); + return [t8.getTensorList(o.id).stack(n, s, a)]; } case "TensorListFromTensor": { - let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = oT(o, n, s); - return t10.addTensorList(a), [a.idTensor]; + let o = I("tensor", r, e, t8), n = I("elementShape", r, e, t8), s = I("elementDType", r, e, t8), a = hT(o, n, s); + return t8.addTensorList(a), [a.idTensor]; } case "TensorListConcat": case "TensorListConcatV2": { - let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id), s = I("dtype", r, e, t10), a = I("elementShape", r, e, t10); + let o = I("tensorListId", r, e, t8), n = t8.getTensorList(o.id), s = I("dtype", r, e, t8), a = I("elementShape", r, e, t8); return [n.concat(s, a)]; } case "TensorListPushBack": { - let o = I("tensorListId", r, e, t10), n = I("tensor", r, e, t10), s = t10.getTensorList(o.id); + let o = I("tensorListId", r, e, t8), n = I("tensor", r, e, t8), s = t8.getTensorList(o.id); return s.pushBack(n), [s.idTensor]; } case "TensorListPopBack": { - let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10); - return [t10.getTensorList(o.id).popBack(n, s)]; + let o = I("tensorListId", r, e, t8), n = I("elementShape", r, e, t8), s = I("elementDType", r, e, t8); + return [t8.getTensorList(o.id).popBack(n, s)]; } case "TensorListSplit": { - let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("lengths", r, e, t10), a = aT(o, s, n); - return t10.addTensorList(a), [a.idTensor]; + let o = I("tensor", r, e, t8), n = I("elementShape", r, e, t8), s = I("lengths", r, e, t8), a = yT(o, s, n); + return t8.addTensorList(a), [a.idTensor]; } case "TensorListLength": { - let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id); + let o = I("tensorListId", r, e, t8), n = t8.getTensorList(o.id); return [ke(n.size(), "int32")]; } case "TensorListResize": { - let o = I("tensorListId", r, e, t10), n = I("size", r, e, t10), a = t10.getTensorList(o.id).resize(n); - return t10.addTensorList(a), [a.idTensor]; + let o = I("tensorListId", r, e, t8), n = I("size", r, e, t8), a = t8.getTensorList(o.id).resize(n); + return t8.addTensorList(a), [a.idTensor]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function uT(r, e, t10) { - let [o, n] = I("fusedOps", r, e, t10), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t10); +function CT(r, e, t8) { + let [o, n] = I("fusedOps", r, e, t8), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t8); if (s) { if (i && u !== 2) throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha."); @@ -10575,202 +10624,202 @@ function uT(r, e, t10) { } if (p) throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported"); - let c = I("strides", r, e, t10), l = Al(r, e, t10), m = I("dataFormat", r, e, t10).toUpperCase(), d = I("dilations", r, e, t10), [f, h] = I("args", r, e, t10); + let c = I("strides", r, e, t8), l = Al(r, e, t8), m = I("dataFormat", r, e, t8).toUpperCase(), d = I("dilations", r, e, t8), [f, h] = I("args", r, e, t8); a && (h = f, f = void 0); - let g = I("leakyreluAlpha", r, e, t10); + let g = I("leakyreluAlpha", r, e, t8); return { stride: c, pad: l, dataFormat: m, dilations: d, biasArg: f, preluArg: h, activationFunc: n, leakyreluAlpha: g }; } -var pT = (r, e, t10, o = Ze) => { +var wT = (r, e, t8, o = Je) => { switch (r.op) { case "Conv1D": { - let n = I("stride", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilation", r, e, t10); - return [o.conv1d(I("x", r, e, t10), I("filter", r, e, t10), n, s, a, i)]; + let n = I("stride", r, e, t8), s = I("pad", r, e, t8), a = I("dataFormat", r, e, t8).toUpperCase(), i = I("dilation", r, e, t8); + return [o.conv1d(I("x", r, e, t8), I("filter", r, e, t8), n, s, a, i)]; } case "Conv2D": { - let n = I("strides", r, e, t10), s = Al(r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10); - return [o.conv2d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, a, [i[1], i[2]])]; + let n = I("strides", r, e, t8), s = Al(r, e, t8), a = I("dataFormat", r, e, t8).toUpperCase(), i = I("dilations", r, e, t8); + return [o.conv2d(I("x", r, e, t8), I("filter", r, e, t8), [n[1], n[2]], s, a, [i[1], i[2]])]; } case "_FusedConv2D": { - let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = uT(r, e, t10); - return [o.fused.conv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; + let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = CT(r, e, t8); + return [o.fused.conv2d({ x: I("x", r, e, t8), filter: I("filter", r, e, t8), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; } case "FusedDepthwiseConv2dNative": { - let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = uT(r, e, t10); - return [o.fused.depthwiseConv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; + let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = CT(r, e, t8); + return [o.fused.depthwiseConv2d({ x: I("x", r, e, t8), filter: I("filter", r, e, t8), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; } case "Conv2DBackpropInput": case "Conv2dTranspose": { - let n = I("outputShape", r, e, t10), s = I("strides", r, e, t10), a = Al(r, e, t10); - return [o.conv2dTranspose(I("x", r, e, t10), I("filter", r, e, t10), n, [s[1], s[2]], a)]; + let n = I("outputShape", r, e, t8), s = I("strides", r, e, t8), a = Al(r, e, t8); + return [o.conv2dTranspose(I("x", r, e, t8), I("filter", r, e, t8), n, [s[1], s[2]], a)]; } case "DepthwiseConv2dNative": case "DepthwiseConv2d": { - let n = I("strides", r, e, t10), s = Al(r, e, t10), a = I("dilations", r, e, t10), i = I("dataFormat", r, e, t10).toUpperCase(); - return [o.depthwiseConv2d(I("input", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, i, [a[1], a[2]])]; + let n = I("strides", r, e, t8), s = Al(r, e, t8), a = I("dilations", r, e, t8), i = I("dataFormat", r, e, t8).toUpperCase(); + return [o.depthwiseConv2d(I("input", r, e, t8), I("filter", r, e, t8), [n[1], n[2]], s, i, [a[1], a[2]])]; } case "Conv3D": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10); - return [o.conv3d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])]; + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("dataFormat", r, e, t8).toUpperCase(), i = I("dilations", r, e, t8); + return [o.conv3d(I("x", r, e, t8), I("filter", r, e, t8), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])]; } case "AvgPool": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); - return [o.avgPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)]; + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8); + return [o.avgPool(I("x", r, e, t8), [a[1], a[2]], [n[1], n[2]], s)]; } case "MaxPool": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); - return [o.maxPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)]; + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8); + return [o.maxPool(I("x", r, e, t8), [a[1], a[2]], [n[1], n[2]], s)]; } case "MaxPoolWithArgmax": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10), i = I("includeBatchInIndex", r, e, t10), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s, i); + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8), i = I("includeBatchInIndex", r, e, t8), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t8), [a[1], a[2]], [n[1], n[2]], s, i); return [p, u]; } case "AvgPool3D": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); - return [o.avgPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8); + return [o.avgPool3d(I("x", r, e, t8), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; } case "MaxPool3D": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); - return [o.maxPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8); + return [o.maxPool3d(I("x", r, e, t8), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; } case "Dilation2D": { - let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dilations", r, e, t10), i = n[1], p = n[2], u = a[1], c = a[2]; - return [o.dilation2d(I("x", r, e, t10), I("filter", r, e, t10), [i, p], s, [u, c], "NHWC")]; + let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("dilations", r, e, t8), i = n[1], p = n[2], u = a[1], c = a[2]; + return [o.dilation2d(I("x", r, e, t8), I("filter", r, e, t8), [i, p], s, [u, c], "NHWC")]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var cT = (r, e, t10, o = Ze) => { +var ST = (r, e, t8, o = Je) => { switch (r.op) { case "Fill": { - let n = I("shape", r, e, t10), s = I("dtype", r, e, t10), a = I("value", r, e, t10); + let n = I("shape", r, e, t8), s = I("dtype", r, e, t8), a = I("value", r, e, t8); return [o.fill(n, a, s)]; } case "LinSpace": { - let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("num", r, e, t10); + let n = I("start", r, e, t8), s = I("stop", r, e, t8), a = I("num", r, e, t8); return [o.linspace(n, s, a)]; } case "Multinomial": { - let n = I("logits", r, e, t10), s = I("numSamples", r, e, t10), a = I("seed", r, e, t10); + let n = I("logits", r, e, t8), s = I("numSamples", r, e, t8), a = I("seed", r, e, t8); return [o.multinomial(n, s, a)]; } case "OneHot": { - let n = I("indices", r, e, t10), s = I("depth", r, e, t10), a = I("onValue", r, e, t10), i = I("offValue", r, e, t10), p = I("dtype", r, e, t10); + let n = I("indices", r, e, t8), s = I("depth", r, e, t8), a = I("onValue", r, e, t8), i = I("offValue", r, e, t8), p = I("dtype", r, e, t8); return [o.oneHot(n, s, a, i, p)]; } case "Ones": - return [o.ones(I("shape", r, e, t10), I("dtype", r, e, t10))]; + return [o.ones(I("shape", r, e, t8), I("dtype", r, e, t8))]; case "OnesLike": - return [o.onesLike(I("x", r, e, t10))]; + return [o.onesLike(I("x", r, e, t8))]; case "RandomStandardNormal": - return [o.randomStandardNormal(I("shape", r, e, t10), I("dtype", r, e, t10), I("seed", r, e, t10))]; + return [o.randomStandardNormal(I("shape", r, e, t8), I("dtype", r, e, t8), I("seed", r, e, t8))]; case "RandomUniform": - return [o.randomUniform(I("shape", r, e, t10), I("minval", r, e, t10), I("maxval", r, e, t10), I("dtype", r, e, t10))]; + return [o.randomUniform(I("shape", r, e, t8), I("minval", r, e, t8), I("maxval", r, e, t8), I("dtype", r, e, t8))]; case "RandomUniformInt": - return [o.randomUniformInt(I("shape", r, e, t10), I("minval", r, e, t10), I("maxval", r, e, t10), I("seed", r, e, t10))]; + return [o.randomUniformInt(I("shape", r, e, t8), I("minval", r, e, t8), I("maxval", r, e, t8), I("seed", r, e, t8))]; case "Range": { - let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("step", r, e, t10); - return [o.range(n, s, a, I("dtype", r, e, t10))]; + let n = I("start", r, e, t8), s = I("stop", r, e, t8), a = I("step", r, e, t8); + return [o.range(n, s, a, I("dtype", r, e, t8))]; } case "TruncatedNormal": { - let n = I("shape", r, e, t10), s = I("mean", r, e, t10), a = I("stdDev", r, e, t10), i = I("seed", r, e, t10); - return [o.truncatedNormal(n, s, a, I("dtype", r, e, t10), i)]; + let n = I("shape", r, e, t8), s = I("mean", r, e, t8), a = I("stdDev", r, e, t8), i = I("seed", r, e, t8); + return [o.truncatedNormal(n, s, a, I("dtype", r, e, t8), i)]; } case "Zeros": - return [o.zeros(I("shape", r, e, t10), I("dtype", r, e, t10))]; + return [o.zeros(I("shape", r, e, t8), I("dtype", r, e, t8))]; case "ZerosLike": - return [o.zerosLike(I("x", r, e, t10))]; + return [o.zerosLike(I("x", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function yS(r, e, t10) { - let o = I("boxes", r, e, t10), n = I("scores", r, e, t10), s = I("maxOutputSize", r, e, t10), a = I("iouThreshold", r, e, t10), i = I("scoreThreshold", r, e, t10), p = I("softNmsSigma", r, e, t10); +function TS(r, e, t8) { + let o = I("boxes", r, e, t8), n = I("scores", r, e, t8), s = I("maxOutputSize", r, e, t8), a = I("iouThreshold", r, e, t8), i = I("scoreThreshold", r, e, t8), p = I("softNmsSigma", r, e, t8); return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p }; } -var lT = async (r, e, t10, o, n = Ze) => { +var IT = async (r, e, t8, o, n = Je) => { switch (r.op) { case "NonMaxSuppressionV5": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = yS(r, e, t10), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = TS(r, e, t8), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c); return [l.selectedIndices, l.selectedScores]; } case "NonMaxSuppressionV4": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = yS(r, e, t10), c = I("padToMaxOutputSize", r, e, t10), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = TS(r, e, t8), c = I("padToMaxOutputSize", r, e, t8), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c); return [l.selectedIndices, l.validOutputs]; } case "NonMaxSuppressionV3": case "NonMaxSuppressionV2": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = yS(r, e, t10); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = TS(r, e, t8); return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)]; } case "Where": { - let s = n.cast(I("condition", r, e, t10), "bool"), a = [await n.whereAsync(s)]; + let s = n.cast(I("condition", r, e, t8), "bool"), a = [await n.whereAsync(s)]; return s.dispose(), a; } case "ListDiff": - return n.setdiff1dAsync(I("x", r, e, t10), I("y", r, e, t10)); + return n.setdiff1dAsync(I("x", r, e, t8), I("y", r, e, t8)); default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var mT = (r, e, t10, o = Ze) => { +var vT = (r, e, t8, o = Je) => { switch (r.op) { case "LowerBound": { - let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10); + let n = I("sortedSequence", r, e, t8), s = I("values", r, e, t8); return [o.lowerBound(n, s)]; } case "TopKV2": { - let n = I("x", r, e, t10), s = I("k", r, e, t10), a = I("sorted", r, e, t10), i = o.topk(n, s, a); + let n = I("x", r, e, t8), s = I("k", r, e, t8), a = I("sorted", r, e, t8), i = o.topk(n, s, a); return [i.values, i.indices]; } case "UpperBound": { - let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10); + let n = I("sortedSequence", r, e, t8), s = I("values", r, e, t8); return [o.upperBound(n, s)]; } case "Unique": { - let n = I("x", r, e, t10), s = o.unique(n); + let n = I("x", r, e, t8), s = o.unique(n); return [s.values, s.indices]; } case "UniqueV2": { - let n = I("x", r, e, t10), s = I("axis", r, e, t10), a = o.unique(n, s); + let n = I("x", r, e, t8), s = I("axis", r, e, t8), a = o.unique(n, s); return [a.values, a.indices]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var dT = (r, e, t10, o = Ze) => { +var kT = (r, e, t8, o = Je) => { switch (r.op) { case "Const": return e[r.name]; case "PlaceholderWithDefault": - let n = I("default", r, e, t10); - return [Bt(r.name, e, t10) || n]; + let n = I("default", r, e, t8); + return [Bt(r.name, e, t8) || n]; case "Placeholder": - return [Bt(r.name, e, t10)]; + return [Bt(r.name, e, t8)]; case "Identity": case "StopGradient": case "FakeQuantWithMinMaxVars": { - let c = I("x", r, e, t10); - return [As(c)]; + let c = I("x", r, e, t8); + return [Ms(c)]; } case "IdentityN": - return I("x", r, e, t10).map((c) => As(c)); + return I("x", r, e, t8).map((c) => Ms(c)); case "Snapshot": - let s = I("x", r, e, t10); - return [As(s)]; + let s = I("x", r, e, t8); + return [Ms(s)]; case "Shape": - return [o.tensor1d(I("x", r, e, t10).shape, "int32")]; + return [o.tensor1d(I("x", r, e, t8).shape, "int32")]; case "ShapeN": - return I("x", r, e, t10).map((c) => o.tensor1d(c.shape)); + return I("x", r, e, t8).map((c) => o.tensor1d(c.shape)); case "Size": - return [o.scalar(I("x", r, e, t10).size, "int32")]; + return [o.scalar(I("x", r, e, t8).size, "int32")]; case "Rank": - return [o.scalar(I("x", r, e, t10).rank, "int32")]; + return [o.scalar(I("x", r, e, t8).rank, "int32")]; case "NoOp": return [o.scalar(1)]; case "Print": - let a = I("x", r, e, t10), i = I("data", r, e, t10), p = I("message", r, e, t10), u = I("summarize", r, e, t10); + let a = I("x", r, e, t8), i = I("data", r, e, t8), p = I("message", r, e, t8), u = I("summarize", r, e, t8); console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(p); for (let c = 0; c < i.length; c++) console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0, u)); @@ -10779,12 +10828,12 @@ var dT = (r, e, t10, o = Ze) => { throw TypeError(`Node type ${r.op} is not implemented`); } }; -var wf = class { +var Sf = class { get id() { return this.handle.id; } - constructor(e, t10) { - this.keyDType = e, this.valueDType = t10, this.handle = ke(0), this.tensorMap = /* @__PURE__ */ new Map(), Er(this.handle); + constructor(e, t8) { + this.keyDType = e, this.valueDType = t8, this.handle = ke(0), this.tensorMap = /* @__PURE__ */ new Map(), Rr(this.handle); } clearAndClose() { this.tensorMap.forEach((e) => e.dispose()), this.tensorMap.clear(), this.handle.dispose(); @@ -10795,43 +10844,43 @@ var wf = class { tensorSize() { return ke(this.size(), "int32"); } - async import(e, t10) { - this.checkKeyAndValueTensor(e, t10); + async import(e, t8) { + this.checkKeyAndValueTensor(e, t8); let o = await e.data(); return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), De(() => { - let n = po(t10), s = o.length, a = n.length; + let n = mo(t8), s = o.length, a = n.length; y.assert(s === a, () => `The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`); for (let i = 0; i < s; i++) { let p = o[i], u = n[i]; - Er(u), this.tensorMap.set(p, u); + Rr(u), this.tensorMap.set(p, u); } return this.handle; }); } - async find(e, t10) { - this.checkKeyAndValueTensor(e, t10); + async find(e, t8) { + this.checkKeyAndValueTensor(e, t8); let o = await e.data(); return De(() => { let n = []; for (let s = 0; s < o.length; s++) { - let a = o[s], i = this.findWithDefault(a, t10); + let a = o[s], i = this.findWithDefault(a, t8); n.push(i); } - return vr(n); + return kr(n); }); } - findWithDefault(e, t10) { + findWithDefault(e, t8) { let o = this.tensorMap.get(e); - return o != null ? o : t10; + return o != null ? o : t8; } - checkKeyAndValueTensor(e, t10) { + checkKeyAndValueTensor(e, t8) { if (e.dtype !== this.keyDType) throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`); - if (t10.dtype !== this.valueDType) - throw new Error(`Expect value dtype ${this.valueDType}, but got ${t10.dtype}`); + if (t8.dtype !== this.valueDType) + throw new Error(`Expect value dtype ${this.valueDType}, but got ${t8.dtype}`); } }; -var fT = async (r, e, t10, o) => { +var NT = async (r, e, t8, o) => { switch (r.op) { case "HashTable": case "HashTableV2": { @@ -10839,7 +10888,7 @@ var fT = async (r, e, t10, o) => { if (n != null) return [n]; { - let s = I("keyDType", r, e, t10), a = I("valueDType", r, e, t10), i = new wf(s, a); + let s = I("keyDType", r, e, t8), a = I("valueDType", r, e, t8), i = new Sf(s, a); return o.addHashTable(r.name, i), [i.handle]; } } @@ -10847,229 +10896,229 @@ var fT = async (r, e, t10, o) => { case "InitializeTableV2": case "LookupTableImport": case "LookupTableImportV2": { - let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("values", r, e, t10); + let n = I("tableHandle", r, e, t8, o), s = I("keys", r, e, t8), a = I("values", r, e, t8); return [await o.getHashTableById(n.id).import(s, a)]; } case "LookupTableFind": case "LookupTableFindV2": { - let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("defaultValue", r, e, t10); + let n = I("tableHandle", r, e, t8, o), s = I("keys", r, e, t8), a = I("defaultValue", r, e, t8); return [await o.getHashTableById(n.id).find(s, a)]; } case "LookupTableSize": case "LookupTableSizeV2": { - let n = I("tableHandle", r, e, t10, o); + let n = I("tableHandle", r, e, t8, o); return [o.getHashTableById(n.id).tensorSize()]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var hT = (r, e, t10, o = Ze) => { +var TT = (r, e, t8, o = Je) => { switch (r.op) { case "ResizeBilinear": { - let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10); + let n = I("images", r, e, t8), s = I("size", r, e, t8), a = I("alignCorners", r, e, t8), i = I("halfPixelCenters", r, e, t8); return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)]; } case "ResizeNearestNeighbor": { - let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10); + let n = I("images", r, e, t8), s = I("size", r, e, t8), a = I("alignCorners", r, e, t8), i = I("halfPixelCenters", r, e, t8); return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)]; } case "CropAndResize": { - let n = I("image", r, e, t10), s = I("boxes", r, e, t10), a = I("boxInd", r, e, t10), i = I("cropSize", r, e, t10), p = I("method", r, e, t10), u = I("extrapolationValue", r, e, t10); + let n = I("image", r, e, t8), s = I("boxes", r, e, t8), a = I("boxInd", r, e, t8), i = I("cropSize", r, e, t8), p = I("method", r, e, t8), u = I("extrapolationValue", r, e, t8); return [o.image.cropAndResize(n, s, a, i, p, u)]; } case "ImageProjectiveTransformV3": { - let n = I("images", r, e, t10), s = I("transforms", r, e, t10), a = I("outputShape", r, e, t10), i = I("fillValue", r, e, t10), p = I("interpolation", r, e, t10), u = I("fillMode", r, e, t10); + let n = I("images", r, e, t8), s = I("transforms", r, e, t8), a = I("outputShape", r, e, t8), i = I("fillValue", r, e, t8), p = I("interpolation", r, e, t8), u = I("fillMode", r, e, t8); return [o.image.transform(n, s, p.toLowerCase(), u.toLowerCase(), i, a)]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var gT = (r, e, t10, o = Ze) => { +var _T = (r, e, t8, o = Je) => { switch (r.op) { case "Equal": - return [o.equal(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.equal(I("a", r, e, t8), I("b", r, e, t8))]; case "NotEqual": - return [o.notEqual(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.notEqual(I("a", r, e, t8), I("b", r, e, t8))]; case "Greater": - return [o.greater(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.greater(I("a", r, e, t8), I("b", r, e, t8))]; case "GreaterEqual": - return [o.greaterEqual(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.greaterEqual(I("a", r, e, t8), I("b", r, e, t8))]; case "Less": - return [o.less(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.less(I("a", r, e, t8), I("b", r, e, t8))]; case "LessEqual": - return [o.lessEqual(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.lessEqual(I("a", r, e, t8), I("b", r, e, t8))]; case "LogicalAnd": - return [o.logicalAnd(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.logicalAnd(I("a", r, e, t8), I("b", r, e, t8))]; case "LogicalNot": - return [o.logicalNot(I("a", r, e, t10))]; + return [o.logicalNot(I("a", r, e, t8))]; case "LogicalOr": - return [o.logicalOr(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.logicalOr(I("a", r, e, t8), I("b", r, e, t8))]; case "Select": case "SelectV2": - return [o.where(I("condition", r, e, t10), I("a", r, e, t10), I("b", r, e, t10))]; + return [o.where(I("condition", r, e, t8), I("a", r, e, t8), I("b", r, e, t8))]; case "BitwiseAnd": - return [o.bitwiseAnd(I("a", r, e, t10), I("b", r, e, t10))]; + return [o.bitwiseAnd(I("a", r, e, t8), I("b", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var xT = (r, e, t10, o = Ze) => { +var $T = (r, e, t8, o = Je) => { switch (r.op) { case "BatchMatMul": case "BatchMatMulV2": case "MatMul": - return [o.matMul(I("a", r, e, t10), I("b", r, e, t10), I("transposeA", r, e, t10), I("transposeB", r, e, t10))]; + return [o.matMul(I("a", r, e, t8), I("b", r, e, t8), I("transposeA", r, e, t8), I("transposeB", r, e, t8))]; case "Einsum": - return [o.einsum(I("equation", r, e, t10), ...I("tensors", r, e, t10))]; + return [o.einsum(I("equation", r, e, t8), ...I("tensors", r, e, t8))]; case "Transpose": - return [o.transpose(I("x", r, e, t10), I("perm", r, e, t10))]; + return [o.transpose(I("x", r, e, t8), I("perm", r, e, t8))]; case "_FusedMatMul": - let [n, s] = I("fusedOps", r, e, t10), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t10), u = I("leakyreluAlpha", r, e, t10); + let [n, s] = I("fusedOps", r, e, t8), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t8), u = I("leakyreluAlpha", r, e, t8); if (a) { if (i && p !== 2) throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha."); if (!i && p !== 1) throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias."); } - let [c, l] = I("args", r, e, t10); - return [o.fused.matMul({ a: I("a", r, e, t10), b: I("b", r, e, t10), transposeA: I("transposeA", r, e, t10), transposeB: I("transposeB", r, e, t10), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })]; + let [c, l] = I("args", r, e, t8); + return [o.fused.matMul({ a: I("a", r, e, t8), b: I("b", r, e, t8), transposeA: I("transposeA", r, e, t8), transposeB: I("transposeB", r, e, t8), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })]; case "MatrixBandPart": - return [o.linalg.bandPart(I("a", r, e, t10), I("numLower", r, e, t10), I("numUpper", r, e, t10))]; + return [o.linalg.bandPart(I("a", r, e, t8), I("numLower", r, e, t8), I("numUpper", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var yT = (r, e, t10, o = Ze) => { +var ET = (r, e, t8, o = Je) => { switch (r.op) { case "EuclideanNorm": - return [o.euclideanNorm(I("x", r, e, t10), I("axis", r, e, t10), I("keepDims", r, e, t10))]; + return [o.euclideanNorm(I("x", r, e, t8), I("axis", r, e, t8), I("keepDims", r, e, t8))]; case "FusedBatchNorm": case "FusedBatchNormV2": - return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))]; + return [o.batchNorm(I("x", r, e, t8), I("mean", r, e, t8), I("variance", r, e, t8), I("offset", r, e, t8), I("scale", r, e, t8), I("epsilon", r, e, t8))]; case "FusedBatchNormV3": - return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))]; + return [o.batchNorm(I("x", r, e, t8), I("mean", r, e, t8), I("variance", r, e, t8), I("offset", r, e, t8), I("scale", r, e, t8), I("epsilon", r, e, t8))]; case "LRN": - return [o.localResponseNormalization(I("x", r, e, t10), I("radius", r, e, t10), I("bias", r, e, t10), I("alpha", r, e, t10), I("beta", r, e, t10))]; + return [o.localResponseNormalization(I("x", r, e, t8), I("radius", r, e, t8), I("bias", r, e, t8), I("alpha", r, e, t8), I("beta", r, e, t8))]; case "Softmax": - return [o.softmax(I("x", r, e, t10))]; + return [o.softmax(I("x", r, e, t8))]; case "LogSoftmax": - return [o.logSoftmax(I("x", r, e, t10))]; + return [o.logSoftmax(I("x", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var bT = (r, e, t10, o = Ze) => { +var RT = (r, e, t8, o = Je) => { switch (r.op) { case "RaggedGather": { - let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t10), I("paramsDenseValues", r, e, t10), I("indices", r, e, t10), I("outputRaggedRank", r, e, t10)); + let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t8), I("paramsDenseValues", r, e, t8), I("indices", r, e, t8), I("outputRaggedRank", r, e, t8)); return n.concat(s); } case "RaggedRange": { - let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t10), I("limits", r, e, t10), I("splits", r, e, t10)); + let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t8), I("limits", r, e, t8), I("splits", r, e, t8)); return [n, s]; } case "RaggedTensorToTensor": - return [o.raggedTensorToTensor(I("shape", r, e, t10), I("values", r, e, t10), I("defaultValue", r, e, t10), I("rowPartitionTensors", r, e, t10), I("rowPartitionTypes", r, e, t10))]; + return [o.raggedTensorToTensor(I("shape", r, e, t8), I("values", r, e, t8), I("defaultValue", r, e, t8), I("rowPartitionTensors", r, e, t8), I("rowPartitionTypes", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var CT = (r, e, t10, o = Ze) => { +var DT = (r, e, t8, o = Je) => { switch (r.op) { case "Max": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.max(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.max(I("x", r, e, t8), i, p)]; } case "Mean": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.mean(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.mean(I("x", r, e, t8), i, p)]; } case "Min": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.min(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.min(I("x", r, e, t8), i, p)]; } case "Sum": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.sum(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.sum(I("x", r, e, t8), i, p)]; } case "All": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.all(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.all(I("x", r, e, t8), i, p)]; } case "Any": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.any(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.any(I("x", r, e, t8), i, p)]; } case "ArgMax": { - let i = I("axis", r, e, t10); - return [o.argMax(I("x", r, e, t10), i)]; + let i = I("axis", r, e, t8); + return [o.argMax(I("x", r, e, t8), i)]; } case "ArgMin": { - let i = I("axis", r, e, t10); - return [o.argMin(I("x", r, e, t10), i)]; + let i = I("axis", r, e, t8); + return [o.argMin(I("x", r, e, t8), i)]; } case "Prod": { - let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); - return [o.prod(I("x", r, e, t10), i, p)]; + let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8); + return [o.prod(I("x", r, e, t8), i, p)]; } case "Cumprod": { - let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10); - return [o.cumprod(I("x", r, e, t10), i, p, u)]; + let i = I("axis", r, e, t8), p = I("exclusive", r, e, t8), u = I("reverse", r, e, t8); + return [o.cumprod(I("x", r, e, t8), i, p, u)]; } case "Cumsum": { - let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10); - return [o.cumsum(I("x", r, e, t10), i, p, u)]; + let i = I("axis", r, e, t8), p = I("exclusive", r, e, t8), u = I("reverse", r, e, t8); + return [o.cumsum(I("x", r, e, t8), i, p, u)]; } case "Bincount": - let n = I("x", r, e, t10), s = I("weights", r, e, t10), a = I("size", r, e, t10); + let n = I("x", r, e, t8), s = I("weights", r, e, t8), a = I("size", r, e, t8); return [o.bincount(n, s, a)]; case "DenseBincount": { - let i = I("x", r, e, t10), p = I("weights", r, e, t10), u = I("size", r, e, t10), c = I("binaryOutput", r, e, t10); + let i = I("x", r, e, t8), p = I("weights", r, e, t8), u = I("size", r, e, t8), c = I("binaryOutput", r, e, t8); return [o.denseBincount(i, p, u, c)]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var wT = (r, e, t10, o = Ze) => { +var AT = (r, e, t8, o = Je) => { switch (r.op) { case "ConcatV2": case "Concat": { - let n = I("n", r, e, t10), s = I("axis", r, e, t10), a = I("tensors", r, e, t10); + let n = I("n", r, e, t8), s = I("axis", r, e, t8), a = I("tensors", r, e, t8); return a = a.slice(0, n), [o.concat(a, s)]; } case "Gather": { - let n = I("x", r, e, t10), s = I("indices", r, e, t10); + let n = I("x", r, e, t8), s = I("indices", r, e, t8); return [o.gather(n, o.cast(s, "int32"), 0)]; } case "GatherV2": { - let n = I("axis", r, e, t10), s = I("batchDims", r, e, t10), a = I("x", r, e, t10), i = I("indices", r, e, t10); + let n = I("axis", r, e, t8), s = I("batchDims", r, e, t8), a = I("x", r, e, t8), i = I("indices", r, e, t8); return [o.gather(a, o.cast(i, "int32"), n, s)]; } case "Reverse": { - let n = I("dims", r, e, t10), s = []; + let n = I("dims", r, e, t8), s = []; for (let i = 0; i < n.length; i++) n[i] && s.push(i); - let a = I("x", r, e, t10); + let a = I("x", r, e, t8); return [o.reverse(a, s)]; } case "ReverseV2": { - let n = I("axis", r, e, t10), s = I("x", r, e, t10); + let n = I("axis", r, e, t8), s = I("x", r, e, t8); return [o.reverse(s, n)]; } case "Slice": { - let n = I("begin", r, e, t10), s = I("size", r, e, t10); - return [o.slice(I("x", r, e, t10), n, s)]; + let n = I("begin", r, e, t8), s = I("size", r, e, t8); + return [o.slice(I("x", r, e, t8), n, s)]; } case "StridedSlice": { - let n = I("begin", r, e, t10), s = I("end", r, e, t10), a = I("strides", r, e, t10), i = I("beginMask", r, e, t10), p = I("endMask", r, e, t10), u = I("ellipsisMask", r, e, t10), c = I("newAxisMask", r, e, t10), l = I("shrinkAxisMask", r, e, t10), m = I("x", r, e, t10); + let n = I("begin", r, e, t8), s = I("end", r, e, t8), a = I("strides", r, e, t8), i = I("beginMask", r, e, t8), p = I("endMask", r, e, t8), u = I("ellipsisMask", r, e, t8), c = I("newAxisMask", r, e, t8), l = I("shrinkAxisMask", r, e, t8), m = I("x", r, e, t8); return [o.stridedSlice(m, n, s, a, i, p, u, c, l)]; } case "Pack": return De(() => { - let n = I("axis", r, e, t10), s = I("tensors", r, e, t10), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => { + let n = I("axis", r, e, t8), s = I("tensors", r, e, t8), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => { let c = y.arraysEqual(u.shape, a); if (!c && !y.arraysEqual(o.squeeze(u).shape, i)) throw new Error("the input tensors shape does not match"); @@ -11078,189 +11127,189 @@ var wT = (r, e, t10, o = Ze) => { return [o.stack(p, n)]; }); case "Unpack": { - let n = I("axis", r, e, t10), s = I("tensor", r, e, t10); + let n = I("axis", r, e, t8), s = I("tensor", r, e, t8); return o.unstack(s, n); } case "Tile": { - let n = I("reps", r, e, t10); - return [o.tile(I("x", r, e, t10), n)]; + let n = I("reps", r, e, t8); + return [o.tile(I("x", r, e, t8), n)]; } case "Split": case "SplitV": { - let n = I("axis", r, e, t10), s = I("numOrSizeSplits", r, e, t10), a = I("x", r, e, t10); + let n = I("axis", r, e, t8), s = I("numOrSizeSplits", r, e, t8), a = I("x", r, e, t8); return o.split(a, s, n); } case "ScatterNd": { - let n = I("indices", r, e, t10), s = I("values", r, e, t10), a = I("shape", r, e, t10); + let n = I("indices", r, e, t8), s = I("values", r, e, t8), a = I("shape", r, e, t8); return [o.scatterND(n, s, a)]; } case "GatherNd": { - let n = I("x", r, e, t10), s = I("indices", r, e, t10); + let n = I("x", r, e, t8), s = I("indices", r, e, t8); return [o.gatherND(n, s)]; } case "SparseToDense": { - let n = I("sparseIndices", r, e, t10), s = I("outputShape", r, e, t10), a = I("sparseValues", r, e, t10), i = I("defaultValue", r, e, t10); + let n = I("sparseIndices", r, e, t8), s = I("outputShape", r, e, t8), a = I("sparseValues", r, e, t8), i = I("defaultValue", r, e, t8); return [o.sparseToDense(n, a, s, a.dtype === i.dtype ? i : o.cast(i, a.dtype))]; } case "TensorScatterUpdate": { - let n = I("indices", r, e, t10), s = I("values", r, e, t10), a = I("tensor", r, e, t10); + let n = I("indices", r, e, t8), s = I("values", r, e, t8), a = I("tensor", r, e, t8); return [o.tensorScatterUpdate(a, n, s)]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var ST = (r, e, t10, o = Ze) => { +var FT = (r, e, t8, o = Je) => { switch (r.op) { case "SparseFillEmptyRows": { - let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t10), I("values", r, e, t10), I("denseShape", r, e, t10), I("defaultValue", r, e, t10)); + let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t8), I("values", r, e, t8), I("denseShape", r, e, t8), I("defaultValue", r, e, t8)); return [n, s, a, i]; } case "SparseReshape": { - let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t10), I("inputShape", r, e, t10), I("newShape", r, e, t10)); + let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t8), I("inputShape", r, e, t8), I("newShape", r, e, t8)); return [n, s]; } case "SparseSegmentMean": - return [o.sparse.sparseSegmentMean(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))]; + return [o.sparse.sparseSegmentMean(I("data", r, e, t8), I("indices", r, e, t8), I("segmentIds", r, e, t8))]; case "SparseSegmentSum": - return [o.sparse.sparseSegmentSum(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))]; + return [o.sparse.sparseSegmentSum(I("data", r, e, t8), I("indices", r, e, t8), I("segmentIds", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var IT = (r, e, t10, o = Ze) => { +var PT = (r, e, t8, o = Je) => { switch (r.op) { case "FFT": - return [o.fft(I("x", r, e, t10))]; + return [o.fft(I("x", r, e, t8))]; case "IFFT": - return [o.ifft(I("x", r, e, t10))]; + return [o.ifft(I("x", r, e, t8))]; case "RFFT": - return [o.rfft(I("x", r, e, t10))]; + return [o.rfft(I("x", r, e, t8))]; case "IRFFT": - return [o.irfft(I("x", r, e, t10))]; + return [o.irfft(I("x", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var vT = (r, e, t10, o = Ze) => { +var OT = (r, e, t8, o = Je) => { switch (r.op) { case "StaticRegexReplace": - return [o.string.staticRegexReplace(I("input", r, e, t10), I("pattern", r, e, t10), I("rewrite", r, e, t10), I("replaceGlobal", r, e, t10))]; + return [o.string.staticRegexReplace(I("input", r, e, t8), I("pattern", r, e, t8), I("rewrite", r, e, t8), I("replaceGlobal", r, e, t8))]; case "StringNGrams": { - let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t10), I("dataSplits", r, e, t10), I("separator", r, e, t10), I("nGramWidths", r, e, t10), I("leftPad", r, e, t10), I("rightPad", r, e, t10), I("padWidth", r, e, t10), I("preserveShortSequences", r, e, t10)); + let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t8), I("dataSplits", r, e, t8), I("separator", r, e, t8), I("nGramWidths", r, e, t8), I("leftPad", r, e, t8), I("rightPad", r, e, t8), I("padWidth", r, e, t8), I("preserveShortSequences", r, e, t8)); return [n, s]; } case "StringSplit": { - let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t10), I("delimiter", r, e, t10), I("skipEmpty", r, e, t10)); + let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t8), I("delimiter", r, e, t8), I("skipEmpty", r, e, t8)); return [n, s, a]; } case "StringToHashBucketFast": - return [o.string.stringToHashBucketFast(I("input", r, e, t10), I("numBuckets", r, e, t10))]; + return [o.string.stringToHashBucketFast(I("input", r, e, t8), I("numBuckets", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var kT = (r, e, t10, o = Ze) => { +var MT = (r, e, t8, o = Je) => { switch (r.op) { case "Cast": - return [o.cast(I("x", r, e, t10), I("dtype", r, e, t10))]; + return [o.cast(I("x", r, e, t8), I("dtype", r, e, t8))]; case "ExpandDims": { - let n = I("axis", r, e, t10); - return [o.expandDims(I("x", r, e, t10), n)]; + let n = I("axis", r, e, t8); + return [o.expandDims(I("x", r, e, t8), n)]; } case "Squeeze": { - let n = I("axis", r, e, t10); - return [o.squeeze(I("x", r, e, t10), n)]; + let n = I("axis", r, e, t8); + return [o.squeeze(I("x", r, e, t8), n)]; } case "Reshape": - return [o.reshape(I("x", r, e, t10), I("shape", r, e, t10))]; + return [o.reshape(I("x", r, e, t8), I("shape", r, e, t8))]; case "EnsureShape": - return [o.ensureShape(I("x", r, e, t10), I("shape", r, e, t10))]; + return [o.ensureShape(I("x", r, e, t8), I("shape", r, e, t8))]; case "MirrorPad": - return [o.mirrorPad(I("x", r, e, t10), I("padding", r, e, t10), I("mode", r, e, t10))]; + return [o.mirrorPad(I("x", r, e, t8), I("padding", r, e, t8), I("mode", r, e, t8))]; case "PadV2": case "Pad": - return [o.pad(I("x", r, e, t10), I("padding", r, e, t10), I("constantValue", r, e, t10))]; + return [o.pad(I("x", r, e, t8), I("padding", r, e, t8), I("constantValue", r, e, t8))]; case "SpaceToBatchND": { - let n = I("blockShape", r, e, t10), s = I("paddings", r, e, t10); - return [o.spaceToBatchND(I("x", r, e, t10), n, s)]; + let n = I("blockShape", r, e, t8), s = I("paddings", r, e, t8); + return [o.spaceToBatchND(I("x", r, e, t8), n, s)]; } case "BatchToSpaceND": { - let n = I("blockShape", r, e, t10), s = I("crops", r, e, t10); - return [o.batchToSpaceND(I("x", r, e, t10), n, s)]; + let n = I("blockShape", r, e, t8), s = I("crops", r, e, t8); + return [o.batchToSpaceND(I("x", r, e, t8), n, s)]; } case "DepthToSpace": { - let n = I("blockSize", r, e, t10), s = I("dataFormat", r, e, t10).toUpperCase(); - return [o.depthToSpace(I("x", r, e, t10), n, s)]; + let n = I("blockSize", r, e, t8), s = I("dataFormat", r, e, t8).toUpperCase(); + return [o.depthToSpace(I("x", r, e, t8), n, s)]; } case "BroadcastTo": - return [o.broadcastTo(I("x", r, e, t10), I("shape", r, e, t10))]; + return [o.broadcastTo(I("x", r, e, t8), I("shape", r, e, t8))]; case "BroadcastArgs": - return [o.broadcastArgs(I("s0", r, e, t10), I("s1", r, e, t10))]; + return [o.broadcastArgs(I("s0", r, e, t8), I("s1", r, e, t8))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function bS(r, e, t10, o, n = De) { +function _S(r, e, t8, o, n = De) { let s = ((a, i, p) => { switch (a.category) { case "arithmetic": - return n(() => eT(a, i, p)); - case "basic_math": - return n(() => tT(a, i, p)); - case "control": - return iT(a, i, p); - case "convolution": - return n(() => pT(a, i, p)); - case "creation": - return n(() => cT(a, i, p)); - case "dynamic": - return lT(a, i, p); - case "evaluation": return n(() => mT(a, i, p)); - case "image": - return n(() => hT(a, i, p)); - case "graph": + case "basic_math": return n(() => dT(a, i, p)); - case "logical": - return n(() => gT(a, i, p)); - case "matrices": - return n(() => xT(a, i, p)); - case "normalization": - return n(() => yT(a, i, p)); - case "ragged": - return n(() => bT(a, i, p)); - case "reduction": - return n(() => CT(a, i, p)); - case "slice_join": + case "control": + return bT(a, i, p); + case "convolution": return n(() => wT(a, i, p)); - case "sparse": + case "creation": return n(() => ST(a, i, p)); - case "spectral": - return n(() => IT(a, i, p)); - case "string": + case "dynamic": + return IT(a, i, p); + case "evaluation": return n(() => vT(a, i, p)); - case "transformation": + case "image": + return n(() => TT(a, i, p)); + case "graph": return n(() => kT(a, i, p)); + case "logical": + return n(() => _T(a, i, p)); + case "matrices": + return n(() => $T(a, i, p)); + case "normalization": + return n(() => ET(a, i, p)); + case "ragged": + return n(() => RT(a, i, p)); + case "reduction": + return n(() => DT(a, i, p)); + case "slice_join": + return n(() => AT(a, i, p)); + case "sparse": + return n(() => FT(a, i, p)); + case "spectral": + return n(() => PT(a, i, p)); + case "string": + return n(() => OT(a, i, p)); + case "transformation": + return n(() => MT(a, i, p)); case "hash_table": - return fT(a, i, p, o); + return NT(a, i, p, o); case "custom": - let u = sf(a.op); + let u = af(a.op); if (u && u.customExecutor) - return u.customExecutor(new yf(a, i, p)); + return u.customExecutor(new bf(a, i, p)); throw TypeError(`Custom op ${a.op} is not registered.`); default: throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`); } - })(r, e, t10); + })(r, e, t8); return y.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s); } var Pl = class { - constructor(e = {}, t10 = {}, o = {}, n = {}, s) { - this.weightMap = e, this.tensorArrayMap = t10, this.tensorListMap = o, this.functionMap = n, this.parseNodeNameCache = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds(); + constructor(e = {}, t8 = {}, o = {}, n = {}, s) { + this.weightMap = e, this.tensorArrayMap = t8, this.tensorListMap = o, this.functionMap = n, this.parseNodeNameCache = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds(); } - newFrame(e, t10) { - return { id: e, frameName: t10, iterationId: 0 }; + newFrame(e, t8) { + return { id: e, frameName: t8, iterationId: 0 }; } set currentContext(e) { this.contexts !== e && (this.contexts = e, this.generateCurrentContextIds()); @@ -11276,14 +11325,14 @@ var Pl = class { } generateCurrentContextIds() { let e = []; - for (let t10 = 0; t10 < this.contexts.length - 1; t10++) { - let o = this.contexts.slice(0, this.contexts.length - t10); + for (let t8 = 0; t8 < this.contexts.length - 1; t8++) { + let o = this.contexts.slice(0, this.contexts.length - t8); e.push(this.contextIdforContexts(o)); } e.push(""), this._currentContextIds = e; } contextIdforContexts(e) { - return e ? e.map((t10) => t10.id === 0 && t10.iterationId === 0 ? "" : `${t10.frameName}-${t10.iterationId}`).join("/") : ""; + return e ? e.map((t8) => t8.id === 0 && t8.iterationId === 0 ? "" : `${t8.frameName}-${t8.iterationId}`).join("/") : ""; } enterFrame(e) { this.contexts && (this.lastId++, this.contexts = this.contexts.slice(), this.contexts.push(this.newFrame(this.lastId, e)), this._currentContextIds.unshift(this.contextIdforContexts(this.contexts))); @@ -11318,19 +11367,19 @@ var Pl = class { return this.tensorListMap[e]; } dispose(e) { - for (let t10 in this.tensorArrayMap) - this.tensorArrayMap[t10].clearAndClose(e); - for (let t10 in this.tensorListMap) - this.tensorListMap[t10].clearAndClose(e); + for (let t8 in this.tensorArrayMap) + this.tensorArrayMap[t8].clearAndClose(e); + for (let t8 in this.tensorListMap) + this.tensorListMap[t8].clearAndClose(e); } }; -function CS(r, e, t10, o) { - let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = new Set(Object.keys(r).map((m) => Nr(m)[0])); +function $S(r, e, t8, o) { + let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = new Set(Object.keys(r).map((m) => Tr(m)[0])); o = o || []; - let c = new Set(o.map((m) => Nr(m.name)[0])), l = [...e]; + let c = new Set(o.map((m) => Tr(m.name)[0])), l = [...e]; for (; l.length > 0; ) { let m = l.pop(); - if ((cu(m) || L5(m) || B5(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t10[m.name] == null && !u.has(m.name) && !c.has(m.name)) { + if ((fu(m) || l8(m) || m8(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t8[m.name] == null && !u.has(m.name) && !c.has(m.name)) { if (m.inputs.length === 0) { s.push(m.name); continue; @@ -11342,8 +11391,8 @@ function CS(r, e, t10, o) { } return { inputs: r, outputs: e, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i }; } -function NT(r, e) { - let { usedNodes: t10, inputs: o } = e, n = Object.keys(o).map((g) => Nr(g)[0]).map((g) => r.nodes[g]), s = r.initNodes || [], a = (g) => t10.has(typeof g == "string" ? g : g.name); +function LT(r, e) { + let { usedNodes: t8, inputs: o } = e, n = Object.keys(o).map((g) => Tr(g)[0]).map((g) => r.nodes[g]), s = r.initNodes || [], a = (g) => t8.has(typeof g == "string" ? g : g.name); function i(g) { return [...new Map(g.map((x) => [x.name, x])).values()]; } @@ -11359,68 +11408,68 @@ function NT(r, e) { for (let b of x.children.filter(a)) --l[b.name] === 0 && (d.push(b.name), m.push(b.name)); } - let f = d.map((g) => c.get(g)), h = A5(f, p); - return F5(h, p), h; + let f = d.map((g) => c.get(g)), h = a8(f, p); + return i8(h, p), h; } -function A5(r, e) { - let t10 = new Map(r.map((a) => [a.name, a])), o = e.map((a) => a.name), n = new Set(o); +function a8(r, e) { + let t8 = new Map(r.map((a) => [a.name, a])), o = e.map((a) => a.name), n = new Set(o); for (; o.length > 0; ) { - let a = o.pop(), i = t10.get(a); + let a = o.pop(), i = t8.get(a); for (let p of i.children) - !t10.has(p.name) || n.has(p.name) || (n.add(p.name), o.push(p.name)); + !t8.has(p.name) || n.has(p.name) || (n.add(p.name), o.push(p.name)); } return r.filter((a) => n.has(a.name)); } -var hc = class extends Error { +var gc = class extends Error { constructor(e) { super(`NodesExecutionOrderError: ${e}`); } }; -function F5(r, e) { - let t10 = new Map(r.map((i, p) => [i.name, p])), o = new Set(e.map((i) => i.name)), n = (i) => o.has(typeof i == "string" ? i : i.name), s = new Set(r.map((i) => i.name)), a = (i) => s.has(typeof i == "string" ? i : i.name); +function i8(r, e) { + let t8 = new Map(r.map((i, p) => [i.name, p])), o = new Set(e.map((i) => i.name)), n = (i) => o.has(typeof i == "string" ? i : i.name), s = new Set(r.map((i) => i.name)), a = (i) => s.has(typeof i == "string" ? i : i.name); for (let i of r) { for (let p of i.children.filter(a)) { - if (!t10.has(p.name)) - throw new hc(`Child ${p.name} of node ${i.name} is unreachable.`); - if (t10.get(i.name) > t10.get(p.name)) - throw new hc(`Node ${i.name} is scheduled to run after its child ${p.name}.`); + if (!t8.has(p.name)) + throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`); + if (t8.get(i.name) > t8.get(p.name)) + throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`); } if (!n(i)) for (let p of i.inputs) { - if (!t10.has(p.name)) - throw new hc(`Input ${p.name} of node ${i.name} is unreachable.`); - if (t10.get(p.name) > t10.get(i.name)) - throw new hc(`Node ${i.name} is scheduled to run before its input ${p.name}.`); + if (!t8.has(p.name)) + throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`); + if (t8.get(p.name) > t8.get(i.name)) + throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`); } } } -function TT(r) { - let e = new Map(r.map((i, p) => [i.name, p])), t10 = Number.MAX_SAFE_INTEGER, o = r.map((i, p) => cu(i) ? t10 : p), n = (i) => { +function BT(r) { + let e = new Map(r.map((i, p) => [i.name, p])), t8 = Number.MAX_SAFE_INTEGER, o = r.map((i, p) => fu(i) ? t8 : p), n = (i) => { let p = o[e.get(i.name)]; return p == null ? -1 : p; }, s = r.map((i, p) => i.children.map(n).reduce((u, c) => Math.max(u, c), o[p])), a = /* @__PURE__ */ new Map(); for (let i = 0; i < r.length; ++i) { let p = s[i]; - if (p === t10) + if (p === t8) continue; let u = r[i], c = r[p]; a.has(c.name) || a.set(c.name, []), a.get(c.name).push(u); } return a; } -var P5 = /* @__PURE__ */ new Set(["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]); -var O5 = /* @__PURE__ */ new Set(["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]); -var M5 = /* @__PURE__ */ new Set(["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]); -function cu(r) { - return P5.has(r.op); +var u8 = /* @__PURE__ */ new Set(["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]); +var p8 = /* @__PURE__ */ new Set(["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]); +var c8 = /* @__PURE__ */ new Set(["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]); +function fu(r) { + return u8.has(r.op); } -function L5(r) { - return O5.has(r.op); +function l8(r) { + return p8.has(r.op); } -function B5(r) { - return M5.has(r.op); +function m8(r) { + return c8.has(r.op); } -var op = class { +var ip = class { get weightIds() { return this.parent ? this.parent.weightIds : this._weightIds; } @@ -11431,8 +11480,8 @@ var op = class { return this.parent ? this.parent.weightMap : this._weightMap; } set weightMap(e) { - let t10 = Object.keys(e).map((o) => e[o].map((n) => n.id)); - this._weightIds = [].concat(...t10), this._weightMap = e; + let t8 = Object.keys(e).map((o) => e[o].map((n) => n.id)); + this._weightIds = [].concat(...t8), this._weightMap = e; } set resourceManager(e) { this._resourceManager = e; @@ -11448,55 +11497,55 @@ var op = class { } get outputNodes() { return this._outputs.map((e) => { - let t10 = e.signatureKey || e.name; - return e.defaultOutput ? `${t10}:${e.defaultOutput}` : t10; + let t8 = e.signatureKey || e.name; + return e.defaultOutput ? `${t8}:${e.defaultOutput}` : t8; }); } get functions() { - return Object.keys(this._functions).reduce((e, t10) => (e[t10] = this._functions[t10].signature, e), {}); + return Object.keys(this._functions).reduce((e, t8) => (e[t8] = this._functions[t8].signature, e), {}); } - constructor(e, t10) { - this.graph = e, this.parent = t10, this.compiledMap = /* @__PURE__ */ new Map(), this.parseNodeNameCache = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPARATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((o) => { - this._functionExecutorMap[o] = new op(e.functions[o], this); + constructor(e, t8) { + this.graph = e, this.parent = t8, this.compiledMap = /* @__PURE__ */ new Map(), this.parseNodeNameCache = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPARATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((o) => { + this._functionExecutorMap[o] = new ip(e.functions[o], this); }); } - getCompilationKey(e, t10) { - let o = e.map((s) => s.name).sort(), n = t10.map((s) => s.name).sort(); + getCompilationKey(e, t8) { + let o = e.map((s) => s.name).sort(), n = t8.map((s) => s.name).sort(); return o.join(this.SEPARATOR) + "--" + n.join(this.SEPARATOR); } - compile(e, t10) { - let o = CS(e, t10, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o; + compile(e, t8) { + let o = $S(e, t8, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o; if (s != null) throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`); if (n.length > 0) { - let u = t10.map((l) => l.name), c = Object.keys(e); + let u = t8.map((l) => l.name), c = Object.keys(e); throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`); } - let i = NT(this.graph, o), p = TT(i); + let i = LT(this.graph, o), p = BT(i); return { orderedNodes: i, nodeLiveUntilMap: p }; } cloneAndKeepTensor(e) { if (e == null) return null; - let t10 = e.clone(); - return Er(t10), t10; + let t8 = e.clone(); + return Rr(t8), t8; } cloneTensorList(e) { return e ? e.map((o) => this.cloneAndKeepTensor(o)) : null; } cloneTensorMap(e) { - return Object.fromEntries(Object.entries(e).map(([t10, o]) => [t10, this.cloneTensorList(o)])); + return Object.fromEntries(Object.entries(e).map(([t8, o]) => [t8, this.cloneTensorList(o)])); } - execute(e, t10) { + execute(e, t8) { this.disposeIntermediateTensors(), e = this.mapInputs(e); let o = Object.keys(e).sort(); - this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10); - let n = o.map((m) => this.graph.nodes[Nr(m)[0]]), s = t10.map((m) => Nr(m)[0]), a = new Set(s), i = s.map((m) => this.graph.nodes[m]); + this.checkInputs(e), this.checkInputShapeAndType(e), t8 = this.mapOutputs(t8), this.checkOutputs(t8); + let n = o.map((m) => this.graph.nodes[Tr(m)[0]]), s = t8.map((m) => Tr(m)[0]), a = new Set(s), i = s.map((m) => this.graph.nodes[m]); i.length === 0 && (i = this._outputs); let p = this.getCompilationKey(n, i), u = this.compiledMap.get(p); u == null && (u = this.compile(e, i), this.compiledMap.set(p, u)); try { - this.keepIntermediateTensors = P().getBool("KEEP_INTERMEDIATE_TENSORS"); + this.keepIntermediateTensors = A().getBool("KEEP_INTERMEDIATE_TENSORS"); } catch (m) { this.keepIntermediateTensors = false, console.warn(m.message); } @@ -11504,33 +11553,33 @@ var op = class { return De(() => { let m = new Pl(this.weightMap, c, l, this.functionExecutorMap, this.parseNodeNameCache), d = Object.assign({}, this.weightMap); this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)), Object.keys(e).forEach((x) => { - let [b, w] = Nr(x, m), S = []; - S[w] = e[x], d[b] = S, this.keepIntermediateTensors && (this.clonedTensorsMap[b] = this.cloneTensorList(S)); + let [b, C] = Tr(x, m), S = []; + S[C] = e[x], d[b] = S, this.keepIntermediateTensors && (this.clonedTensorsMap[b] = this.cloneTensorList(S)); }); let f = this.getFrozenTensorIds(d), { orderedNodes: h, nodeLiveUntilMap: g } = u; for (let x of h) { if (d[x.name]) continue; - let b = bS(x, d, m, this._resourceManager); + let b = _S(x, d, m, this._resourceManager); if (y.isPromise(b)) throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`); d[x.name] = b, this.keepIntermediateTensors && (this.clonedTensorsMap[x.name] = this.cloneTensorList(b)), this.checkTensorForDisposalWithNodeLiveUntilInfo(x, d, m, f, a, g.get(x.name)); } - return this.parent == null && m.dispose(f), t10.map((x) => Bt(x, d, m)); + return this.parent == null && m.dispose(f), t8.map((x) => Bt(x, d, m)); }); } getFrozenTensorIds(e) { - let t10 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id))); - return new Set(t10); + let t8 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id))); + return new Set(t8); } - checkTensorForDisposal(e, t10, o, n, s, a, i) { - if (!(cu(t10) || a.has(e))) { + checkTensorForDisposal(e, t8, o, n, s, a, i) { + if (!(fu(t8) || a.has(e))) { for (let p of o[e]) - p != null && (i[p.id] = (i[p.id] || 0) + t10.children.length); - for (let p of t10.inputs) { - if (cu(p)) + p != null && (i[p.id] = (i[p.id] || 0) + t8.children.length); + for (let p of t8.inputs) { + if (fu(p)) continue; - let u = Qw(p.name, o, n); + let u = aS(p.name, o, n); if (u != null) for (let c of u) { if (!c || c.kept || s.has(c.id)) @@ -11541,100 +11590,100 @@ var op = class { } } } - checkTensorForDisposalWithNodeLiveUntilInfo(e, t10, o, n, s, a) { + checkTensorForDisposalWithNodeLiveUntilInfo(e, t8, o, n, s, a) { function i(p) { - return cu(p) || s.has(p.name); + return fu(p) || s.has(p.name); } - if (!(cu(e) || a == null)) + if (!(fu(e) || a == null)) for (let p of a) { if (i(p)) continue; - let u = Qw(p.name, t10, o); + let u = aS(p.name, t8, o); for (let c of u) !c || c.kept || n.has(c.id) || c.dispose(); } } - async executeAsync(e, t10) { - return this._executeAsync(e, t10); + async executeAsync(e, t8) { + return this._executeAsync(e, t8); } disposeIntermediateTensors() { this.clonedTensorsMap && (Object.values(this.clonedTensorsMap).forEach((e) => { - for (let t10 of e) - t10 && !t10.isDisposed && t10.dispose(); + for (let t8 of e) + t8 && !t8.isDisposed && t8.dispose(); }), this.clonedTensorsMap = null); } getIntermediateTensors() { return this.clonedTensorsMap; } - async _executeAsync(e, t10, o = false, n = {}, s = {}) { - this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10)); + async _executeAsync(e, t8, o = false, n = {}, s = {}) { + this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t8 = this.mapOutputs(t8), this.checkOutputs(t8)); try { - this.keepIntermediateTensors = P().getBool("KEEP_INTERMEDIATE_TENSORS"); + this.keepIntermediateTensors = A().getBool("KEEP_INTERMEDIATE_TENSORS"); } catch (m) { this.keepIntermediateTensors = false, console.warn(m.message); } let a = new Pl(this.weightMap, n, s, this.functionExecutorMap, this.parseNodeNameCache); this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)); - let i = await this.executeWithControlFlow(e, a, t10, o), p = t10.map((m) => Bt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(e).map((m) => e[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]); + let i = await this.executeWithControlFlow(e, a, t8, o), p = t8.map((m) => Bt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(e).map((m) => e[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]); return Object.values(i).forEach((m) => { m.forEach((d) => { d && !d.isDisposed && !l.has(d.id) && d.dispose(); }); }), this.parent == null && a.dispose(l), p; } - async executeFunctionAsync(e, t10, o) { + async executeFunctionAsync(e, t8, o) { let n = e.reduce((s, a, i) => (s[this.inputs[i].name] = a, s), {}); - return this._executeAsync(n, this.outputNodes, true, t10, o); + return this._executeAsync(n, this.outputNodes, true, t8, o); } - async executeWithControlFlow(e, t10, o, n) { - let s = Object.keys(e), a = s.map((S) => this.graph.nodes[Nr(S)[0]]), i = o.map((S) => Nr(S)[0]), p = new Set(i), u = i.map((S) => this.graph.nodes[S]); + async executeWithControlFlow(e, t8, o, n) { + let s = Object.keys(e), a = s.map((S) => this.graph.nodes[Tr(S)[0]]), i = o.map((S) => Tr(S)[0]), p = new Set(i), u = i.map((S) => this.graph.nodes[S]); u.length === 0 && (u = this._outputs); - let { usedNodes: c, missingInputs: l, dynamicNode: m, syncInputs: d } = CS(e, u, this.weightMap, this._initNodes), f = [...a, ...this.graph.weights, ...this._initNodes || []].map((S) => ({ node: S, contexts: t10.currentContext })), h = Object.assign({}, this.weightMap); + let { usedNodes: c, missingInputs: l, dynamicNode: m, syncInputs: d } = $S(e, u, this.weightMap, this._initNodes), f = [...a, ...this.graph.weights, ...this._initNodes || []].map((S) => ({ node: S, contexts: t8.currentContext })), h = Object.assign({}, this.weightMap); Object.keys(e).forEach((S) => { - let [k, _] = Nr(S), E = []; + let [k, _] = Tr(S), E = []; E[_] = e[S], h[k] = E; }); let g = {}, x = this.getFrozenTensorIds(h), b = {}; for (; f.length > 0; ) { - let S = this.processStack(a, f, t10, h, b, x, p, g, c); + let S = this.processStack(a, f, t8, h, b, x, p, g, c); await Promise.all(S); } m == null && !n && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead."); - let w = u.filter((S) => !cu(S) && !Bt(S.name, h, t10)).map((S) => S.name); - if (w.length > 0) { + let C = u.filter((S) => !fu(S) && !Bt(S.name, h, t8)).map((S) => S.name); + if (C.length > 0) { let S = ""; - throw m != null && (S = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`), new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`); + throw m != null && (S = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`), new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`); } return h; } - processStack(e, t10, o, n, s, a, i, p, u) { + processStack(e, t8, o, n, s, a, i, p, u) { let c = []; - for (; t10.length > 0; ) { - let l = t10.pop(); + for (; t8.length > 0; ) { + let l = t8.pop(); o.currentContext = l.contexts; let m = ""; - if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = Ds(l.node.name, o)), n[l.node.name] == null) { - let d = bS(l.node, n, o, this._resourceManager); - m || ([m] = Ds(l.node.name, o)); + if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = Os(l.node.name, o)), n[l.node.name] == null) { + let d = _S(l.node, n, o, this._resourceManager); + m || ([m] = Os(l.node.name, o)); let f = o.currentContext; - y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u)); + y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t8, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t8, o, n, s, u)); } else - this.processChildNodes(l.node, t10, o, n, s, u); + this.processChildNodes(l.node, t8, o, n, s, u); } return c; } - processChildNodes(e, t10, o, n, s, a) { + processChildNodes(e, t8, o, n, s, a) { e.children.forEach((i) => { - let [p] = Ds(i.name, o); - s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Bt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Bt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i }))); + let [p] = Os(i.name, o); + s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Bt(u, n, o)) && (s[p] = true, t8.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Bt(u, n, o)) && (s[p] = true, t8.push({ contexts: o.currentContext, node: i }))); }); } dispose() { - Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t10) => t10.dispose())); + Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t8) => t8.dispose())); } checkInputShapeAndType(e) { - Object.keys(e).forEach((t10) => { - let o = e[t10], [n] = Nr(t10), s = this.graph.nodes[n]; + Object.keys(e).forEach((t8) => { + let o = e[t8], [n] = Tr(t8), s = this.graph.nodes[n]; if (s.attrParams.shape && s.attrParams.shape.value) { let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p); y.assert(i, () => `The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`); @@ -11643,43 +11692,43 @@ var op = class { }); } mapInputs(e) { - var t10, o; + var t8, o; let n = {}; for (let s in e) { - let a = (o = (t10 = this._signature) === null || t10 === void 0 ? void 0 : t10.inputs) === null || o === void 0 ? void 0 : o[s]; + let a = (o = (t8 = this._signature) === null || t8 === void 0 ? void 0 : t8.inputs) === null || o === void 0 ? void 0 : o[s]; a != null ? n[a.name] = e[s] : n[s] = e[s]; } return n; } checkInputs(e) { - let t10 = Object.keys(e).filter((o) => { - let [n] = Nr(o); + let t8 = Object.keys(e).filter((o) => { + let [n] = Tr(o); return this.graph.nodes[n] == null; }); - if (t10.length > 0) - throw new Error(`The dict provided in model.execute(dict) has keys: [${t10}] that are not part of graph`); + if (t8.length > 0) + throw new Error(`The dict provided in model.execute(dict) has keys: [${t8}] that are not part of graph`); } mapOutputs(e) { - return e.map((t10) => { + return e.map((t8) => { var o, n; - let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t10]; - return s != null ? s.name : t10; + let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t8]; + return s != null ? s.name : t8; }, {}); } checkOutputs(e) { - e.forEach((t10) => { - let [o] = Nr(t10); + e.forEach((t8) => { + let [o] = Tr(t8); if (!this.graph.nodes[o]) - throw new Error(`The output '${t10}' is not found in the graph`); + throw new Error(`The output '${t8}' is not found in the graph`); }); } }; -var Sf = class { - constructor(e = {}, t10 = {}) { - this.hashTableNameToHandle = e, this.hashTableMap = t10; +var If = class { + constructor(e = {}, t8 = {}) { + this.hashTableNameToHandle = e, this.hashTableMap = t8; } - addHashTable(e, t10) { - this.hashTableNameToHandle[e] = t10.handle, this.hashTableMap[t10.id] = t10; + addHashTable(e, t8) { + this.hashTableNameToHandle[e] = t8.handle, this.hashTableMap[t8.id] = t8; } getHashTableHandleByName(e) { return this.hashTableNameToHandle[e]; @@ -11694,8 +11743,8 @@ var Sf = class { this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e]; } }; -var z5 = "?tfjs-format=file"; -var V5 = "model.json"; +var d8 = "?tfjs-format=file"; +var f8 = "model.json"; var Ol = class { get modelVersion() { return this.version; @@ -11724,8 +11773,8 @@ var Ol = class { get modelStructuredOutputKeys() { return this.structuredOutputKeys; } - constructor(e, t10 = {}, o = pi) { - this.modelUrl = e, this.loadOptions = t10, this.version = "n/a", this.io = o, t10 == null && (this.loadOptions = {}), this.resourceManager = new Sf(); + constructor(e, t8 = {}, o = mi) { + this.modelUrl = e, this.loadOptions = t8, this.version = "n/a", this.io = o, t8 == null && (this.loadOptions = {}), this.resourceManager = new If(); } findIOHandler() { let e = this.modelUrl; @@ -11734,36 +11783,36 @@ var Ol = class { else if (this.loadOptions.requestInit != null) this.handler = this.io.browserHTTPRequest(e, this.loadOptions); else { - let t10 = this.io.getLoadHandlers(e, this.loadOptions); - if (t10.length === 0) - t10.push(this.io.browserHTTPRequest(e, this.loadOptions)); - else if (t10.length > 1) - throw new Error(`Found more than one (${t10.length}) load handlers for URL '${[e]}'`); - this.handler = t10[0]; + let t8 = this.io.getLoadHandlers(e, this.loadOptions); + if (t8.length === 0) + t8.push(this.io.browserHTTPRequest(e, this.loadOptions)); + else if (t8.length > 1) + throw new Error(`Found more than one (${t8.length}) load handlers for URL '${[e]}'`); + this.handler = t8[0]; } } load() { if (this.findIOHandler(), this.handler.load == null) throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented."); let e = this.handler.load(); - return y.isPromise(e) ? e.then((t10) => this.loadSync(t10)) : this.loadSync(e); + return y.isPromise(e) ? e.then((t8) => this.loadSync(t8)) : this.loadSync(e); } loadSync(e) { this.artifacts = e; - let t10 = this.artifacts.modelTopology, o = this.artifacts.signature; + let t8 = this.artifacts.modelTopology, o = this.artifacts.signature; if (this.artifacts.userDefinedMetadata != null) { let s = this.artifacts.userDefinedMetadata; s.signature != null && (o = s.signature), s.structuredOutputKeys != null && (this.structuredOutputKeys = s.structuredOutputKeys); } - this.signature = o, this.version = `${t10.versions.producer}.${t10.versions.minConsumer}`; + this.signature = o, this.version = `${t8.versions.producer}.${t8.versions.minConsumer}`; let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs); - if (this.executor = new op(Fl.Instance.transformGraph(t10, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) { + if (this.executor = new ip(Fl.Instance.transformGraph(t8, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) { let s = Fl.Instance.transformGraph(e.modelInitializer); - this.initializer = new op(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature; + this.initializer = new ip(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature; } return true; } - async save(e, t10) { + async save(e, t8) { if (typeof e == "string") { let o = this.io.getSaveHandlers(e); if (o.length === 0) @@ -11778,23 +11827,23 @@ var Ol = class { } addStructuredOutputNames(e) { if (this.structuredOutputKeys) { - let t10 = e instanceof pt ? [e] : e, o = {}; - return t10.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o; + let t8 = e instanceof ut ? [e] : e, o = {}; + return t8.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o; } return e; } - predict(e, t10) { + predict(e, t8) { let o = this.execute(e, this.outputNodes); return this.addStructuredOutputNames(o); } - async predictAsync(e, t10) { + async predictAsync(e, t8) { let o = await this.executeAsync(e, this.outputNodes); return this.addStructuredOutputNames(o); } normalizeInputs(e) { - var t10; - if (!(e instanceof pt) && !Array.isArray(e)) { - let s = (t10 = this.signature) === null || t10 === void 0 ? void 0 : t10.inputs; + var t8; + if (!(e instanceof ut) && !Array.isArray(e)) { + let s = (t8 = this.signature) === null || t8 === void 0 ? void 0 : t8.inputs; if (s != null) for (let a in s) { let i = s[a]; @@ -11824,21 +11873,21 @@ var Ol = class { } setResourceIdToCapturedInput(e) { if (this.resourceIdToCapturedInput = {}, this.initializerSignature) { - let t10 = this.initializerSignature.outputs, o = Object.keys(t10); + let t8 = this.initializerSignature.outputs, o = Object.keys(t8); for (let n = 0; n < o.length; n++) { - let s = o[n], a = t10[s]; + let s = o[n], a = t8[s]; this.resourceIdToCapturedInput[a.resourceId] = e[n]; } } } - execute(e, t10) { - this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10); - let o = this.executor.execute(e, t10); + execute(e, t8) { + this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t8 = this.normalizeOutputs(t8); + let o = this.executor.execute(e, t8); return o.length > 1 ? o : o[0]; } - async executeAsync(e, t10) { - this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10); - let o = await this.executor.executeAsync(e, t10); + async executeAsync(e, t8) { + this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t8 = this.normalizeOutputs(t8); + let o = await this.executor.executeAsync(e, t8); return o.length > 1 ? o : o[0]; } getIntermediateTensors() { @@ -11848,20 +11897,20 @@ var Ol = class { this.executor.disposeIntermediateTensors(); } convertTensorMapToTensorsMap(e) { - return Object.keys(e).reduce((t10, o) => (t10[o] = [e[o]], t10), {}); + return Object.keys(e).reduce((t8, o) => (t8[o] = [e[o]], t8), {}); } dispose() { this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Ot(this.resourceIdToCapturedInput)), this.resourceManager.dispose(); } }; -async function W5(r, e = {}, t10 = pi) { +async function h8(r, e = {}, t8 = mi) { if (r == null) throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model"); - e == null && (e = {}), e.fromTFHub && typeof r == "string" && (r = G5(r)); - let o = new Ol(r, e, t10); + e == null && (e = {}), e.fromTFHub && typeof r == "string" && (r = x8(r)); + let o = new Ol(r, e, t8); return await o.load(), o; } -function U5(r) { +function g8(r) { if (r == null) throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model"); let e; @@ -11875,66 +11924,66 @@ function U5(r) { throw new Error("Model JSON is missing 'modelTopology'"); if (!("weightsManifest" in o)) throw new Error("Model JSON is missing 'weightsManifest'"); - let s = pi.getWeightSpecs(o.weightsManifest), a = pi.getModelArtifactsForJSONSync(o, s, n); - e = pi.fromMemorySync(a); + let s = mi.getWeightSpecs(o.weightsManifest), a = mi.getModelArtifactsForJSONSync(o, s, n); + e = mi.fromMemorySync(a); } else if ("load" in r) e = r; else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r) - e = pi.fromMemorySync(r); + e = mi.fromMemorySync(r); else throw new Error("Unknown model format"); - let t10 = new Ol(e); - return t10.load(), t10; + let t8 = new Ol(e); + return t8.load(), t8; } -function G5(r) { - return r.endsWith("/") || (r = r + "/"), `${r}${V5}${z5}`; +function x8(r) { + return r.endsWith("/") || (r = r + "/"), `${r}${f8}${d8}`; } -var H5 = "4.5.0"; -function Y(r, e) { - Array.isArray(r) || (r = [r]), r.forEach((t10) => { - t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`); +var y8 = "4.7.0"; +function Q(r, e) { + Array.isArray(r) || (r = [r]), r.forEach((t8) => { + t8 != null && y.assert(t8.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`); }); } -var K5 = Wt.whereImpl; -var lu = class extends ro { +var b8 = Wt.whereImpl; +var hu = class extends so { nextDataId() { - return lu.nextDataId++; + return hu.nextDataId++; } constructor() { - super(), this.blockSize = 48, this.firstUse = true, this.data = new Lo(this, ur()); + super(), this.blockSize = 48, this.firstUse = true, this.data = new Bo(this, ur()); } - write(e, t10, o) { - this.firstUse && (this.firstUse = false, P().get("IS_NODE") && C.warn(` + write(e, t8, o) { + this.firstUse && (this.firstUse = false, A().get("IS_NODE") && w.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. ============================`)); let n = { id: this.nextDataId() }; return this.data.set(n, { values: e, dtype: o, refCount: 1 }), n; } - makeTensorInfo(e, t10, o) { + makeTensorInfo(e, t8, o) { let n; - if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) { + if (t8 === "string" && o != null && o.length > 0 && y.isString(o[0])) { let s = o.map((a) => y.encodeString(a)); - n = this.write(s, e, t10); + n = this.write(s, e, t8); } else - n = this.write(o, e, t10); - return { dataId: n, shape: e, dtype: t10 }; + n = this.write(o, e, t8); + return { dataId: n, shape: e, dtype: t8 }; } refCount(e) { return this.data.has(e) ? this.data.get(e).refCount : 0; } incRef(e) { - let t10 = this.data.get(e); - t10.refCount++; + let t8 = this.data.get(e); + t8.refCount++; } decRef(e) { if (this.data.has(e)) { - let t10 = this.data.get(e); - t10.refCount--; + let t8 = this.data.get(e); + t8.refCount--; } } - move(e, t10, o, n, s) { - this.data.set(e, { values: t10, dtype: n, refCount: s }); + move(e, t8, o, n, s) { + this.data.set(e, { values: t8, dtype: n, refCount: s }); } numDataIds() { return this.data.numDataIds(); @@ -11943,30 +11992,30 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return this.readSync(e); } readSync(e) { - let { dtype: t10, complexTensorInfos: o } = this.data.get(e); - if (t10 === "complex64") { + let { dtype: t8, complexTensorInfos: o } = this.data.get(e); + if (t8 === "complex64") { let n = this.readSync(o.real.dataId), s = this.readSync(o.imag.dataId); - return C.mergeRealAndImagArrays(n, s); + return w.mergeRealAndImagArrays(n, s); } - return y.convertBackendValuesAndArrayBuffer(this.data.get(e).values, t10); + return y.convertBackendValuesAndArrayBuffer(this.data.get(e).values, t8); } bufferSync(e) { - let t10 = this.readSync(e.dataId); + let t8 = this.readSync(e.dataId); if (e.dtype === "string") try { - let o = t10.map((n) => y.decodeString(n)); + let o = t8.map((n) => y.decodeString(n)); return me(e.shape, e.dtype, o); } catch (o) { throw new Error("Failed to decode encoded string bytes into utf-8"); } - return me(e.shape, e.dtype, t10); + return me(e.shape, e.dtype, t8); } - makeOutput(e, t10, o) { - return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t10, o, e), this); + makeOutput(e, t8, o) { + return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t8, o, e), this); } - disposeData(e, t10 = false) { + disposeData(e, t8 = false) { if (this.data.has(e)) { - if (this.data.get(e).refCount--, !t10 && this.data.get(e).refCount > 0) + if (this.data.get(e).refCount--, !t8 && this.data.get(e).refCount > 0) return false; let { complexTensorInfos: o } = this.data.get(e); o != null && (this.disposeData(o.real.dataId, true), this.disposeData(o.imag.dataId, true)), this.data.delete(e); @@ -11977,16 +12026,16 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram this.disposeData(e.dataId); } async time(e) { - let t10 = y.now(); - return e(), { kernelMs: y.now() - t10 }; + let t8 = y.now(); + return e(), { kernelMs: y.now() - t8 }; } memory() { return { unreliable: true, reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."] }; } where(e) { - Y([e], "where"); - let t10 = this.readSync(e.dataId); - return K5(e.shape, t10); + Q([e], "where"); + let t8 = this.readSync(e.dataId); + return b8(e.shape, t8); } dispose() { } @@ -11997,33 +12046,33 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return super.epsilon(); } }; -lu.nextDataId = 0; -var Sc = {}; -He(Sc, { addImpl: () => IS, bincountImpl: () => yc, bincountReduceImpl: () => If, bitwiseAndImpl: () => vS, castImpl: () => SS, ceilImpl: () => kS, concatImpl: () => np, equalImpl: () => NS, expImpl: () => _S, expm1Impl: () => ES, floorDivImpl: () => DS, floorImpl: () => RS, gatherNdImpl: () => vf, gatherV2Impl: () => kf, greaterEqualImpl: () => FS, greaterImpl: () => AS, lessEqualImpl: () => OS, lessImpl: () => PS, linSpaceImpl: () => Nf, logImpl: () => MS, maxImpl: () => Tf, maximumImpl: () => LS, minimumImpl: () => BS, multiplyImpl: () => Ml, negImpl: () => zS, notEqualImpl: () => VS, prodImpl: () => WS, raggedGatherImpl: () => _f, raggedRangeImpl: () => $f, raggedTensorToTensorImpl: () => Ef, rangeImpl: () => ap, rsqrtImpl: () => US, scatterImpl: () => Fs, sigmoidImpl: () => n_, simpleAbsImpl: () => wS, sliceImpl: () => ip, sparseFillEmptyRowsImpl: () => Rf, sparseReshapeImpl: () => Df, sparseSegmentReductionImpl: () => wc, sqrtImpl: () => i_, squaredDifferenceImpl: () => HS, staticRegexReplaceImpl: () => KS, stridedSliceImpl: () => Af, stringNGramsImpl: () => up, stringSplitImpl: () => pp, stringToHashBucketFastImpl: () => cp, subImpl: () => jS, tileImpl: () => Ff, topKImpl: () => Pf, transposeImpl: () => bc, uniqueImpl: () => lp }); -function wS(r) { +hu.nextDataId = 0; +var Ic = {}; +Ke(Ic, { addImpl: () => DS, bincountImpl: () => bc, bincountReduceImpl: () => vf, bitwiseAndImpl: () => AS, castImpl: () => RS, ceilImpl: () => FS, concatImpl: () => up, equalImpl: () => PS, expImpl: () => MS, expm1Impl: () => BS, floorDivImpl: () => VS, floorImpl: () => zS, gatherNdImpl: () => kf, gatherV2Impl: () => Nf, greaterEqualImpl: () => US, greaterImpl: () => WS, lessEqualImpl: () => HS, lessImpl: () => GS, linSpaceImpl: () => Tf, logImpl: () => KS, maxImpl: () => _f, maximumImpl: () => qS, minimumImpl: () => jS, multiplyImpl: () => Ml, negImpl: () => XS, notEqualImpl: () => YS, prodImpl: () => QS, raggedGatherImpl: () => $f, raggedRangeImpl: () => Ef, raggedTensorToTensorImpl: () => Rf, rangeImpl: () => cp, rsqrtImpl: () => ZS, scatterImpl: () => Ls, sigmoidImpl: () => g_, simpleAbsImpl: () => ES, sliceImpl: () => lp, sparseFillEmptyRowsImpl: () => Df, sparseReshapeImpl: () => Af, sparseSegmentReductionImpl: () => Sc, sqrtImpl: () => b_, squaredDifferenceImpl: () => eI, staticRegexReplaceImpl: () => tI, stridedSliceImpl: () => Ff, stringNGramsImpl: () => mp, stringSplitImpl: () => dp, stringToHashBucketFastImpl: () => fp, subImpl: () => oI, tileImpl: () => Pf, topKImpl: () => Of, transposeImpl: () => Cc, uniqueImpl: () => hp }); +function ES(r) { let e = new Float32Array(r.length); - for (let t10 = 0; t10 < r.length; ++t10) - e[t10] = Math.abs(r[t10]); + for (let t8 = 0; t8 < r.length; ++t8) + e[t8] = Math.abs(r[t8]); return e; } -var q5 = (r) => { - let { x: e } = r.inputs, t10 = r.backend; - Y(e, "abs"); - let o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId).values; - return o = wS(n), t10.makeOutput(o, e.shape, e.dtype); +var C8 = (r) => { + let { x: e } = r.inputs, t8 = r.backend; + Q(e, "abs"); + let o = new Float32Array(y.sizeFromShape(e.shape)), n = t8.data.get(e.dataId).values; + return o = ES(n), t8.makeOutput(o, e.shape, e.dtype); }; -var _T = { kernelName: Gs, backendName: "cpu", kernelFunc: q5 }; +var zT = { kernelName: js, backendName: "cpu", kernelFunc: C8 }; function ze(r) { - return (e, t10, o, n, s) => { - let a = C.assertAndGetBroadcastShape(e, t10), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, d = y.computeStrides(e), f = y.computeStrides(t10), h = C.getBroadcastDims(e, a), g = C.getBroadcastDims(t10, a); + return (e, t8, o, n, s) => { + let a = w.assertAndGetBroadcastShape(e, t8), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t8.length, d = y.computeStrides(e), f = y.computeStrides(t8), h = w.getBroadcastDims(e, a), g = w.getBroadcastDims(t8, a); if (h.length + g.length === 0) for (let x = 0; x < c.length; ++x) c[x] = r(o[x % o.length], n[x % n.length]); else for (let x = 0; x < c.length; ++x) { - let b = y.indexToLoc(x, i, p), w = b.slice(-l); - h.forEach((E) => w[E] = 0); - let S = y.locToIndex(w, l, d), k = b.slice(-m); + let b = y.indexToLoc(x, i, p), C = b.slice(-l); + h.forEach((E) => C[E] = 0); + let S = y.locToIndex(C, l, d), k = b.slice(-m); g.forEach((E) => k[E] = 0); let _ = y.locToIndex(k, m, f); c[x] = r(o[S], n[_]); @@ -12032,79 +12081,79 @@ function ze(r) { }; } function Kt(r) { - let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = t10.makeTensorInfo(o.shape, "complex64"), p = t10.data.get(i.dataId); - return p.complexTensorInfos = { real: t10.makeTensorInfo(o.shape, "float32", s), imag: t10.makeTensorInfo(n.shape, "float32", a) }, i; + let { inputs: e, backend: t8 } = r, { real: o, imag: n } = e, s = t8.data.get(o.dataId).values, a = t8.data.get(n.dataId).values, i = t8.makeTensorInfo(o.shape, "complex64"), p = t8.data.get(i.dataId); + return p.complexTensorInfos = { real: t8.makeTensorInfo(o.shape, "float32", s), imag: t8.makeTensorInfo(n.shape, "float32", a) }, i; } -var $T = { kernelName: Ti, backendName: "cpu", kernelFunc: Kt }; -function gc(r, e, t10 = "float32") { - if (t10 === "complex64") { - let n = gc(r, e, "float32"), s = gc(r, e, "float32"); +var VT = { kernelName: Ri, backendName: "cpu", kernelFunc: Kt }; +function xc(r, e, t8 = "float32") { + if (t8 === "complex64") { + let n = xc(r, e, "float32"), s = xc(r, e, "float32"); return Kt({ inputs: { real: n, imag: s }, backend: r }); } - let o = y.makeZerosTypedArray(y.sizeFromShape(e), t10); - return r.makeTensorInfo(e, t10, o); + let o = y.makeZerosTypedArray(y.sizeFromShape(e), t8); + return r.makeTensorInfo(e, t8, o); } function lr(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; - return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; + let { inputs: e, backend: t8 } = r, { x: o } = e; + return t8.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -var ET = { kernelName: xo, backendName: "cpu", kernelFunc: lr }; -function To(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.real, s = t10.data.get(n.dataId).values; - return t10.makeTensorInfo(n.shape, n.dtype, s); +var WT = { kernelName: Co, backendName: "cpu", kernelFunc: lr }; +function Eo(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.data.get(o.dataId).complexTensorInfos.real, s = t8.data.get(n.dataId).values; + return t8.makeTensorInfo(n.shape, n.dtype, s); } -var RT = { kernelName: zi, backendName: "cpu", kernelFunc: To }; -function SS(r, e, t10, o) { +var UT = { kernelName: Gi, backendName: "cpu", kernelFunc: Eo }; +function RS(r, e, t8, o) { if (o === "int32") { let n = Int32Array.from(r); return [e, "int32", n]; } if (o === "bool") { - let n = y.toTypedArray([0], t10), [s, a] = ze((i, p) => i !== p ? 1 : 0)(e, [], r, n, "bool"); + let n = y.toTypedArray([0], t8), [s, a] = ze((i, p) => i !== p ? 1 : 0)(e, [], r, n, "bool"); return [a, "bool", s]; } - throw new Error(`Error in Cast: failed to cast ${t10} to ${o}`); + throw new Error(`Error in Cast: failed to cast ${t8} to ${o}`); } -function _o(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o; +function Ro(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return lr({ inputs: { x: n }, backend: t10 }); - let c = gc(t10, n.shape, n.dtype), l = _o({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), m = Kt({ inputs: { real: l, imag: c }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), m; + return lr({ inputs: { x: n }, backend: t8 }); + let c = xc(t8, n.shape, n.dtype), l = Ro({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }), m = Kt({ inputs: { real: l, imag: c }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(c), t8.disposeIntermediateTensorInfo(l), m; } if (n.dtype === "complex64") { - let c = To({ inputs: { input: n }, backend: t10 }), l = _o({ inputs: { x: c }, backend: t10, attrs: { dtype: s } }); - return t10.disposeIntermediateTensorInfo(c), l; + let c = Eo({ inputs: { input: n }, backend: t8 }), l = Ro({ inputs: { x: c }, backend: t8, attrs: { dtype: s } }); + return t8.disposeIntermediateTensorInfo(c), l; } if (!y.hasEncodingLoss(n.dtype, s)) { - let c = lr({ inputs: { x: n }, backend: t10 }); + let c = lr({ inputs: { x: n }, backend: t8 }); return { dataId: c.dataId, shape: c.shape, dtype: s }; } - let a = t10.data.get(n.dataId).values, [i, p, u] = SS(a, n.shape, n.dtype, s); - return t10.makeTensorInfo(i, p, u); + let a = t8.data.get(n.dataId).values, [i, p, u] = RS(a, n.shape, n.dtype, s); + return t8.makeTensorInfo(i, p, u); } -var DT = { kernelName: ho, backendName: "cpu", kernelFunc: _o }; -function je(r, e, t10, o) { - return t10 == null ? ({ inputs: n, backend: s }) => { +var GT = { kernelName: yo, backendName: "cpu", kernelFunc: Ro }; +function Ye(r, e, t8, o) { + return t8 == null ? ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; - Y([a, i], r); - let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? C.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? C.fromUint8ToStringArray(c) : c, d = o || a.dtype, [f, h] = e(a.shape, i.shape, l, m, d); + Q([a, i], r); + let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? w.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? w.fromUint8ToStringArray(c) : c, d = o || a.dtype, [f, h] = e(a.shape, i.shape, l, m, d); return p.makeTensorInfo(h, d, f); } : ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; if (a.dtype === "complex64" || i.dtype === "complex64") { - let u = _o({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = _o({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, w = p.data.get(x.dataId).values, S = p.data.get(b.dataId).values, [k, _, E] = t10(a.shape, i.shape, d, f, w, S), R = p.makeTensorInfo(E, "float32", k), D = p.makeTensorInfo(E, "float32", _), F = Kt({ inputs: { real: R, imag: D }, backend: p }); - return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(D), F; + let u = Ro({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = Ro({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, S = p.data.get(b.dataId).values, [k, _, E] = t8(a.shape, i.shape, d, f, C, S), R = p.makeTensorInfo(E, "float32", k), D = p.makeTensorInfo(E, "float32", _), P = Kt({ inputs: { real: R, imag: D }, backend: p }); + return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(D), P; } else { let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, d] = e(a.shape, i.shape, u, c, l); return p.makeTensorInfo(d, l, m); } }; } -function xc(r) { - return (e, t10, o, n, s, a) => { - let i = C.assertAndGetBroadcastShape(e, t10), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = C.getBroadcastDims(e, i), f = C.getBroadcastDims(t10, i), h = C.mergeRealAndImagArrays(o, n), g = C.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), w = t10.length, S = y.computeStrides(t10); +function yc(r) { + return (e, t8, o, n, s, a) => { + let i = w.assertAndGetBroadcastShape(e, t8), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = w.getBroadcastDims(e, i), f = w.getBroadcastDims(t8, i), h = w.mergeRealAndImagArrays(o, n), g = w.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), C = t8.length, S = y.computeStrides(t8); if (d.length + f.length === 0) for (let k = 0; k < l.length; k++) { let _ = k % h.length, E = k % g.length, R = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]); @@ -12114,20 +12163,20 @@ function xc(r) { for (let k = 0; k < l.length; k++) { let _ = y.indexToLoc(k, u, c), E = _.slice(-x); d.forEach((M) => E[M] = 0); - let R = y.locToIndex(E, x, b), D = _.slice(-w); + let R = y.locToIndex(E, x, b), D = _.slice(-C); f.forEach((M) => D[M] = 0); - let F = y.locToIndex(D, w, S), O = r(h[R * 2], h[R * 2 + 1], g[F * 2], g[F * 2 + 1]); + let P = y.locToIndex(D, C, S), O = r(h[R * 2], h[R * 2 + 1], g[P * 2], g[P * 2 + 1]); l[k] = O.real, m[k] = O.imag; } return [l, m, i]; }; } -var IS = ze((r, e) => r + e); -var j5 = xc((r, e, t10, o) => ({ real: r + t10, imag: e + o })); -var _a = je(no, IS, j5); -var AT = { kernelName: no, backendName: "cpu", kernelFunc: _a }; -function yc(r, e, t10, o, n) { - let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t10); +var DS = ze((r, e) => r + e); +var w8 = yc((r, e, t8, o) => ({ real: r + t8, imag: e + o })); +var Aa = Ye(io, DS, w8); +var HT = { kernelName: io, backendName: "cpu", kernelFunc: Aa }; +function bc(r, e, t8, o, n) { + let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t8); for (let i = 0; i < r.length; i++) { let p = r[i]; if (p < 0) @@ -12136,53 +12185,53 @@ function yc(r, e, t10, o, n) { } return a; } -function If(r, e, t10, o = false) { - let n = r.shape[0], s = r.shape[1], a = me([n, t10], e.dtype); +function vf(r, e, t8, o = false) { + let n = r.shape[0], s = r.shape[1], a = me([n, t8], e.dtype); for (let i = 0; i < n; i++) for (let p = 0; p < s; p++) { let u = r.get(i, p); if (u < 0) throw new Error("Input x must be non-negative!"); - u >= t10 || (o ? a.set(1, i, u) : e.size > 0 ? a.set(a.get(i, u) + e.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u)); + u >= t8 || (o ? a.set(1, i, u) : e.size > 0 ? a.set(a.get(i, u) + e.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u)); } return a; } -var vS = ze((r, e) => r & e); -var X5 = je(ml, vS); -var FT = { kernelName: ml, backendName: "cpu", kernelFunc: X5 }; -function jt(r) { - return (e, t10, o) => { - let n = y.getArrayFromDType(t10, e.length); +var AS = ze((r, e) => r & e); +var S8 = Ye(Ha, AS); +var KT = { kernelName: Ha, backendName: "cpu", kernelFunc: S8 }; +function Xt(r) { + return (e, t8, o) => { + let n = y.getArrayFromDType(t8, e.length); for (let s = 0; s < e.length; ++s) n[s] = r(e[s], o); return n; }; } -function Ie(r, e, t10) { - let o = jt(e); - return Dr(r, o, t10); +function Ie(r, e, t8) { + let o = Xt(e); + return Ar(r, o, t8); } -function Dr(r, e, t10) { +function Ar(r, e, t8) { return ({ inputs: o, attrs: n, backend: s }) => { let { x: a } = o; - Y(a, r); + Q(a, r); let i = s, p = i.data.get(a.dataId).values, u; if (a.dtype === "string") { if (!Array.isArray(p)) throw new Error("String tensor's value was not an instance of Array"); - u = C.fromUint8ToStringArray(p); + u = w.fromUint8ToStringArray(p); } else u = p; - let c = t10 || a.dtype, l = e(u, c, n); + let c = t8 || a.dtype, l = e(u, c, n); return i.makeTensorInfo(a.shape, c, l); }; } -var kS = jt((r) => Math.ceil(r)); -var Y5 = Dr(Jo, kS); -var PT = { kernelName: Jo, backendName: "cpu", kernelFunc: Y5 }; -function np(r, e, t10, o) { - let n = y.getArrayFromDType(t10, y.sizeFromShape(e)); - if (o && t10 !== "string") { +var FS = Xt((r) => Math.ceil(r)); +var I8 = Ar(en, FS); +var qT = { kernelName: en, backendName: "cpu", kernelFunc: I8 }; +function up(r, e, t8, o) { + let n = y.getArrayFromDType(t8, y.sizeFromShape(e)); + if (o && t8 !== "string") { let s = 0; r.forEach((a) => { let i = y.sizeFromShape(a.shape); @@ -12191,7 +12240,7 @@ function np(r, e, t10, o) { } else { let s = 0; r.forEach((a) => { - let i = t10 === "string" ? C.fromUint8ToStringArray(a.vals) : a.vals, p = 0; + let i = t8 === "string" ? w.fromUint8ToStringArray(a.vals) : a.vals, p = 0; for (let u = 0; u < a.shape[0]; ++u) { let c = u * e[1] + s; for (let l = 0; l < a.shape[1]; ++l) @@ -12202,23 +12251,23 @@ function np(r, e, t10, o) { } return n; } -var NS = ze((r, e) => r === e ? 1 : 0); -var TS = je(hn, NS, null, "bool"); -var OT = { kernelName: hn, backendName: "cpu", kernelFunc: TS }; -var _S = jt((r) => Math.exp(r)); -var $S = Dr(gn, _S, "float32"); -var MT = { kernelName: gn, backendName: "cpu", kernelFunc: $S }; -var ES = jt((r) => Math.expm1(r)); -var Q5 = Dr(xn, ES); -var LT = { kernelName: xn, backendName: "cpu", kernelFunc: Q5 }; -var RS = jt((r) => Math.floor(r)); -var Z5 = Dr(bn, RS); -var BT = { kernelName: bn, backendName: "cpu", kernelFunc: Z5 }; -var DS = ze((r, e) => Math.floor(r / e)); -var J5 = je(Cn, DS, null, "int32"); -var zT = { kernelName: Cn, backendName: "cpu", kernelFunc: J5 }; -function vf(r, e, t10, o, n, s, a, i, p) { - let u = me([o, s], t10); +var PS = ze((r, e) => r === e ? 1 : 0); +var OS = Ye(xn, PS, null, "bool"); +var jT = { kernelName: xn, backendName: "cpu", kernelFunc: OS }; +var MS = Xt((r) => Math.exp(r)); +var LS = Ar(yn, MS, "float32"); +var XT = { kernelName: yn, backendName: "cpu", kernelFunc: LS }; +var BS = Xt((r) => Math.expm1(r)); +var v8 = Ar(bn, BS); +var YT = { kernelName: bn, backendName: "cpu", kernelFunc: v8 }; +var zS = Xt((r) => Math.floor(r)); +var k8 = Ar(wn, zS); +var QT = { kernelName: wn, backendName: "cpu", kernelFunc: k8 }; +var VS = ze((r, e) => Math.floor(r / e)); +var N8 = Ye(Sn, VS, null, "int32"); +var ZT = { kernelName: Sn, backendName: "cpu", kernelFunc: N8 }; +function kf(r, e, t8, o, n, s, a, i, p) { + let u = me([o, s], t8); for (let c = 0; c < o; c++) { let l = [], m = 0; for (let d = 0; d < n; d++) { @@ -12232,8 +12281,8 @@ function vf(r, e, t10, o, n, s, a, i, p) { } return u; } -function kf(r, e, t10) { - let o = me(t10, r.dtype); +function Nf(r, e, t8) { + let o = me(t8, r.dtype); for (let n = 0; n < o.size; ++n) { let a = o.indexToLoc(n).slice(), i = a[0], p = a[2], u = e.locToIndex([i, p]); a[2] = e.values[u]; @@ -12242,30 +12291,30 @@ function kf(r, e, t10) { } return o; } -var AS = ze((r, e) => r > e ? 1 : 0); -var e8 = je(In, AS, null, "bool"); -var VT = { kernelName: In, backendName: "cpu", kernelFunc: e8 }; -var FS = ze((r, e) => r >= e ? 1 : 0); -var t8 = je(vn, FS, null, "bool"); -var WT = { kernelName: vn, backendName: "cpu", kernelFunc: t8 }; -var PS = ze((r, e) => r < e ? 1 : 0); -var r8 = je($n, PS, null, "bool"); -var UT = { kernelName: $n, backendName: "cpu", kernelFunc: r8 }; -var OS = ze((r, e) => r <= e ? 1 : 0); -var o8 = je(En, OS, null, "bool"); -var GT = { kernelName: En, backendName: "cpu", kernelFunc: o8 }; -function Nf(r, e, t10) { - let o = (e - r) / (t10 - 1), n = y.makeZerosTypedArray(t10, "float32"); +var WS = ze((r, e) => r > e ? 1 : 0); +var T8 = Ye(kn, WS, null, "bool"); +var JT = { kernelName: kn, backendName: "cpu", kernelFunc: T8 }; +var US = ze((r, e) => r >= e ? 1 : 0); +var _8 = Ye(Nn, US, null, "bool"); +var e_ = { kernelName: Nn, backendName: "cpu", kernelFunc: _8 }; +var GS = ze((r, e) => r < e ? 1 : 0); +var $8 = Ye(Rn, GS, null, "bool"); +var t_ = { kernelName: Rn, backendName: "cpu", kernelFunc: $8 }; +var HS = ze((r, e) => r <= e ? 1 : 0); +var E8 = Ye(Dn, HS, null, "bool"); +var r_ = { kernelName: Dn, backendName: "cpu", kernelFunc: E8 }; +function Tf(r, e, t8) { + let o = (e - r) / (t8 - 1), n = y.makeZerosTypedArray(t8, "float32"); n[0] = r; for (let s = 1; s < n.length; s++) n[s] = n[s - 1] + o; return n; } -var MS = jt((r) => Math.log(r)); -var n8 = Dr(Dn, MS); -var HT = { kernelName: Dn, backendName: "cpu", kernelFunc: n8 }; -function Tf(r, e, t10, o) { - let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t10)); +var KS = Xt((r) => Math.log(r)); +var R8 = Ar(Fn, KS); +var o_ = { kernelName: Fn, backendName: "cpu", kernelFunc: R8 }; +function _f(r, e, t8, o) { + let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t8)); for (let s = 0; s < n.length; ++s) { let a = s * e, i = r[a]; for (let p = 0; p < e; ++p) { @@ -12276,32 +12325,32 @@ function Tf(r, e, t10, o) { } return n; } -var LS = ze((r, e) => Math.max(r, e)); -var s8 = je(Bn, LS); -var KT = { kernelName: Bn, backendName: "cpu", kernelFunc: s8 }; -var BS = ze((r, e) => Math.min(r, e)); -var a8 = je(Un, BS); -var qT = { kernelName: Un, backendName: "cpu", kernelFunc: a8 }; +var qS = ze((r, e) => Math.max(r, e)); +var D8 = Ye(Vn, qS); +var n_ = { kernelName: Vn, backendName: "cpu", kernelFunc: D8 }; +var jS = ze((r, e) => Math.min(r, e)); +var A8 = Ye(Hn, jS); +var s_ = { kernelName: Hn, backendName: "cpu", kernelFunc: A8 }; var Ml = ze((r, e) => r * e); -var i8 = xc((r, e, t10, o) => ({ real: r * t10 - e * o, imag: r * o + e * t10 })); -var sp = je(Kn, Ml, i8); -var jT = { kernelName: Kn, backendName: "cpu", kernelFunc: sp }; -function zS(r, e, t10) { - let o = y.createScalarValue(-1, t10); - return Ml([], e, o, r, t10); +var F8 = yc((r, e, t8, o) => ({ real: r * t8 - e * o, imag: r * o + e * t8 })); +var pp = Ye(Xn, Ml, F8); +var a_ = { kernelName: Xn, backendName: "cpu", kernelFunc: pp }; +function XS(r, e, t8) { + let o = y.createScalarValue(-1, t8); + return Ml([], e, o, r, t8); } -function u8(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; - Y(o, "neg"); - let n = t10.data.get(o.dataId).values, [s, a] = zS(n, o.shape, o.dtype); - return t10.makeTensorInfo(a, o.dtype, s); +function P8(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; + Q(o, "neg"); + let n = t8.data.get(o.dataId).values, [s, a] = XS(n, o.shape, o.dtype); + return t8.makeTensorInfo(a, o.dtype, s); } -var XT = { kernelName: oa, backendName: "cpu", kernelFunc: u8 }; -var VS = ze((r, e) => r !== e ? 1 : 0); -var p8 = je(qn, VS, null, "bool"); -var YT = { kernelName: qn, backendName: "cpu", kernelFunc: p8 }; -function bc(r, e, t10, o, n) { - let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t10, y.sizeFromShape(n)); +var i_ = { kernelName: ua, backendName: "cpu", kernelFunc: P8 }; +var YS = ze((r, e) => r !== e ? 1 : 0); +var O8 = Ye(Yn, YS, null, "bool"); +var u_ = { kernelName: Yn, backendName: "cpu", kernelFunc: O8 }; +function Cc(r, e, t8, o, n) { + let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t8, y.sizeFromShape(n)); for (let c = 0; c < a; ++c) { let l = y.indexToLoc(c, s, i), m = new Array(l.length); for (let f = 0; f < m.length; f++) @@ -12311,46 +12360,46 @@ function bc(r, e, t10, o, n) { } return u; } -function St(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { perm: s } = t10; - Y(n, "transpose"); +function It(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n } = e, { perm: s } = t8; + Q(n, "transpose"); let a = n.shape.length, i = new Array(a); for (let l = 0; l < i.length; l++) i[l] = n.shape[s[l]]; - let p = o.data.get(n.dataId).values, u = bc(p, n.shape, n.dtype, s, i); + let p = o.data.get(n.dataId).values, u = Cc(p, n.shape, n.dtype, s, i); return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype }; } -var QT = { kernelName: ao, backendName: "cpu", kernelFunc: St }; -function WS(r, e, t10, o) { - let [n, s] = C.computeOutAndReduceShapes(r, o), a = dt(e, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s); +var p_ = { kernelName: po, backendName: "cpu", kernelFunc: It }; +function QS(r, e, t8, o) { + let [n, s] = w.computeOutAndReduceShapes(r, o), a = dt(e, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s); for (let u = 0; u < i.length; ++u) { let c = u * p, l = 1; for (let m = 0; m < p; ++m) - l *= t10[c + m]; + l *= t8[c + m]; i[u] = l; } return { outVals: i, outShape: n, outDtype: a }; } -function c8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - Y(n, "prod"); - let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = C.getAxesPermutation(p, i), c = p, l = n, m = []; - u != null && (l = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), m.push(l), c = C.getInnerMostAxes(c.length, i)); - let d = t10.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = WS(l.shape, l.dtype, d, c), x = h; - return a && (x = C.expandShapeToKeepDim(h, p)), m.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.makeTensorInfo(x, g, f); +function M8(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + Q(n, "prod"); + let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = w.getAxesPermutation(p, i), c = p, l = n, m = []; + u != null && (l = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), m.push(l), c = w.getInnerMostAxes(c.length, i)); + let d = t8.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = QS(l.shape, l.dtype, d, c), x = h; + return a && (x = w.expandShapeToKeepDim(h, p)), m.forEach((b) => t8.disposeIntermediateTensorInfo(b)), t8.makeTensorInfo(x, g, f); } -var ZT = { kernelName: es, backendName: "cpu", kernelFunc: c8 }; -function l8(r, e, t10) { +var c_ = { kernelName: os, backendName: "cpu", kernelFunc: M8 }; +function L8(r, e, t8) { r.forEach((o, n) => { - if (o < 0 || o >= t10) { + if (o < 0 || o >= t8) { let s = y.indexToLoc(n, e.length, y.computeStrides(e)).join(","); - throw new Error(`indices[${s}] = ${o} is not in [0, ${t10})`); + throw new Error(`indices[${s}] = ${o} is not in [0, ${t8})`); } }); } -function m8(r, e) { - for (let t10 = 0; t10 < r.length; ++t10) { - let o = r[t10], n = t10 === r.length - 1 ? e : r[t10 + 1].length; +function B8(r, e) { + for (let t8 = 0; t8 < r.length; ++t8) { + let o = r[t8], n = t8 === r.length - 1 ? e : r[t8 + 1].length; if (o.length === 0) throw new Error("Ragged splits may not be empty"); if (o[0] < 0) @@ -12362,9 +12411,9 @@ function m8(r, e) { throw new Error("Ragged splits must be sorted in ascending order"); } } -function d8(r, e, t10, o) { - let n = [], s = 0, a = e.length - 1 + t10.length, i = new Array(a).fill(null).map(() => [0]); - m8(t10, o); +function z8(r, e, t8, o) { + let n = [], s = 0, a = e.length - 1 + t8.length, i = new Array(a).fill(null).map(() => [0]); + B8(t8, o); let p = 1; for (let u = 0; u < e.length - 1; ++u) { p *= e[u]; @@ -12374,8 +12423,8 @@ function d8(r, e, t10, o) { } for (let u = 0; u < r.length; ++u) { let c = r[u], l = r[u] + 1; - for (let m = 0; m < t10.length; ++m) { - let d = t10[m], f = m + e.length - 1; + for (let m = 0; m < t8.length; ++m) { + let d = t8[m], f = m + e.length - 1; if (f >= 0) { let h = i[f], g = h[h.length - 1] - d[c]; for (let x = c; x < l; ++x) @@ -12387,50 +12436,50 @@ function d8(r, e, t10, o) { } return { outSplits: i, valueSlices: n, numValues: s }; } -function f8(r) { +function V8(r) { let e = []; - for (let t10 = 0; t10 < r.length; ++t10) { - let o = r[t10].length, n = y.getArrayFromDType("int32", o); - e.push(n), r[t10].forEach((s, a) => n[a] = s); + for (let t8 = 0; t8 < r.length; ++t8) { + let o = r[t8].length, n = y.getArrayFromDType("int32", o); + e.push(n), r[t8].forEach((s, a) => n[a] = s); } return e; } -function JT(r, e) { - let t10 = r.slice(0, e); - for (; t10.length < e; ) - t10.push(1); +function l_(r, e) { + let t8 = r.slice(0, e); + for (; t8.length < e; ) + t8.push(1); for (let o = e; o < r.length; o++) - t10[e - 1] *= r[o]; - return t10; + t8[e - 1] *= r[o]; + return t8; } -function h8(r, e, t10, o, n, s) { - let a = JT(e, 2)[1], i = JT(s, 2)[1], p = 0; - for (let u of t10) +function W8(r, e, t8, o, n, s) { + let a = l_(e, 2)[1], i = l_(s, 2)[1], p = 0; + for (let u of t8) for (let c = u[0]; c < u[1]; ++c) { for (let l = 0; l < o; ++l) n[p * i + l] = r[c * a + l]; ++p; } } -function g8(r, e, t10, o, n) { +function U8(r, e, t8, o, n) { let s = e.slice(); s[0] = n; - let a = y.getArrayFromDType(t10, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0]; - return h8(r, e, o, p, a, s), [a, s]; + let a = y.getArrayFromDType(t8, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0]; + return W8(r, e, o, p, a, s), [a, s]; } -function _f(r, e, t10, o, n, s, a, i) { +function $f(r, e, t8, o, n, s, a, i) { if (r.length === 0) throw new Error("paramsNestedSplits must be non empty"); if (e[0].length === 0) throw new Error("Split tensors must not be scalars"); let p = e[0][0] - 1; - if (l8(s, a, p), o.length === 0) + if (L8(s, a, p), o.length === 0) throw new Error("params.rank must be nonzero"); - let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = d8(s, a, r, u), d = f8(c), f = g8(t10, o, n, l, m); + let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = z8(s, a, r, u), d = V8(c), f = U8(t8, o, n, l, m); return [d, f[0], f[1]]; } -var e_ = 2147483647; -function $f(r, e, t10, o, n, s, a) { +var m_ = 2147483647; +function Ef(r, e, t8, o, n, s, a) { if (e.length > 1) throw new Error("starts must be a scalar or vector"); if (n.length > 1) @@ -12445,97 +12494,97 @@ function $f(r, e, t10, o, n, s, a) { let l = c.length === 0 ? 1 : c[0], m = y.getArrayFromDType("int32", l + 1); m[0] = 0; for (let g = 0; g < l; ++g) { - let x = i ? r[0] : r[g], b = p ? o[0] : o[g], w = u ? s[0] : s[g]; - if (w === 0) + let x = i ? r[0] : r[g], b = p ? o[0] : o[g], C = u ? s[0] : s[g]; + if (C === 0) throw new Error("Requires delta != 0"); let S; - if (w > 0 && b < x || w < 0 && b > x) + if (C > 0 && b < x || C < 0 && b > x) S = 0; - else if (S = Math.ceil(Math.abs((b - x) / w)), S > e_) - throw new Error(`Requires ((limit - start) / delta) <= ${e_}`); + else if (S = Math.ceil(Math.abs((b - x) / C)), S > m_) + throw new Error(`Requires ((limit - start) / delta) <= ${m_}`); m[g + 1] = m[g] + S; } - let d = m[l], f = y.getArrayFromDType(t10, d), h = 0; + let d = m[l], f = y.getArrayFromDType(t8, d), h = 0; for (let g = 0; g < l; ++g) { - let x = m[g + 1] - m[g], b = i ? r[0] : r[g], w = u ? s[0] : s[g]; + let x = m[g + 1] - m[g], b = i ? r[0] : r[g], C = u ? s[0] : s[g]; for (let S = 0; S < x; ++S) - f[h++] = b, b += w; + f[h++] = b, b += C; } return [m, f]; } -var $o = C.RowPartitionType; -var Cc = class { - constructor(e, t10, o, n, s, a, i, p, u, c) { - this.shape = e, this.shapeShape = t10, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = C.getRowPartitionTypesHelper(c), this.raggedRank = C.getRaggedRank(this.rowPartitionTypes); +var Do = w.RowPartitionType; +var wc = class { + constructor(e, t8, o, n, s, a, i, p, u, c) { + this.shape = e, this.shapeShape = t8, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = w.getRowPartitionTypesHelper(c), this.raggedRank = w.getRaggedRank(this.rowPartitionTypes); } getRowPartitionTypeByDimension(e) { - return this.rowPartitionTypes[0] === $o.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e]; + return this.rowPartitionTypes[0] === Do.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e]; } getRowPartitionTensor(e) { - return this.rowPartitionTypes[0] === $o.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e]; + return this.rowPartitionTypes[0] === Do.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e]; } getMaxWidth(e) { - let t10 = this.getRowPartitionTensor(e - 1); + let t8 = this.getRowPartitionTensor(e - 1); switch (this.getRowPartitionTypeByDimension(e - 1)) { - case $o.VALUE_ROWIDS: - return Cc.getMaxWidthValueRowID(t10); - case $o.ROW_SPLITS: - return Cc.getMaxWidthRowSplit(t10); + case Do.VALUE_ROWIDS: + return wc.getMaxWidthValueRowID(t8); + case Do.ROW_SPLITS: + return wc.getMaxWidthRowSplit(t8); default: - throw new Error(`Cannot handle partition type ${$o[this.getRowPartitionTypeByDimension(e - 1)]}`); + throw new Error(`Cannot handle partition type ${Do[this.getRowPartitionTypeByDimension(e - 1)]}`); } } static getMaxWidthRowSplit(e) { - let t10 = e.length; - if (t10 === 0 || t10 === 1) + let t8 = e.length; + if (t8 === 0 || t8 === 1) return 0; let o = 0; - for (let n = 0; n < t10 - 1; ++n) { + for (let n = 0; n < t8 - 1; ++n) { let s = e[n + 1] - e[n]; s > o && (o = s); } return o; } static getMaxWidthValueRowID(e) { - let t10 = e.length; - if (t10 === 0) + let t8 = e.length; + if (t8 === 0) return 0; let o = 0, n = e[0], s = 0; - for (let a = 1; a < t10; ++a) { + for (let a = 1; a < t8; ++a) { let i = e[a]; i !== n && (n = i, s = Math.max(a - o, s), o = a); } - return Math.max(t10 - o, s); + return Math.max(t8 - o, s); } - tensorShapeFromTensor(e, t10, o = true) { - if (t10.length === 0) { + tensorShapeFromTensor(e, t8, o = true) { + if (t8.length === 0) { if (e[0] === -1) return []; throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1."); } - return r_(e, o); + return f_(e, o); } calculateOutputSize(e) { - let t10 = this.valuesShape, o = this.defaultValueShape; - C.validateDefaultValueShape(o, t10); - let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = C.combineRaggedTensorToTensorShapes(this.raggedRank, n, t10); + let t8 = this.valuesShape, o = this.defaultValueShape; + w.validateDefaultValueShape(o, t8); + let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = w.combineRaggedTensorToTensorShapes(this.raggedRank, n, t8); a[0] < 0 && (a[0] = e); for (let i = 1; i <= this.raggedRank; ++i) a[i] < 0 && (a[i] = this.getMaxWidth(i)); return a; } - calculateFirstParentOutputIndex(e, t10, o) { + calculateFirstParentOutputIndex(e, t8, o) { let n = Math.min(e, o), s = [], a = 0; - for (let i = 0; i < n; ++i, a += t10) + for (let i = 0; i < n; ++i, a += t8) s.push(a); for (let i = n; i < e; ++i) s.push(-1); return y.assert(s.length === e, () => "Final length of result must be equal to firstDimension."), s; } - calculateOutputIndexRowSplit(e, t10, o, n) { + calculateOutputIndexRowSplit(e, t8, o, n) { let s = e.length, a = []; for (let i = 0; i < s - 1; ++i) { - let p = e[i + 1] - e[i], u = Math.min(n, p), c = t10[i]; + let p = e[i + 1] - e[i], u = Math.min(n, p), c = t8[i]; c === -1 && (u = 0); for (let l = 0; l < u; ++l) a.push(c), c += o; @@ -12546,23 +12595,23 @@ var Cc = class { throw new Error("Invalid row split size."); return a; } - calculateOutputIndexValueRowID(e, t10, o, n) { + calculateOutputIndexValueRowID(e, t8, o, n) { let s = e.length, a = []; if (s === 0) return []; let i = 0, p = e[0]; - if (p >= t10.length) - throw new Error(`Got currentValueRowId=${p}, which is not less than ${t10.length}`); - let u = t10[p]; + if (p >= t8.length) + throw new Error(`Got currentValueRowId=${p}, which is not less than ${t8.length}`); + let u = t8[p]; a.push(u); for (let c = 1; c < s; ++c) { let l = e[c]; if (l === p) u >= 0 && (++i, i < n ? u += o : u = -1); else { - if (i = 0, p = l, l >= t10.length) - throw new Error(`Got nextValueRowId=${l} which is not less than ${t10.length}`); - u = t10[l]; + if (i = 0, p = l, l >= t8.length) + throw new Error(`Got nextValueRowId=${l} which is not less than ${t8.length}`); + u = t8[l]; } a.push(u); } @@ -12570,74 +12619,74 @@ var Cc = class { throw new Error("Invalid row ids."); return a; } - calculateOutputIndex(e, t10, o, n) { + calculateOutputIndex(e, t8, o, n) { let s = this.getRowPartitionTensor(e), a = this.getRowPartitionTypeByDimension(e); switch (a) { - case $o.VALUE_ROWIDS: - return this.calculateOutputIndexValueRowID(s, t10, o, n); - case $o.ROW_SPLITS: - if (s.length - 1 > t10.length) - throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t10.length}`); - return this.calculateOutputIndexRowSplit(s, t10, o, n); + case Do.VALUE_ROWIDS: + return this.calculateOutputIndexValueRowID(s, t8, o, n); + case Do.ROW_SPLITS: + if (s.length - 1 > t8.length) + throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t8.length}`); + return this.calculateOutputIndexRowSplit(s, t8, o, n); default: - throw new Error(`Unsupported partition type: ${$o[a]}`); + throw new Error(`Unsupported partition type: ${Do[a]}`); } } getFirstDimensionSize() { let e = this.rowPartitionValues[0]; if (this.rowPartitionTypes.length === 0) throw new Error("No row_partition_types given."); - let t10 = this.rowPartitionTypes[0]; - switch (t10) { - case $o.FIRST_DIM_SIZE: + let t8 = this.rowPartitionTypes[0]; + switch (t8) { + case Do.FIRST_DIM_SIZE: return e[0]; - case $o.VALUE_ROWIDS: + case Do.VALUE_ROWIDS: throw new Error("Cannot handle VALUE_ROWIDS in first dimension."); - case $o.ROW_SPLITS: + case Do.ROW_SPLITS: return this.rowPartitionValuesShapes[0][0] - 1; default: - throw new Error(`Cannot handle type ${$o[t10]}`); + throw new Error(`Cannot handle type ${Do[t8]}`); } } compute() { if (this.rowPartitionValues[0].length <= 0) throw new Error("Invalid first partition input. Tensor requires at least one element."); - let t10 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t10), n = new Array(this.raggedRank + 1); + let t8 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t8), n = new Array(this.raggedRank + 1); n[n.length - 1] = 1; for (let p = n.length - 2; p >= 0; --p) n[p] = n[p + 1] * o[p + 1]; - let s = r_(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s)); + let s = f_(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s)); if (n[0] * o[0] > 0) { - let p = this.calculateFirstParentOutputIndex(t10, n[0], o[0]); + let p = this.calculateFirstParentOutputIndex(t8, n[0], o[0]); for (let u = 1; u <= this.raggedRank; ++u) p = this.calculateOutputIndex(u - 1, p, n[u], o[u]); this.setOutput(this.raggedRank, p, a, s); } return [s, a]; } - setOutput(e, t10, o, n) { + setOutput(e, t8, o, n) { if (o.length === 0) return; let s = this.values, a = o, i = n.slice(); i = i.slice(e + 1); - let p = y.sizeFromShape(i), u = t10.length, c = this.defaultValue; + let p = y.sizeFromShape(i), u = t8.length, c = this.defaultValue; if (c.length !== p && c.length !== 1) { let f = this.defaultValueShape; De(() => { let h = W(c, f); - c = ru(h, i).dataSync(); + c = au(h, i).dataSync(); }); } let l = 0, m = 0, d = 0; for (let f = 0; f <= u; ++f) { - let h = f < u ? t10[f] : -1; + let h = f < u ? t8[f] : -1; if (h === d) { ++d; continue; } if (m < d) { let g = s.subarray(l * p), x = a.subarray(m * p), b = (d - m) * p; - t_(x, g, b); + d_(x, g, b); } if (f >= u) { let g = o.length; @@ -12649,18 +12698,18 @@ var Cc = class { else for (; h > d; ) { let g = a.slice(d * p); - t_(g, c, p), ++d; + d_(g, c, p), ++d; } h < 0 ? (l = f + 1, m = d) : (l = f, m = d, d = m + 1); } } }; -function t_(r, e, t10) { - for (let o = 0; o < t10; o++) +function d_(r, e, t8) { + for (let o = 0; o < t8; o++) r[o] = e[o]; } -function r_(r, e) { - let t10 = []; +function f_(r, e) { + let t8 = []; for (let o of r) { if (o < 0) { if (!e) @@ -12669,30 +12718,30 @@ function r_(r, e) { throw new Error(`Dimension ${o} must be >= -1`); o = -1; } - t10.push(o); + t8.push(o); } - return t10; + return t8; } -function Ef(r, e, t10, o, n, s, a, i, p, u) { - return new Cc(r, e, t10, o, n, s, a, i, p, u).compute(); +function Rf(r, e, t8, o, n, s, a, i, p, u) { + return new wc(r, e, t8, o, n, s, a, i, p, u).compute(); } -function ap(r, e, t10, o) { - let n = r === e, s = r < e && t10 < 0, a = e < r && t10 > 1; +function cp(r, e, t8, o) { + let n = r === e, s = r < e && t8 < 0, a = e < r && t8 > 1; if (n || s || a) return y.makeZerosTypedArray(0, o); - let i = Math.abs(Math.ceil((e - r) / t10)), p = y.makeZerosTypedArray(i, o); - e < r && t10 === 1 && (t10 = -1), p[0] = r; + let i = Math.abs(Math.ceil((e - r) / t8)), p = y.makeZerosTypedArray(i, o); + e < r && t8 === 1 && (t8 = -1), p[0] = r; for (let u = 1; u < p.length; u++) - p[u] = p[u - 1] + t10; + p[u] = p[u - 1] + t8; return p; } -var US = jt((r) => 1 / Math.sqrt(r)); -var x8 = Dr(us, US); -var o_ = { kernelName: us, backendName: "cpu", kernelFunc: x8 }; -function Fs(r, e, t10, o, n, s, a, i, p, u) { +var ZS = Xt((r) => 1 / Math.sqrt(r)); +var G8 = Ar(ls, ZS); +var h_ = { kernelName: ls, backendName: "cpu", kernelFunc: G8 }; +function Ls(r, e, t8, o, n, s, a, i, p, u) { let c = [o / n, n], l = r.values, m = e.values; if (o === 0) - return me(t10, e.dtype); + return me(t8, e.dtype); let d = p instanceof tt ? p : me(c, e.dtype); typeof p == "string" || typeof p == "number" ? d.values.fill(p) : typeof p == "boolean" && d.values.fill(+p); for (let f = 0; f < s; f++) { @@ -12702,52 +12751,52 @@ function Fs(r, e, t10, o, n, s, a, i, p, u) { h.push(b), g += b * i[x]; } if (g < 0 || g >= o / n) - throw new Error(`Invalid indices: ${h} does not index into ${t10}`); + throw new Error(`Invalid indices: ${h} does not index into ${t8}`); for (let x = 0; x < n; x++) u ? d.values[g * n + x] += m[f * n + x] : d.values[g * n + x] = e.rank === 0 ? m[0] : m[f * n + x]; } return d; } -var n_ = jt((r) => 1 / (1 + Math.exp(-r))); -var GS = Ie(hs, (r) => 1 / (1 + Math.exp(-r))); -var s_ = { kernelName: hs, backendName: "cpu", kernelFunc: GS }; -function ip(r, e, t10, o, n) { - let s = ct.isSliceContinous(o, e, t10), a = y.sizeFromShape(t10), i = y.computeStrides(o); +var g_ = Xt((r) => 1 / (1 + Math.exp(-r))); +var JS = Ie(bs, (r) => 1 / (1 + Math.exp(-r))); +var x_ = { kernelName: bs, backendName: "cpu", kernelFunc: JS }; +function lp(r, e, t8, o, n) { + let s = ct.isSliceContinous(o, e, t8), a = y.sizeFromShape(t8), i = y.computeStrides(o); if (s) { let l = ct.computeFlatOffset(e, i); return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a); } - let p = n === "string" ? C.fromUint8ToStringArray(r) : r, u = me(o, n, p), c = me(t10, n); + let p = n === "string" ? w.fromUint8ToStringArray(r) : r, u = me(o, n, p), c = me(t8, n); for (let l = 0; l < c.size; ++l) { let m = c.indexToLoc(l), d = m.map((f, h) => f + e[h]); c.set(u.get(...d), ...m); } - return n === "string" ? C.fromStringArrayToUint8(c.values) : c.values; + return n === "string" ? w.fromStringArrayToUint8(c.values) : c.values; } -function Eo(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o; - Y(n, "slice"); +function Ao(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o; + Q(n, "slice"); let [i, p] = ct.parseSliceParams(n, s, a); ct.assertParamsValid(n, i, p); - let u = t10.data.get(n.dataId).values, c = ip(u, i, p, n.shape, n.dtype); - return t10.makeTensorInfo(p, n.dtype, c); + let u = t8.data.get(n.dataId).values, c = lp(u, i, p, n.shape, n.dtype); + return t8.makeTensorInfo(p, n.dtype, c); } -var a_ = { kernelName: pa, backendName: "cpu", kernelFunc: Eo }; -function Rf(r, e, t10, o, n, s, a) { +var y_ = { kernelName: fa, backendName: "cpu", kernelFunc: Ao }; +function Df(r, e, t8, o, n, s, a) { let i = e[0], p = s[0], u = new Array(p), c = new Array(i), l = e[1]; if (p === 0) { if (i !== 0) - throw new Error(C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i)); - let g = y.getArrayFromDType(t10, 0), x = y.getArrayFromDType(n, 0); + throw new Error(w.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i)); + let g = y.getArrayFromDType(t8, 0), x = y.getArrayFromDType(n, 0); return [g, [0, l], x, u, c]; } let m = true, d = 0, f = new Array(p).fill(0); for (let g = 0; g < i; ++g) { let x = r[g * l]; if (x < 0) - throw new Error(C.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, x)); + throw new Error(w.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, x)); if (x >= p) - throw new Error(C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, x, p)); + throw new Error(w.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, x, p)); ++f[x], m = m && x >= d, d = x; } let h = true; @@ -12761,16 +12810,16 @@ function Rf(r, e, t10, o, n, s, a) { c[b] = b; return [g, [i, l], x, u, c]; } else { - let g = f[p - 1], x = y.getArrayFromDType(t10, g * l), b = y.getArrayFromDType(n, g), w = new Array(p).fill(0); + let g = f[p - 1], x = y.getArrayFromDType(t8, g * l), b = y.getArrayFromDType(n, g), C = new Array(p).fill(0); for (let S = 0; S < i; ++S) { - let k = r[S * l], _ = w[k], E = (k === 0 ? 0 : f[k - 1]) + _; - w[k]++; + let k = r[S * l], _ = C[k], E = (k === 0 ? 0 : f[k - 1]) + _; + C[k]++; for (let R = 0; R < l; ++R) x[E * l + R] = r[S * l + R]; b[E] = o[S], c[S] = E; } for (let S = 0; S < p; ++S) - if (w[S] === 0) { + if (C[S] === 0) { let _ = S === 0 ? 0 : f[S - 1]; x[_ * l + 0] = S; for (let E = 1; E < l; ++E) @@ -12780,30 +12829,30 @@ function Rf(r, e, t10, o, n, s, a) { return [x, [g, l], b, u, c]; } } -function Df(r, e, t10, o, n) { +function Af(r, e, t8, o, n) { let s = y.sizeFromShape(o), a = e[0], i = n.length, p = [], u = 1, c = -1; for (let g = 0; g < i; ++g) { let x = n[g]; if (x === -1) { if (c !== -1) - throw new Error(C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g)); + throw new Error(w.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g)); c = g, p.push(1); } else { if (x < 0) - throw new Error(C.getSparseReshapeNegativeOutputDimErrorMessage(g, x)); + throw new Error(w.getSparseReshapeNegativeOutputDimErrorMessage(g, x)); u *= x, p.push(x); } } if (c !== -1) { if (u <= 0) - throw new Error(C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage()); + throw new Error(w.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage()); let g = Math.trunc(s / u); if (u * g !== s) - throw new Error(C.getSparseReshapeInputOutputMultipleErrorMessage(o, p)); + throw new Error(w.getSparseReshapeInputOutputMultipleErrorMessage(o, p)); p[c] = g; } if (y.sizeFromShape(p) !== s) - throw new Error(C.getSparseReshapeInputOutputMismatchErrorMessage(o, p)); + throw new Error(w.getSparseReshapeInputOutputMismatchErrorMessage(o, p)); let m = o.length, d = []; if (m > 0) { d[m - 1] = 1; @@ -12816,7 +12865,7 @@ function Df(r, e, t10, o, n) { for (let g = i - 2; g >= 0; --g) f[g] = f[g + 1] * p[g + 1]; } - let h = y.getArrayFromDType(t10, a * i); + let h = y.getArrayFromDType(t8, a * i); for (let g = 0; g < a; ++g) { let x = 0; for (let b = 0; b < m; ++b) @@ -12826,92 +12875,92 @@ function Df(r, e, t10, o, n) { } return [h, [a, i], p]; } -function wc(r, e, t10, o, n, s = false, a = 0) { +function Sc(r, e, t8, o, n, s = false, a = 0) { let i = o.length, p = [e[0], r.length / e[0]], u = p[1], l = i > 0 ? n[i - 1] + 1 : 0; if (l < 0) - throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let m = e.slice(); m[0] = l; - let d = m.reduce((w, S) => w * S, 1), f = y.getArrayFromDType(t10, d); + let d = m.reduce((C, S) => C * S, 1), f = y.getArrayFromDType(t8, d); if (i === 0) return l > 0 && f.fill(a), [f, m]; if (l <= 0) - throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let h = 0, g = 1, x = 0, b = n[h]; for (; ; ) { - let w = 0; + let C = 0; if (g < i) { - if (w = n[g], b === w) { + if (C = n[g], b === C) { ++g; continue; } - if (b >= w) - throw new Error(C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage()); + if (b >= C) + throw new Error(w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage()); } if (b < 0 || b >= l) - throw new Error(C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b, l)); + throw new Error(w.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b, l)); b > x && f.fill(a, x * u, b * u); for (let S = h; S < g; ++S) { let k = o[S]; if (k < 0 || k >= p[0]) - throw new Error(C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(S, o[S], p[0])); + throw new Error(w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(S, o[S], p[0])); for (let _ = 0; _ < u; _++) f[b * u + _] += r[k * u + _]; } if (s) for (let S = 0; S < u; S++) f[b * u + S] /= g - h; - if (h = g, ++g, x = b + 1, b = w, g > i) + if (h = g, ++g, x = b + 1, b = C, g > i) break; } return x < l && f.fill(a, x * u, l * u), [f, m]; } -var i_ = jt((r) => Math.sqrt(r)); -var y8 = Ie(xs, (r) => Math.sqrt(r)); -var u_ = { kernelName: xs, backendName: "cpu", kernelFunc: y8 }; -var HS = ze((r, e) => { - let t10 = r - e; - return t10 * t10; +var b_ = Xt((r) => Math.sqrt(r)); +var H8 = Ie(ws, (r) => Math.sqrt(r)); +var C_ = { kernelName: ws, backendName: "cpu", kernelFunc: H8 }; +var eI = ze((r, e) => { + let t8 = r - e; + return t8 * t8; }); -var b8 = je(ws, HS); -var p_ = { kernelName: ws, backendName: "cpu", kernelFunc: b8 }; -var KS = jt((r, e) => { - let { pattern: t10, replaceGlobal: o, rewrite: n } = e; - return r.replace(new RegExp(t10, o ? "g" : ""), n); +var K8 = Ye(ks, eI); +var w_ = { kernelName: ks, backendName: "cpu", kernelFunc: K8 }; +var tI = Xt((r, e) => { + let { pattern: t8, replaceGlobal: o, rewrite: n } = e; + return r.replace(new RegExp(t8, o ? "g" : ""), n); }); -var C8 = Dr(_u, KS); -var c_ = { kernelName: _u, backendName: "cpu", kernelFunc: C8 }; -function Af(r, e, t10, o) { +var q8 = Ar(Du, tI); +var S_ = { kernelName: Du, backendName: "cpu", kernelFunc: q8 }; +function Ff(r, e, t8, o) { let n = me(r, e.dtype); for (let s = 0; s < n.size; s++) { let a = n.indexToLoc(s), i = new Array(a.length); for (let p = 0; p < i.length; p++) - i[p] = a[p] * t10[p] + o[p]; + i[p] = a[p] * t8[p] + o[p]; n.set(e.get(...i), ...a); } return n; } -var qS = class { - constructor(e, t10, o, n, s, a) { - this.separator = y.encodeString(e), this.nGramWidths = t10, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a; +var rI = class { + constructor(e, t8, o, n, s, a) { + this.separator = y.encodeString(e), this.nGramWidths = t8, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a; } getPadWidth(e) { return Math.min(this.padWidth < 0 ? e - 1 : this.padWidth, e - 1); } - getNumNGrams(e, t10) { - let o = this.getPadWidth(t10); - return Math.max(0, e + 2 * o - t10 + 1); + getNumNGrams(e, t8) { + let o = this.getPadWidth(t8); + return Math.max(0, e + 2 * o - t8 + 1); } - createNGrams(e, t10, o, n, s, a) { + createNGrams(e, t8, o, n, s, a) { for (let i = 0; i < s; ++i) { - let p = this.getPadWidth(a), u = Math.max(0, p - i), c = Math.max(0, p - (s - (i + 1))), l = a - (u + c), m = t10 + (u > 0 ? 0 : i - p), d = 0; + let p = this.getPadWidth(a), u = Math.max(0, p - i), c = Math.max(0, p - (s - (i + 1))), l = a - (u + c), m = t8 + (u > 0 ? 0 : i - p), d = 0; d += u * this.leftPad.length; for (let b = 0; b < l; ++b) d += e[m + b].length; d += c * this.rightPad.length; let f = u + c + l - 1; d += f * this.separator.length, o[n + i] = new Uint8Array(d); - let h = o[n + i], g = 0, x = (b) => b.forEach((w) => h[g++] = w); + let h = o[n + i], g = 0, x = (b) => b.forEach((C) => h[g++] = C); for (let b = 0; b < u; ++b) x(this.leftPad), x(this.separator); for (let b = 0; b < l - 1; ++b) @@ -12927,17 +12976,17 @@ var qS = class { } } } - compute(e, t10) { - let o = e.length, n = t10.length; + compute(e, t8) { + let o = e.length, n = t8.length; if (n > 0) { - let p = t10[0]; + let p = t8[0]; if (p !== 0) throw new Error(`First split value must be 0, got ${p}`); for (let u = 1; u < n; ++u) { - let c = t10[u] >= p; - if (c = c && t10[u] <= o, !c) - throw new Error(`Invalid split value ${t10[u]}, must be in [${p}, ${o}]`); - p = t10[u]; + let c = t8[u] >= p; + if (c = c && t8[u] <= o, !c) + throw new Error(`Invalid split value ${t8[u]}, must be in [${p}, ${o}]`); + p = t8[u]; } if (p !== o) throw new Error(`Last split value must be data size. Expected ${o}, got ${p}`); @@ -12951,19 +13000,19 @@ var qS = class { } a[0] = 0; for (let p = 1; p <= s; ++p) { - let u = t10[p] - t10[p - 1], c = 0; + let u = t8[p] - t8[p - 1], c = 0; this.nGramWidths.forEach((l) => { c += this.getNumNGrams(u, l); }), this.preserveShort && u > 0 && c === 0 && (c = 1), a[p] = a[p - 1] + c; } let i = new Array(a[s]); for (let p = 0; p < s; ++p) { - let u = t10[p], c = a[p]; + let u = t8[p], c = a[p]; if (this.nGramWidths.forEach((l) => { - let m = t10[p + 1] - t10[p], d = this.getNumNGrams(m, l); + let m = t8[p + 1] - t8[p], d = this.getNumNGrams(m, l); this.createNGrams(e, u, i, c, d, l), c += d; }), this.preserveShort && c === a[p]) { - let l = t10[p + 1] - t10[p]; + let l = t8[p + 1] - t8[p]; if (l === 0) continue; let m = l + 2 * this.padWidth, d = 1; @@ -12973,10 +13022,10 @@ var qS = class { return [i, a]; } }; -function up(r, e, t10, o, n, s, a, i) { - return new qS(t10, o, n, s, a, i).compute(r, e); +function mp(r, e, t8, o, n, s, a, i) { + return new rI(t8, o, n, s, a, i).compute(r, e); } -function w8(r, e, t10, o) { +function j8(r, e, t8, o) { if (!r.length) return; if (e.length === 0) { @@ -12988,23 +13037,23 @@ function w8(r, e, t10, o) { let s = e[0], a = r.indexOf(s); for (; a !== -1; ) { let i = r.subarray(0, a); - (!t10 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s); + (!t8 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s); } - (!t10 || r.length !== 0) && o.push(r); + (!t8 || r.length !== 0) && o.push(r); return; } let n = 0; for (let s = 0; s < r.length + 1; s++) if (s === r.length || e.indexOf(r[s]) !== -1) { let a = r.subarray(n, s); - (!t10 || a.length !== 0) && o.push(a), n = s + 1; + (!t8 || a.length !== 0) && o.push(a), n = s + 1; } } -function pp(r, e, t10) { +function dp(r, e, t8) { let o = r.length, n = [], s = 0, a = 0, i = new Array(o); for (let m = 0; m < o; ++m) { let d = n.length; - w8(r[m], e, t10, n); + j8(r[m], e, t8, n); let f = n.length - d; i[m] = f, s += f, a = Math.max(a, f); } @@ -13014,21 +13063,21 @@ function pp(r, e, t10) { p[l * 2] = m, p[l * 2 + 1] = d, u[l] = n[l], ++l; return [p, u, c]; } -function cp(r, e) { - let t10 = y.getArrayFromDType("int32", r.length); +function fp(r, e) { + let t8 = y.getArrayFromDType("int32", r.length); for (let o = 0; o < r.length; ++o) - t10[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned(); - return t10; + t8[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned(); + return t8; } -var jS = ze((r, e) => r - e); -var S8 = xc((r, e, t10, o) => ({ real: r - t10, imag: e - o })); -var Ll = je(Is, jS, S8); -var l_ = { kernelName: Is, backendName: "cpu", kernelFunc: Ll }; -function Ff(r, e) { - let t10 = new Array(r.rank); - for (let n = 0; n < t10.length; n++) - t10[n] = r.shape[n] * e[n]; - let o = me(t10, r.dtype); +var oI = ze((r, e) => r - e); +var X8 = yc((r, e, t8, o) => ({ real: r - t8, imag: e - o })); +var Ll = Ye(Ts, oI, X8); +var I_ = { kernelName: Ts, backendName: "cpu", kernelFunc: Ll }; +function Pf(r, e) { + let t8 = new Array(r.rank); + for (let n = 0; n < t8.length; n++) + t8[n] = r.shape[n] * e[n]; + let o = me(t8, r.dtype); for (let n = 0; n < o.values.length; ++n) { let s = o.indexToLoc(n), a = new Array(r.rank); for (let p = 0; p < a.length; p++) @@ -13039,54 +13088,54 @@ function Ff(r, e) { return o; } var Bl = (r, e) => { - let t10 = e.value - r.value; - return t10 === 0 ? r.index - e.index : t10; + let t8 = e.value - r.value; + return t8 === 0 ? r.index - e.index : t8; }; -function m_(r, e, t10 = 0, o = r.length - 1) { - for (; o > t10; ) { - if (o - t10 > 600) { - let i = o - t10 + 1, p = e - t10 + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(t10, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l)); - m_(r, e, m, d); +function v_(r, e, t8 = 0, o = r.length - 1) { + for (; o > t8; ) { + if (o - t8 > 600) { + let i = o - t8 + 1, p = e - t8 + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(t8, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l)); + v_(r, e, m, d); } - let n = r[e], s = t10, a = o; - for (y.swap(r, t10, e), Bl(r[o], n) > 0 && y.swap(r, t10, o); s < a; ) { + let n = r[e], s = t8, a = o; + for (y.swap(r, t8, e), Bl(r[o], n) > 0 && y.swap(r, t8, o); s < a; ) { for (y.swap(r, s, a), s++, a--; Bl(r[s], n) < 0; ) s = s + 1; for (; Bl(r[a], n) > 0; ) a = a - 1; } - Bl(r[t10], n) === 0 ? y.swap(r, t10, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t10 = a + 1), e <= a && (o = a - 1); + Bl(r[t8], n) === 0 ? y.swap(r, t8, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t8 = a + 1), e <= a && (o = a - 1); } } -function Pf(r, e, t10, o, n) { - let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t10, a * o), u = y.getTypedArrayFromDType("int32", a * o); +function Of(r, e, t8, o, n) { + let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t8, a * o), u = y.getTypedArrayFromDType("int32", a * o); for (let l = 0; l < a; l++) { let m = l * i, d = r.subarray(m, m + i), f = new Array(d.length); - d.forEach((b, w) => f[w] = { value: b, index: w }), o < f.length && (m_(f, o), f = f.slice(0, o)), n && f.sort(Bl); + d.forEach((b, C) => f[C] = { value: b, index: C }), o < f.length && (v_(f, o), f = f.slice(0, o)), n && f.sort(Bl); let h = l * o, g = p.subarray(h, h + o), x = u.subarray(h, h + o); for (let b = 0; b < o; b++) g[b] = f[b].value, x[b] = f[b].index; } let c = e.slice(); - return c[c.length - 1] = o, [me(c, t10, p), me(c, "int32", u)]; + return c[c.length - 1] = o, [me(c, t8, p), me(c, "int32", u)]; } -function lp(r, e, t10, o) { - let n = y.parseAxisParam(e, t10)[0], s = [1, t10[0], 1]; +function hp(r, e, t8, o) { + let n = y.parseAxisParam(e, t8)[0], s = [1, t8[0], 1]; for (let f = 0; f < n; f++) - s[0] *= t10[f]; - s[1] = t10[n]; - for (let f = n + 1; f < t10.length; f++) - s[2] *= t10[f]; - let a = /* @__PURE__ */ new Map(), i = new Int32Array(t10[n]), p = new tt(s, o, r), u = [], c = s[0] === 1 && s[2] === 1; - for (let f = 0; f < t10[n]; f++) { + s[0] *= t8[f]; + s[1] = t8[n]; + for (let f = n + 1; f < t8.length; f++) + s[2] *= t8[f]; + let a = /* @__PURE__ */ new Map(), i = new Int32Array(t8[n]), p = new tt(s, o, r), u = [], c = s[0] === 1 && s[2] === 1; + for (let f = 0; f < t8[n]; f++) { let h; if (c) h = r[f].toString(); else { let x = []; for (let b = 0; b < s[0]; b++) - for (let w = 0; w < s[2]; w++) - x.push(p.get(b, f, w)); + for (let C = 0; C < s[2]; C++) + x.push(p.get(b, f, C)); h = x.join(","); } let g = a.get(h); @@ -13105,251 +13154,251 @@ function lp(r, e, t10, o) { for (let x = 0; x < s[2]; x++) m.set(p.get(g, f, x), g, h, x); }); - let d = t10.slice(); + let d = t8.slice(); return d[n] = l[1], { outputValues: m.values, outputShape: d, indices: i }; } -var I8 = "4.5.0"; -eu("cpu", () => new lu(), 1); -var XS = Ie(fn, (r) => r >= 0 ? r : Math.exp(r) - 1); -var d_ = { kernelName: fn, backendName: "cpu", kernelFunc: XS }; -function YS(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o; - Y([n], "leakyRelu"); - let a = y.sizeFromShape(n.shape), i = t10.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a); +var Y8 = "4.7.0"; +nu("cpu", () => new hu(), 1); +var nI = Ie(hn, (r) => r >= 0 ? r : Math.exp(r) - 1); +var k_ = { kernelName: hn, backendName: "cpu", kernelFunc: nI }; +function sI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { alpha: s } = o; + Q([n], "leakyRelu"); + let a = y.sizeFromShape(n.shape), i = t8.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a); for (let u = 0; u < i.length; u++) p[u] = i[u] < 0 ? s * i[u] : i[u]; - return t10.makeTensorInfo(n.shape, "float32", p); + return t8.makeTensorInfo(n.shape, "float32", p); } -var f_ = { kernelName: _n, backendName: "cpu", kernelFunc: YS }; -var v8 = ze((r, e) => r < 0 ? e * r : r); -function QS(r) { - let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e; - Y([o, n], "prelu"); - let s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, [i, p] = v8(o.shape, n.shape, s, a, "float32"); - return t10.makeTensorInfo(p, "float32", i); +var N_ = { kernelName: En, backendName: "cpu", kernelFunc: sI }; +var Q8 = ze((r, e) => r < 0 ? e * r : r); +function aI(r) { + let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e; + Q([o, n], "prelu"); + let s = t8.data.get(o.dataId).values, a = t8.data.get(n.dataId).values, [i, p] = Q8(o.shape, n.shape, s, a, "float32"); + return t8.makeTensorInfo(p, "float32", i); } -var h_ = { kernelName: Jn, backendName: "cpu", kernelFunc: QS }; -var ZS = Ie(rs, (r) => Math.max(0, r)); -var g_ = { kernelName: rs, backendName: "cpu", kernelFunc: ZS }; -var JS = Ie(ss, (r) => Math.min(Math.max(0, r), 6)); -var x_ = { kernelName: ss, backendName: "cpu", kernelFunc: JS }; -function mp(r, e, t10, o, n) { - if (t10 === "linear") +var T_ = { kernelName: rs, backendName: "cpu", kernelFunc: aI }; +var iI = Ie(ss, (r) => Math.max(0, r)); +var __ = { kernelName: ss, backendName: "cpu", kernelFunc: iI }; +var uI = Ie(us, (r) => Math.min(Math.max(0, r), 6)); +var $_ = { kernelName: us, backendName: "cpu", kernelFunc: uI }; +function gp(r, e, t8, o, n) { + if (t8 === "linear") return lr({ inputs: { x: e }, backend: r }); - if (t10 === "relu") - return ZS({ inputs: { x: e }, backend: r }); - if (t10 === "elu") - return XS({ inputs: { x: e }, backend: r }); - if (t10 === "relu6") + if (t8 === "relu") + return iI({ inputs: { x: e }, backend: r }); + if (t8 === "elu") + return nI({ inputs: { x: e }, backend: r }); + if (t8 === "relu6") + return uI({ inputs: { x: e }, backend: r }); + if (t8 === "prelu") + return aI({ inputs: { x: e, alpha: o }, backend: r }); + if (t8 === "leakyrelu") + return sI({ inputs: { x: e }, backend: r, attrs: { alpha: n } }); + if (t8 === "sigmoid") return JS({ inputs: { x: e }, backend: r }); - if (t10 === "prelu") - return QS({ inputs: { x: e, alpha: o }, backend: r }); - if (t10 === "leakyrelu") - return YS({ inputs: { x: e }, backend: r, attrs: { alpha: n } }); - if (t10 === "sigmoid") - return GS({ inputs: { x: e }, backend: r }); - throw new Error(`Activation ${t10} has not been implemented for the CPU backend.`); + throw new Error(`Activation ${t8} has not been implemented for the CPU backend.`); } function Ve(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i); - y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t10.incRef(n.dataId); - let u = t10.data.get(n.dataId); + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i); + y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t8.incRef(n.dataId); + let u = t8.data.get(n.dataId); if (u.complexTensorInfos != null) { let c = u.complexTensorInfos.real, l = u.complexTensorInfos.imag; c.shape = i, l.shape = i; } return { dataId: n.dataId, shape: i, dtype: n.dtype }; } -var y_ = { kernelName: ia, backendName: "cpu", kernelFunc: Ve }; -function eI(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; - Y([n, s], "matMul"); - let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), w = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); +var E_ = { kernelName: ma, backendName: "cpu", kernelFunc: Ve }; +function pI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; + Q([n, s], "matMul"); + let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`); - let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: S } }), E = Ve({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), R = a ? _.shape[1] : _.shape[2], D = a ? _.shape[2] : _.shape[1], F = i ? E.shape[1] : E.shape[2], O = Math.max(g, x), M = t10.data.get(_.dataId).values, L = t10.data.get(E.dataId).values, B = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, j, H] = a ? [B[0], 1, B[1]] : [B[0], B[1], 1], [X, J, re] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], ne = D * F, ee = me([O, D, F], _.dtype), oe = ee.values, ie = t10.blockSize; + let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Ve({ inputs: { x: n }, backend: t8, attrs: { shape: S } }), E = Ve({ inputs: { x: s }, backend: t8, attrs: { shape: k } }), R = a ? _.shape[1] : _.shape[2], D = a ? _.shape[2] : _.shape[1], P = i ? E.shape[1] : E.shape[2], O = Math.max(g, x), M = t8.data.get(_.dataId).values, L = t8.data.get(E.dataId).values, B = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, j, q] = a ? [B[0], 1, B[1]] : [B[0], B[1], 1], [Y, J, re] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], ne = D * P, ee = me([O, D, P], _.dtype), oe = ee.values, ie = t8.blockSize; for (let le = 0; le < O; le++) { - let ye = le % g, _e = le % x; + let be = le % g, _e = le % x; for (let ve = 0; ve < D; ve += ie) { let Fe = Math.min(ve + ie, D); - for (let Pe = 0; Pe < F; Pe += ie) { - let st = Math.min(Pe + ie, F); + for (let Pe = 0; Pe < P; Pe += ie) { + let st = Math.min(Pe + ie, P); for (let lt = 0; lt < R; lt += ie) { - let We = Math.min(lt + ie, R); + let Ue = Math.min(lt + ie, R); for (let mt = ve; mt < Fe; mt++) for (let it = Pe; it < st; it++) { - let ht = 0; - for (let gt = lt; gt < We; gt++) { - let Or = M[ye * U + mt * j + gt * H], Mt = L[gt * X + it * J + _e * re]; - ht += Or * Mt; + let gt = 0; + for (let xt = lt; xt < Ue; xt++) { + let Lr = M[be * U + mt * j + xt * q], Mt = L[xt * Y + it * J + _e * re]; + gt += Lr * Mt; } - oe[le * ne + (mt * F + it)] += ht; + oe[le * ne + (mt * P + it)] += gt; } } } } } - return t10.disposeIntermediateTensorInfo(_), t10.disposeIntermediateTensorInfo(E), t10.makeTensorInfo(w, ee.dtype, ee.values); + return t8.disposeIntermediateTensorInfo(_), t8.disposeIntermediateTensorInfo(E), t8.makeTensorInfo(C, ee.dtype, ee.values); } -var b_ = { kernelName: Qo, backendName: "cpu", kernelFunc: eI }; -function k8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = []; - m = eI({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t10 }), a && (d = _a({ inputs: { a: m, b: a }, backend: t10 }), h.push(m), m = d), c && (f = mp(t10, m, c, i, l), h.push(m), m = f); +var R_ = { kernelName: Zo, backendName: "cpu", kernelFunc: pI }; +function Z8(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = []; + m = pI({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t8 }), a && (d = Aa({ inputs: { a: m, b: a }, backend: t8 }), h.push(m), m = d), c && (f = gp(t8, m, c, i, l), h.push(m), m = f); for (let x of h) - t10.disposeIntermediateTensorInfo(x); + t8.disposeIntermediateTensorInfo(x); return m; } -var C_ = { kernelName: bo, backendName: "cpu", kernelFunc: k8 }; -var N8 = Ie(zo, (r) => Math.acos(r)); -var w_ = { kernelName: zo, backendName: "cpu", kernelFunc: N8 }; -var T8 = Ie(Vo, (r) => Math.acosh(r)); -var S_ = { kernelName: Vo, backendName: "cpu", kernelFunc: T8 }; -function _8(r) { - let { inputs: e, backend: t10 } = r, o = e; - Y(e, "addN"); - let n = o.map((i) => t10.data.get(i.dataId).values), s = me(o[0].shape, o[0].dtype), a = s.values; +var D_ = { kernelName: So, backendName: "cpu", kernelFunc: Z8 }; +var J8 = Ie(Vo, (r) => Math.acos(r)); +var A_ = { kernelName: Vo, backendName: "cpu", kernelFunc: J8 }; +var eY = Ie(Wo, (r) => Math.acosh(r)); +var F_ = { kernelName: Wo, backendName: "cpu", kernelFunc: eY }; +function tY(r) { + let { inputs: e, backend: t8 } = r, o = e; + Q(e, "addN"); + let n = o.map((i) => t8.data.get(i.dataId).values), s = me(o[0].shape, o[0].dtype), a = s.values; for (let i = 0; i < o.length; i++) { let p = n[i]; for (let u = 0; u < a.length; u++) a[u] += p[u]; } - return t10.makeTensorInfo(s.shape, s.dtype, s.values); + return t8.makeTensorInfo(s.shape, s.dtype, s.values); } -var I_ = { kernelName: Wo, backendName: "cpu", kernelFunc: _8 }; -function $8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - Y(n, "all"); - let i = y.parseAxisParam(s, n.shape), p = i, u = C.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = C.getInnerMostAxes(p.length, n.shape.length)), C.assertAxesAreInnerMostDims("all", p, c.shape.length); - let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values; +var P_ = { kernelName: Uo, backendName: "cpu", kernelFunc: tY }; +function rY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + Q(n, "all"); + let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n; + u != null && (c = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("all", p, c.shape.length); + let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t8.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { - let b = x * d, w = h[b]; + let b = x * d, C = h[b]; for (let S = 0; S < d; ++S) { let k = h[b + S]; - w = w && k; + C = C && k; } - f[x] = w; + f[x] = C; } - u != null && t10.disposeIntermediateTensorInfo(c); - let g = t10.makeTensorInfo(l, c.dtype, f); + u != null && t8.disposeIntermediateTensorInfo(c); + let g = t8.makeTensorInfo(l, c.dtype, f); if (a) { - let x = C.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: x } }); - return t10.disposeIntermediateTensorInfo(g), b; + let x = w.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: x } }); + return t8.disposeIntermediateTensorInfo(g), b; } return g; } -var v_ = { kernelName: Uo, backendName: "cpu", kernelFunc: $8 }; -function E8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - Y(n, "any"); - let i = y.parseAxisParam(s, n.shape), p = i, u = C.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = C.getInnerMostAxes(p.length, n.shape.length)), C.assertAxesAreInnerMostDims("any", p, c.shape.length); - let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values; +var O_ = { kernelName: Go, backendName: "cpu", kernelFunc: rY }; +function oY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + Q(n, "any"); + let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n; + u != null && (c = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("any", p, c.shape.length); + let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t8.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { - let b = x * d, w = h[b]; + let b = x * d, C = h[b]; for (let S = 0; S < d; ++S) { let k = h[b + S]; - w = w || k; + C = C || k; } - f[x] = w; + f[x] = C; } - u != null && t10.disposeIntermediateTensorInfo(c); - let g = t10.makeTensorInfo(l, c.dtype, f); + u != null && t8.disposeIntermediateTensorInfo(c); + let g = t8.makeTensorInfo(l, c.dtype, f); if (a) { - let x = C.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: x } }); - return t10.disposeIntermediateTensorInfo(g), b; + let x = w.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: x } }); + return t8.disposeIntermediateTensorInfo(g), b; } return g; } -var k_ = { kernelName: Go, backendName: "cpu", kernelFunc: E8 }; -function R8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o; - Y(n, "argMax"); - let a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], C.assertAxesAreInnerMostDims("argMax", a, p.shape.length); - let [c, l] = C.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values; +var M_ = { kernelName: Ho, backendName: "cpu", kernelFunc: oY }; +function nY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o; + Q(n, "argMax"); + let a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = It({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMax", a, p.shape.length); + let [c, l] = w.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t8.data.get(p.dataId).values; for (let g = 0; g < d.length; ++g) { - let x = g * f, b = h[x], w = 0; + let x = g * f, b = h[x], C = 0; for (let S = 0; S < f; ++S) { let k = h[x + S]; - k > b && (b = k, w = S); + k > b && (b = k, C = S); } - d[g] = w; + d[g] = C; } - return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d); + return u.forEach((g) => t8.disposeIntermediateTensorInfo(g)), t8.makeTensorInfo(c, "int32", d); } -var N_ = { kernelName: Hs, backendName: "cpu", kernelFunc: R8 }; -function D8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o; - Y(n, "argMin"); - let a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], C.assertAxesAreInnerMostDims("argMin", a, p.shape.length); - let [c, l] = C.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values; +var L_ = { kernelName: Xs, backendName: "cpu", kernelFunc: nY }; +function sY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o; + Q(n, "argMin"); + let a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = It({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMin", a, p.shape.length); + let [c, l] = w.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t8.data.get(p.dataId).values; for (let g = 0; g < d.length; ++g) { - let x = g * f, b = h[x], w = 0; + let x = g * f, b = h[x], C = 0; for (let S = 0; S < f; ++S) { let k = h[x + S]; - k < b && (b = k, w = S); + k < b && (b = k, C = S); } - d[g] = w; + d[g] = C; } - return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d); + return u.forEach((g) => t8.disposeIntermediateTensorInfo(g)), t8.makeTensorInfo(c, "int32", d); } -var T_ = { kernelName: Ks, backendName: "cpu", kernelFunc: D8 }; -var A8 = Ie(Ho, (r) => Math.asin(r)); -var __ = { kernelName: Ho, backendName: "cpu", kernelFunc: A8 }; -var F8 = Ie(Ko, (r) => Math.asinh(r)); -var $_ = { kernelName: Ko, backendName: "cpu", kernelFunc: F8 }; -var P8 = Ie(qo, (r) => Math.atan(r)); -var E_ = { kernelName: qo, backendName: "cpu", kernelFunc: P8 }; -var O8 = ze((r, e) => Math.atan2(r, e)); -var M8 = je(Xo, O8); -var R_ = { kernelName: Xo, backendName: "cpu", kernelFunc: M8 }; -var L8 = Ie(jo, (r) => Math.atanh(r)); -var D_ = { kernelName: jo, backendName: "cpu", kernelFunc: L8 }; -function Ic(r, e, t10, o, n, s) { - let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = me(n.outShape, t10), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], w = n.outShape[3]; +var B_ = { kernelName: Ys, backendName: "cpu", kernelFunc: sY }; +var aY = Ie(Ko, (r) => Math.asin(r)); +var z_ = { kernelName: Ko, backendName: "cpu", kernelFunc: aY }; +var iY = Ie(qo, (r) => Math.asinh(r)); +var V_ = { kernelName: qo, backendName: "cpu", kernelFunc: iY }; +var uY = Ie(jo, (r) => Math.atan(r)); +var W_ = { kernelName: jo, backendName: "cpu", kernelFunc: uY }; +var pY = ze((r, e) => Math.atan2(r, e)); +var cY = Ye(Yo, pY); +var U_ = { kernelName: Yo, backendName: "cpu", kernelFunc: cY }; +var lY = Ie(Xo, (r) => Math.atanh(r)); +var G_ = { kernelName: Xo, backendName: "cpu", kernelFunc: lY }; +function vc(r, e, t8, o, n, s) { + let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = me(n.outShape, t8), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], C = n.outShape[3]; for (let S = 0; S < n.batchSize; ++S) { let k = S * x, _ = S * o[0]; for (let E = 0; E < n.inChannels; ++E) for (let R = 0; R < n.outHeight; ++R) { - let D = R * a - m, F = Math.max(0, D), O = Math.min(n.inHeight, c + D), M = k + R * b; + let D = R * a - m, P = Math.max(0, D), O = Math.min(n.inHeight, c + D), M = k + R * b; for (let L = 0; L < n.outWidth; ++L) { - let B = L * i - d, z = Math.max(0, B), U = Math.min(n.inWidth, l + B), j = f, H = 0, X = 0; - for (let re = F; re < O; re += p) { + let B = L * i - d, z = Math.max(0, B), U = Math.min(n.inWidth, l + B), j = f, q = 0, Y = 0; + for (let re = P; re < O; re += p) { let ne = _ + re * o[1]; for (let ee = z; ee < U; ee += u) { let oe = ne + ee * o[2], ie = r[oe + E]; - s === "max" && ie > j ? j = ie : s === "avg" && (H += ie, X++); + s === "max" && ie > j ? j = ie : s === "avg" && (q += ie, Y++); } if (isNaN(j)) break; } - let J = M + L * w + E; - g[J] = s === "avg" ? H / X : j; + let J = M + L * C + E; + g[J] = s === "avg" ? q / Y : j; } } } return h; } -function Of(r, e, t10, o, n = false, s = false) { - let a = me(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = me(e, t10, r); +function Mf(r, e, t8, o, n = false, s = false) { + let a = me(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = me(e, t8, r); for (let g = 0; g < o.batchSize; ++g) for (let x = 0; x < o.inChannels; ++x) for (let b = 0; b < o.outHeight; ++b) { - let w = b * i - d, S = w; + let C = b * i - d, S = C; for (; S < 0; ) S += u; - let k = Math.min(o.inHeight, l + w); + let k = Math.min(o.inHeight, l + C); for (let _ = 0; _ < o.outWidth; ++_) { let E = _ * p - f, R = E; for (; R < 0; ) R += c; - let D = Math.min(o.inWidth, m + E), F = Number.NEGATIVE_INFINITY, O = -1; + let D = Math.min(o.inWidth, m + E), P = Number.NEGATIVE_INFINITY, O = -1; for (let M = S; M < k; M += u) { - let L = M - w; + let L = M - C; for (let B = R; B < D; B += c) { let z = B - E, U = h.get(g, M, B, x); - U > F && (F = U, n ? O = s ? ((g * o.inHeight + M) * o.inWidth + B) * o.inChannels + x : (M * o.inWidth + B) * o.inChannels + x : O = L * m + z); + U > P && (P = U, n ? O = s ? ((g * o.inHeight + M) * o.inWidth + B) * o.inChannels + x : (M * o.inWidth + B) * o.inChannels + x : O = L * m + z); } } a.set(O, g, b, _, x); @@ -13357,33 +13406,33 @@ function Of(r, e, t10, o, n = false, s = false) { } return a; } -function Mf(r, e, t10, o, n, s) { - let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, w = me(n.outShape, t10), S = w.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], E = n.outShape[3] * n.outShape[4], R = n.outShape[4]; +function Lf(r, e, t8, o, n, s) { + let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, C = me(n.outShape, t8), S = C.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], E = n.outShape[3] * n.outShape[4], R = n.outShape[4]; for (let D = 0; D < n.batchSize; ++D) { - let F = D * k, O = D * o[0]; + let P = D * k, O = D * o[0]; for (let M = 0; M < n.inChannels; ++M) for (let L = 0; L < n.outDepth; ++L) { let B = L * a - h, z = B; for (; z < 0; ) z += u; - let U = Math.min(n.inDepth, m + B), j = F + L * _; - for (let H = 0; H < n.outHeight; ++H) { - let X = H * i - g, J = X; + let U = Math.min(n.inDepth, m + B), j = P + L * _; + for (let q = 0; q < n.outHeight; ++q) { + let Y = q * i - g, J = Y; for (; J < 0; ) J += c; - let re = Math.min(n.inHeight, d + X), ne = j + H * E; + let re = Math.min(n.inHeight, d + Y), ne = j + q * E; for (let ee = 0; ee < n.outWidth; ++ee) { let oe = ee * p - x, ie = oe; for (; ie < 0; ) ie += l; - let le = Math.min(n.inWidth, f + oe), ye = ne + ee * R, _e = b, ve = 0, Fe = 0; + let le = Math.min(n.inWidth, f + oe), be = ne + ee * R, _e = b, ve = 0, Fe = 0; for (let st = z; st < U; st += u) { let lt = O + st * o[1]; - for (let We = J; We < re; We += c) { - let mt = lt + We * o[2]; + for (let Ue = J; Ue < re; Ue += c) { + let mt = lt + Ue * o[2]; for (let it = ie; it < le; it += l) { - let ht = mt + it * o[3], gt = r[ht + M]; - if (s === "max" && gt > _e ? _e = gt : s === "avg" && (ve += gt, Fe++), isNaN(_e)) + let gt = mt + it * o[3], xt = r[gt + M]; + if (s === "max" && xt > _e ? _e = xt : s === "avg" && (ve += xt, Fe++), isNaN(_e)) break; } if (isNaN(_e)) @@ -13392,22 +13441,22 @@ function Mf(r, e, t10, o, n, s) { if (isNaN(_e)) break; } - let Pe = ye + M; + let Pe = be + M; S[Pe] = s === "avg" ? ve / Math.max(Fe, 1) : _e; } } } } - return w; + return C; } -function A_(r, e) { - let t10 = me(e.outShape, "int32"), o = e.strideDepth, n = e.strideHeight, s = e.strideWidth, a = e.dilationDepth, i = e.dilationHeight, p = e.dilationWidth, u = e.effectiveFilterDepth, c = e.effectiveFilterHeight, l = e.effectiveFilterWidth, m = e.padInfo.front, d = e.padInfo.top, f = e.padInfo.left; +function H_(r, e) { + let t8 = me(e.outShape, "int32"), o = e.strideDepth, n = e.strideHeight, s = e.strideWidth, a = e.dilationDepth, i = e.dilationHeight, p = e.dilationWidth, u = e.effectiveFilterDepth, c = e.effectiveFilterHeight, l = e.effectiveFilterWidth, m = e.padInfo.front, d = e.padInfo.top, f = e.padInfo.left; for (let h = 0; h < e.batchSize; ++h) for (let g = 0; g < e.inChannels; ++g) for (let x = 0; x < e.outDepth; ++x) { - let b = x * o - m, w = b; - for (; w < 0; ) - w += a; + let b = x * o - m, C = b; + for (; C < 0; ) + C += a; let S = Math.min(e.inDepth, u + b); for (let k = 0; k < e.outHeight; ++k) { let _ = k * n - d, E = _; @@ -13415,196 +13464,196 @@ function A_(r, e) { E += i; let R = Math.min(e.inHeight, c + _); for (let D = 0; D < e.outWidth; ++D) { - let F = D * s - f, O = F; + let P = D * s - f, O = P; for (; O < 0; ) O += p; - let M = Math.min(e.inWidth, l + F), L = Number.NEGATIVE_INFINITY, B = -1; - for (let z = w; z < S; z += a) { + let M = Math.min(e.inWidth, l + P), L = Number.NEGATIVE_INFINITY, B = -1; + for (let z = C; z < S; z += a) { let U = z - b; for (let j = E; j < R; j += i) { - let H = j - _; - for (let X = O; X < M; X += p) { - let J = X - F, re = r.get(h, z, j, X, g); - re >= L && (L = re, B = U * c * l + H * c + J); + let q = j - _; + for (let Y = O; Y < M; Y += p) { + let J = Y - P, re = r.get(h, z, j, Y, g); + re >= L && (L = re, B = U * c * l + q * c + J); } } } - t10.set(B, h, x, k, D, g); + t8.set(B, h, x, k, D, g); } } } - return t10; + return t8; } -function B8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; - Y(n, "avgPool"); +function mY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e; + Q(n, "avgPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; - y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); - let c = C.computePool2DInfo(n.shape, s, a, u, i, p), l; + y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); + let c = w.computePool2DInfo(n.shape, s, a, u, i, p), l; if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - l = lr({ inputs: { x: n }, backend: t10 }); + l = lr({ inputs: { x: n }, backend: t8 }); else { - let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Ic(m, n.shape, n.dtype, d, c, "avg"); - l = t10.makeTensorInfo(c.outShape, n.dtype, f.values); + let m = t8.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = vc(m, n.shape, n.dtype, d, c, "avg"); + l = t8.makeTensorInfo(c.outShape, n.dtype, f.values); } return l; } -var F_ = { kernelName: Yo, backendName: "cpu", kernelFunc: B8 }; -function z8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; - Y(n, "avgPool3d"); - let c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = Mf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg"); - return t10.makeTensorInfo(m.shape, "float32", m.values); +var K_ = { kernelName: Qo, backendName: "cpu", kernelFunc: mY }; +function dY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; + Q(n, "avgPool3d"); + let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.data.get(n.dataId).values, m = Lf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg"); + return t8.makeTensorInfo(m.shape, "float32", m.values); } -var P_ = { kernelName: qs, backendName: "cpu", kernelFunc: z8 }; -function V8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; - Y([n, s], "avgPool3DGrad"); - let c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, w = c.dilationWidth, S = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = S - 1 - c.padInfo.front, R = _ - 1 - c.padInfo.left, D = k - 1 - c.padInfo.top, F = me(s.shape, "float32"), O = 1 / (f * h * g), M = t10.bufferSync(n); +var q_ = { kernelName: Qs, backendName: "cpu", kernelFunc: dY }; +function fY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; + Q([n, s], "avgPool3DGrad"); + let c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, C = c.dilationWidth, S = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = S - 1 - c.padInfo.front, R = _ - 1 - c.padInfo.left, D = k - 1 - c.padInfo.top, P = me(s.shape, "float32"), O = 1 / (f * h * g), M = t8.bufferSync(n); for (let L = 0; L < c.batchSize; ++L) for (let B = 0; B < c.inChannels; ++B) for (let z = 0; z < c.inDepth; ++z) for (let U = 0; U < c.inHeight; ++U) for (let j = 0; j < c.inWidth; ++j) { - let H = z - E, X = U - D, J = j - R, re = 0; + let q = z - E, Y = U - D, J = j - R, re = 0; for (let ne = 0; ne < S; ne += x) { - let ee = (H + ne) / l; + let ee = (q + ne) / l; if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee)) for (let oe = 0; oe < k; oe += b) { - let ie = (X + oe) / m; + let ie = (Y + oe) / m; if (!(ie < 0 || ie >= c.outHeight || Math.floor(ie) !== ie)) - for (let le = 0; le < _; le += w) { - let ye = (J + le) / d; - if (ye < 0 || ye >= c.outWidth || Math.floor(ye) !== ye) + for (let le = 0; le < _; le += C) { + let be = (J + le) / d; + if (be < 0 || be >= c.outWidth || Math.floor(be) !== be) continue; - let _e = M.get(L, ee, ie, ye, B); + let _e = M.get(L, ee, ie, be, B); re += _e; } } } - F.set(re * O, L, z, U, j, B); + P.set(re * O, L, z, U, j, B); } - return t10.makeTensorInfo(F.shape, F.dtype, F.values); + return t8.makeTensorInfo(P.shape, P.dtype, P.values); } -var O_ = { kernelName: Ni, backendName: "cpu", kernelFunc: V8 }; -function W8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s; - Y([n, s], "avgPoolGrad"); - let { filterSize: i, strides: p, pad: u } = o, c = C.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, w = b - 1 - c.padInfo.left, S = x - 1 - c.padInfo.top, k = me(a.shape, "float32"), _ = 1 / (d * f), E = t10.data.get(n.dataId).values, R = me(n.shape, "float32", E); +var j_ = { kernelName: Ei, backendName: "cpu", kernelFunc: fY }; +function hY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s; + Q([n, s], "avgPoolGrad"); + let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, C = b - 1 - c.padInfo.left, S = x - 1 - c.padInfo.top, k = me(a.shape, "float32"), _ = 1 / (d * f), E = t8.data.get(n.dataId).values, R = me(n.shape, "float32", E); for (let D = 0; D < c.batchSize; ++D) - for (let F = 0; F < c.inChannels; ++F) + for (let P = 0; P < c.inChannels; ++P) for (let O = 0; O < c.inHeight; ++O) for (let M = 0; M < c.inWidth; ++M) { - let L = O - S, B = M - w, z = 0; + let L = O - S, B = M - C, z = 0; for (let U = 0; U < x; U += h) { let j = (L + U) / l; if (!(j < 0 || j >= c.outHeight || Math.floor(j) !== j)) - for (let H = 0; H < b; H += g) { - let X = (B + H) / m; - if (X < 0 || X >= c.outWidth || Math.floor(X) !== X) + for (let q = 0; q < b; q += g) { + let Y = (B + q) / m; + if (Y < 0 || Y >= c.outWidth || Math.floor(Y) !== Y) continue; - let J = R.get(D, j, X, F); + let J = R.get(D, j, Y, P); z += J; } } - k.set(z * _, D, O, M, F); + k.set(z * _, D, O, M, P); } - return t10.makeTensorInfo(k.shape, k.dtype, k.values); + return t8.makeTensorInfo(k.shape, k.dtype, k.values); } -var M_ = { kernelName: Gp, backendName: "cpu", kernelFunc: W8 }; -function U8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e; - y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), Y([n, i, p, s, a], "batchNorm"); +var X_ = { kernelName: $i, backendName: "cpu", kernelFunc: hY }; +function gY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e; + y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), Q([n, i, p, s, a], "batchNorm"); let { varianceEpsilon: u } = o; u == null && (u = 1e-3); - let c = t10.data.get(n.dataId).values, l = t10.data.get(i.dataId).values, m = t10.data.get(p.dataId).values, d = s ? t10.data.get(s.dataId).values : new Float32Array([1]), f = a ? t10.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, w = l.length, S = 0, k = 0, _ = 0, E = 0; + let c = t8.data.get(n.dataId).values, l = t8.data.get(i.dataId).values, m = t8.data.get(p.dataId).values, d = s ? t8.data.get(s.dataId).values : new Float32Array([1]), f = a ? t8.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, C = l.length, S = 0, k = 0, _ = 0, E = 0; for (let R = 0; R < c.length; ++R) - h[R] = f[S++] + (c[R] - l[k++]) * d[_++] / Math.sqrt(m[E++] + u), S >= g && (S = 0), k >= w && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0); - return t10.makeTensorInfo(n.shape, n.dtype, h); + h[R] = f[S++] + (c[R] - l[k++]) * d[_++] / Math.sqrt(m[E++] + u), S >= g && (S = 0), k >= C && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0); + return t8.makeTensorInfo(n.shape, n.dtype, h); } -var L_ = { kernelName: wn, backendName: "cpu", kernelFunc: U8 }; -function G8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; - Y([n], "batchToSpaceND"); - let i = s.reduce((x, b) => x * b), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = St({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = Ve({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = Eo({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } }); - return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g; +var Y_ = { kernelName: In, backendName: "cpu", kernelFunc: gY }; +function xY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; + Q([n], "batchToSpaceND"); + let i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = Ve({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), f = It({ inputs: { x: d }, backend: t8, attrs: { perm: u } }), h = Ve({ inputs: { x: f }, backend: t8, attrs: { shape: c } }), g = Ao({ inputs: { x: h }, backend: t8, attrs: { begin: l, size: m } }); + return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), g; } -var B_ = { kernelName: js, backendName: "cpu", kernelFunc: G8 }; -function H8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = yc(i, p, s.dtype, s.shape, a); - return t10.makeTensorInfo([a], s.dtype, u); +var Q_ = { kernelName: Zs, backendName: "cpu", kernelFunc: xY }; +function yY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, u = bc(i, p, s.dtype, s.shape, a); + return t8.makeTensorInfo([a], s.dtype, u); } -var z_ = { kernelName: Zo, backendName: "cpu", kernelFunc: H8 }; -function K8(r) { - let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = C.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); - return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i)); +var Z_ = { kernelName: Jo, backendName: "cpu", kernelFunc: yY }; +function bY(r) { + let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e, s = t8.data.get(o.dataId).values, a = t8.data.get(n.dataId).values, i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); + return t8.makeTensorInfo([i.length], "int32", Int32Array.from(i)); } -var V_ = { kernelName: Xs, backendName: "cpu", kernelFunc: K8 }; -var q8 = Ie(go, (r, e) => { - let t10 = e; - return r > t10.clipValueMax ? t10.clipValueMax : r < t10.clipValueMin ? t10.clipValueMin : r; +var J_ = { kernelName: Js, backendName: "cpu", kernelFunc: bY }; +var CY = Ie(bo, (r, e) => { + let t8 = e; + return r > t8.clipValueMax ? t8.clipValueMax : r < t8.clipValueMin ? t8.clipValueMin : r; }); -var W_ = { kernelName: go, backendName: "cpu", kernelFunc: q8 }; -var j8 = (r) => { - let { x: e } = r.inputs, t10 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values; +var e$ = { kernelName: bo, backendName: "cpu", kernelFunc: CY }; +var wY = (r) => { + let { x: e } = r.inputs, t8 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t8.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t8.data.get(s.dataId).values, p = t8.data.get(a.dataId).values; for (let u = 0; u < i.length; u++) { let c = i[u], l = p[u]; o[u] = Math.hypot(c, l); } - return t10.makeOutput(o, e.shape, "float32"); + return t8.makeOutput(o, e.shape, "float32"); }; -var U_ = { kernelName: _i, backendName: "cpu", kernelFunc: j8 }; -function $a(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.imag, s = t10.data.get(n.dataId).values; - return t10.makeTensorInfo(n.shape, n.dtype, s); +var t$ = { kernelName: Di, backendName: "cpu", kernelFunc: wY }; +function Fa(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.data.get(o.dataId).complexTensorInfos.imag, s = t8.data.get(n.dataId).values; + return t8.makeTensorInfo(n.shape, n.dtype, s); } -var G_ = { kernelName: Mi, backendName: "cpu", kernelFunc: $a }; -function mu(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape); - C.assertParamsConsistent(a, s); - let i = C.computeOutShape(e.map((h) => h.shape), s); +var r$ = { kernelName: Vi, backendName: "cpu", kernelFunc: Fa }; +function gu(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape); + w.assertParamsConsistent(a, s); + let i = w.computeOutShape(e.map((h) => h.shape), s); if (y.sizeFromShape(i) === 0) - return t10.makeTensorInfo(i, e[0].dtype, []); + return t8.makeTensorInfo(i, e[0].dtype, []); let p = e.filter((h) => y.sizeFromShape(h.shape) > 0); if (p.length === 1) - return lr({ inputs: { x: p[0] }, backend: t10 }); + return lr({ inputs: { x: p[0] }, backend: t8 }); if (p[0].dtype === "complex64") { - let h = p.map((S) => To({ inputs: { input: S }, backend: t10 })), g = p.map((S) => $a({ inputs: { input: S }, backend: t10 })), x = mu({ inputs: h, backend: t10, attrs: { axis: s } }), b = mu({ inputs: g, backend: t10, attrs: { axis: s } }), w = Kt({ inputs: { real: x, imag: b }, backend: t10 }); - return h.forEach((S) => t10.disposeIntermediateTensorInfo(S)), g.forEach((S) => t10.disposeIntermediateTensorInfo(S)), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), w; + let h = p.map((S) => Eo({ inputs: { input: S }, backend: t8 })), g = p.map((S) => Fa({ inputs: { input: S }, backend: t8 })), x = gu({ inputs: h, backend: t8, attrs: { axis: s } }), b = gu({ inputs: g, backend: t8, attrs: { axis: s } }), C = Kt({ inputs: { real: x, imag: b }, backend: t8 }); + return h.forEach((S) => t8.disposeIntermediateTensorInfo(S)), g.forEach((S) => t8.disposeIntermediateTensorInfo(S)), t8.disposeIntermediateTensorInfo(x), t8.disposeIntermediateTensorInfo(b), C; } let u = p.map((h) => { let x = [-1, y.sizeFromShape(h.shape.slice(s))]; - return Ve({ inputs: { x: h }, backend: t10, attrs: { shape: x } }); - }), c = u.map((h) => ({ vals: t10.data.get(h.dataId).values, shape: h.shape })); - i = C.computeOutShape(u.map((h) => h.shape), 1); - let l = u[0].shape[0] === 1, m = np(c, i, e[0].dtype, l), d = C.computeOutShape(p.map((h) => h.shape), s), f = t10.makeTensorInfo(d, e[0].dtype, m); - return u.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f; + return Ve({ inputs: { x: h }, backend: t8, attrs: { shape: x } }); + }), c = u.map((h) => ({ vals: t8.data.get(h.dataId).values, shape: h.shape })); + i = w.computeOutShape(u.map((h) => h.shape), 1); + let l = u[0].shape[0] === 1, m = up(c, i, e[0].dtype, l), d = w.computeOutShape(p.map((h) => h.shape), s), f = t8.makeTensorInfo(d, e[0].dtype, m); + return u.forEach((h) => t8.disposeIntermediateTensorInfo(h)), f; } -var H_ = { kernelName: Ys, backendName: "cpu", kernelFunc: mu }; -function tI(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o; - Y([n, s], "conv2d"); - let l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, w = m.dataFormat === "channelsLast", S = new tt(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], R = w ? k[1] : k[2], D = w ? k[2] : 1, F = w ? 1 : k[1], O = S.strides[0], M = w ? S.strides[1] : S.strides[2], L = w ? S.strides[2] : 1, B = w ? 1 : S.strides[1], z = t10.data.get(n.dataId).values, U = t10.data.get(s.dataId).values, j = S.values; - for (let H = 0; H < m.batchSize; ++H) { - let X = H * E, J = H * O; +var o$ = { kernelName: ea, backendName: "cpu", kernelFunc: gu }; +function cI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o; + Q([n, s], "conv2d"); + let l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, C = m.dataFormat === "channelsLast", S = new tt(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], R = C ? k[1] : k[2], D = C ? k[2] : 1, P = C ? 1 : k[1], O = S.strides[0], M = C ? S.strides[1] : S.strides[2], L = C ? S.strides[2] : 1, B = C ? 1 : S.strides[1], z = t8.data.get(n.dataId).values, U = t8.data.get(s.dataId).values, j = S.values; + for (let q = 0; q < m.batchSize; ++q) { + let Y = q * E, J = q * O; for (let re = 0; re < m.outHeight; ++re) { let ne = J + re * M, ee = re * m.strideHeight - b; for (let oe = 0; oe < d; ++oe) { let ie = ee + oe * h; if (ie < 0 || ie >= m.inHeight) continue; - let le = oe * _[0], ye = X + ie * R; + let le = oe * _[0], be = Y + ie * R; for (let _e = 0; _e < m.outWidth; ++_e) { let ve = ne + _e * L, Fe = _e * m.strideWidth - x; for (let Pe = 0; Pe < f; ++Pe) { let st = Fe + Pe * g; if (st < 0 || st >= m.inWidth) continue; - let lt = le + Pe * _[1], We = ye + st * D, mt = lt; + let lt = le + Pe * _[1], Ue = be + st * D, mt = lt; for (let it = 0; it < m.inChannels; ++it) { - let ht = z[We + it * F]; - for (let gt = 0; gt < m.outChannels; ++gt) - j[ve + gt * B] += ht * U[mt + gt]; + let gt = z[Ue + it * P]; + for (let xt = 0; xt < m.outChannels; ++xt) + j[ve + xt * B] += gt * U[mt + xt]; mt += m.outChannels; } } @@ -13612,96 +13661,96 @@ function tI(r) { } } } - return t10.makeTensorInfo(S.shape, S.dtype, j); + return t8.makeTensorInfo(S.shape, S.dtype, j); } -var K_ = { kernelName: en, backendName: "cpu", kernelFunc: tI }; -function X8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o; - Y([n, s], "conv2dBackpropFilter"); - let l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new tt(m.filterShape, "float32"), w = m.padInfo.left, S = m.padInfo.top, k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = new tt(n.shape, n.dtype, k), R = new tt(s.shape, s.dtype, _); +var n$ = { kernelName: tn, backendName: "cpu", kernelFunc: cI }; +function SY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o; + Q([n, s], "conv2dBackpropFilter"); + let l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new tt(m.filterShape, "float32"), C = m.padInfo.left, S = m.padInfo.top, k = t8.data.get(n.dataId).values, _ = t8.data.get(s.dataId).values, E = new tt(n.shape, n.dtype, k), R = new tt(s.shape, s.dtype, _); for (let D = 0; D < h; ++D) { - let F = Math.max(0, Math.ceil((S - D) / d)), O = Math.min(m.outHeight, (m.inHeight + S - D) / d); + let P = Math.max(0, Math.ceil((S - D) / d)), O = Math.min(m.outHeight, (m.inHeight + S - D) / d); for (let M = 0; M < g; ++M) { - let L = Math.max(0, Math.ceil((w - M) / f)), B = Math.min(m.outWidth, (m.inWidth + w - M) / f); + let L = Math.max(0, Math.ceil((C - M) / f)), B = Math.min(m.outWidth, (m.inWidth + C - M) / f); for (let z = 0; z < m.inChannels; ++z) for (let U = 0; U < m.outChannels; ++U) { let j = 0; - for (let H = 0; H < m.batchSize; ++H) - for (let X = F; X < O; ++X) { - let J = D + X * d - S; + for (let q = 0; q < m.batchSize; ++q) + for (let Y = P; Y < O; ++Y) { + let J = D + Y * d - S; for (let re = L; re < B; ++re) { - let ne = M + re * f - w; - x ? j += E.get(H, J, ne, z) * R.get(H, X, re, U) : j += E.get(H, z, J, ne) * R.get(H, U, X, re); + let ne = M + re * f - C; + x ? j += E.get(q, J, ne, z) * R.get(q, Y, re, U) : j += E.get(q, z, J, ne) * R.get(q, U, Y, re); } } b.set(j, D, M, z, U); } } } - return t10.makeTensorInfo(b.shape, b.dtype, b.values); + return t8.makeTensorInfo(b.shape, b.dtype, b.values); } -var q_ = { kernelName: $i, backendName: "cpu", kernelFunc: X8 }; -function Y8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o; - Y([n, s], "conv2dBackpropInput"); - let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = C.convertConv2DDataFormat(u), f = C.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new tt(f.inShape, "float32"), g = h.values, x = t10.data.get(n.dataId).values, b = t10.data.get(s.dataId).values, [w, S, k] = l, { batchSize: _, filterHeight: E, filterWidth: R, inChannels: D, inHeight: F, inWidth: O, outChannels: M, outHeight: L, outWidth: B, strideHeight: z, strideWidth: U } = f; +var s$ = { kernelName: Ai, backendName: "cpu", kernelFunc: SY }; +function IY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o; + Q([n, s], "conv2dBackpropInput"); + let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = w.convertConv2DDataFormat(u), f = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new tt(f.inShape, "float32"), g = h.values, x = t8.data.get(n.dataId).values, b = t8.data.get(s.dataId).values, [C, S, k] = l, { batchSize: _, filterHeight: E, filterWidth: R, inChannels: D, inHeight: P, inWidth: O, outChannels: M, outHeight: L, outWidth: B, strideHeight: z, strideWidth: U } = f; d = f.dataFormat; - let j = E - 1 - f.padInfo.top, H = R - 1 - f.padInfo.left, X = d === "channelsLast", J = h.strides[0], re = X ? h.strides[1] : h.strides[2], ne = X ? h.strides[2] : 1, ee = X ? 1 : h.strides[1], oe = m[0], ie = X ? m[1] : m[2], le = X ? m[2] : 1, ye = X ? 1 : m[1]; + let j = E - 1 - f.padInfo.top, q = R - 1 - f.padInfo.left, Y = d === "channelsLast", J = h.strides[0], re = Y ? h.strides[1] : h.strides[2], ne = Y ? h.strides[2] : 1, ee = Y ? 1 : h.strides[1], oe = m[0], ie = Y ? m[1] : m[2], le = Y ? m[2] : 1, be = Y ? 1 : m[1]; for (let _e = 0; _e < _; ++_e) for (let ve = 0; ve < D; ++ve) - for (let Fe = 0; Fe < F; ++Fe) { + for (let Fe = 0; Fe < P; ++Fe) { let Pe = Fe - j, st = Math.max(0, Math.ceil(Pe / z)), lt = Math.min(L, (E + Pe) / z); - for (let We = 0; We < O; ++We) { - let mt = We - H, it = Math.max(0, Math.ceil(mt / U)), ht = Math.min(B, (R + mt) / U), gt = 0; + for (let Ue = 0; Ue < O; ++Ue) { + let mt = Ue - q, it = Math.max(0, Math.ceil(mt / U)), gt = Math.min(B, (R + mt) / U), xt = 0; for (let Mt = st; Mt < lt; ++Mt) { - let Qr = Mt * z - Pe; - for (let or = it; or < ht; ++or) { - let Tt = or * U - mt, nr = oe * _e + ie * Mt + le * or, sr = w * (E - 1 - Qr) + S * (R - 1 - Tt) + k * ve; - for (let Zr = 0; Zr < M; ++Zr) { - let Jr = x[nr + ye * Zr], fr = b[sr + Zr]; - gt += Jr * fr; + let eo = Mt * z - Pe; + for (let or = it; or < gt; ++or) { + let Tt = or * U - mt, nr = oe * _e + ie * Mt + le * or, sr = C * (E - 1 - eo) + S * (R - 1 - Tt) + k * ve; + for (let to = 0; to < M; ++to) { + let ro = x[nr + be * to], fr = b[sr + to]; + xt += ro * fr; } } } - let Or = J * _e + re * Fe + ne * We + ee * ve; - g[Or] = gt; + let Lr = J * _e + re * Fe + ne * Ue + ee * ve; + g[Lr] = xt; } } - return t10.makeTensorInfo(h.shape, h.dtype, h.values); + return t8.makeTensorInfo(h.shape, h.dtype, h.values); } -var j_ = { kernelName: tn, backendName: "cpu", kernelFunc: Y8 }; -function Q8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; - Y([n, s], "conv3d"); - let u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, w = g.top, S = new tt(u.outShape, n.dtype), k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = S.values, R = y.computeStrides(n.shape), D = y.computeStrides(s.shape); - for (let F = 0; F < u.batchSize; ++F) { - let O = F * R[0], M = F * S.strides[0]; +var a$ = { kernelName: rn, backendName: "cpu", kernelFunc: IY }; +function vY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; + Q([n, s], "conv3d"); + let u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, C = g.top, S = new tt(u.outShape, n.dtype), k = t8.data.get(n.dataId).values, _ = t8.data.get(s.dataId).values, E = S.values, R = y.computeStrides(n.shape), D = y.computeStrides(s.shape); + for (let P = 0; P < u.batchSize; ++P) { + let O = P * R[0], M = P * S.strides[0]; for (let L = 0; L < u.outDepth; ++L) { let B = M + L * S.strides[1], z = L * u.strideDepth - x; for (let U = 0; U < c; ++U) { let j = z + U * d; if (j < 0 || j >= u.inDepth) continue; - let H = U * D[0], X = O + j * R[1]; + let q = U * D[0], Y = O + j * R[1]; for (let J = 0; J < u.outHeight; ++J) { - let re = B + J * S.strides[2], ne = J * u.strideHeight - w; + let re = B + J * S.strides[2], ne = J * u.strideHeight - C; for (let ee = 0; ee < l; ++ee) { let oe = ne + ee * f; if (oe < 0 || oe >= u.inHeight) continue; - let ie = H + ee * D[1], le = X + oe * R[2]; - for (let ye = 0; ye < u.outWidth; ++ye) { - let _e = re + ye * u.outChannels, ve = ye * u.strideWidth - b; + let ie = q + ee * D[1], le = Y + oe * R[2]; + for (let be = 0; be < u.outWidth; ++be) { + let _e = re + be * u.outChannels, ve = be * u.strideWidth - b; for (let Fe = 0; Fe < m; ++Fe) { let Pe = ve + Fe * h; if (Pe < 0 || Pe >= u.inWidth) continue; - let st = ie + Fe * D[2], lt = le + Pe * u.inChannels, We = st; + let st = ie + Fe * D[2], lt = le + Pe * u.inChannels, Ue = st; for (let mt = 0; mt < u.inChannels; ++mt) { let it = k[lt + mt]; - for (let ht = 0; ht < u.outChannels; ++ht) - E[_e + ht] += it * _[We + ht]; - We += u.outChannels; + for (let gt = 0; gt < u.outChannels; ++gt) + E[_e + gt] += it * _[Ue + gt]; + Ue += u.outChannels; } } } @@ -13710,66 +13759,66 @@ function Q8(r) { } } } - return t10.makeTensorInfo(S.shape, S.dtype, S.values); + return t8.makeTensorInfo(S.shape, S.dtype, S.values); } -var X_ = { kernelName: rn, backendName: "cpu", kernelFunc: Q8 }; -function Z8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o; - Y([n, s], "conv3dBackpropFilterV2"); - let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = C.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new tt(l.filterShape, "float32"), w = b.values, [S, k, _, E] = b.strides, R = t10.data.get(s.dataId).values, [D, F, O, M] = c, L = t10.data.get(n.dataId).values, [B, z, U, j] = u, H = l.padInfo.front, X = l.padInfo.left, J = l.padInfo.top; +var i$ = { kernelName: on, backendName: "cpu", kernelFunc: vY }; +function kY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o; + Q([n, s], "conv3dBackpropFilterV2"); + let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = w.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new tt(l.filterShape, "float32"), C = b.values, [S, k, _, E] = b.strides, R = t8.data.get(s.dataId).values, [D, P, O, M] = c, L = t8.data.get(n.dataId).values, [B, z, U, j] = u, q = l.padInfo.front, Y = l.padInfo.left, J = l.padInfo.top; for (let re = 0; re < h; ++re) { - let ne = Math.max(0, Math.ceil((H - re) / m)), ee = Math.min(l.outDepth, (l.inDepth + H - re) / m), oe = re * S; + let ne = Math.max(0, Math.ceil((q - re) / m)), ee = Math.min(l.outDepth, (l.inDepth + q - re) / m), oe = re * S; for (let ie = 0; ie < g; ++ie) { - let le = Math.max(0, Math.ceil((J - ie) / d)), ye = Math.min(l.outHeight, (l.inHeight + J - ie) / d), _e = ie * k + oe; + let le = Math.max(0, Math.ceil((J - ie) / d)), be = Math.min(l.outHeight, (l.inHeight + J - ie) / d), _e = ie * k + oe; for (let ve = 0; ve < x; ++ve) { - let Fe = Math.max(0, Math.ceil((X - ve) / f)), Pe = Math.min(l.outWidth, (l.inWidth + X - ve) / f), st = ve * _ + _e; + let Fe = Math.max(0, Math.ceil((Y - ve) / f)), Pe = Math.min(l.outWidth, (l.inWidth + Y - ve) / f), st = ve * _ + _e; for (let lt = 0; lt < l.inChannels; ++lt) { - let We = lt * E + st; + let Ue = lt * E + st; for (let mt = 0; mt < l.outChannels; ++mt) { let it = 0; - for (let ht = 0; ht < l.batchSize; ++ht) { - let gt = ht * B, Or = ht * D; + for (let gt = 0; gt < l.batchSize; ++gt) { + let xt = gt * B, Lr = gt * D; for (let Mt = ne; Mt < ee; ++Mt) { - let or = (re + Mt * m - H) * z + gt, Tt = Mt * F + Or; - for (let nr = le; nr < ye; ++nr) { - let Zr = (ie + nr * d - J) * U + or, Jr = nr * O + Tt; + let or = (re + Mt * m - q) * z + xt, Tt = Mt * P + Lr; + for (let nr = le; nr < be; ++nr) { + let to = (ie + nr * d - J) * U + or, ro = nr * O + Tt; for (let fr = Fe; fr < Pe; ++fr) { - let Mo = (ve + fr * f - X) * j + Zr, Vs = fr * M + Jr; - it += L[Mo + lt] * R[Vs + mt]; + let Lo = (ve + fr * f - Y) * j + to, Hs = fr * M + ro; + it += L[Lo + lt] * R[Hs + mt]; } } } } - w[We + mt] = it; + C[Ue + mt] = it; } } } } } - return t10.makeTensorInfo(b.shape, b.dtype, b.values); + return t8.makeTensorInfo(b.shape, b.dtype, b.values); } -var Y_ = { kernelName: za, backendName: "cpu", kernelFunc: Z8 }; -function J8(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o; - Y([n], "conv3dBackpropInputV2"); - let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = C.computeConv3DInfo(p, s.shape, i, 1, a), m = new tt(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t10.data.get(n.dataId).values, [w, S, k, _] = u, E = t10.data.get(s.dataId).values, [R, D, F, O] = c, { batchSize: M, filterDepth: L, filterHeight: B, filterWidth: z, inChannels: U, inDepth: j, inHeight: H, inWidth: X, outChannels: J, outDepth: re, outHeight: ne, outWidth: ee, strideDepth: oe, strideHeight: ie, strideWidth: le } = l, ye = L - 1 - l.padInfo.front, _e = B - 1 - l.padInfo.top, ve = z - 1 - l.padInfo.left; +var u$ = { kernelName: Ka, backendName: "cpu", kernelFunc: kY }; +function NY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o; + Q([n], "conv3dBackpropInputV2"); + let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = w.computeConv3DInfo(p, s.shape, i, 1, a), m = new tt(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t8.data.get(n.dataId).values, [C, S, k, _] = u, E = t8.data.get(s.dataId).values, [R, D, P, O] = c, { batchSize: M, filterDepth: L, filterHeight: B, filterWidth: z, inChannels: U, inDepth: j, inHeight: q, inWidth: Y, outChannels: J, outDepth: re, outHeight: ne, outWidth: ee, strideDepth: oe, strideHeight: ie, strideWidth: le } = l, be = L - 1 - l.padInfo.front, _e = B - 1 - l.padInfo.top, ve = z - 1 - l.padInfo.left; for (let Fe = 0; Fe < M; ++Fe) for (let Pe = 0; Pe < U; ++Pe) for (let st = 0; st < j; ++st) { - let lt = st - ye, We = Math.max(0, Math.ceil(lt / oe)), mt = Math.min(re, (L + lt) / oe); - for (let it = 0; it < H; ++it) { - let ht = it - _e, gt = Math.max(0, Math.ceil(ht / ie)), Or = Math.min(ne, (B + ht) / ie); - for (let Mt = 0; Mt < X; ++Mt) { - let Qr = Mt - ve, or = Math.max(0, Math.ceil(Qr / le)), Tt = Math.min(ee, (z + Qr) / le), nr = 0; - for (let sr = We; sr < mt; ++sr) { - let Zr = sr * oe - lt; - for (let Jr = gt; Jr < Or; ++Jr) { - let fr = Jr * ie - ht; - for (let Fa = or; Fa < Tt; ++Fa) { - let Mo = Fa * le - Qr, Vs = w * Fe + S * sr + k * Jr + _ * Fa, Xt = R * (L - 1 - Zr) + D * (B - 1 - fr) + F * (z - 1 - Mo) + O * Pe; - for (let Pa = 0; Pa < J; ++Pa) { - let el = b[Vs + Pa], tl = E[Xt + Pa]; - nr += el * tl; + let lt = st - be, Ue = Math.max(0, Math.ceil(lt / oe)), mt = Math.min(re, (L + lt) / oe); + for (let it = 0; it < q; ++it) { + let gt = it - _e, xt = Math.max(0, Math.ceil(gt / ie)), Lr = Math.min(ne, (B + gt) / ie); + for (let Mt = 0; Mt < Y; ++Mt) { + let eo = Mt - ve, or = Math.max(0, Math.ceil(eo / le)), Tt = Math.min(ee, (z + eo) / le), nr = 0; + for (let sr = Ue; sr < mt; ++sr) { + let to = sr * oe - lt; + for (let ro = xt; ro < Lr; ++ro) { + let fr = ro * ie - gt; + for (let Ba = or; Ba < Tt; ++Ba) { + let Lo = Ba * le - eo, Hs = C * Fe + S * sr + k * ro + _ * Ba, Yt = R * (L - 1 - to) + D * (B - 1 - fr) + P * (z - 1 - Lo) + O * Pe; + for (let za = 0; za < J; ++za) { + let tl = b[Hs + za], rl = E[Yt + za]; + nr += tl * rl; } } } @@ -13778,470 +13827,497 @@ function J8(r) { } } } - return t10.makeTensorInfo(m.shape, m.dtype, m.values); + return t8.makeTensorInfo(m.shape, m.dtype, m.values); } -var Q_ = { kernelName: on, backendName: "cpu", kernelFunc: J8 }; -var eY = Ie(nn, (r) => Math.cos(r)); -var Z_ = { kernelName: nn, backendName: "cpu", kernelFunc: eY }; -var tY = Ie(sn, (r) => Math.cosh(r)); -var J_ = { kernelName: sn, backendName: "cpu", kernelFunc: tY }; -function rY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = me([f, h, g, d], "float32"), b = t10.data.get(s.dataId).values, w = t10.data.get(a.dataId).values, S = t10.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape); +var p$ = { kernelName: nn, backendName: "cpu", kernelFunc: NY }; +var TY = Ie(sn, (r) => Math.cos(r)); +var c$ = { kernelName: sn, backendName: "cpu", kernelFunc: TY }; +var _Y = Ie(an, (r) => Math.cosh(r)); +var l$ = { kernelName: an, backendName: "cpu", kernelFunc: _Y }; +function $Y(r) { + let { inputs: e, backend: t8, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = me([f, h, g, d], "float32"), b = t8.data.get(s.dataId).values, C = t8.data.get(a.dataId).values, S = t8.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape); for (let E = 0; E < f; E++) { - let R = E * 4, D = b[R], F = b[R + 1], O = b[R + 2], M = b[R + 3], L = w[E]; + let R = E * 4, D = b[R], P = b[R + 1], O = b[R + 2], M = b[R + 3], L = C[E]; if (L >= c) continue; - let B = h > 1 ? (O - D) * (l - 1) / (h - 1) : 0, z = g > 1 ? (M - F) * (m - 1) / (g - 1) : 0; + let B = h > 1 ? (O - D) * (l - 1) / (h - 1) : 0, z = g > 1 ? (M - P) * (m - 1) / (g - 1) : 0; for (let U = 0; U < h; U++) { let j = h > 1 ? D * (l - 1) + U * B : 0.5 * (D + O) * (l - 1); if (j < 0 || j > l - 1) { - for (let H = 0; H < g; H++) - for (let X = 0; X < d; X++) { - let J = X + H * _[2] + U * _[1] + E * _[0]; + for (let q = 0; q < g; q++) + for (let Y = 0; Y < d; Y++) { + let J = Y + q * _[2] + U * _[1] + E * _[0]; x.values[J] = u; } continue; } if (p === "bilinear") { - let H = Math.floor(j), X = Math.ceil(j), J = j - H; + let q = Math.floor(j), Y = Math.ceil(j), J = j - q; for (let re = 0; re < g; re++) { - let ne = g > 1 ? F * (m - 1) + re * z : 0.5 * (F + M) * (m - 1); + let ne = g > 1 ? P * (m - 1) + re * z : 0.5 * (P + M) * (m - 1); if (ne < 0 || ne > m - 1) { for (let le = 0; le < d; le++) { - let ye = le + re * _[2] + U * _[1] + E * _[0]; - x.values[ye] = u; + let be = le + re * _[2] + U * _[1] + E * _[0]; + x.values[be] = u; } continue; } let ee = Math.floor(ne), oe = Math.ceil(ne), ie = ne - ee; for (let le = 0; le < d; le++) { - let ye = le + ee * k[2] + H * k[1] + L * k[0], _e = S[ye]; - ye = le + oe * k[2] + H * k[1] + L * k[0]; - let ve = S[ye]; - ye = le + ee * k[2] + X * k[1] + L * k[0]; - let Fe = S[ye]; - ye = le + oe * k[2] + X * k[1] + L * k[0]; - let Pe = S[ye], st = _e + (ve - _e) * ie, lt = Fe + (Pe - Fe) * ie; - ye = le + re * _[2] + U * _[1] + E * _[0], x.values[ye] = st + (lt - st) * J; + let be = le + ee * k[2] + q * k[1] + L * k[0], _e = S[be]; + be = le + oe * k[2] + q * k[1] + L * k[0]; + let ve = S[be]; + be = le + ee * k[2] + Y * k[1] + L * k[0]; + let Fe = S[be]; + be = le + oe * k[2] + Y * k[1] + L * k[0]; + let Pe = S[be], st = _e + (ve - _e) * ie, lt = Fe + (Pe - Fe) * ie; + be = le + re * _[2] + U * _[1] + E * _[0], x.values[be] = st + (lt - st) * J; } } } else - for (let H = 0; H < g; ++H) { - let X = g > 1 ? F * (m - 1) + H * z : 0.5 * (F + M) * (m - 1); - if (X < 0 || X > m - 1) { + for (let q = 0; q < g; ++q) { + let Y = g > 1 ? P * (m - 1) + q * z : 0.5 * (P + M) * (m - 1); + if (Y < 0 || Y > m - 1) { for (let ne = 0; ne < d; ne++) { - let ee = ne + H * _[2] + U * _[1] + E * _[0]; + let ee = ne + q * _[2] + U * _[1] + E * _[0]; x.values[ee] = u; } continue; } - let J = Math.round(X), re = Math.round(j); + let J = Math.round(Y), re = Math.round(j); for (let ne = 0; ne < d; ne++) { - let ee = ne + J * k[2] + re * k[1] + L * k[0], oe = ne + H * _[2] + U * _[1] + E * _[0]; + let ee = ne + J * k[2] + re * k[1] + L * k[0], oe = ne + q * _[2] + U * _[1] + E * _[0]; x.values[oe] = S[ee]; } } } } - return t10.makeTensorInfo(x.shape, x.dtype, x.values); + return t8.makeTensorInfo(x.shape, x.dtype, x.values); } -var e$ = { kernelName: pn, backendName: "cpu", kernelFunc: rY }; -function oY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - Y(n, "cumprod"); - let p = C.getAxesPermutation([s], n.shape.length), u = n; - p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } })); - let c = C.getInnerMostAxes(1, n.shape.length)[0]; +var m$ = { kernelName: cn, backendName: "cpu", kernelFunc: $Y }; +function EY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + Q(n, "cumprod"); + let p = w.getAxesPermutation([s], n.shape.length), u = n; + p != null && (u = It({ inputs: { x: n }, backend: t8, attrs: { perm: p } })); + let c = w.getInnerMostAxes(1, n.shape.length)[0]; if (c !== u.shape.length - 1) throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`); - let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; + let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t8.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; for (let x = 0; x < d.length; x += f) for (let b = 0; b < f; b++) { - let w = h(x, b); + let C = h(x, b); if (b === 0) - m[w] = a ? 1 : d[w]; + m[C] = a ? 1 : d[C]; else { let S = h(x, b - 1); - m[w] = a ? d[S] * m[S] : d[w] * m[S]; + m[C] = a ? d[S] * m[S] : d[C] * m[S]; } } - let g = t10.makeTensorInfo(u.shape, l, m); + let g = t8.makeTensorInfo(u.shape, l, m); if (p != null) { - let x = C.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } }); - return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b; + let x = w.getUndoAxesPermutation(p), b = It({ inputs: { x: g }, backend: t8, attrs: { perm: x } }); + return t8.disposeIntermediateTensorInfo(g), t8.disposeIntermediateTensorInfo(u), b; } return g; } -var t$ = { kernelName: an, backendName: "cpu", kernelFunc: oY }; -function nY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - Y(n, "cumsum"); - let p = C.getAxesPermutation([s], n.shape.length), u = n; - p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } })); - let c = C.getInnerMostAxes(1, n.shape.length)[0]; +var d$ = { kernelName: un, backendName: "cpu", kernelFunc: EY }; +function RY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + Q(n, "cumsum"); + let p = w.getAxesPermutation([s], n.shape.length), u = n; + p != null && (u = It({ inputs: { x: n }, backend: t8, attrs: { perm: p } })); + let c = w.getInnerMostAxes(1, n.shape.length)[0]; if (c !== u.shape.length - 1) throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`); - let l = dt(u.dtype, "int32"), m = y.makeZerosTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; + let l = dt(u.dtype, "int32"), m = y.makeZerosTypedArray(y.sizeFromShape(u.shape), l), d = t8.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; for (let x = 0; x < d.length; x += f) for (let b = 0; b < f; b++) { - let w = h(x, b); + let C = h(x, b); if (b === 0) - m[w] = a ? 0 : d[w]; + m[C] = a ? 0 : d[C]; else { let S = h(x, b - 1); - m[w] = a ? d[S] + m[S] : d[w] + m[S]; + m[C] = a ? d[S] + m[S] : d[C] + m[S]; } } - let g = t10.makeTensorInfo(u.shape, l, m); + let g = t8.makeTensorInfo(u.shape, l, m); if (p != null) { - let x = C.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } }); - return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b; + let x = w.getUndoAxesPermutation(p), b = It({ inputs: { x: g }, backend: t8, attrs: { perm: x } }); + return t8.disposeIntermediateTensorInfo(g), t8.disposeIntermediateTensorInfo(u), b; } return g; } -var r$ = { kernelName: un, backendName: "cpu", kernelFunc: nY }; -function sY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; +var f$ = { kernelName: pn, backendName: "cpu", kernelFunc: RY }; +function DY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; if (n.shape.length === 1) { - let p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = yc(p, u, s.dtype, s.shape, a); - return t10.makeTensorInfo([a], s.dtype, c); + let p = t8.data.get(n.dataId).values, u = t8.data.get(s.dataId).values, c = bc(p, u, s.dtype, s.shape, a); + return t8.makeTensorInfo([a], s.dtype, c); } else if (n.shape.length === 2) { - let p = t10.bufferSync(n), u = t10.bufferSync(s), c = If(p, u, a, i); - return t10.makeTensorInfo(c.shape, s.dtype, c.values); + let p = t8.bufferSync(n), u = t8.bufferSync(s), c = vf(p, u, a, i); + return t8.makeTensorInfo(c.shape, s.dtype, c.values); } throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`); } -var o$ = { kernelName: Qs, backendName: "cpu", kernelFunc: sY }; -function aY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o; +var h$ = { kernelName: ta, backendName: "cpu", kernelFunc: DY }; +function AY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o; y.assert(a === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`); - let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t10.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0; + let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t8.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0; for (let x = 0; x < i; ++x) for (let b = 0; b < l; ++b) { - let w = Math.floor(b / s), S = b % s; + let C = Math.floor(b / s), S = b % s; for (let k = 0; k < m; ++k) { let _ = Math.floor(k / s), E = k % s, R = (S * s + E) * d; for (let D = 0; D < d; ++D) { - let O = D + R + c * (_ + u * (w + p * x)); + let O = D + R + c * (_ + u * (C + p * x)); h[g++] = f[O]; } } } - return t10.makeTensorInfo([i, l, m, d], n.dtype, h); + return t8.makeTensorInfo([i, l, m, d], n.dtype, h); } -var n$ = { kernelName: cn, backendName: "cpu", kernelFunc: aY }; -function rI(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o; - Y([n, s], "depthwiseConv2DNative"); +var g$ = { kernelName: ln, backendName: "cpu", kernelFunc: AY }; +function lI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o; + Q([n, s], "depthwiseConv2DNative"); let c = y.computeStrides(n.shape), l = y.computeStrides(s.shape), m = p; - m == null && (m = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`); - let d = C.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, w = b.left, S = b.top, k = d.outChannels / d.inChannels, _ = new tt(d.outShape, n.dtype), E = t10.data.get(n.dataId).values, R = t10.data.get(s.dataId).values, D = _.values; - for (let F = 0; F < d.batchSize; ++F) { - let O = F * c[0], M = F * _.strides[0]; + m == null && (m = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`); + let d = w.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, C = b.left, S = b.top, k = d.outChannels / d.inChannels, _ = new tt(d.outShape, n.dtype), E = t8.data.get(n.dataId).values, R = t8.data.get(s.dataId).values, D = _.values; + for (let P = 0; P < d.batchSize; ++P) { + let O = P * c[0], M = P * _.strides[0]; for (let L = 0; L < d.outHeight; ++L) { let B = M + L * _.strides[1], z = L * d.strideHeight - S; for (let U = 0; U < f; ++U) { let j = z + U * g; if (j < 0 || j >= d.inHeight) continue; - let H = U * l[0], X = O + j * c[1]; + let q = U * l[0], Y = O + j * c[1]; for (let J = 0; J < d.outWidth; ++J) { - let re = B + J * _.strides[2], ne = J * d.strideWidth - w; + let re = B + J * _.strides[2], ne = J * d.strideWidth - C; for (let ee = 0; ee < h; ++ee) { let oe = ne + ee * x; if (oe < 0 || oe >= d.inWidth) continue; - let ie = H + ee * l[1], le = X + oe * d.inChannels, ye = re, _e = ie; + let ie = q + ee * l[1], le = Y + oe * d.inChannels, be = re, _e = ie; for (let ve = 0; ve < d.inChannels; ++ve) { let Fe = E[le + ve]; for (let Pe = 0; Pe < k; ++Pe) - D[ye + Pe] += Fe * R[_e + Pe]; - ye += k, _e += k; + D[be + Pe] += Fe * R[_e + Pe]; + be += k, _e += k; } } } } } } - return t10.makeTensorInfo(_.shape, _.dtype, _.values); + return t8.makeTensorInfo(_.shape, _.dtype, _.values); } -var s$ = { kernelName: ln, backendName: "cpu", kernelFunc: rI }; -function iY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o; - Y([n, s], "depthwiseConv2dNativeBackpropFilter"); - let l = C.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new tt(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, w = l.outChannels / l.inChannels, S = t10.data.get(n.dataId).values, k = new tt(n.shape, n.dtype, S), _ = t10.data.get(s.dataId).values, E = new tt(s.shape, s.dtype, _); +var x$ = { kernelName: mn, backendName: "cpu", kernelFunc: lI }; +function FY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o; + Q([n, s], "depthwiseConv2dNativeBackpropFilter"); + let l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new tt(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, C = l.outChannels / l.inChannels, S = t8.data.get(n.dataId).values, k = new tt(n.shape, n.dtype, S), _ = t8.data.get(s.dataId).values, E = new tt(s.shape, s.dtype, _); for (let R = 0; R < f; ++R) { - let D = Math.max(0, Math.ceil((b - R) / m)), F = Math.min(l.outHeight, (l.inHeight + b - R) / m); + let D = Math.max(0, Math.ceil((b - R) / m)), P = Math.min(l.outHeight, (l.inHeight + b - R) / m); for (let O = 0; O < h; ++O) { let M = Math.max(0, Math.ceil((x - O) / d)), L = Math.min(l.outWidth, (l.inWidth + x - O) / d); for (let B = 0; B < l.outChannels; ++B) { - let z = Math.trunc(B / w), U = B % w, j = 0; - for (let H = 0; H < l.batchSize; ++H) - for (let X = D; X < F; ++X) { - let J = R + X * m - b; + let z = Math.trunc(B / C), U = B % C, j = 0; + for (let q = 0; q < l.batchSize; ++q) + for (let Y = D; Y < P; ++Y) { + let J = R + Y * m - b; for (let re = M; re < L; ++re) { let ne = O + re * d - x; - j += k.get(H, J, ne, z) * E.get(H, X, re, B); + j += k.get(q, J, ne, z) * E.get(q, Y, re, B); } } g.set(j, R, O, z, U); } } } - return t10.makeTensorInfo(g.shape, g.dtype, g.values); + return t8.makeTensorInfo(g.shape, g.dtype, g.values); } -var a$ = { kernelName: Ei, backendName: "cpu", kernelFunc: iY }; -function uY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o; - Y([n, s], "depthwiseConv2DNativeBackpropInput"); - let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = C.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new tt(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, w = t10.data.get(n.dataId).values, [S, k, _] = l, E = t10.data.get(s.dataId).values, [R, D, F] = m, { batchSize: O, filterHeight: M, filterWidth: L, inChannels: B, inHeight: z, inWidth: U, outChannels: j, outHeight: H, outWidth: X, strideHeight: J, strideWidth: re } = d, ne = M - 1 - d.padInfo.top, ee = L - 1 - d.padInfo.left, oe = j / B; +var y$ = { kernelName: Fi, backendName: "cpu", kernelFunc: FY }; +function PY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o; + Q([n, s], "depthwiseConv2DNativeBackpropInput"); + let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new tt(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, C = t8.data.get(n.dataId).values, [S, k, _] = l, E = t8.data.get(s.dataId).values, [R, D, P] = m, { batchSize: O, filterHeight: M, filterWidth: L, inChannels: B, inHeight: z, inWidth: U, outChannels: j, outHeight: q, outWidth: Y, strideHeight: J, strideWidth: re } = d, ne = M - 1 - d.padInfo.top, ee = L - 1 - d.padInfo.left, oe = j / B; for (let ie = 0; ie < O; ++ie) for (let le = 0; le < B; ++le) - for (let ye = 0; ye < z; ++ye) { - let _e = ye - ne, ve = Math.max(0, Math.ceil(_e / J)), Fe = Math.min(H, (M + _e) / J); + for (let be = 0; be < z; ++be) { + let _e = be - ne, ve = Math.max(0, Math.ceil(_e / J)), Fe = Math.min(q, (M + _e) / J); for (let Pe = 0; Pe < U; ++Pe) { - let st = Pe - ee, lt = Math.max(0, Math.ceil(st / re)), We = Math.min(X, (L + st) / re), mt = 0; + let st = Pe - ee, lt = Math.max(0, Math.ceil(st / re)), Ue = Math.min(Y, (L + st) / re), mt = 0; for (let it = ve; it < Fe; ++it) { - let ht = it * J - _e; - for (let gt = lt; gt < We; ++gt) { - let Or = gt * re - st, Mt = S * ie + k * it + _ * gt, Qr = R * (M - 1 - ht) + D * (L - 1 - Or) + F * le; + let gt = it * J - _e; + for (let xt = lt; xt < Ue; ++xt) { + let Lr = xt * re - st, Mt = S * ie + k * it + _ * xt, eo = R * (M - 1 - gt) + D * (L - 1 - Lr) + P * le; for (let or = 0; or < oe; ++or) { - let Tt = le * oe + or, nr = w[Mt + Tt], sr = E[Qr + or]; + let Tt = le * oe + or, nr = C[Mt + Tt], sr = E[eo + or]; mt += nr * sr; } } } - h[g * ie + x * ye + b * Pe + le] = mt; + h[g * ie + x * be + b * Pe + le] = mt; } } - return t10.makeTensorInfo(f.shape, f.dtype, f.values); + return t8.makeTensorInfo(f.shape, f.dtype, f.values); } -var i$ = { kernelName: Ri, backendName: "cpu", kernelFunc: uY }; -function pY(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t10.data.get(o.dataId).values, a = me([n, n], o.dtype), i = a.values; +var b$ = { kernelName: Pi, backendName: "cpu", kernelFunc: PY }; +function OY(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t8.data.get(o.dataId).values, a = me([n, n], o.dtype), i = a.values; for (let u = 0; u < s.length; u++) i[u * n + u] = s[u]; let p = [...o.shape, ...o.shape]; - return t10.makeTensorInfo(p, a.dtype, a.values); + return t8.makeTensorInfo(p, a.dtype, a.values); } -var u$ = { kernelName: Zs, backendName: "cpu", kernelFunc: pY }; -var p$ = { kernelName: mn, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => { - let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t10, p = e, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: w, strideHeight: S, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: D, outShape: F } = C.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = y.sizeFromShape(F), M = F.length, L = y.getArrayFromDType(o.dtype, O); +var C$ = { kernelName: ra, backendName: "cpu", kernelFunc: OY }; +var w$ = { kernelName: dn, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t8 }) => { + let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t8, p = e, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: C, strideHeight: S, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: D, outShape: P } = w.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = y.sizeFromShape(P), M = P.length, L = y.getArrayFromDType(o.dtype, O); for (let z = 0; z < d; ++z) for (let U = 0; U < x; ++U) { - let j = U * S - w.top; - for (let H = 0; H < b; ++H) { - let X = H * k - w.left; + let j = U * S - C.top; + for (let q = 0; q < b; ++q) { + let Y = q * k - C.left; for (let J = 0; J < g; ++J) { let re = Number.MIN_SAFE_INTEGER; for (let ee = 0; ee < _; ++ee) { let oe = j + ee * R; if (oe >= 0 && oe < f) for (let ie = 0; ie < E; ++ie) { - let le = X + ie * D; + let le = Y + ie * D; if (le >= 0 && le < h) { - let ye = y.locToIndex([z, oe, le, J], c, y.computeStrides(o.shape)), _e = y.locToIndex([ee, ie, J], m, y.computeStrides(n.shape)), ve = u[ye] + l[_e]; + let be = y.locToIndex([z, oe, le, J], c, y.computeStrides(o.shape)), _e = y.locToIndex([ee, ie, J], m, y.computeStrides(n.shape)), ve = u[be] + l[_e]; ve > re && (re = ve); } } } - let ne = y.locToIndex([z, U, H, J], M, y.computeStrides(F)); + let ne = y.locToIndex([z, U, q, J], M, y.computeStrides(P)); L[ne] = re; } } } - return { dataId: p.write(y.toTypedArray(L, o.dtype), F, o.dtype), shape: F, dtype: o.dtype }; + return { dataId: p.write(y.toTypedArray(L, o.dtype), P, o.dtype), shape: P, dtype: o.dtype }; } }; -var c$ = { kernelName: Ai, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => { - let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: w, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = C.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); - y.assert(s.rank === D.length, () => `Error in ${Ai}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); - let F = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(n.shape, n.dtype); +var S$ = { kernelName: Mi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t8 }) => { + let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t8, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); + y.assert(s.rank === D.length, () => `Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); + let P = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(n.shape, n.dtype); for (let L = 0; L < m; ++L) for (let B = 0; B < g; ++B) { - let z = B * w - b.top; + let z = B * C - b.top; for (let U = 0; U < x; ++U) { let j = U * S - b.left; - for (let H = 0; H < h; ++H) { - let X = Number.MIN_SAFE_INTEGER, J = 0, re = 0; + for (let q = 0; q < h; ++q) { + let Y = Number.MIN_SAFE_INTEGER, J = 0, re = 0; for (let ne = 0; ne < k; ++ne) { let ee = z + ne * E; if (ee >= 0 && ee < d) for (let oe = 0; oe < _; ++oe) { let ie = j + oe * R; if (ie >= 0 && ie < f) { - let le = c[L][ee][ie][H] + l[ne][oe][H]; - le > X && (X = le, J = ne, re = oe); + let le = c[L][ee][ie][q] + l[ne][oe][q]; + le > Y && (Y = le, J = ne, re = oe); } } } - O[J][re][H] += F[L][B][U][H]; + O[J][re][q] += P[L][B][U][q]; } } } return { dataId: u.write(y.toTypedArray(O, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype }; } }; -var l$ = { kernelName: Di, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => { - let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: w, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = C.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); - y.assert(s.rank === D.length, () => `Error in ${Di}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); - let F = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(o.shape, o.dtype); +var I$ = { kernelName: Oi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t8 }) => { + let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t8, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); + y.assert(s.rank === D.length, () => `Error in ${Oi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); + let P = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(o.shape, o.dtype); for (let L = 0; L < m; ++L) for (let B = 0; B < g; ++B) { - let z = B * w - b.top; + let z = B * C - b.top; for (let U = 0; U < x; ++U) { let j = U * S - b.left; - for (let H = 0; H < h; ++H) { - let X = Number.MIN_SAFE_INTEGER, J = z < 0 ? 0 : z, re = j < 0 ? 0 : j; + for (let q = 0; q < h; ++q) { + let Y = Number.MIN_SAFE_INTEGER, J = z < 0 ? 0 : z, re = j < 0 ? 0 : j; for (let ne = 0; ne < k; ++ne) { let ee = z + ne * E; if (ee >= 0 && ee < d) for (let oe = 0; oe < _; ++oe) { let ie = j + oe * R; if (ie >= 0 && ie < f) { - let le = c[L][ee][ie][H] + l[ne][oe][H]; - le > X && (X = le, J = ee, re = ie); + let le = c[L][ee][ie][q] + l[ne][oe][q]; + le > Y && (Y = le, J = ee, re = ie); } } } - O[L][J][re][H] += F[L][B][U][H]; + O[L][J][re][q] += P[L][B][U][q]; } } } return { dataId: u.write(y.toTypedArray(O, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -function li(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - Y(n, "sum"); +function MY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { image: n } = e, { canvas: s, options: a } = o, { contextOptions: i, imageOptions: p } = a || {}, u = (p == null ? void 0 : p.alpha) || 1, c = (i == null ? void 0 : i.contextType) || "2d"; + if (c !== "2d") + throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`); + let l = s.getContext(c, (i == null ? void 0 : i.contextAttributes) || {}); + if (l == null) + throw new Error(`Could not get the context with ${c} type.`); + let [m, d] = n.shape.slice(0, 2), f = n.shape.length === 2 ? 1 : n.shape[2], h = t8.data.get(n.dataId).values, g = n.dtype === "float32" ? 255 : 1, x = new Uint8ClampedArray(d * m * 4); + for (let C = 0; C < m * d; ++C) { + let S = [0, 0, 0, 255 * u]; + for (let _ = 0; _ < f; _++) { + let E = h[C * f + _]; + if (n.dtype === "float32") { + if (E < 0 || E > 1) + throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${E}.`); + } else if (n.dtype === "int32" && (E < 0 || E > 255)) + throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${E}.`); + f === 1 ? (S[0] = E * g, S[1] = E * g, S[2] = E * g) : S[_] = E * g; + } + let k = C * 4; + x[k + 0] = Math.round(S[0]), x[k + 1] = Math.round(S[1]), x[k + 2] = Math.round(S[2]), x[k + 3] = Math.round(S[3]); + } + s.width = d, s.height = m; + let b = new ImageData(x, d, m); + return l.putImageData(b, 0, 0), n; +} +var v$ = { kernelName: qm, backendName: "cpu", kernelFunc: MY }; +function fi(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + Q(n, "sum"); let i; - n.dtype === "bool" ? i = _o({ inputs: { x: n }, backend: t10, attrs: { dtype: "int32" } }) : i = lr({ inputs: { x: n }, backend: t10 }); - let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = C.getAxesPermutation(u, p), l = u, m = i; - c != null && (m = St({ inputs: { x: i }, backend: t10, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, p)), C.assertAxesAreInnerMostDims("sum", l, m.shape.length); - let [d, f] = C.computeOutAndReduceShapes(m.shape, l), h = C.upcastType(m.dtype, "int32"), g = gc(t10, d, h), x = y.sizeFromShape(f), b = t10.data.get(g.dataId).values, w = t10.data.get(m.dataId).values; + n.dtype === "bool" ? i = Ro({ inputs: { x: n }, backend: t8, attrs: { dtype: "int32" } }) : i = lr({ inputs: { x: n }, backend: t8 }); + let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = w.getAxesPermutation(u, p), l = u, m = i; + c != null && (m = It({ inputs: { x: i }, backend: t8, attrs: { perm: c } }), l = w.getInnerMostAxes(l.length, p)), w.assertAxesAreInnerMostDims("sum", l, m.shape.length); + let [d, f] = w.computeOutAndReduceShapes(m.shape, l), h = w.upcastType(m.dtype, "int32"), g = xc(t8, d, h), x = y.sizeFromShape(f), b = t8.data.get(g.dataId).values, C = t8.data.get(m.dataId).values; for (let S = 0; S < b.length; ++S) { let k = S * x, _ = 0; for (let E = 0; E < x; ++E) - _ += w[k + E]; + _ += C[k + E]; b[S] = _; } if (a) { - let S = C.expandShapeToKeepDim(g.shape, u), k = g; - g = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: S } }), t10.disposeIntermediateTensorInfo(k); + let S = w.expandShapeToKeepDim(g.shape, u), k = g; + g = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: S } }), t8.disposeIntermediateTensorInfo(k); } - return t10.disposeIntermediateTensorInfo(i), c != null && t10.disposeIntermediateTensorInfo(m), g; + return t8.disposeIntermediateTensorInfo(i), c != null && t8.disposeIntermediateTensorInfo(m), g; } -var m$ = { kernelName: ys, backendName: "cpu", kernelFunc: li }; -function cY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = C.decodeEinsumEquation(n, s.length); - C.checkEinsumDimSizes(a.length, p, s); - let { path: u, steps: c } = C.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; +var k$ = { kernelName: Ss, backendName: "cpu", kernelFunc: fi }; +function LY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); + w.checkEinsumDimSizes(a.length, p, s); + let { path: u, steps: c } = w.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { - let { permutationIndices: x, expandDims: b } = C.getEinsumPermutation(d, p[g]), w; - C.isIdentityPermutation(x) ? w = s[g] : (w = St({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(w)); - let S = w.shape.slice(); + let { permutationIndices: x, expandDims: b } = w.getEinsumPermutation(d, p[g]), C; + w.isIdentityPermutation(x) ? C = s[g] : (C = It({ inputs: { x: s[g] }, backend: t8, attrs: { perm: x } }), f.push(C)); + let S = C.shape.slice(); for (let k = 0; k < b.length; ++k) S.splice(b[k], 0, 1); - y.arraysEqual(w.shape, S) || (w = Ve({ inputs: { x: w }, backend: t10, attrs: { shape: S } }), f.push(w)), m === null ? m = w : (m = sp({ inputs: { a: w, b: m }, backend: t10 }), f.push(m)); + y.arraysEqual(C.shape, S) || (C = Ve({ inputs: { x: C }, backend: t8, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = pp({ inputs: { a: C, b: m }, backend: t8 }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = li({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = fi({ inputs: { x: m }, backend: t8, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) - h !== m && t10.disposeIntermediateTensorInfo(h); + h !== m && t8.disposeIntermediateTensorInfo(h); return m; } -var d$ = { kernelName: Fi, backendName: "cpu", kernelFunc: cY }; -function lY(r) { - let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e; - Y([o, n], "eluGrad"); - let s = new Float32Array(y.sizeFromShape(n.shape)), a = t10.data.get(n.dataId).values, i = t10.data.get(o.dataId).values; +var N$ = { kernelName: Li, backendName: "cpu", kernelFunc: LY }; +function BY(r) { + let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e; + Q([o, n], "eluGrad"); + let s = new Float32Array(y.sizeFromShape(n.shape)), a = t8.data.get(n.dataId).values, i = t8.data.get(o.dataId).values; for (let p = 0; p < a.length; ++p) { let u = a[p]; u >= 0 ? s[p] = i[p] : s[p] = i[p] * (u + 1); } - return t10.makeTensorInfo(n.shape, "float32", s); + return t8.makeTensorInfo(n.shape, "float32", s); } -var f$ = { kernelName: Va, backendName: "cpu", kernelFunc: lY }; -var mY = C.ERF_P; -var dY = C.ERF_A1; -var fY = C.ERF_A2; -var hY = C.ERF_A3; -var gY = C.ERF_A4; -var xY = C.ERF_A5; -var yY = Ie(Wa, (r) => { - let e = Math.sign(r), t10 = Math.abs(r), o = 1 / (1 + mY * t10); - return e * (1 - ((((xY * o + gY) * o + hY) * o + fY) * o + dY) * o * Math.exp(-t10 * t10)); +var T$ = { kernelName: qa, backendName: "cpu", kernelFunc: BY }; +var zY = w.ERF_P; +var VY = w.ERF_A1; +var WY = w.ERF_A2; +var UY = w.ERF_A3; +var GY = w.ERF_A4; +var HY = w.ERF_A5; +var KY = Ie(gn, (r) => { + let e = Math.sign(r), t8 = Math.abs(r), o = 1 / (1 + zY * t8); + return e * (1 - ((((HY * o + GY) * o + UY) * o + WY) * o + VY) * o * Math.exp(-t8 * t8)); }); -var h$ = { kernelName: Wa, backendName: "cpu", kernelFunc: yY }; -function vc(r) { - let { inputs: e, backend: t10, attrs: o } = r, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; - return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Ve({ inputs: { x: n }, backend: t10, attrs: { shape: i } }); +var _$ = { kernelName: gn, backendName: "cpu", kernelFunc: KY }; +function kc(r) { + let { inputs: e, backend: t8, attrs: o } = r, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; + return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Ve({ inputs: { x: n }, backend: t8, attrs: { shape: i } }); } -var g$ = { kernelName: Js, backendName: "cpu", kernelFunc: vc }; -var bY = ze((r, e) => r / e); -var zl = je(dn, bY); -var Vl = { kernelName: dn, backendName: "cpu", kernelFunc: zl }; -function Lf(r, e, t10) { - let o = r.shape, n = o[0], s = o[1], a = t10.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c); +var $$ = { kernelName: oa, backendName: "cpu", kernelFunc: kc }; +var qY = ze((r, e) => r / e); +var zl = Ye(fn, qY); +var Vl = { kernelName: fn, backendName: "cpu", kernelFunc: zl }; +function Bf(r, e, t8) { + let o = r.shape, n = o[0], s = o[1], a = t8.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c); for (let g = 0; g < n; g++) { - let x = Eo({ inputs: { x: i }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), b = Eo({ inputs: { x: p }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), w = Kt({ inputs: { real: x, imag: b }, backend: t10 }), { real: S, imag: k } = CY(w, e, t10), _ = C.mergeRealAndImagArrays(S, k); + let x = Ao({ inputs: { x: i }, backend: t8, attrs: { begin: [g, 0], size: [1, s] } }), b = Ao({ inputs: { x: p }, backend: t8, attrs: { begin: [g, 0], size: [1, s] } }), C = Kt({ inputs: { real: x, imag: b }, backend: t8 }), { real: S, imag: k } = jY(C, e, t8), _ = w.mergeRealAndImagArrays(S, k); for (let E = 0; E < s; E++) { - let R = C.getComplexWithIndex(_, E); + let R = w.getComplexWithIndex(_, E); l[g * s + E] = R.real, m[g * s + E] = R.imag; } - t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), t10.disposeIntermediateTensorInfo(w); + t8.disposeIntermediateTensorInfo(x), t8.disposeIntermediateTensorInfo(b), t8.disposeIntermediateTensorInfo(C); } - let d = t10.makeTensorInfo(u, "float32", l), f = t10.makeTensorInfo(u, "float32", m), h = Kt({ inputs: { real: d, imag: f }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), h; + let d = t8.makeTensorInfo(u, "float32", l), f = t8.makeTensorInfo(u, "float32", m), h = Kt({ inputs: { real: d, imag: f }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), h; } -function CY(r, e, t10) { - let o = y.sizeFromShape(r.shape), n = t10.data.get(r.dataId), s = t10.data.get(n.complexTensorInfos.real.dataId).values, a = t10.data.get(n.complexTensorInfos.imag.dataId).values; - if (wY(o)) { - let i = oI(s, a, o, e, t10), p = [r.shape[0], r.shape[1]]; +function jY(r, e, t8) { + let o = y.sizeFromShape(r.shape), n = t8.data.get(r.dataId), s = t8.data.get(n.complexTensorInfos.real.dataId).values, a = t8.data.get(n.complexTensorInfos.imag.dataId).values; + if (XY(o)) { + let i = mI(s, a, o, e, t8), p = [r.shape[0], r.shape[1]]; if (e) { - let u = t10.makeTensorInfo(p, "float32", i.real), c = t10.makeTensorInfo(p, "float32", i.imag), l = t10.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = lr({ inputs: { x: l }, backend: t10 }), d = Vl.kernelFunc({ inputs: { a: u, b: l }, backend: t10 }), f = Vl.kernelFunc({ inputs: { a: c, b: m }, backend: t10 }), h = t10.data.get(d.dataId).values, g = t10.data.get(f.dataId).values; - return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), { real: h, imag: g }; + let u = t8.makeTensorInfo(p, "float32", i.real), c = t8.makeTensorInfo(p, "float32", i.imag), l = t8.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = lr({ inputs: { x: l }, backend: t8 }), d = Vl.kernelFunc({ inputs: { a: u, b: l }, backend: t8 }), f = Vl.kernelFunc({ inputs: { a: c, b: m }, backend: t8 }), h = t8.data.get(d.dataId).values, g = t8.data.get(f.dataId).values; + return t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(c), t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), { real: h, imag: g }; } return i; } else { - let i = C.mergeRealAndImagArrays(s, a), p = SY(i, o, e); - return C.splitRealAndImagArrays(p); + let i = w.mergeRealAndImagArrays(s, a), p = YY(i, o, e); + return w.splitRealAndImagArrays(p); } } -function wY(r) { +function XY(r) { return (r & r - 1) === 0; } -function oI(r, e, t10, o, n) { - if (t10 === 1) +function mI(r, e, t8, o, n) { + if (t8 === 1) return { real: r, imag: e }; - let s = C.mergeRealAndImagArrays(r, e), a = t10 / 2, i = C.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Kt({ inputs: { real: l, imag: m }, backend: n }), f = C.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), w = n.makeTensorInfo(x, "float32", g), S = Kt({ inputs: { real: b, imag: w }, backend: n }), k = oI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], D = n.makeTensorInfo(R, "float32", _), F = n.makeTensorInfo(R, "float32", E), O = Kt({ inputs: { real: D, imag: F }, backend: n }), M = oI(h, g, a, o, n), L = M.real, B = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), j = n.makeTensorInfo(z, "float32", B), H = Kt({ inputs: { real: U, imag: j }, backend: n }), X = C.exponents(t10, o), J = [X.real.length], re = n.makeTensorInfo(J, "float32", X.real), ne = n.makeTensorInfo(J, "float32", X.imag), ee = Kt({ inputs: { real: re, imag: ne }, backend: n }), oe = sp({ inputs: { a: ee, b: H }, backend: n }), ie = _a({ inputs: { a: O, b: oe }, backend: n }), le = Ll({ inputs: { a: O, b: oe }, backend: n }), ye = To({ inputs: { input: ie }, backend: n }), _e = To({ inputs: { input: le }, backend: n }), ve = $a({ inputs: { input: ie }, backend: n }), Fe = $a({ inputs: { input: le }, backend: n }), Pe = mu({ inputs: [ye, _e], backend: n, attrs: { axis: 0 } }), st = mu({ inputs: [ve, Fe], backend: n, attrs: { axis: 0 } }), lt = n.data.get(Pe.dataId).values, We = n.data.get(st.dataId).values; - return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(w), n.disposeIntermediateTensorInfo(S), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(F), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(j), n.disposeIntermediateTensorInfo(H), n.disposeIntermediateTensorInfo(re), n.disposeIntermediateTensorInfo(ne), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(le), n.disposeIntermediateTensorInfo(ye), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(_e), n.disposeIntermediateTensorInfo(Fe), n.disposeIntermediateTensorInfo(Pe), n.disposeIntermediateTensorInfo(st), { real: lt, imag: We }; + let s = w.mergeRealAndImagArrays(r, e), a = t8 / 2, i = w.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Kt({ inputs: { real: l, imag: m }, backend: n }), f = w.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), S = Kt({ inputs: { real: b, imag: C }, backend: n }), k = mI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], D = n.makeTensorInfo(R, "float32", _), P = n.makeTensorInfo(R, "float32", E), O = Kt({ inputs: { real: D, imag: P }, backend: n }), M = mI(h, g, a, o, n), L = M.real, B = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), j = n.makeTensorInfo(z, "float32", B), q = Kt({ inputs: { real: U, imag: j }, backend: n }), Y = w.exponents(t8, o), J = [Y.real.length], re = n.makeTensorInfo(J, "float32", Y.real), ne = n.makeTensorInfo(J, "float32", Y.imag), ee = Kt({ inputs: { real: re, imag: ne }, backend: n }), oe = pp({ inputs: { a: ee, b: q }, backend: n }), ie = Aa({ inputs: { a: O, b: oe }, backend: n }), le = Ll({ inputs: { a: O, b: oe }, backend: n }), be = Eo({ inputs: { input: ie }, backend: n }), _e = Eo({ inputs: { input: le }, backend: n }), ve = Fa({ inputs: { input: ie }, backend: n }), Fe = Fa({ inputs: { input: le }, backend: n }), Pe = gu({ inputs: [be, _e], backend: n, attrs: { axis: 0 } }), st = gu({ inputs: [ve, Fe], backend: n, attrs: { axis: 0 } }), lt = n.data.get(Pe.dataId).values, Ue = n.data.get(st.dataId).values; + return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(S), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(j), n.disposeIntermediateTensorInfo(q), n.disposeIntermediateTensorInfo(re), n.disposeIntermediateTensorInfo(ne), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(le), n.disposeIntermediateTensorInfo(be), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(_e), n.disposeIntermediateTensorInfo(Fe), n.disposeIntermediateTensorInfo(Pe), n.disposeIntermediateTensorInfo(st), { real: lt, imag: Ue }; } -function SY(r, e, t10) { +function YY(r, e, t8) { let o = new Float32Array(e * 2); for (let n = 0; n < e; n++) { let s = 0, a = 0; for (let i = 0; i < e; i++) { - let p = C.exponent(n * i, e, t10), u = C.getComplexWithIndex(r, i); + let p = w.exponent(n * i, e, t8), u = w.getComplexWithIndex(r, i); s += u.real * p.real - u.imag * p.imag, a += u.real * p.imag + u.imag * p.real; } - t10 && (s /= e, a /= e), C.assignToTypedArray(o, s, a, n); + t8 && (s /= e, a /= e), w.assignToTypedArray(o, s, a, n); } return o; } -function IY(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Lf(i, false, t10), u = Ve({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } }); - return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u; +function QY(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: t8, attrs: { shape: [a, s] } }), p = Bf(i, false, t8), u = Ve({ inputs: { x: p }, backend: t8, attrs: { shape: o.shape } }); + return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(p), u; } -var x$ = { kernelName: Pi, backendName: "cpu", kernelFunc: IY }; +var E$ = { kernelName: Bi, backendName: "cpu", kernelFunc: QY }; function Wl(r) { - let { backend: e, attrs: t10 } = r, { shape: o, value: n, dtype: s } = t10, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); - return vY(i, n, a), e.makeTensorInfo(o, a, i); + let { backend: e, attrs: t8 } = r, { shape: o, value: n, dtype: s } = t8, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); + return ZY(i, n, a), e.makeTensorInfo(o, a, i); } -var y$ = { kernelName: ea, backendName: "cpu", kernelFunc: Wl }; -function vY(r, e, t10) { +var R$ = { kernelName: na, backendName: "cpu", kernelFunc: Wl }; +function ZY(r, e, t8) { r.fill(e); } -var b$ = { kernelName: yn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { image: o } = r, n = t10, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values; +var D$ = { kernelName: Cn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { image: o } = r, n = t8, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values; for (let m = 0; m < a; m++) { let d = m * p * i * u; for (let f = 0; f < i; f++) { @@ -14249,9 +14325,9 @@ var b$ = { kernelName: yn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: for (let g = 0; g < p; g++) { let x = g * u; for (let b = 0; b < u; b++) { - let w = Math.round(p - g - 1), S = d + h + x + b, k = c[S]; - if (w >= 0 && w < p) { - let _ = w * u, E = d + h + _ + b; + let C = Math.round(p - g - 1), S = d + h + x + b, k = c[S]; + if (C >= 0 && C < p) { + let _ = C * u, E = d + h + _ + b; k = c[E]; } s[S] = k; @@ -14261,115 +14337,115 @@ var b$ = { kernelName: yn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: } return { dataId: n.write(s, o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -function kY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = tI({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); +function JY(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = cI({ inputs: { x: n, filter: s }, backend: t8, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); if (a) { let g = h; if (c === "NCHW" && a.shape.length === 1 && a.shape[0] !== 1) { - let x = Ve({ inputs: { x: a }, backend: t10, attrs: { shape: [a.shape[0], 1, 1] } }); - h = _a({ inputs: { a: h, b: x }, backend: t10 }), t10.disposeIntermediateTensorInfo(x); + let x = Ve({ inputs: { x: a }, backend: t8, attrs: { shape: [a.shape[0], 1, 1] } }); + h = Aa({ inputs: { a: h, b: x }, backend: t8 }), t8.disposeIntermediateTensorInfo(x); } else - h = _a({ inputs: { a: h, b: a }, backend: t10 }); - t10.disposeIntermediateTensorInfo(g); + h = Aa({ inputs: { a: h, b: a }, backend: t8 }); + t8.disposeIntermediateTensorInfo(g); } if (d) { let g = h; if (c === "NCHW" && d === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) { - let x = Ve({ inputs: { x: i }, backend: t10, attrs: { shape: [i.shape[0], 1, 1] } }); - h = mp(t10, h, d, x, f), t10.disposeIntermediateTensorInfo(x); + let x = Ve({ inputs: { x: i }, backend: t8, attrs: { shape: [i.shape[0], 1, 1] } }); + h = gp(t8, h, d, x, f), t8.disposeIntermediateTensorInfo(x); } else - h = mp(t10, h, d, i, f); - t10.disposeIntermediateTensorInfo(g); + h = gp(t8, h, d, i, f); + t8.disposeIntermediateTensorInfo(g); } return h; } -var C$ = { kernelName: Co, backendName: "cpu", kernelFunc: kY }; -function NY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = rI({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); +var A$ = { kernelName: Io, backendName: "cpu", kernelFunc: JY }; +function e7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = lI({ inputs: { x: n, filter: s }, backend: t8, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); if (a) { let g = h; - h = _a({ inputs: { a: h, b: a }, backend: t10 }), t10.disposeIntermediateTensorInfo(g); + h = Aa({ inputs: { a: h, b: a }, backend: t8 }), t8.disposeIntermediateTensorInfo(g); } if (d) { let g = h; - h = mp(t10, h, d, i, f), t10.disposeIntermediateTensorInfo(g); + h = gp(t8, h, d, i, f), t8.disposeIntermediateTensorInfo(g); } return h; } -var w$ = { kernelName: wo, backendName: "cpu", kernelFunc: NY }; -function TY(r) { - let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = C.prepareAndValidate(o, n); +var F$ = { kernelName: vo, backendName: "cpu", kernelFunc: e7 }; +function t7(r) { + let { inputs: e, backend: t8 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = w.prepareAndValidate(o, n); if (u === 0) - return t10.makeTensorInfo(p, o.dtype, []); - let m = t10.data.get(n.dataId).values, d = t10.bufferSync(o), f = vf(m, d, o.dtype, u, i, c, l, o.shape, s); - return t10.makeTensorInfo(p, o.dtype, f.values); + return t8.makeTensorInfo(p, o.dtype, []); + let m = t8.data.get(n.dataId).values, d = t8.bufferSync(o), f = kf(m, d, o.dtype, u, i, c, l, o.shape, s); + return t8.makeTensorInfo(p, o.dtype, f.values); } -var S$ = { kernelName: Sn, backendName: "cpu", kernelFunc: TY }; -function _Y(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o; - Y([n, s], "gatherV2"); - let p = y.parseAxisParam(a, n.shape)[0], u = t10.data.get(s.dataId).values, c = n.shape[p]; +var P$ = { kernelName: vn, backendName: "cpu", kernelFunc: t7 }; +function r7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o; + Q([n, s], "gatherV2"); + let p = y.parseAxisParam(a, n.shape)[0], u = t8.data.get(s.dataId).values, c = n.shape[p]; for (let S = 0; S < u.length; ++S) { let k = u[S]; y.assert(k <= c - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${c - 1}]`); } let l = i; i == null && (l = 0); - let m = y.sizeFromShape(s.shape), d = C.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Ve({ inputs: { x: s }, backend: t10, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t10.bufferSync(h), b = t10.bufferSync(f), w = kf(b, x, g); - return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), t10.makeTensorInfo(d.outputShape, w.dtype, w.values); + let m = y.sizeFromShape(s.shape), d = w.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Ve({ inputs: { x: n }, backend: t8, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Ve({ inputs: { x: s }, backend: t8, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t8.bufferSync(h), b = t8.bufferSync(f), C = Nf(b, x, g); + return t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), t8.makeTensorInfo(d.outputShape, C.dtype, C.values); } -var I$ = { kernelName: ta, backendName: "cpu", kernelFunc: _Y }; -function $Y(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Lf(i, true, t10), u = Ve({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } }); - return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u; +var O$ = { kernelName: sa, backendName: "cpu", kernelFunc: r7 }; +function o7(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: t8, attrs: { shape: [a, s] } }), p = Bf(i, true, t8), u = Ve({ inputs: { x: p }, backend: t8, attrs: { shape: o.shape } }); + return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(p), u; } -var v$ = { kernelName: Oi, backendName: "cpu", kernelFunc: $Y }; -var EY = Ie(kn, (r) => Number.isFinite(r) ? 1 : 0, "bool"); -var k$ = { kernelName: kn, backendName: "cpu", kernelFunc: EY }; -var RY = Ie(Nn, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool"); -var N$ = { kernelName: Nn, backendName: "cpu", kernelFunc: RY }; -var DY = Ie(Tn, (r) => Number.isNaN(r) ? 1 : 0, "bool"); -var T$ = { kernelName: Tn, backendName: "cpu", kernelFunc: DY }; -function AY(r) { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = Nf(o, n, s); +var M$ = { kernelName: zi, backendName: "cpu", kernelFunc: o7 }; +var n7 = Ie(Tn, (r) => Number.isFinite(r) ? 1 : 0, "bool"); +var L$ = { kernelName: Tn, backendName: "cpu", kernelFunc: n7 }; +var s7 = Ie(_n, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool"); +var B$ = { kernelName: _n, backendName: "cpu", kernelFunc: s7 }; +var a7 = Ie($n, (r) => Number.isNaN(r) ? 1 : 0, "bool"); +var z$ = { kernelName: $n, backendName: "cpu", kernelFunc: a7 }; +function i7(r) { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, num: s } = t8, a = Tf(o, n, s); return e.makeTensorInfo([a.length], "float32", a); } -var _$ = { kernelName: Rn, backendName: "cpu", kernelFunc: AY }; -var FY = Ie(An, (r) => Math.log1p(r)); -var $$ = { kernelName: An, backendName: "cpu", kernelFunc: FY }; -var PY = ze((r, e) => r && e); -var OY = je(Fn, PY, null, "bool"); -var E$ = { kernelName: Fn, backendName: "cpu", kernelFunc: OY }; -var MY = Ie(Pn, (r) => r ? 0 : 1, "bool"); -var R$ = { kernelName: Pn, backendName: "cpu", kernelFunc: MY }; -var LY = ze((r, e) => r || e); -var BY = je(On, LY, null, "bool"); -var D$ = { kernelName: On, backendName: "cpu", kernelFunc: BY }; -function zY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; - Y(n, "LRN"); - let u = n.shape[3], c = u - 1, l = t10.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m); +var V$ = { kernelName: An, backendName: "cpu", kernelFunc: i7 }; +var u7 = Ie(Pn, (r) => Math.log1p(r)); +var W$ = { kernelName: Pn, backendName: "cpu", kernelFunc: u7 }; +var p7 = ze((r, e) => r && e); +var c7 = Ye(On, p7, null, "bool"); +var U$ = { kernelName: On, backendName: "cpu", kernelFunc: c7 }; +var l7 = Ie(Mn, (r) => r ? 0 : 1, "bool"); +var G$ = { kernelName: Mn, backendName: "cpu", kernelFunc: l7 }; +var m7 = ze((r, e) => r || e); +var d7 = Ye(Ln, m7, null, "bool"); +var H$ = { kernelName: Ln, backendName: "cpu", kernelFunc: d7 }; +function f7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; + Q(n, "LRN"); + let u = n.shape[3], c = u - 1, l = t8.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m); function f(h) { - let g = h % u, x = h - g + Math.max(0, g - s), b = h - g + Math.min(g + s, c), w = 0; + let g = h % u, x = h - g + Math.max(0, g - s), b = h - g + Math.min(g + s, c), C = 0; for (; x <= b; x++) { let S = l[x]; - w += S * S; + C += S * S; } - return w; + return C; } for (let h = 0; h < m; h++) { let g = f(h), x = l[h] * Math.pow(a + i * g, -p); d[h] = x; } - return t10.makeTensorInfo(n.shape, n.dtype, d); + return t8.makeTensorInfo(n.shape, n.dtype, d); } -var A$ = { kernelName: Mn, backendName: "cpu", kernelFunc: zY }; -function VY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; - Y(a, "LRNGrad"); - let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t10.data.get(a.dataId).values, f = t10.data.get(n.dataId).values, h = t10.data.get(s.dataId).values, g = new Float32Array(l), x = l; +var K$ = { kernelName: Bn, backendName: "cpu", kernelFunc: f7 }; +function h7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; + Q(a, "LRNGrad"); + let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t8.data.get(a.dataId).values, f = t8.data.get(n.dataId).values, h = t8.data.get(s.dataId).values, g = new Float32Array(l), x = l; for (let b = 0; b < x; b++) { - let w = b % m, S = b - w + Math.max(0, w - i), k = b - w + Math.min(m, w + i + 1), _ = 0; + let C = b % m, S = b - C + Math.max(0, C - i), k = b - C + Math.min(m, C + i + 1), _ = 0; for (let E = S; E < k; E++) _ += Math.pow(f[E], 2); _ = u * _ + p; @@ -14378,363 +14454,363 @@ function VY(r) { b === E && (R += Math.pow(_, -c)), R *= d[b], g[E] += R; } } - return t10.makeTensorInfo(a.shape, n.dtype, g); + return t8.makeTensorInfo(a.shape, n.dtype, g); } -var F$ = { kernelName: Ua, backendName: "cpu", kernelFunc: VY }; -function nI(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t10, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = C.getAxesPermutation(l, u), d = i.data.get(n.dataId).values; +var q$ = { kernelName: ja, backendName: "cpu", kernelFunc: h7 }; +function dI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t8, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = w.getAxesPermutation(l, u), d = i.data.get(n.dataId).values; if (m != null) { let S = new Array(u); for (let k = 0; k < S.length; k++) S[k] = p[m[k]]; - d = bc(d, p, n.dtype, m, S), l = C.getInnerMostAxes(l.length, u), p = S; + d = Cc(d, p, n.dtype, m, S), l = w.getInnerMostAxes(l.length, u), p = S; } - Y(n, "max"), C.assertAxesAreInnerMostDims("max", l, u); - let [f, h] = C.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = Tf(d, g, f, n.dtype), b = i.write(x, f, n.dtype), w = f; - return a && (w = C.expandShapeToKeepDim(f, c)), { dataId: b, shape: w, dtype: n.dtype }; + Q(n, "max"), w.assertAxesAreInnerMostDims("max", l, u); + let [f, h] = w.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = _f(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f; + return a && (C = w.expandShapeToKeepDim(f, c)), { dataId: b, shape: C, dtype: n.dtype }; } -var P$ = { kernelName: Ln, backendName: "cpu", kernelFunc: nI }; -function WY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; - Y(n, "maxPool"); +var j$ = { kernelName: zn, backendName: "cpu", kernelFunc: dI }; +function g7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e; + Q(n, "maxPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; - y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); - let c = C.computePool2DInfo(n.shape, s, a, u, i, p), l; + y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); + let c = w.computePool2DInfo(n.shape, s, a, u, i, p), l; if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - l = lr({ inputs: { x: n }, backend: t10 }); + l = lr({ inputs: { x: n }, backend: t8 }); else { - let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Ic(m, n.shape, n.dtype, d, c, "max"); - l = t10.makeTensorInfo(c.outShape, n.dtype, f.values); + let m = t8.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = vc(m, n.shape, n.dtype, d, c, "max"); + l = t8.makeTensorInfo(c.outShape, n.dtype, f.values); } return l; } -var O$ = { kernelName: zn, backendName: "cpu", kernelFunc: WY }; -function UY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; - Y(n, "maxPool3d"); - let c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = Mf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max"); - return t10.makeTensorInfo(m.shape, "float32", m.values); +var X$ = { kernelName: Wn, backendName: "cpu", kernelFunc: g7 }; +function x7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; + Q(n, "maxPool3d"); + let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.data.get(n.dataId).values, m = Lf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max"); + return t8.makeTensorInfo(m.shape, "float32", m.values); } -var M$ = { kernelName: ra, backendName: "cpu", kernelFunc: UY }; -function GY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; - Y([n, s], "maxPool3DGrad"); - let c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.bufferSync(s), m = A_(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, w = c.effectiveFilterDepth, S = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = w - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, R = S - 1 - c.padInfo.top, D = me(s.shape, "float32"), F = t10.bufferSync(n); +var Y$ = { kernelName: aa, backendName: "cpu", kernelFunc: x7 }; +function y7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; + Q([n, s], "maxPool3DGrad"); + let c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = t8.bufferSync(s), m = H_(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, S = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, R = S - 1 - c.padInfo.top, D = me(s.shape, "float32"), P = t8.bufferSync(n); for (let O = 0; O < c.batchSize; ++O) for (let M = 0; M < c.inChannels; ++M) for (let L = 0; L < c.inDepth; ++L) for (let B = 0; B < c.inHeight; ++B) for (let z = 0; z < c.inWidth; ++z) { - let U = L - _, j = B - R, H = z - E, X = 0; - for (let J = 0; J < w; J += g) { + let U = L - _, j = B - R, q = z - E, Y = 0; + for (let J = 0; J < C; J += g) { let re = (U + J) / d; if (!(re < 0 || re >= c.outDepth || Math.floor(re) !== re)) for (let ne = 0; ne < S; ne += x) { let ee = (j + ne) / f; if (!(ee < 0 || ee >= c.outHeight || Math.floor(ee) !== ee)) for (let oe = 0; oe < k; oe += b) { - let ie = (H + oe) / h; + let ie = (q + oe) / h; if (ie < 0 || ie >= c.outWidth || Math.floor(ie) !== ie) continue; - let le = w * S * k - 1 - m.get(O, re, ee, ie, M), ye = J * S * k + ne * k + oe, _e = le === ye ? 1 : 0; + let le = C * S * k - 1 - m.get(O, re, ee, ie, M), be = J * S * k + ne * k + oe, _e = le === be ? 1 : 0; if (_e === 0) continue; - let ve = F.get(O, re, ee, ie, M); - X += ve * _e; + let ve = P.get(O, re, ee, ie, M); + Y += ve * _e; } } } - D.set(X, O, L, B, z, M); + D.set(Y, O, L, B, z, M); } - return t10.makeTensorInfo(D.shape, D.dtype, D.values); + return t8.makeTensorInfo(D.shape, D.dtype, D.values); } -var L$ = { kernelName: Li, backendName: "cpu", kernelFunc: GY }; -function HY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; - Y([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = C.computePool2DInfo(i.shape, p, u, 1, c, l), d = t10.data.get(i.dataId).values, f = me(m.outShape, i.dtype, Of(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, w = m.effectiveFilterHeight, S = m.effectiveFilterWidth, k = S - 1 - m.padInfo.left, _ = w - 1 - m.padInfo.top, E = me(i.shape, "float32"), R = t10.data.get(n.dataId).values, D = me(n.shape, "float32", R); - for (let F = 0; F < m.batchSize; ++F) +var Q$ = { kernelName: Ui, backendName: "cpu", kernelFunc: y7 }; +function b7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; + Q([s, a], "maxPoolGrad"); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = t8.data.get(i.dataId).values, f = me(m.outShape, i.dtype, Mf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, S = m.effectiveFilterWidth, k = S - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = me(i.shape, "float32"), R = t8.data.get(n.dataId).values, D = me(n.shape, "float32", R); + for (let P = 0; P < m.batchSize; ++P) for (let O = 0; O < m.inChannels; ++O) for (let M = 0; M < m.inHeight; ++M) for (let L = 0; L < m.inWidth; ++L) { let B = M - _, z = L - k, U = 0; - for (let j = 0; j < w; j += x) { - let H = (B + j) / h; - if (!(H < 0 || H >= m.outHeight || Math.floor(H) !== H)) - for (let X = 0; X < S; X += b) { - let J = (z + X) / g; + for (let j = 0; j < C; j += x) { + let q = (B + j) / h; + if (!(q < 0 || q >= m.outHeight || Math.floor(q) !== q)) + for (let Y = 0; Y < S; Y += b) { + let J = (z + Y) / g; if (J < 0 || J >= m.outWidth || Math.floor(J) !== J) continue; - let re = w * S - 1 - f.get(F, H, J, O), ne = j * S + X, ee = re === ne ? 1 : 0; + let re = C * S - 1 - f.get(P, q, J, O), ne = j * S + Y, ee = re === ne ? 1 : 0; if (ee === 0) continue; - let oe = D.get(F, H, J, O); + let oe = D.get(P, q, J, O); U += oe * ee; } } - E.set(U, F, M, L, O); + E.set(U, P, M, L, O); } - return t10.makeTensorInfo(E.shape, E.dtype, E.values); + return t8.makeTensorInfo(E.shape, E.dtype, E.values); } -var B$ = { kernelName: Hp, backendName: "cpu", kernelFunc: HY }; -function z$(r, e, t10, o, n) { - let s = y.computeStrides(e), a = Ic(r, e, t10, s, n, "max"), i = Of(r, e, t10, n, true, o); +var Z$ = { kernelName: Wi, backendName: "cpu", kernelFunc: b7 }; +function J$(r, e, t8, o, n) { + let s = y.computeStrides(e), a = vc(r, e, t8, s, n, "max"), i = Mf(r, e, t8, n, true, o); return [a.values, i.values]; } -var V$ = { kernelName: Bi, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10; - Y(o, "MaxPoolWithArgmax"); - let u = p.data.get(o.dataId).values, c = C.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = z$(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype); +var eE = { kernelName: ia, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t8; + Q(o, "MaxPoolWithArgmax"); + let u = p.data.get(o.dataId).values, c = w.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = J$(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype); return [{ dataId: d, shape: c.outShape, dtype: o.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }]; } }; -function KY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = C.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t10.makeTensorInfo([], "float32", new Float32Array([c])); +function C7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = w.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t8.makeTensorInfo([], "float32", new Float32Array([c])); l.push(m); - let d = _o({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }); + let d = Ro({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }); l.push(d); - let f = zl({ inputs: { a: d, b: m }, backend: t10 }); + let f = zl({ inputs: { a: d, b: m }, backend: t8 }); l.push(f); - let h = li({ inputs: { x: f }, backend: t10, attrs: { axis: s, keepDims: a } }); - return l.forEach((g) => t10.disposeIntermediateTensorInfo(g)), h; + let h = fi({ inputs: { x: f }, backend: t8, attrs: { axis: s, keepDims: a } }); + return l.forEach((g) => t8.disposeIntermediateTensorInfo(g)), h; } -var W$ = { kernelName: Vn, backendName: "cpu", kernelFunc: KY }; -function qY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - Y(n, "min"); - let i = y.parseAxisParam(s, n.shape), p = i, u = C.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = C.getInnerMostAxes(p.length, n.shape.length)), C.assertAxesAreInnerMostDims("min", p, c.shape.length); - let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values; +var tE = { kernelName: Un, backendName: "cpu", kernelFunc: C7 }; +function w7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + Q(n, "min"); + let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n; + u != null && (c = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", p, c.shape.length); + let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t8.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { - let b = x * d, w = h[b]; + let b = x * d, C = h[b]; for (let S = 0; S < d; ++S) { let k = h[b + S]; - (Number.isNaN(k) || k < w) && (w = k); + (Number.isNaN(k) || k < C) && (C = k); } - f[x] = w; + f[x] = C; } - u != null && t10.disposeIntermediateTensorInfo(c); - let g = t10.makeTensorInfo(l, c.dtype, f); + u != null && t8.disposeIntermediateTensorInfo(c); + let g = t8.makeTensorInfo(l, c.dtype, f); if (a) { - let x = C.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: x } }); - return t10.disposeIntermediateTensorInfo(g), b; + let x = w.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: x } }); + return t8.disposeIntermediateTensorInfo(g), b; } return g; } -var U$ = { kernelName: Wn, backendName: "cpu", kernelFunc: qY }; -function jY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o; - Y(n, "mirrorPad"); - let i = s.map((w, S) => w[0] + n.shape[S] + w[1]), p = s.map((w) => w[0]), u = s.map((w, S) => w[0] + n.shape[S]), c = a === "reflect" ? 0 : 1, l = t10.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f); - for (let w = 0; w < f; w++) { - let S = y.indexToLoc(w, h, g); +var rE = { kernelName: Gn, backendName: "cpu", kernelFunc: w7 }; +function S7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o; + Q(n, "mirrorPad"); + let i = s.map((C, S) => C[0] + n.shape[S] + C[1]), p = s.map((C) => C[0]), u = s.map((C, S) => C[0] + n.shape[S]), c = a === "reflect" ? 0 : 1, l = t8.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f); + for (let C = 0; C < f; C++) { + let S = y.indexToLoc(C, h, g); for (let _ = 0; _ < h; _++) S[_] < p[_] ? S[_] = p[_] * 2 - S[_] - c : S[_] >= u[_] && (S[_] = (u[_] - 1) * 2 - S[_] + c); S = S.map((_, E) => _ - p[E]); let k = y.locToIndex(S, m, d); - x[w] = l[k]; + x[C] = l[k]; } - return { dataId: t10.write(x, i, n.dtype), shape: i, dtype: n.dtype }; + return { dataId: t8.write(x, i, n.dtype), shape: i, dtype: n.dtype }; } -var G$ = { kernelName: Gn, backendName: "cpu", kernelFunc: jY }; -var XY = ze((r, e) => { - let t10 = r % e; - return r < 0 && e < 0 || r >= 0 && e >= 0 ? t10 : (t10 + e) % e; +var oE = { kernelName: Kn, backendName: "cpu", kernelFunc: S7 }; +var I7 = ze((r, e) => { + let t8 = r % e; + return r < 0 && e < 0 || r >= 0 && e >= 0 ? t8 : (t8 + e) % e; }); -var YY = je(Ga, XY); -var H$ = { kernelName: Ga, backendName: "cpu", kernelFunc: YY }; -var q$ = Bp(Rw()); -function sI(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s; +var v7 = Ye(qn, I7); +var nE = { kernelName: qn, backendName: "cpu", kernelFunc: v7 }; +var aE = Up(zw()); +function fI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s; if (i === -1 && (i = a - 1), i !== a - 1) throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`); - let p = y.parseAxisParam([i], n.shape), u = nI({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: p, keepDims: false } }), c = C.expandShapeToKeepDim(u.shape, p), l = Ve({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), m = Ll({ inputs: { a: n, b: l }, backend: t10 }), d = $S({ inputs: { x: m }, backend: t10 }), f = li({ inputs: { x: d }, backend: t10, attrs: { axis: p, keepDims: false } }), h = Ve({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = zl({ inputs: { a: d, b: h }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g; + let p = y.parseAxisParam([i], n.shape), u = dI({ inputs: { x: n }, backend: t8, attrs: { reductionIndices: p, keepDims: false } }), c = w.expandShapeToKeepDim(u.shape, p), l = Ve({ inputs: { x: u }, backend: t8, attrs: { shape: c } }), m = Ll({ inputs: { a: n, b: l }, backend: t8 }), d = LS({ inputs: { x: m }, backend: t8 }), f = fi({ inputs: { x: d }, backend: t8, attrs: { axis: p, keepDims: false } }), h = Ve({ inputs: { x: f }, backend: t8, attrs: { shape: c } }), g = zl({ inputs: { a: d, b: h }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), g; } -var K$ = { kernelName: bs, backendName: "cpu", kernelFunc: sI }; -function QY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o; - Y(n, "multinomial"); - let p = i ? n : sI({ inputs: { logits: n }, backend: t10, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t10.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32"); +var sE = { kernelName: Is, backendName: "cpu", kernelFunc: fI }; +function k7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o; + Q(n, "multinomial"); + let p = i ? n : fI({ inputs: { logits: n }, backend: t8, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t8.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32"); for (let f = 0; f < u; ++f) { let h = f * c, g = new Float32Array(c - 1); g[0] = l[h]; - for (let w = 1; w < g.length; ++w) - g[w] = g[w - 1] + l[h + w]; - let x = q$.alea(a.toString()), b = f * s; - for (let w = 0; w < s; ++w) { + for (let C = 1; C < g.length; ++C) + g[C] = g[C - 1] + l[h + C]; + let x = aE.alea(a.toString()), b = f * s; + for (let C = 0; C < s; ++C) { let S = x(); - d[b + w] = g.length; + d[b + C] = g.length; for (let k = 0; k < g.length; k++) if (S < g[k]) { - d[b + w] = k; + d[b + C] = k; break; } } } - return i || t10.disposeIntermediateTensorInfo(p), t10.makeTensorInfo(m, "int32", d); + return i || t8.disposeIntermediateTensorInfo(p), t8.makeTensorInfo(m, "int32", d); } -var j$ = { kernelName: Hn, backendName: "cpu", kernelFunc: QY }; -var ZY = Wt.nonMaxSuppressionV3Impl; -function JY(r) { - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o; - Y(n, "NonMaxSuppression"); - let u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, { selectedIndices: l } = ZY(u, c, a, i, p); - return t10.makeTensorInfo([l.length], "int32", new Int32Array(l)); +var iE = { kernelName: jn, backendName: "cpu", kernelFunc: k7 }; +var N7 = Wt.nonMaxSuppressionV3Impl; +function T7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o; + Q(n, "NonMaxSuppression"); + let u = t8.data.get(n.dataId).values, c = t8.data.get(s.dataId).values, { selectedIndices: l } = N7(u, c, a, i, p); + return t8.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var X$ = { kernelName: jn, backendName: "cpu", kernelFunc: JY }; -var eQ = Wt.nonMaxSuppressionV4Impl; -function tQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o; - Y(n, "NonMaxSuppressionPadded"); - let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = eQ(c, l, a, i, p, u); - return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))]; +var uE = { kernelName: Qn, backendName: "cpu", kernelFunc: T7 }; +var _7 = Wt.nonMaxSuppressionV4Impl; +function $7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o; + Q(n, "NonMaxSuppressionPadded"); + let c = t8.data.get(n.dataId).values, l = t8.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = _7(c, l, a, i, p, u); + return [t8.makeTensorInfo([m.length], "int32", new Int32Array(m)), t8.makeTensorInfo([], "int32", new Int32Array([d]))]; } -var Y$ = { kernelName: Ha, backendName: "cpu", kernelFunc: tQ }; -var rQ = Wt.nonMaxSuppressionV5Impl; -function oQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o; - Y(n, "NonMaxSuppressionWithScore"); - let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = rQ(c, l, m, d, f, h); - return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))]; +var pE = { kernelName: Xa, backendName: "cpu", kernelFunc: $7 }; +var E7 = Wt.nonMaxSuppressionV5Impl; +function R7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o; + Q(n, "NonMaxSuppressionWithScore"); + let c = t8.data.get(n.dataId).values, l = t8.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = E7(c, l, m, d, f, h); + return [t8.makeTensorInfo([g.length], "int32", new Int32Array(g)), t8.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var Q$ = { kernelName: Xn, backendName: "cpu", kernelFunc: oQ }; -function nQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o; - Y(n, "oneHot"); +var cE = { kernelName: Zn, backendName: "cpu", kernelFunc: R7 }; +function D7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o; + Q(n, "oneHot"); let u = y.sizeFromShape(n.shape), c = new Float32Array(u * a); c.fill(p); - let l = t10.data.get(n.dataId).values; + let l = t8.data.get(n.dataId).values; for (let m = 0; m < u; ++m) l[m] >= 0 && l[m] < a && (c[m * a + l[m]] = i); - return t10.makeTensorInfo([...n.shape, a], s, c); + return t8.makeTensorInfo([...n.shape, a], s, c); } -var Z$ = { kernelName: Yn, backendName: "cpu", kernelFunc: nQ }; +var lE = { kernelName: Jn, backendName: "cpu", kernelFunc: D7 }; function Ul(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; + let { inputs: e, backend: t8 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("zerosLike is not supported for string tensors"); if (o.dtype === "complex64") { - let n = To({ inputs: { input: o }, backend: t10 }), s = Ul({ inputs: { x: n }, backend: t10 }), a = $a({ inputs: { input: o }, backend: t10 }), i = Ul({ inputs: { x: a }, backend: t10 }), p = Kt({ inputs: { real: s, imag: i }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; + let n = Eo({ inputs: { input: o }, backend: t8 }), s = Ul({ inputs: { x: n }, backend: t8 }), a = Fa({ inputs: { input: o }, backend: t8 }), i = Ul({ inputs: { x: a }, backend: t8 }), p = Kt({ inputs: { real: s, imag: i }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p; } else - return Wl({ backend: t10, attrs: { shape: o.shape, value: 0, dtype: o.dtype } }); + return Wl({ backend: t8, attrs: { shape: o.shape, value: 0, dtype: o.dtype } }); } -var J$ = { kernelName: fa, backendName: "cpu", kernelFunc: Ul }; -function eE(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; +var mE = { kernelName: ba, backendName: "cpu", kernelFunc: Ul }; +function dE(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("onesLike is not supported for string tensors"); if (o.dtype === "complex64") { - let n = To({ inputs: { input: o }, backend: t10 }), s = eE({ inputs: { x: n }, backend: t10 }), a = $a({ inputs: { input: o }, backend: t10 }), i = Ul({ inputs: { x: a }, backend: t10 }), p = Kt({ inputs: { real: s, imag: i }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; + let n = Eo({ inputs: { input: o }, backend: t8 }), s = dE({ inputs: { x: n }, backend: t8 }), a = Fa({ inputs: { input: o }, backend: t8 }), i = Ul({ inputs: { x: a }, backend: t8 }), p = Kt({ inputs: { real: s, imag: i }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p; } else - return Wl({ backend: t10, attrs: { shape: o.shape, value: 1, dtype: o.dtype } }); + return Wl({ backend: t8, attrs: { shape: o.shape, value: 1, dtype: o.dtype } }); } -var tE = { kernelName: na, backendName: "cpu", kernelFunc: eE }; -function aI(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; +var fE = { kernelName: pa, backendName: "cpu", kernelFunc: dE }; +function hI(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return vc({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); + return kc({ inputs: { input: e[0] }, backend: t8, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = vc({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); + let l = kc({ inputs: { input: c }, backend: t8, attrs: { dim: n } }); return i.push(l), l; - }), u = mu({ inputs: p, backend: t10, attrs: { axis: n } }); - return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u; + }), u = gu({ inputs: p, backend: t8, attrs: { axis: n } }); + return i.forEach((c) => t8.disposeIntermediateTensorInfo(c)), u; } -var rE = { kernelName: sa, backendName: "cpu", kernelFunc: aI }; -function sQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; - Y(n, "pad"); - let i = s.map((b, w) => b[0] + n.shape[w] + b[1]), p = s.map((b) => b[0]), u = t10.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d); +var hE = { kernelName: ca, backendName: "cpu", kernelFunc: hI }; +function A7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; + Q(n, "pad"); + let i = s.map((b, C) => b[0] + n.shape[C] + b[1]), p = s.map((b) => b[0]), u = t8.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d); a !== 0 && g.fill(a); for (let b = 0; b < c; b++) { let S = y.indexToLoc(b, l, m).map((_, E) => _ + p[E]), k = y.locToIndex(S, f, h); g[k] = u[b]; } - return { dataId: t10.write(g, i, n.dtype), shape: i, dtype: n.dtype }; + return { dataId: t8.write(g, i, n.dtype), shape: i, dtype: n.dtype }; } -var Bf = { kernelName: Qn, backendName: "cpu", kernelFunc: sQ }; -var aQ = ze((r, e) => Math.pow(r, e)); -var iQ = je(Zn, aQ); -var oE = { kernelName: Zn, backendName: "cpu", kernelFunc: iQ }; -function uQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, [m, d, f] = _f(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d); +var zf = { kernelName: es, backendName: "cpu", kernelFunc: A7 }; +var F7 = ze((r, e) => Math.pow(r, e)); +var P7 = Ye(ts, F7); +var gE = { kernelName: ts, backendName: "cpu", kernelFunc: P7 }; +function O7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t8.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t8.data.get(s.dataId).values, l = t8.data.get(a.dataId).values, [m, d, f] = $f(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t8.makeTensorInfo([x.length], "int32", x)), g = t8.makeTensorInfo(f, s.dtype, d); return h.concat([g]); } -var nE = { kernelName: Kp, backendName: "cpu", kernelFunc: uQ }; -function pQ(r) { - let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = $f(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c); +var xE = { kernelName: jp, backendName: "cpu", kernelFunc: O7 }; +function M7(r) { + let { inputs: e, backend: t8 } = r, { starts: o, limits: n, deltas: s } = e, a = t8.data.get(o.dataId).values, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, [u, c] = Ef(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t8.makeTensorInfo([u.length], "int32", u), m = t8.makeTensorInfo([c.length], o.dtype, c); return [l, m]; } -var sE = { kernelName: qp, backendName: "cpu", kernelFunc: pQ }; -function cQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, m = i.map((g) => t10.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = Ef(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); - return t10.makeTensorInfo(f, s.dtype, h); +var yE = { kernelName: Xp, backendName: "cpu", kernelFunc: M7 }; +function L7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t8.data.get(n.dataId).values, c = t8.data.get(s.dataId).values, l = t8.data.get(a.dataId).values, m = i.map((g) => t8.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = Rf(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); + return t8.makeTensorInfo(f, s.dtype, h); } -var aE = { kernelName: jp, backendName: "cpu", kernelFunc: cQ }; -function lQ(r) { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, dtype: s, step: a } = t10, i = ap(o, n, a, s); +var bE = { kernelName: Yp, backendName: "cpu", kernelFunc: L7 }; +function B7(r) { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, dtype: s, step: a } = t8, i = cp(o, n, a, s); return e.makeTensorInfo([i.length], s, i); } -var iE = { kernelName: aa, backendName: "cpu", kernelFunc: lQ }; -var mQ = Ie(ts, (r) => 1 / r); -var uE = { kernelName: ts, backendName: "cpu", kernelFunc: mQ }; -function dQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; - Y(n, "resizeBilinear"); - let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], w = 0, S = x[0] / b[0], k = x[1] / b[1]; +var CE = { kernelName: la, backendName: "cpu", kernelFunc: B7 }; +var z7 = Ie(ns, (r) => 1 / r); +var wE = { kernelName: ns, backendName: "cpu", kernelFunc: z7 }; +function V7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; + Q(n, "resizeBilinear"); + let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t8.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = 0, S = x[0] / b[0], k = x[1] / b[1]; for (let _ = 0; _ < l; _++) for (let E = 0; E < u; E++) { let R; a ? R = S * (E + 0.5) - 0.5 : R = S * E; - let D = Math.max(0, Math.floor(R)), F = R - D, O = Math.min(m - 1, Math.ceil(R)), M = _ * p[0] + D * p[1], L = _ * p[0] + O * p[1]; + let D = Math.max(0, Math.floor(R)), P = R - D, O = Math.min(m - 1, Math.ceil(R)), M = _ * p[0] + D * p[1], L = _ * p[0] + O * p[1]; for (let B = 0; B < c; B++) { let z; a ? z = k * (B + 0.5) - 0.5 : z = k * B; - let U = Math.max(0, Math.floor(z)), j = z - U, H = Math.min(d - 1, Math.ceil(z)), X = M + U * p[2], J = L + U * p[2], re = M + H * p[2], ne = L + H * p[2]; + let U = Math.max(0, Math.floor(z)), j = z - U, q = Math.min(d - 1, Math.ceil(z)), Y = M + U * p[2], J = L + U * p[2], re = M + q * p[2], ne = L + q * p[2]; for (let ee = 0; ee < f; ee++) { - let oe = h[X + ee], ie = h[J + ee], le = h[re + ee], ye = h[ne + ee], _e = oe + (le - oe) * j, ve = ie + (ye - ie) * j, Fe = _e + (ve - _e) * F; - g[w++] = Fe; + let oe = h[Y + ee], ie = h[J + ee], le = h[re + ee], be = h[ne + ee], _e = oe + (le - oe) * j, ve = ie + (be - ie) * j, Fe = _e + (ve - _e) * P; + g[C++] = Fe; } } } - return t10.makeTensorInfo([l, u, c, f], "float32", g); + return t8.makeTensorInfo([l, u, c, f], "float32", g); } -var pE = { kernelName: ns, backendName: "cpu", kernelFunc: dQ }; -function fQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; - Y([s, n], "resizeBilinearGrad"); - let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], w = t10.data.get(s.dataId).values, S = 0; +var SE = { kernelName: is, backendName: "cpu", kernelFunc: V7 }; +function W7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; + Q([s, n], "resizeBilinearGrad"); + let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], C = t8.data.get(s.dataId).values, S = 0; for (let k = 0; k < p; k++) { let _ = k * i[0]; for (let E = 0; E < m; E++) { - let R = E * x, D = Math.floor(R), F = Math.min(Math.ceil(R), u - 1), O = _ + D * i[1], M = _ + F * i[1], L = R - D, B = 1 - L; + let R = E * x, D = Math.floor(R), P = Math.min(Math.ceil(R), u - 1), O = _ + D * i[1], M = _ + P * i[1], L = R - D, B = 1 - L; for (let z = 0; z < d; z++) { - let U = z * b, j = Math.floor(U), H = Math.min(Math.ceil(U), c - 1), X = U - j, J = 1 - X, re = O + j * i[2], ne = O + H * i[2], ee = M + j * i[2], oe = M + H * i[2], ie = B * J, le = B * X, ye = L * J, _e = L * X; + let U = z * b, j = Math.floor(U), q = Math.min(Math.ceil(U), c - 1), Y = U - j, J = 1 - Y, re = O + j * i[2], ne = O + q * i[2], ee = M + j * i[2], oe = M + q * i[2], ie = B * J, le = B * Y, be = L * J, _e = L * Y; for (let ve = 0; ve < l; ve++) { - let Fe = w[S++]; - f[re + ve] += Fe * ie, f[ne + ve] += Fe * le, f[ee + ve] += Fe * ye, f[oe + ve] += Fe * _e; + let Fe = C[S++]; + f[re + ve] += Fe * ie, f[ne + ve] += Fe * le, f[ee + ve] += Fe * be, f[oe + ve] += Fe * _e; } } } } - return t10.makeTensorInfo([p, c, u, l], "float32", f); + return t8.makeTensorInfo([p, c, u, l], "float32", f); } -var cE = { kernelName: qa, backendName: "cpu", kernelFunc: fQ }; -function hQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; - Y(n, "resizeNearestNeighbor"); - let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], w = x[0] / b[0], S = x[1] / b[1], k = 0; +var IE = { kernelName: Qa, backendName: "cpu", kernelFunc: W7 }; +function U7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; + Q(n, "resizeNearestNeighbor"); + let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t8.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = x[0] / b[0], S = x[1] / b[1], k = 0; for (let _ = 0; _ < l; _++) { let E = _ * p[0]; for (let R = 0; R < u; R++) { - let D = a ? w * (R + 0.5) : w * R, F = Math.min(m - 1, s ? Math.round(D) : Math.floor(D)); - a && (F = Math.max(0, F)); - let O = E + F * p[1]; + let D = a ? C * (R + 0.5) : C * R, P = Math.min(m - 1, s ? Math.round(D) : Math.floor(D)); + a && (P = Math.max(0, P)); + let O = E + P * p[1]; for (let M = 0; M < c; M++) { let L = a ? S * (M + 0.5) : S * M, B = Math.min(d - 1, s ? Math.round(L) : Math.floor(L)); a && (B = Math.max(0, B)); @@ -14746,74 +14822,74 @@ function hQ(r) { } } } - return t10.makeTensorInfo([l, u, c, f], n.dtype, g); + return t8.makeTensorInfo([l, u, c, f], n.dtype, g); } -var lE = { kernelName: os, backendName: "cpu", kernelFunc: hQ }; -function gQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; - Y([s, n], "resizeNearestNeighborGrad"); - let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = t10.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], w = x[0] / b[0], S = x[1] / b[1], k = 1 / w, _ = 1 / S, E = Math.ceil(k) * 2 + 2, R = Math.ceil(_) * 2 + 2; +var vE = { kernelName: as, backendName: "cpu", kernelFunc: U7 }; +function G7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; + Q([s, n], "resizeNearestNeighborGrad"); + let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = t8.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], C = x[0] / b[0], S = x[1] / b[1], k = 1 / C, _ = 1 / S, E = Math.ceil(k) * 2 + 2, R = Math.ceil(_) * 2 + 2; for (let D = 0; D < u; D++) { - let F = D * i[0]; + let P = D * i[0]; for (let O = 0; O < c; O++) { - let M = F + O * i[1], L = Math.floor(O * k), B = Math.floor(L - E / 2); + let M = P + O * i[1], L = Math.floor(O * k), B = Math.floor(L - E / 2); for (let z = 0; z < l; z++) { - let U = M + z * i[2], j = Math.floor(z * _), H = Math.floor(j - R / 2); - for (let X = 0; X < m; X++) { + let U = M + z * i[2], j = Math.floor(z * _), q = Math.floor(j - R / 2); + for (let Y = 0; Y < m; Y++) { let J = 0; for (let re = 0; re < E; re++) { let ne = re + B; if (ne < 0 || ne >= d) continue; - let ee = F + ne * p[1], oe = ne * w, ie = Math.min(c - 1, a ? Math.round(oe) : Math.floor(oe)); + let ee = P + ne * p[1], oe = ne * C, ie = Math.min(c - 1, a ? Math.round(oe) : Math.floor(oe)); if (O === ie) for (let le = 0; le < R; le++) { - let ye = le + H; - if (ye < 0 || ye >= f) + let be = le + q; + if (be < 0 || be >= f) continue; - let _e = ee + ye * p[2], ve = ye * S, Fe = Math.min(l - 1, a ? Math.round(ve) : Math.floor(ve)); - z === Fe && (J += g[_e + X]); + let _e = ee + be * p[2], ve = be * S, Fe = Math.min(l - 1, a ? Math.round(ve) : Math.floor(ve)); + z === Fe && (J += g[_e + Y]); } } - h[U + X] = J; + h[U + Y] = J; } } } } - return t10.makeTensorInfo(n.shape, n.dtype, h); + return t8.makeTensorInfo(n.shape, n.dtype, h); } -var mE = { kernelName: Ka, backendName: "cpu", kernelFunc: gQ }; -function xQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o; - Y(n, "reverse"); +var kE = { kernelName: Ya, backendName: "cpu", kernelFunc: G7 }; +function H7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o; + Q(n, "reverse"); let a = n.shape.length, i = y.parseAxisParam(s, n.shape); if (a === 0) - return lr({ inputs: { x: n }, backend: t10 }); - let p = new tt(n.shape, n.dtype), u = t10.bufferSync(n); + return lr({ inputs: { x: n }, backend: t8 }); + let p = new tt(n.shape, n.dtype), u = t8.bufferSync(n); for (let c = 0; c < p.size; c++) { let l = p.indexToLoc(c), m = l.slice(); i.forEach((d) => m[d] = n.shape[d] - 1 - m[d]), p.set(u.get(...m), ...l); } - return t10.makeTensorInfo(p.shape, p.dtype, p.values); + return t8.makeTensorInfo(p.shape, p.dtype, p.values); } -var dE = { kernelName: as, backendName: "cpu", kernelFunc: xQ }; -var fE = { kernelName: _s, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = C.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values; +var NE = { kernelName: ps, backendName: "cpu", kernelFunc: H7 }; +var TE = { kernelName: Ds, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t8, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = w.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values; for (let S = 0; S < u; S++) { let k = S * l * c * m; for (let _ = 0; _ < c; _++) { let E = _ * (l * m); for (let R = 0; R < l; R++) { let D = R * m; - for (let F = 0; F < m; F++) { - let O = [u, _, R, F], M = O[2], L = O[1], B = (M - d) * x - (L - f) * g, z = (M - d) * g + (L - f) * x; + for (let P = 0; P < m; P++) { + let O = [u, _, R, P], M = O[2], L = O[1], B = (M - d) * x - (L - f) * g, z = (M - d) * g + (L - f) * x; B = Math.round(B + d), z = Math.round(z + f); let U = s; - if (typeof s != "number" && (F === 3 ? U = h : U = s[F]), B >= 0 && B < l && z >= 0 && z < c) { - let H = z * (l * m), X = B * m, J = k + H + X + F; + if (typeof s != "number" && (P === 3 ? U = h : U = s[P]), B >= 0 && B < l && z >= 0 && z < c) { + let q = z * (l * m), Y = B * m, J = k + q + Y + P; U = b[J]; } - let j = k + E + D + F; + let j = k + E + D + P; p[j] = U; } } @@ -14821,82 +14897,82 @@ var fE = { kernelName: _s, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: } return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -var yQ = Ie(is, (r) => { +var K7 = Ie(cs, (r) => { let e = Math.floor(r); return r - e < 0.5 ? Math.floor(r) : r - e > 0.5 ? Math.ceil(r) : e % 2 === 0 ? e : e + 1; }); -var hE = { kernelName: is, backendName: "cpu", kernelFunc: yQ }; -function bQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(s, n, a), m = true, d = t10.bufferSync(n), f = t10.bufferSync(s), h = Fs(d, f, a, l, u, p, i, c, 0, m); - return t10.makeTensorInfo(a, h.dtype, h.values); +var _E = { kernelName: cs, backendName: "cpu", kernelFunc: K7 }; +function q7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = true, d = t8.bufferSync(n), f = t8.bufferSync(s), h = Ls(d, f, a, l, u, p, i, c, 0, m); + return t8.makeTensorInfo(a, h.dtype, h.values); } -var gE = { kernelName: ps, backendName: "cpu", kernelFunc: bQ }; -function CQ(r, e) { - let t10 = 0, o = r.length, n = 0; - for (; t10 < o; ) - n = Math.floor((t10 + o) / 2), r[n] < e ? t10 = n + 1 : o = n; +var $E = { kernelName: ms, backendName: "cpu", kernelFunc: q7 }; +function j7(r, e) { + let t8 = 0, o = r.length, n = 0; + for (; t8 < o; ) + n = Math.floor((t8 + o) / 2), r[n] < e ? t8 = n + 1 : o = n; return o; } -function wQ(r, e) { - let t10 = 0, o = r.length, n = 0; - for (; t10 < o; ) - n = Math.floor((t10 + o) / 2), r[n] <= e ? t10 = n + 1 : o = n; +function X7(r, e) { + let t8 = 0, o = r.length, n = 0; + for (; t8 < o; ) + n = Math.floor((t8 + o) / 2), r[n] <= e ? t8 = n + 1 : o = n; return o; } -function xE(r, e, t10, o, n, s) { - let a = y.getArrayFromDType("int32", t10 * n); - for (let i = 0; i < t10; ++i) { +function EE(r, e, t8, o, n, s) { + let a = y.getArrayFromDType("int32", t8 * n); + for (let i = 0; i < t8; ++i) { let p = r.slice(i * o, (i + 1) * o), u = i * n; for (let c = 0; c < n; ++c) - a[u + c] = s === "left" ? CQ(p, e[c + u]) : wQ(p, e[c + u]); + a[u + c] = s === "left" ? j7(p, e[c + u]) : X7(p, e[c + u]); } return a; } -function SQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = xE(i, p, n.shape[0], n.shape[1], s.shape[1], a); - return t10.makeTensorInfo(s.shape, "int32", u); +function Y7(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, u = EE(i, p, n.shape[0], n.shape[1], s.shape[1], a); + return t8.makeTensorInfo(s.shape, "int32", u); } -var yE = { kernelName: ls, backendName: "cpu", kernelFunc: SQ }; -function IQ(r) { - let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e; - Y([o, n, s], "select"); - let a = o.shape.length, i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); +var RE = { kernelName: fs, backendName: "cpu", kernelFunc: Y7 }; +function Q7(r) { + let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e; + Q([o, n, s], "select"); + let a = o.shape.length, i = t8.data.get(o.dataId).values, p = t8.data.get(n.dataId).values, u = t8.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); for (let f = 0; f < i.length; f++) for (let h = 0; h < d; h++) i[f] === 1 ? l[m++] = p[f] : l[m++] = u[f]; - return t10.makeTensorInfo(n.shape, c, l); + return t8.makeTensorInfo(n.shape, c, l); } -var bE = { kernelName: ua, backendName: "cpu", kernelFunc: IQ }; -var vQ = C.SELU_SCALEALPHA; -var kQ = C.SELU_SCALE; -var NQ = Ie(ms, (r) => r >= 0 ? kQ * r : vQ * (Math.exp(r) - 1)); -var CE = { kernelName: ms, backendName: "cpu", kernelFunc: NQ }; -var TQ = Ie(fs, (r) => r < 0 ? -1 : r > 0 ? 1 : 0); -var wE = { kernelName: fs, backendName: "cpu", kernelFunc: TQ }; -var _Q = Ie(ds, (r) => Math.sin(r)); -var SE = { kernelName: ds, backendName: "cpu", kernelFunc: _Q }; -var $Q = Ie(ja, (r) => Math.sinh(r)); -var IE = { kernelName: ja, backendName: "cpu", kernelFunc: $Q }; -var EQ = 11920928955078125e-23; -var vE = Math.log(EQ) + 2; -var RQ = Ie(gs, (r) => { - let e = r > -vE, t10 = r < vE, o = Math.exp(r), n; - return t10 ? n = o : e ? n = r : n = Math.log(1 + o), n; +var DE = { kernelName: da, backendName: "cpu", kernelFunc: Q7 }; +var Z7 = w.SELU_SCALEALPHA; +var J7 = w.SELU_SCALE; +var eQ = Ie(hs, (r) => r >= 0 ? J7 * r : Z7 * (Math.exp(r) - 1)); +var AE = { kernelName: hs, backendName: "cpu", kernelFunc: eQ }; +var tQ = Ie(ys, (r) => r < 0 ? -1 : r > 0 ? 1 : 0); +var FE = { kernelName: ys, backendName: "cpu", kernelFunc: tQ }; +var rQ = Ie(gs, (r) => Math.sin(r)); +var PE = { kernelName: gs, backendName: "cpu", kernelFunc: rQ }; +var oQ = Ie(xs, (r) => Math.sinh(r)); +var OE = { kernelName: xs, backendName: "cpu", kernelFunc: oQ }; +var nQ = 11920928955078125e-23; +var ME = Math.log(nQ) + 2; +var sQ = Ie(Cs, (r) => { + let e = r > -ME, t8 = r < ME, o = Math.exp(r), n; + return t8 ? n = o : e ? n = r : n = Math.log(1 + o), n; }); -var kE = { kernelName: gs, backendName: "cpu", kernelFunc: RQ }; -function DQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; - Y([n], "spaceToBatchND"); +var LE = { kernelName: Cs, backendName: "cpu", kernelFunc: sQ }; +function aQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; + Q([n], "spaceToBatchND"); let i = y.sizeFromShape(s), p = [[0, 0]]; p.push(...a); for (let _ = 1 + s.length; _ < n.shape.length; ++_) p.push([0, 0]); - let u = Bf.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = C.getReshaped(u.shape, s, i, false), l = C.getPermuted(c.length, s.length, false), m = C.getReshapedPermuted(u.shape, s, i, false), h = Ve({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = St({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Ve({ inputs: { x: b }, backend: t10, attrs: { shape: m } }); - return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(b), k; + let u = zf.kernelFunc({ inputs: { x: n }, backend: t8, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = Ve({ inputs: { x: u }, backend: t8, attrs: { shape: c } }), b = It({ inputs: { x: h }, backend: t8, attrs: { perm: l } }), k = Ve({ inputs: { x: b }, backend: t8, attrs: { shape: m } }); + return t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(b), k; } -var NE = { kernelName: ca, backendName: "cpu", kernelFunc: DQ }; -function AQ(r) { - let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; +var BE = { kernelName: ha, backendName: "cpu", kernelFunc: aQ }; +function iQ(r) { + let { inputs: e, backend: t8 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; if (s.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw: ${s.shape}`); @@ -14909,12 +14985,12 @@ function AQ(r) { if (a.shape.length !== 0) throw new Error(`Default value must be a scalar, saw: ${a.shape}`); - let i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = t10.data.get(a.dataId).values[0], [l, m, d, f, h] = Rf(i, o.shape, o.dtype, p, n.dtype, u, c); - return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; + let i = t8.data.get(o.dataId).values, p = t8.data.get(n.dataId).values, u = t8.data.get(s.dataId).values, c = t8.data.get(a.dataId).values[0], [l, m, d, f, h] = Df(i, o.shape, o.dtype, p, n.dtype, u, c); + return [t8.makeTensorInfo(m, o.dtype, l), t8.makeTensorInfo([m[0]], n.dtype, d), t8.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t8.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; } -var TE = { kernelName: Vi, backendName: "cpu", kernelFunc: AQ }; -function FQ(r) { - let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; +var zE = { kernelName: Hi, backendName: "cpu", kernelFunc: iQ }; +function uQ(r) { + let { inputs: e, backend: t8 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); @@ -14923,12 +14999,12 @@ function FQ(r) { ${n.shape}`); if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); - let a = Array.from(t10.data.get(n.dataId).values), i = t10.data.get(o.dataId).values, p = Array.from(t10.data.get(s.dataId).values), [u, c, l] = Df(i, o.shape, o.dtype, a, p); - return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; + let a = Array.from(t8.data.get(n.dataId).values), i = t8.data.get(o.dataId).values, p = Array.from(t8.data.get(s.dataId).values), [u, c, l] = Af(i, o.shape, o.dtype, a, p); + return [t8.makeTensorInfo(c, o.dtype, u), t8.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; } -var _E = { kernelName: Xa, backendName: "cpu", kernelFunc: FQ }; -function PQ(r) { - let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; +var VE = { kernelName: Za, backendName: "cpu", kernelFunc: uQ }; +function pQ(r) { + let { inputs: e, backend: t8 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -14939,12 +15015,12 @@ function PQ(r) { ${s.shape}`); if (n.shape[0] !== s.shape[0]) throw new Error("segmentIds and indices should have same size."); - let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = wc(a, o.shape, o.dtype, i, p, true); - return t10.makeTensorInfo(c, o.dtype, u); + let a = t8.data.get(o.dataId).values, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, [u, c] = Sc(a, o.shape, o.dtype, i, p, true); + return t8.makeTensorInfo(c, o.dtype, u); } -var $E = { kernelName: Wi, backendName: "cpu", kernelFunc: PQ }; -function OQ(r) { - let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; +var WE = { kernelName: Ki, backendName: "cpu", kernelFunc: pQ }; +function cQ(r) { + let { inputs: e, backend: t8 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -14955,238 +15031,238 @@ function OQ(r) { ${s.shape}`); if (n.shape[0] !== s.shape[0]) throw new Error("segmentIds and indices should have same size."); - let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = wc(a, o.shape, o.dtype, i, p); - return t10.makeTensorInfo(c, o.dtype, u); + let a = t8.data.get(o.dataId).values, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, [u, c] = Sc(a, o.shape, o.dtype, i, p); + return t8.makeTensorInfo(c, o.dtype, u); } -var EE = { kernelName: Ui, backendName: "cpu", kernelFunc: OQ }; -function MQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = C.calculateShapes(s, n, i), d = false, f = t10.bufferSync(n), h; +var UE = { kernelName: qi, backendName: "cpu", kernelFunc: cQ }; +function lQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false, f = t8.bufferSync(n), h; switch (s.dtype) { case "bool": { - let g = t10.bufferSync(s), x = !!t10.data.get(a.dataId).values[0]; - h = Fs(f, g, i, m, c, u, p, l, x, d); + let g = t8.bufferSync(s), x = !!t8.data.get(a.dataId).values[0]; + h = Ls(f, g, i, m, c, u, p, l, x, d); break; } case "float32": { - let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0]; - h = Fs(f, g, i, m, c, u, p, l, x, d); + let g = t8.bufferSync(s), x = t8.data.get(a.dataId).values[0]; + h = Ls(f, g, i, m, c, u, p, l, x, d); break; } case "int32": { - let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0]; - h = Fs(f, g, i, m, c, u, p, l, x, d); + let g = t8.bufferSync(s), x = t8.data.get(a.dataId).values[0]; + h = Ls(f, g, i, m, c, u, p, l, x, d); break; } case "string": { - let g = t10.bufferSync(s), x = y.decodeString(t10.data.get(a.dataId).values[0]); - h = Fs(f, g, i, m, c, u, p, l, x, d); + let g = t8.bufferSync(s), x = y.decodeString(t8.data.get(a.dataId).values[0]); + h = Ls(f, g, i, m, c, u, p, l, x, d); break; } default: throw new Error(`Unsupported type ${s.dtype}`); } - return t10.makeTensorInfo(i, h.dtype, h.values); + return t8.makeTensorInfo(i, h.dtype, h.values); } -var RE = { kernelName: Cs, backendName: "cpu", kernelFunc: MQ }; -function LQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); +var GE = { kernelName: vs, backendName: "cpu", kernelFunc: lQ }; +function mQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); return p.map((l) => { let m = [...c]; m[i] = l; - let d = Eo({ inputs: { x: n }, backend: t10, attrs: { begin: u, size: m } }); + let d = Ao({ inputs: { x: n }, backend: t8, attrs: { begin: u, size: m } }); return u[i] += l, d; }); } -var DE = { kernelName: la, backendName: "cpu", kernelFunc: LQ }; -var AE = { kernelName: Gi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => { - let { x: t10 } = r, o = e; - Y(t10, "square"); - let n = o.data.get(t10.dataId).values, s = new Float32Array(n.length); +var HE = { kernelName: ga, backendName: "cpu", kernelFunc: mQ }; +var KE = { kernelName: ji, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => { + let { x: t8 } = r, o = e; + Q(t8, "square"); + let n = o.data.get(t8.dataId).values, s = new Float32Array(n.length); for (let i = 0; i < n.length; ++i) { let p = n[i]; s[i] = p * p; } - return { dataId: o.write(s, t10.shape, t10.dtype), shape: t10.shape, dtype: t10.dtype }; + return { dataId: o.write(s, t8.shape, t8.dtype), shape: t8.shape, dtype: t8.dtype }; } }; -var BQ = Ie(yo, (r, e) => { - let t10 = e; - return isNaN(r) ? NaN : r > 0 ? 1 : t10.alpha; +var dQ = Ie(wo, (r, e) => { + let t8 = e; + return isNaN(r) ? NaN : r > 0 ? 1 : t8.alpha; }); -var FE = { kernelName: yo, backendName: "cpu", kernelFunc: BQ }; -function zQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o; - Y(n, "stridedSlice"); - let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +var qE = { kernelName: wo, backendName: "cpu", kernelFunc: dQ }; +function fQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o; + Q(n, "stridedSlice"); + let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: f } }); + k = Ve({ inputs: { x: n }, backend: t8, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ct.computeOutShape(b, w, S), E = Eo({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } }); - k = Ve({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(E); + let _ = ct.computeOutShape(b, C, S), E = Ao({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: _ } }); + k = Ve({ inputs: { x: E }, backend: t8, attrs: { shape: f } }), t8.disposeIntermediateTensorInfo(E); } else { - let _ = t10.bufferSync(n), E = Af(d, _, S, b); - k = t10.makeTensorInfo(f, E.dtype, E.values); + let _ = t8.bufferSync(n), E = Ff(d, _, S, b); + k = t8.makeTensorInfo(f, E.dtype, E.values); } return k; } -var PE = { kernelName: Ss, backendName: "cpu", kernelFunc: zQ }; -function VQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.data.get(c.dataId).values, d = t10.data.get(l.dataId).values, [f, h] = up(m, d, n, s, a, i, p, u); - return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)]; +var jE = { kernelName: Ns, backendName: "cpu", kernelFunc: fQ }; +function hQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t8.data.get(c.dataId).values, d = t8.data.get(l.dataId).values, [f, h] = mp(m, d, n, s, a, i, p, u); + return [t8.makeTensorInfo([f.length], "string", f), t8.makeTensorInfo(l.shape, "int32", h)]; } -var OE = { kernelName: ma, backendName: "cpu", kernelFunc: VQ }; -function WQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; +var XE = { kernelName: xa, backendName: "cpu", kernelFunc: hQ }; +function gQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (s.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${s.shape}`); if (a.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`); - let i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values[0], [u, c, l] = pp(i, p, n), m = c.length; - return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))]; + let i = t8.data.get(s.dataId).values, p = t8.data.get(a.dataId).values[0], [u, c, l] = dp(i, p, n), m = c.length; + return [t8.makeTensorInfo([m, 2], "int32", u), t8.makeTensorInfo([m], "string", c), t8.makeTensorInfo([2], "int32", new Int32Array(l))]; } -var ME = { kernelName: Hi, backendName: "cpu", kernelFunc: WQ }; -function UQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; +var YE = { kernelName: Xi, backendName: "cpu", kernelFunc: gQ }; +function xQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (n <= 0) throw new Error("Number of buckets must be at least 1"); - let a = t10.data.get(s.dataId).values, i = cp(a, n); - return t10.makeTensorInfo(s.shape, "int32", i); + let a = t8.data.get(s.dataId).values, i = fp(a, n); + return t8.makeTensorInfo(s.shape, "int32", i); } -var LE = { kernelName: Ki, backendName: "cpu", kernelFunc: UQ }; -var GQ = Ie(vs, (r) => Math.tan(r)); -var BE = { kernelName: vs, backendName: "cpu", kernelFunc: GQ }; -var HQ = Ie(ks, (r) => Math.tanh(r)); -var zE = { kernelName: ks, backendName: "cpu", kernelFunc: HQ }; -function KQ(r) { - let { inputs: e, backend: t10 } = r, { tensor: o, indices: n, updates: s } = e, { sliceRank: a, numUpdates: i, sliceSize: p, strides: u, outputSize: c } = C.calculateShapes(s, n, o.shape), l = false, m = t10.bufferSync(n), d = t10.bufferSync(s), f = t10.bufferSync(o), h = Fs(m, d, o.shape, c, p, i, a, u, f, l); - return t10.makeTensorInfo(o.shape, h.dtype, h.values); +var QE = { kernelName: Yi, backendName: "cpu", kernelFunc: xQ }; +var yQ = Ie(_s, (r) => Math.tan(r)); +var ZE = { kernelName: _s, backendName: "cpu", kernelFunc: yQ }; +var bQ = Ie($s, (r) => Math.tanh(r)); +var JE = { kernelName: $s, backendName: "cpu", kernelFunc: bQ }; +function CQ(r) { + let { inputs: e, backend: t8 } = r, { tensor: o, indices: n, updates: s } = e, { sliceRank: a, numUpdates: i, sliceSize: p, strides: u, outputSize: c } = w.calculateShapes(s, n, o.shape), l = false, m = t8.bufferSync(n), d = t8.bufferSync(s), f = t8.bufferSync(o), h = Ls(m, d, o.shape, c, p, i, a, u, f, l); + return t8.makeTensorInfo(o.shape, h.dtype, h.values); } -var VE = { kernelName: cs, backendName: "cpu", kernelFunc: KQ }; -function qQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o; - Y(n, "tile"); - let a = Ff(t10.bufferSync(n), s); - return t10.makeTensorInfo(a.shape, a.dtype, a.values); +var eR = { kernelName: ds, backendName: "cpu", kernelFunc: CQ }; +function wQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reps: s } = o; + Q(n, "tile"); + let a = Pf(t8.bufferSync(n), s); + return t8.makeTensorInfo(a.shape, a.dtype, a.values); } -var WE = { kernelName: so, backendName: "cpu", kernelFunc: qQ }; -function jQ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o; - Y(n, "topk"); - let i = t10.data.get(n.dataId).values, [p, u] = Pf(i, n.shape, n.dtype, s, a); - return [t10.makeTensorInfo(p.shape, p.dtype, p.values), t10.makeTensorInfo(u.shape, u.dtype, u.values)]; +var tR = { kernelName: uo, backendName: "cpu", kernelFunc: wQ }; +function SQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o; + Q(n, "topk"); + let i = t8.data.get(n.dataId).values, [p, u] = Of(i, n.shape, n.dtype, s, a); + return [t8.makeTensorInfo(p.shape, p.dtype, p.values), t8.makeTensorInfo(u.shape, u.dtype, u.values)]; } -var UE = { kernelName: Ns, backendName: "cpu", kernelFunc: jQ }; -function XQ(r) { - let { inputs: e, attrs: t10, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t10, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], w = x[1], S = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], R = k[2], D = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g)); +var rR = { kernelName: Es, backendName: "cpu", kernelFunc: SQ }; +function IQ(r) { + let { inputs: e, attrs: t8, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t8, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], C = x[1], S = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], R = k[2], D = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g)); D.fill(p); - let F = o.data.get(n.dataId).values, O = o.data.get(s.dataId).values; + let P = o.data.get(n.dataId).values, O = o.data.get(s.dataId).values; for (let L = 0; L < c; ++L) { let B = s.shape[0] === 1 ? O : O.subarray(L * 8, L * 8 + 8); for (let z = 0; z < f; ++z) for (let U = 0; U < h; ++U) for (let j = 0; j < d; ++j) { - let H, X = B[6] * U + B[7] * z + 1; - if (X === 0) + let q, Y = B[6] * U + B[7] * z + 1; + if (Y === 0) continue; - let J = (B[0] * U + B[1] * z + B[2]) / X, re = (B[3] * U + B[4] * z + B[5]) / X, ne = GE(J, m, i), ee = GE(re, l, i); + let J = (B[0] * U + B[1] * z + B[2]) / Y, re = (B[3] * U + B[4] * z + B[5]) / Y, ne = oR(J, m, i), ee = oR(re, l, i); switch (a) { case "nearest": - H = e7(F, l, m, b, w, S, L, ee, ne, j, p); + q = _Q(P, l, m, b, C, S, L, ee, ne, j, p); break; case "bilinear": - H = t7(F, l, m, b, w, S, L, ee, ne, j, p); + q = $Q(P, l, m, b, C, S, L, ee, ne, j, p); break; default: throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${a}`); } let oe = L * _ + z * E + U * R + j; - D[oe] = H; + D[oe] = q; } return o.makeTensorInfo(g, n.dtype, D); } return { dataId: o.write(D, g, n.dtype), shape: n.shape, dtype: n.dtype }; } -var HE = { kernelName: Ts, backendName: "cpu", kernelFunc: XQ }; -function GE(r, e, t10) { - switch (t10) { +var nR = { kernelName: Rs, backendName: "cpu", kernelFunc: IQ }; +function oR(r, e, t8) { + switch (t8) { case "reflect": - return YQ(r, e); + return vQ(r, e); case "wrap": - return QQ(r, e); + return kQ(r, e); case "nearest": - return JQ(r, e); + return TQ(r, e); case "constant": default: - return ZQ(r, e); + return NQ(r, e); } } -function YQ(r, e) { - let t10 = r; - if (t10 < 0) +function vQ(r, e) { + let t8 = r; + if (t8 < 0) if (e <= 1) - t10 = 0; + t8 = 0; else { let o = 2 * e; - t10 < o && (t10 = o * Math.trunc(-t10 / o) + t10), t10 = t10 < -e ? t10 + o : -t10 - 1; + t8 < o && (t8 = o * Math.trunc(-t8 / o) + t8), t8 = t8 < -e ? t8 + o : -t8 - 1; } - else if (t10 > e - 1) + else if (t8 > e - 1) if (e <= 1) - t10 = 0; + t8 = 0; else { let o = 2 * e; - t10 -= o * Math.trunc(t10 / o), t10 >= e && (t10 = o - t10 - 1); + t8 -= o * Math.trunc(t8 / o), t8 >= e && (t8 = o - t8 - 1); } - return y.clamp(0, t10, e - 1); + return y.clamp(0, t8, e - 1); } -function QQ(r, e) { - let t10 = r; - if (t10 < 0) +function kQ(r, e) { + let t8 = r; + if (t8 < 0) if (e <= 1) - t10 = 0; + t8 = 0; else { let o = e - 1; - t10 += e * (Math.trunc(-t10 / o) + 1); + t8 += e * (Math.trunc(-t8 / o) + 1); } - else if (t10 > e - 1) + else if (t8 > e - 1) if (e <= 1) - t10 = 0; + t8 = 0; else { let o = e - 1; - t10 -= e * Math.trunc(t10 / o); + t8 -= e * Math.trunc(t8 / o); } - return y.clamp(0, t10, e - 1); + return y.clamp(0, t8, e - 1); } -function ZQ(r, e) { +function NQ(r, e) { return r; } -function JQ(r, e) { +function TQ(r, e) { return y.clamp(0, r, e - 1); } -function Gl(r, e, t10, o, n, s, a, i, p, u, c) { +function Gl(r, e, t8, o, n, s, a, i, p, u, c) { let l = a * o + i * n + p * s + u; - return 0 <= i && i < e && 0 <= p && p < t10 ? r[l] : c; + return 0 <= i && i < e && 0 <= p && p < t8 ? r[l] : c; } -function e7(r, e, t10, o, n, s, a, i, p, u, c) { +function _Q(r, e, t8, o, n, s, a, i, p, u, c) { let l = Math.round(i), m = Math.round(p); - return Gl(r, e, t10, o, n, s, a, l, m, u, c); + return Gl(r, e, t8, o, n, s, a, l, m, u, c); } -function t7(r, e, t10, o, n, s, a, i, p, u, c) { - let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * Gl(r, e, t10, o, n, s, a, l, m, u, c) + (p - m) * Gl(r, e, t10, o, n, s, a, l, f, u, c), g = (f - p) * Gl(r, e, t10, o, n, s, a, d, m, u, c) + (p - m) * Gl(r, e, t10, o, n, s, a, d, f, u, c); +function $Q(r, e, t8, o, n, s, a, i, p, u, c) { + let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * Gl(r, e, t8, o, n, s, a, l, m, u, c) + (p - m) * Gl(r, e, t8, o, n, s, a, l, f, u, c), g = (f - p) * Gl(r, e, t8, o, n, s, a, d, m, u, c) + (p - m) * Gl(r, e, t8, o, n, s, a, d, f, u, c); return (d - i) * h + (i - l) * g; } -function r7(r) { - let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e; - Y(s, "unique"); - let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = lp(a, n, s.shape, s.dtype); +function EQ(r) { + let { inputs: e, attrs: t8, backend: o } = r, { axis: n } = t8, { x: s } = e; + Q(s, "unique"); + let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = hp(a, n, s.shape, s.dtype); return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)]; } -var KE = { kernelName: qi, backendName: "cpu", kernelFunc: r7 }; -function o7(r) { - let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; +var sR = { kernelName: Qi, backendName: "cpu", kernelFunc: EQ }; +function RQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n.shape.length, i = n.shape[s], p = new Array(a - 1), u = 0; for (let d = 0; d < a; d++) @@ -15196,112 +15272,112 @@ function o7(r) { let m = new Array(i); for (let d = 0; d < m.length; d++) { c[s] = d; - let f = Eo({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: l } }); - m[d] = Ve({ inputs: { x: f }, backend: t10, attrs: { shape: p } }), t10.disposeIntermediateTensorInfo(f); + let f = Ao({ inputs: { x: n }, backend: t8, attrs: { begin: c, size: l } }); + m[d] = Ve({ inputs: { x: f }, backend: t8, attrs: { shape: p } }), t8.disposeIntermediateTensorInfo(f); } return m; } -var qE = { kernelName: da, backendName: "cpu", kernelFunc: o7 }; -function n7(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o; - Y(n, "unsortedSegmentSum"); +var aR = { kernelName: ya, backendName: "cpu", kernelFunc: RQ }; +function DQ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o; + Q(n, "unsortedSegmentSum"); let i = n.shape.length, p = s.shape.length, u = [], c = [], l = i - p, m = s; for (let f = 0; f < l; ++f) { - let h = vc({ inputs: { input: m }, backend: t10, attrs: { dim: f + 1 } }); + let h = kc({ inputs: { input: m }, backend: t8, attrs: { dim: f + 1 } }); m = h, c.push(h); } for (let f = 0; f < a; ++f) { - let h = y.createScalarValue(f, "int32"), g = t10.makeTensorInfo([], "int32", h), x = TS({ inputs: { a: g, b: m }, backend: t10 }), b = _o({ inputs: { x }, backend: t10, attrs: { dtype: "float32" } }), w = sp({ inputs: { a: b, b: n }, backend: t10 }), S = li({ inputs: { x: w }, backend: t10, attrs: { axis: 0, keepDims: false } }); - u.push(S), c.push(g), c.push(x), c.push(b), c.push(w), c.push(S); + let h = y.createScalarValue(f, "int32"), g = t8.makeTensorInfo([], "int32", h), x = OS({ inputs: { a: g, b: m }, backend: t8 }), b = Ro({ inputs: { x }, backend: t8, attrs: { dtype: "float32" } }), C = pp({ inputs: { a: b, b: n }, backend: t8 }), S = fi({ inputs: { x: C }, backend: t8, attrs: { axis: 0, keepDims: false } }); + u.push(S), c.push(g), c.push(x), c.push(b), c.push(C), c.push(S); } - let d = aI({ inputs: u, backend: t10, attrs: { axis: 0 } }); - return c.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d; + let d = hI({ inputs: u, backend: t8, attrs: { axis: 0 } }); + return c.forEach((f) => t8.disposeIntermediateTensorInfo(f)), d; } -var jE = { kernelName: ji, backendName: "cpu", kernelFunc: n7 }; -var s7 = [C_, _T, w_, S_, AT, I_, v_, k_, N_, T_, __, $_, E_, R_, D_, F_, P_, O_, M_, b_, L_, B_, z_, FT, V_, DT, PT, W_, $T, U_, H_, K_, q_, j_, X_, Y_, Q_, Z_, J_, e$, t$, r$, o$, n$, s$, a$, i$, u$, p$, c$, l$, d$, d_, f$, OT, h$, MT, g$, LT, x$, y$, b$, BT, zT, C$, w$, S$, I$, VT, WT, ET, v$, G_, k$, N$, T$, f_, UT, GT, _$, HT, $$, E$, R$, D$, A$, F$, P$, KT, O$, M$, L$, B$, V$, W$, U$, qT, G$, H$, j$, jT, XT, X$, Y$, Q$, YT, Z$, tE, rE, Bf, oE, h_, ZT, nE, sE, aE, iE, RT, Vl, uE, g_, x_, y_, pE, cE, lE, mE, dE, fE, hE, o_, gE, yE, bE, CE, s_, wE, SE, IE, a_, K$, kE, NE, TE, _E, $E, EE, RE, DE, u_, AE, p_, c_, FE, PE, OE, ME, LE, l_, m$, BE, zE, VE, WE, UE, HE, QT, KE, qE, jE, J$]; -for (let r of s7) - Ya(r); -var _c = {}; -He(_c, { assertNotComplex: () => Ps, bindCanvasToFramebuffer: () => f7, bindColorTextureToFramebuffer: () => jl, bindTextureToProgramUniformSampler: () => SI, bindTextureUnit: () => ZE, bindVertexBufferToProgramAttribute: () => Hf, callAndCheck: () => ce, canBeRepresented: () => cI, createFragmentShader: () => mI, createFramebuffer: () => bI, createProgram: () => dI, createStaticIndexBuffer: () => gI, createStaticVertexBuffer: () => hI, createTexture: () => xI, createVertexShader: () => lI, getBatchDim: () => di, getExtensionOrThrow: () => kc, getFramebufferErrorMessage: () => JE, getMaxTexturesInShader: () => kI, getNumChannels: () => m7, getProgramUniformLocation: () => wI, getProgramUniformLocationOrThrow: () => CI, getRowsCols: () => fi, getShapeAs3D: () => Tc, getTextureShapeFromLogicalShape: () => II, getWebGLDisjointQueryTimerVersion: () => NI, getWebGLErrorMessage: () => QE, getWebGLMaxTextureSize: () => vI, hasExtension: () => Hr, isCapableOfRenderingToFloatTexture: () => TI, isDownloadFloatTextureEnabled: () => _I, isReshapeFree: () => fu, isWebGLFenceEnabled: () => $I, isWebGLVersionEnabled: () => qf, linkProgram: () => fI, logShaderSourceAndInfoLog: () => Gf, resetMaxTextureSize: () => h7, resetMaxTexturesInShader: () => g7, unbindColorTextureFromFramebuffer: () => Kf, unbindTextureUnit: () => d7, validateFramebuffer: () => Nc, validateProgram: () => ql, validateTextureSize: () => yI }); -var dp = {}; -var zf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true }; -function iI(r, e) { - dp[r] = e; +var iR = { kernelName: Zi, backendName: "cpu", kernelFunc: DQ }; +var AQ = [D_, zT, A_, F_, HT, P_, O_, M_, L_, B_, z_, V_, W_, U_, G_, K_, q_, j_, X_, R_, Y_, Q_, Z_, KT, J_, GT, qT, e$, VT, t$, o$, n$, s$, a$, i$, u$, p$, c$, l$, m$, d$, f$, h$, g$, x$, y$, b$, C$, w$, S$, I$, v$, N$, k_, T$, jT, _$, XT, $$, YT, E$, R$, D$, QT, ZT, A$, F$, P$, O$, JT, e_, WT, M$, r$, L$, B$, z$, N_, t_, r_, V$, o_, W$, U$, G$, H$, K$, q$, j$, n_, X$, Y$, Q$, Z$, eE, tE, rE, s_, oE, nE, iE, a_, i_, uE, pE, cE, u_, lE, fE, hE, zf, gE, T_, c_, xE, yE, bE, CE, UT, Vl, wE, __, $_, E_, SE, IE, vE, kE, NE, TE, _E, h_, $E, RE, DE, AE, x_, FE, PE, OE, y_, sE, LE, BE, zE, VE, WE, UE, GE, HE, C_, KE, w_, S_, qE, jE, XE, YE, QE, I_, k$, ZE, JE, eR, tR, rR, nR, p_, sR, aR, iR, mE]; +for (let r of AQ) + Ja(r); +var $c = {}; +Ke($c, { assertNotComplex: () => Bs, bindCanvasToFramebuffer: () => WQ, bindColorTextureToFramebuffer: () => jl, bindTextureToProgramUniformSampler: () => RI, bindTextureUnit: () => lR, bindVertexBufferToProgramAttribute: () => Kf, callAndCheck: () => ce, canBeRepresented: () => bI, createFragmentShader: () => wI, createFramebuffer: () => _I, createProgram: () => SI, createStaticIndexBuffer: () => kI, createStaticVertexBuffer: () => vI, createTexture: () => NI, createVertexShader: () => CI, getBatchDim: () => gi, getExtensionOrThrow: () => Nc, getFramebufferErrorMessage: () => mR, getMaxTexturesInShader: () => FI, getNumChannels: () => zQ, getProgramUniformLocation: () => EI, getProgramUniformLocationOrThrow: () => $I, getRowsCols: () => xi, getShapeAs3D: () => _c, getTextureShapeFromLogicalShape: () => DI, getWebGLDisjointQueryTimerVersion: () => PI, getWebGLErrorMessage: () => cR, getWebGLMaxTextureSize: () => AI, hasExtension: () => qr, isCapableOfRenderingToFloatTexture: () => OI, isDownloadFloatTextureEnabled: () => MI, isReshapeFree: () => yu, isWebGLFenceEnabled: () => LI, isWebGLVersionEnabled: () => jf, linkProgram: () => II, logShaderSourceAndInfoLog: () => Hf, resetMaxTextureSize: () => UQ, resetMaxTexturesInShader: () => GQ, unbindColorTextureFromFramebuffer: () => qf, unbindTextureUnit: () => VQ, validateFramebuffer: () => Tc, validateProgram: () => ql, validateTextureSize: () => TI }); +var xp = {}; +var Vf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true }; +function gI(r, e) { + xp[r] = e; } -function Gr(r, e) { - if (!(r in dp) || e != null) { - let o = i7(r, e); +function Kr(r, e) { + if (!(r in xp) || e != null) { + let o = PQ(r, e); if (o !== null) - dp[r] = o; + xp[r] = o; else return console.log("Could not get context for WebGL version", r), null; } - let t10 = dp[r]; - return t10 == null || t10.isContextLost() ? (delete dp[r], Gr(r)) : (t10.disable(t10.DEPTH_TEST), t10.disable(t10.STENCIL_TEST), t10.disable(t10.BLEND), t10.disable(t10.DITHER), t10.disable(t10.POLYGON_OFFSET_FILL), t10.disable(t10.SAMPLE_COVERAGE), t10.enable(t10.SCISSOR_TEST), t10.enable(t10.CULL_FACE), t10.cullFace(t10.BACK), dp[r]); + let t8 = xp[r]; + return t8 == null || t8.isContextLost() ? (delete xp[r], Kr(r)) : (t8.disable(t8.DEPTH_TEST), t8.disable(t8.STENCIL_TEST), t8.disable(t8.BLEND), t8.disable(t8.DITHER), t8.disable(t8.POLYGON_OFFSET_FILL), t8.disable(t8.SAMPLE_COVERAGE), t8.enable(t8.SCISSOR_TEST), t8.enable(t8.CULL_FACE), t8.cullFace(t8.BACK), xp[r]); } -function a7(r) { - if (!P().getBool("IS_SAFARI") && typeof OffscreenCanvas != "undefined" && r === 2) +function FQ(r) { + if (!A().getBool("IS_SAFARI") && typeof OffscreenCanvas != "undefined" && r === 2) return new OffscreenCanvas(300, 150); if (typeof document != "undefined") return document.createElement("canvas"); throw new Error("Cannot create a canvas in this context"); } -function i7(r, e) { +function PQ(r, e) { if (r !== 1 && r !== 2) throw new Error("Cannot get WebGL rendering context, WebGL is disabled."); - let t10 = e == null ? a7(r) : e; - return t10.addEventListener("webglcontextlost", (o) => { - o.preventDefault(), delete dp[r]; - }, false), P().getBool("SOFTWARE_WEBGL_ENABLED") && (zf.failIfMajorPerformanceCaveat = false), r === 1 ? t10.getContext("webgl", zf) || t10.getContext("experimental-webgl", zf) : t10.getContext("webgl2", zf); + let t8 = e == null ? FQ(r) : e; + return t8.addEventListener("webglcontextlost", (o) => { + o.preventDefault(), delete xp[r]; + }, false), A().getBool("SOFTWARE_WEBGL_ENABLED") && (Vf.failIfMajorPerformanceCaveat = false), r === 1 ? t8.getContext("webgl", Vf) || t8.getContext("experimental-webgl", Vf) : t8.getContext("webgl2", Vf); } -var du; +var xu; (function(r) { r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH"; -})(du || (du = {})); +})(xu || (xu = {})); var mr; (function(r) { r[r.RENDER = 0] = "RENDER", r[r.UPLOAD = 1] = "UPLOAD", r[r.PIXELS = 2] = "PIXELS", r[r.DOWNLOAD = 3] = "DOWNLOAD"; })(mr || (mr = {})); -var er; +var tr; (function(r) { r[r.UNPACKED_FLOAT16 = 0] = "UNPACKED_FLOAT16", r[r.UNPACKED_FLOAT32 = 1] = "UNPACKED_FLOAT32", r[r.PACKED_4X1_UNSIGNED_BYTE = 2] = "PACKED_4X1_UNSIGNED_BYTE", r[r.PACKED_2X2_FLOAT32 = 3] = "PACKED_2X2_FLOAT32", r[r.PACKED_2X2_FLOAT16 = 4] = "PACKED_2X2_FLOAT16"; -})(er || (er = {})); -function fp(r, e) { +})(tr || (tr = {})); +function yp(r, e) { return [e, r]; } -function XE(r, e) { +function uR(r, e) { return r * e; } function Hl(r) { - let e = y.sizeFromShape(r), t10 = Math.ceil(e / 4); - return y.sizeToSquarishShape(t10); + let e = y.sizeFromShape(r), t8 = Math.ceil(e / 4); + return y.sizeToSquarishShape(t8); } -function Ea(r, e) { +function Pa(r, e) { return [Math.max(1, Math.ceil(e / 2)), Math.max(1, Math.ceil(r / 2))]; } -function YE(r, e) { - let [t10, o] = Ea(r, e); - return t10 * o * 4; +function pR(r, e) { + let [t8, o] = Pa(r, e); + return t8 * o * 4; } function Kl(r, e) { - let t10 = r, o, n, s, a, i, p, u, c, l, m; - return P().getNumber("WEBGL_VERSION") === 2 ? (o = t10.R32F, n = t10.R16F, s = t10.RGBA16F, a = t10.RGBA32F, i = t10.RED, u = 4, c = 1, l = t10.HALF_FLOAT, m = t10.FLOAT, p = t10.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t10.RGBA, i = r.RGBA, u = 4, c = 4, l = e != null ? e.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m }; + let t8 = r, o, n, s, a, i, p, u, c, l, m; + return A().getNumber("WEBGL_VERSION") === 2 ? (o = t8.R32F, n = t8.R16F, s = t8.RGBA16F, a = t8.RGBA32F, i = t8.RED, u = 4, c = 1, l = t8.HALF_FLOAT, m = t8.FLOAT, p = t8.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t8.RGBA, i = r.RGBA, u = 4, c = 4, l = e != null ? e.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m }; } function ce(r, e) { - let t10 = e(); - return P().getBool("DEBUG") && u7(r), t10; + let t8 = e(); + return A().getBool("DEBUG") && OQ(r), t8; } -function u7(r) { +function OQ(r) { let e = r.getError(); if (e !== r.NO_ERROR) - throw new Error("WebGL Error: " + QE(r, e)); + throw new Error("WebGL Error: " + cR(r, e)); } -var p7 = 596e-10; -var c7 = 65504; -function cI(r) { - return !!(P().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || p7 < Math.abs(r) && Math.abs(r) < c7); +var MQ = 596e-10; +var LQ = 65504; +function bI(r) { + return !!(A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || MQ < Math.abs(r) && Math.abs(r) < LQ); } -function QE(r, e) { +function cR(r, e) { switch (e) { case r.NO_ERROR: return "NO_ERROR"; @@ -15321,31 +15397,31 @@ function QE(r, e) { return `Unknown error code ${e}`; } } -function kc(r, e) { - return mi(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.'); +function Nc(r, e) { + return hi(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.'); } -function lI(r, e) { - let t10 = mi(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader."); - if (ce(r, () => r.shaderSource(t10, e)), ce(r, () => r.compileShader(t10)), r.getShaderParameter(t10, r.COMPILE_STATUS) === false) - throw console.log(r.getShaderInfoLog(t10)), new Error("Failed to compile vertex shader."); - return t10; +function CI(r, e) { + let t8 = hi(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader."); + if (ce(r, () => r.shaderSource(t8, e)), ce(r, () => r.compileShader(t8)), r.getShaderParameter(t8, r.COMPILE_STATUS) === false) + throw console.log(r.getShaderInfoLog(t8)), new Error("Failed to compile vertex shader."); + return t8; } -function mI(r, e) { - let t10 = mi(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); - if (ce(r, () => r.shaderSource(t10, e)), ce(r, () => r.compileShader(t10)), P().get("ENGINE_COMPILE_ONLY")) - return t10; - if (r.getShaderParameter(t10, r.COMPILE_STATUS) === false) - throw Gf(e, r.getShaderInfoLog(t10)), new Error("Failed to compile fragment shader."); - return t10; +function wI(r, e) { + let t8 = hi(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); + if (ce(r, () => r.shaderSource(t8, e)), ce(r, () => r.compileShader(t8)), A().get("ENGINE_COMPILE_ONLY")) + return t8; + if (r.getShaderParameter(t8, r.COMPILE_STATUS) === false) + throw Hf(e, r.getShaderInfoLog(t8)), new Error("Failed to compile fragment shader."); + return t8; } -var l7 = /ERROR: [0-9]+:([0-9]+):/g; -function Gf(r, e) { - let t10 = l7.exec(e); - if (t10 == null) { +var BQ = /ERROR: [0-9]+:([0-9]+):/g; +function Hf(r, e) { + let t8 = BQ.exec(e); + if (t8 == null) { console.log(`Couldn't parse line number in error: ${e}`), console.log(r); return; } - let o = +t10[1], n = r.split(` + let o = +t8[1], n = r.split(` `), s = n.length.toString().length + 2, a = n.map((l, m) => y.rightPad((m + 1).toString(), s) + l), i = 0; for (let l = 0; l < a.length; l++) i = Math.max(a[l].length, i); @@ -15355,79 +15431,79 @@ function Gf(r, e) { `)[0]), console.log(`%c ${y.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(` `)); } -function dI(r) { - return mi(r, () => r.createProgram(), "Unable to create WebGLProgram."); +function SI(r) { + return hi(r, () => r.createProgram(), "Unable to create WebGLProgram."); } -function fI(r, e) { - if (ce(r, () => r.linkProgram(e)), !P().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false) +function II(r, e) { + if (ce(r, () => r.linkProgram(e)), !A().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false) throw console.log(r.getProgramInfoLog(e)), new Error("Failed to link vertex and fragment shaders."); } function ql(r, e) { if (ce(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false) throw console.log(r.getProgramInfoLog(e)), new Error("Shader program validation failed."); } -function hI(r, e) { - let t10 = mi(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); - return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), ce(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t10; +function vI(r, e) { + let t8 = hi(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); + return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t8)), ce(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t8; } -function gI(r, e) { - let t10 = mi(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); - return ce(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t10)), ce(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t10; +function kI(r, e) { + let t8 = hi(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); + return ce(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t8)), ce(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t8; } -function m7() { - return P().getNumber("WEBGL_VERSION") === 2 ? 1 : 4; +function zQ() { + return A().getNumber("WEBGL_VERSION") === 2 ? 1 : 4; } -function xI(r) { - return mi(r, () => r.createTexture(), "Unable to create WebGLTexture."); +function NI(r) { + return hi(r, () => r.createTexture(), "Unable to create WebGLTexture."); } -function yI(r, e) { - let t10 = P().getNumber("WEBGL_MAX_TEXTURE_SIZE"); +function TI(r, e) { + let t8 = A().getNumber("WEBGL_MAX_TEXTURE_SIZE"); if (r <= 0 || e <= 0) { let o = `[${r}x${e}]`; throw new Error("Requested texture size " + o + " is invalid."); } - if (r > t10 || e > t10) { - let o = `[${r}x${e}]`, n = `[${t10}x${t10}]`; + if (r > t8 || e > t8) { + let o = `[${r}x${e}]`, n = `[${t8}x${t8}]`; throw new Error("Requested texture size " + o + " greater than WebGL maximum on this browser / GPU " + n + "."); } } -function bI(r) { - return mi(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer."); +function _I(r) { + return hi(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer."); } -function Hf(r, e, t10, o, n, s, a) { - let i = r.getAttribLocation(e, t10); +function Kf(r, e, t8, o, n, s, a) { + let i = r.getAttribLocation(e, t8); return i === -1 ? false : (ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), ce(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), ce(r, () => r.enableVertexAttribArray(i)), true); } -function ZE(r, e, t10) { - eR(r, t10), ce(r, () => r.activeTexture(r.TEXTURE0 + t10)), ce(r, () => r.bindTexture(r.TEXTURE_2D, e)); +function lR(r, e, t8) { + dR(r, t8), ce(r, () => r.activeTexture(r.TEXTURE0 + t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, e)); } -function d7(r, e) { - eR(r, e), ce(r, () => r.activeTexture(r.TEXTURE0 + e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); +function VQ(r, e) { + dR(r, e), ce(r, () => r.activeTexture(r.TEXTURE0 + e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); } -function CI(r, e, t10) { - return mi(r, () => r.getUniformLocation(e, t10), 'uniform "' + t10 + '" not present in program.'); +function $I(r, e, t8) { + return hi(r, () => r.getUniformLocation(e, t8), 'uniform "' + t8 + '" not present in program.'); } -function wI(r, e, t10) { - return r.getUniformLocation(e, t10); +function EI(r, e, t8) { + return r.getUniformLocation(e, t8); } -function SI(r, e, t10, o) { - ce(r, () => ZE(r, e, o)), ce(r, () => r.uniform1i(t10, o)); +function RI(r, e, t8, o) { + ce(r, () => lR(r, e, o)), ce(r, () => r.uniform1i(t8, o)); } -function f7(r) { +function WQ(r) { ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), ce(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), ce(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height)); } -function jl(r, e, t10) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t10)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0)); +function jl(r, e, t8) { + ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t8)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0)); } -function Kf(r, e) { +function qf(r, e) { ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0)); } -function Nc(r) { +function Tc(r) { let e = r.checkFramebufferStatus(r.FRAMEBUFFER); if (e !== r.FRAMEBUFFER_COMPLETE) - throw new Error("Error binding framebuffer: " + JE(r, e)); + throw new Error("Error binding framebuffer: " + mR(r, e)); } -function JE(r, e) { +function mR(r, e) { switch (e) { case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT: return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT"; @@ -15441,159 +15517,159 @@ function JE(r, e) { return `unknown error ${e}`; } } -function mi(r, e, t10) { +function hi(r, e, t8) { let o = ce(r, () => e()); if (o == null) - throw new Error(t10); + throw new Error(t8); return o; } -function eR(r, e) { - let t10 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0; - if (o < r.TEXTURE0 || o > t10) { - let n = `[gl.TEXTURE0, gl.TEXTURE${t10}]`; +function dR(r, e) { + let t8 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0; + if (o < r.TEXTURE0 || o > t8) { + let n = `[gl.TEXTURE0, gl.TEXTURE${t8}]`; throw new Error(`textureUnit must be in ${n}.`); } } -function di(r, e = 2) { +function gi(r, e = 2) { return y.sizeFromShape(r.slice(0, r.length - e)); } -function fi(r) { +function xi(r) { if (r.length === 0) throw Error("Cannot get rows and columns of an empty shape array."); return [r.length > 1 ? r[r.length - 2] : 1, r[r.length - 1]]; } -function Tc(r) { +function _c(r) { let e = [1, 1, 1]; - return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [di(r), ...fi(r)]), e; + return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [gi(r), ...xi(r)]), e; } -function II(r, e = false) { - let t10 = P().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = P().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); - o === 1 / 0 && P().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t10 / 2), e && (t10 = t10 * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape); +function DI(r, e = false) { + let t8 = A().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = A().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); + o === 1 / 0 && A().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t8 / 2), e && (t8 = t8 * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape); let n = y.sizeFromShape(r), s = null; - r.length <= 1 && n <= t10 ? s = [1, n] : r.length === 2 && r[0] <= t10 && r[1] <= t10 ? s = r : r.length === 3 && r[0] * r[1] <= t10 && r[2] <= t10 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t10 && r[1] * r[2] <= t10 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t10 && r[3] <= t10 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t10 && r[1] * r[2] * r[3] <= t10 && (s = [r[0], r[1] * r[2] * r[3]]); + r.length <= 1 && n <= t8 ? s = [1, n] : r.length === 2 && r[0] <= t8 && r[1] <= t8 ? s = r : r.length === 3 && r[0] * r[1] <= t8 && r[2] <= t8 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t8 && r[1] * r[2] <= t8 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t8 && r[3] <= t8 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t8 && r[1] * r[2] * r[3] <= t8 && (s = [r[0], r[1] * r[2] * r[3]]); let a = s != null && Math.max(...s) > o && Math.min(...s) <= (e ? 2 : 1) && Math.min(...s) > 0; if (s == null || a) if (e) { - let i = di(r), p = 2, u = 2; - r.length && ([p, u] = fi(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2); + let i = gi(r), p = 2, u = 2; + r.length && ([p, u] = xi(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2); } else s = y.sizeToSquarishShape(n); return s; } -function Vf(r) { +function Wf(r) { return r % 2 === 0; } -function fu(r, e) { +function yu(r, e) { if (r = r.slice(-2), e = e.slice(-2), y.arraysEqual(r, e) || !r.length || !e.length || r[0] === 0 || r[1] === 0 || e[0] === 0 || e[1] === 0) return true; if (r.length !== e.length) { - let t10 = r[r.length - 1], o = e[e.length - 1]; - if (t10 === o || Vf(t10) && Vf(o) && (r[0] === 1 || e[0] === 1)) + let t8 = r[r.length - 1], o = e[e.length - 1]; + if (t8 === o || Wf(t8) && Wf(o) && (r[0] === 1 || e[0] === 1)) return true; } - return r[1] === e[1] && Vf(r[0]) && Vf(e[0]); + return r[1] === e[1] && Wf(r[0]) && Wf(e[0]); } -var Wf; var Uf; -function vI(r) { - if (Wf == null) { - let e = Gr(r); - Wf = e.getParameter(e.MAX_TEXTURE_SIZE); +var Gf; +function AI(r) { + if (Uf == null) { + let e = Kr(r); + Uf = e.getParameter(e.MAX_TEXTURE_SIZE); } - return Wf; + return Uf; } -function h7() { - Wf = null; -} -function g7() { +function UQ() { Uf = null; } -function kI(r) { - if (Uf == null) { - let e = Gr(r); - Uf = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS); - } - return Math.min(16, Uf); +function GQ() { + Gf = null; } -function NI(r) { +function FI(r) { + if (Gf == null) { + let e = Kr(r); + Gf = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS); + } + return Math.min(16, Gf); +} +function PI(r) { if (r === 0) return 0; - let e, t10 = Gr(r); - return Hr(t10, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : Hr(t10, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e; + let e, t8 = Kr(r); + return qr(t8, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : qr(t8, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e; } -function Hr(r, e) { +function qr(r, e) { return r.getExtension(e) != null; } -function qf(r) { +function jf(r) { try { - if (Gr(r) != null) + if (Kr(r) != null) return true; } catch (e) { return console.log("Error when getting WebGL context: ", e), false; } return false; } -function TI(r) { +function OI(r) { if (r === 0) return false; - let e = Gr(r); + let e = Kr(r); if (r === 1) { - if (!Hr(e, "OES_texture_float")) + if (!qr(e, "OES_texture_float")) return false; - } else if (!Hr(e, "EXT_color_buffer_float")) + } else if (!qr(e, "EXT_color_buffer_float")) return false; - return pI(e); + return yI(e); } -function _I(r) { +function MI(r) { if (r === 0) return false; - let e = Gr(r); + let e = Kr(r); if (r === 1) { - if (!Hr(e, "OES_texture_float") || !Hr(e, "WEBGL_color_buffer_float")) + if (!qr(e, "OES_texture_float") || !qr(e, "WEBGL_color_buffer_float")) return false; } else { - if (Hr(e, "EXT_color_buffer_float")) - return pI(e); + if (qr(e, "EXT_color_buffer_float")) + return yI(e); let o = "EXT_color_buffer_half_float"; - if (Hr(e, o)) { + if (qr(e, o)) { let n = e.getExtension(o); - return x7(e, n); + return HQ(e, n); } return false; } - return pI(e); + return yI(e); } -function pI(r) { - let e = Kl(r), t10 = r.createTexture(); - r.bindTexture(r.TEXTURE_2D, t10); +function yI(r) { + let e = Kl(r), t8 = r.createTexture(); + r.bindTexture(r.TEXTURE_2D, t8); let o = 1, n = 1; r.texImage2D(r.TEXTURE_2D, 0, e.internalFormatFloat, o, n, 0, e.textureFormatFloat, e.textureTypeFloat, null); let s = r.createFramebuffer(); - r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t10, 0); + r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t8, 0); let a = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE; - return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t10), r.deleteFramebuffer(s), a; + return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t8), r.deleteFramebuffer(s), a; } -function x7(r, e) { - let t10 = Kl(r, e), o = r.createTexture(); +function HQ(r, e) { + let t8 = Kl(r, e), o = r.createTexture(); r.bindTexture(r.TEXTURE_2D, o); let n = 1, s = 1; - r.texImage2D(r.TEXTURE_2D, 0, t10.internalFormatHalfFloat, n, s, 0, t10.textureFormatFloat, t10.textureTypeHalfFloat, null); + r.texImage2D(r.TEXTURE_2D, 0, t8.internalFormatHalfFloat, n, s, 0, t8.textureFormatFloat, t8.textureTypeHalfFloat, null); let a = r.createFramebuffer(); r.bindFramebuffer(r.FRAMEBUFFER, a), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, o, 0); let i = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE; return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(o), r.deleteFramebuffer(a), i; } -function $I(r) { - return r !== 2 ? false : Gr(r).fenceSync != null; +function LI(r) { + return r !== 2 ? false : Kr(r).fenceSync != null; } -function Ps(r, e) { - Array.isArray(r) || (r = [r]), r.forEach((t10) => { - t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`); +function Bs(r, e) { + Array.isArray(r) || (r = [r]), r.forEach((t8) => { + t8 != null && y.assert(t8.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`); }); } -var Se = P(); +var Se = A(); Se.registerFlag("HAS_WEBGL", () => Se.getNumber("WEBGL_VERSION") > 0); -Se.registerFlag("WEBGL_VERSION", () => qf(2) ? 2 : qf(1) ? 1 : 0); +Se.registerFlag("WEBGL_VERSION", () => jf(2) ? 2 : jf(1) ? 1 : 0); Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); Se.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Se.get("WEBGL_VERSION") === 2); Se.registerFlag("WEBGL_CPU_FORWARD", () => true); @@ -15609,23 +15685,23 @@ Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Se.getBool("WEBGL_PACK")); Se.registerFlag("WEBGL_PACK_REDUCE", () => Se.getBool("WEBGL_PACK")); Se.registerFlag("WEBGL_LAZILY_UNPACK", () => Se.getBool("WEBGL_PACK")); Se.registerFlag("WEBGL_CONV_IM2COL", () => Se.getBool("WEBGL_PACK")); -Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => vI(Se.getNumber("WEBGL_VERSION"))); -Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => kI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => AI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => FI(Se.getNumber("WEBGL_VERSION"))); Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { let r = Se.getNumber("WEBGL_VERSION"); - return r === 0 ? 0 : NI(r); + return r === 0 ? 0 : PI(r); }); -Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !Zi.isMobile()); -Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => TI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !ru.isMobile()); +Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => OI(Se.getNumber("WEBGL_VERSION"))); Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Se.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")); -Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => _I(Se.getNumber("WEBGL_VERSION"))); -Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => $I(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => MI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => LI(Se.getNumber("WEBGL_VERSION"))); Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0); Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r) => { if (r < 0 && r !== -1) throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`); }); -Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => Zi.isMobile() ? 1 : -1, (r) => { +Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => ru.isMobile() ? 1 : -1, (r) => { if (r < 0 && r !== -1) throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`); }); @@ -15639,9 +15715,9 @@ Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0); Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false); Se.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false); Se.registerFlag("ENGINE_COMPILE_ONLY", () => false); -function It() { - let r, e, t10, o, n, s, a, i, p, u; - return P().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t10 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = P().getBool("WEBGL2_ISNAN_CUSTOM") ? ` +function vt() { + let r, e, t8, o, n, s, a, i, p, u; + return A().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t8 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = A().getBool("WEBGL2_ISNAN_CUSTOM") ? ` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -15662,7 +15738,7 @@ function It() { ivec4 newRound(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `) : (r = "", e = "attribute", t10 = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = ` + `) : (r = "", e = "attribute", t8 = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = ` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; @@ -15687,52 +15763,52 @@ function It() { ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `), { version: r, attribute: e, varyingVs: t10, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u }; + `), { version: r, attribute: e, varyingVs: t8, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u }; } -function Os(r, e, t10 = "index") { +function zs(r, e, t8 = "index") { let o = y.computeStrides(e); return o.map((n, s) => { - let a = `int ${r[s]} = ${t10} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`; + let a = `int ${r[s]} = ${t8} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t8} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`; return `${a}; ${i};`; }).join(""); } -function hp(r, e, t10 = "index") { +function bp(r, e, t8 = "index") { let o = y.computeStrides(e); return o.map((n, s) => { - let a = `int ${r[s]} = ${t10} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`; + let a = `int ${r[s]} = ${t8} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t8} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`; return `${a}; ${i};`; }).join(""); } -function y7(r, e) { - let t10 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t10 - 1); - n[t10 - 2] = o[t10 - 1]; - for (let s = t10 - 3; s >= 0; --s) +function KQ(r, e) { + let t8 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t8 - 1); + n[t8 - 2] = o[t8 - 1]; + for (let s = t8 - 3; s >= 0; --s) n[s] = `(${n[s + 1]} * ${o[s + 1]})`; return n; } -function tR(r, e, t10 = "index") { - let o = r.map((s, a) => a), n = y7(o, e); +function fR(r, e, t8 = "index") { + let o = r.map((s, a) => a), n = KQ(o, e); return n.map((s, a) => { - let i = `int ${r[a]} = ${t10} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t10} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`; + let i = `int ${r[a]} = ${t8} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t8} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`; return `${i}; ${p};`; }).join(""); } -function $c(r) { - let e = y.computeStrides(r).map((t10) => t10.toString()); +function Ec(r) { + let e = y.computeStrides(r).map((t8) => t8.toString()); return ` int getFlatIndex(ivec3 coords) { return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z; } `; } -function Ec() { +function Rc() { return ` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } `; } -var jf = ` +var Xf = ` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -15772,13 +15848,13 @@ var jf = ` return c / 255.0; } `; -var { getBroadcastDims: rR } = C; -function oR(r, e, t10) { +var { getBroadcastDims: hR } = w; +function gR(r, e, t8) { let o = []; if (r.forEach((d) => { let f = y.sizeFromShape(d.shapeInfo.logicalShape); - if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), t10.enableShapeUniforms) { - let { uniformShape: h } = Xf(t10.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape); + if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), t8.enableShapeUniforms) { + let { uniformShape: h } = Yf(t8.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape); switch (h.length) { case 1: o.push(`uniform int ${d.name}Shape;`); @@ -15797,7 +15873,7 @@ function oR(r, e, t10) { } o.push(`uniform ivec2 ${d.name}TexShape;`); } - }), t10.enableShapeUniforms) { + }), t8.enableShapeUniforms) { switch (e.logicalShape.length) { case 1: o.push("uniform int outShape;"); @@ -15816,112 +15892,112 @@ function oR(r, e, t10) { } o.push("uniform ivec2 outTexShape;"); } - t10.customUniforms && t10.customUniforms.forEach((d) => { + t8.customUniforms && t8.customUniforms.forEach((d) => { o.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`); }); let n = o.join(` -`), s = r.map((d) => b7(d, e, t10.packedInputs, t10.enableShapeUniforms)).join(` -`), a = e.texShape, i = It(), p = S7(i), u, c, l = k7(i); - return e.isPacked ? (u = C7(e.logicalShape, a, t10.enableShapeUniforms), c = v7(i)) : (u = w7(e.logicalShape, a, t10.enableShapeUniforms), c = I7(i)), t10.packedInputs && (l += $7), [l, p, c, n, u, s, t10.userCode].join(` +`), s = r.map((d) => qQ(d, e, t8.packedInputs, t8.enableShapeUniforms)).join(` +`), a = e.texShape, i = vt(), p = YQ(i), u, c, l = JQ(i); + return e.isPacked ? (u = jQ(e.logicalShape, a, t8.enableShapeUniforms), c = ZQ(i)) : (u = XQ(e.logicalShape, a, t8.enableShapeUniforms), c = QQ(i)), t8.packedInputs && (l += oZ), [l, p, c, n, u, s, t8.userCode].join(` `); } -function Dc(r, e = false) { - let t10 = r.shapeInfo.logicalShape; - switch (t10.length) { +function Ac(r, e = false) { + let t8 = r.shapeInfo.logicalShape; + switch (t8.length) { case 0: - return V7(r, e); + return hZ(r, e); case 1: - return U7(r, e); + return xZ(r, e); case 2: - return H7(r, e); + return bZ(r, e); case 3: - return q7(r, e); + return wZ(r, e); case 4: - return X7(r, e); + return IZ(r, e); case 5: - return Y7(r); + return vZ(r); case 6: - return Q7(r); + return kZ(r); default: - throw new Error(`${t10.length}-D input sampling is not yet supported`); + throw new Error(`${t8.length}-D input sampling is not yet supported`); } } -function nR(r, e) { +function xR(r, e) { switch (r.shapeInfo.logicalShape.length) { case 0: - return z7(r); + return fZ(r); case 1: - return W7(r, e); + return gZ(r, e); case 2: - return G7(r, e); + return yZ(r, e); case 3: - return K7(r, e); + return CZ(r, e); default: - return j7(r, e); + return SZ(r, e); } } -function b7(r, e, t10 = false, o) { +function qQ(r, e, t8 = false, o) { let n = ""; - t10 ? n += nR(r, o) : n += Dc(r, o); + t8 ? n += xR(r, o) : n += Ac(r, o); let s = r.shapeInfo.logicalShape, a = e.logicalShape; - return s.length <= a.length && (t10 ? n += Z7(r, e) : n += J7(r, e)), n; + return s.length <= a.length && (t8 ? n += NZ(r, e) : n += TZ(r, e)), n; } -function C7(r, e, t10) { +function jQ(r, e, t8) { switch (r.length) { case 0: - return sR(); + return yR(); case 1: - return E7(r, e, t10); + return nZ(r, e, t8); case 2: - return L7(r, e, t10); + return mZ(r, e, t8); case 3: - return D7(r, e, t10); + return aZ(r, e, t8); default: - return F7(r, e, t10); + return uZ(r, e, t8); } } -function w7(r, e, t10) { +function XQ(r, e, t8) { switch (r.length) { case 0: - return sR(); + return yR(); case 1: - return R7(r, e, t10); + return sZ(r, e, t8); case 2: - return B7(r, e, t10); + return dZ(r, e, t8); case 3: - return A7(r, e, t10); + return iZ(r, e, t8); case 4: - return P7(r, e, t10); + return pZ(r, e, t8); case 5: - return O7(r, e); + return cZ(r, e); case 6: - return M7(r, e); + return lZ(r, e); default: throw new Error(`${r.length}-D output sampling is not yet supported`); } } -function S7(r) { +function YQ(r) { return ` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } `; } -function I7(r) { +function QQ(r) { return ` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } `; } -function v7(r) { +function ZQ(r) { return ` void setOutput(vec4 val) { ${r.output} = val; } `; } -function k7(r) { +function JQ(r) { return `${r.version} precision highp float; precision highp int; @@ -15977,12 +16053,12 @@ function k7(r) { return fract((p3.x + p3.y) * p3.z); } - ${N7} - ${T7} - ${_7} + ${eZ} + ${tZ} + ${rZ} `; } -var N7 = ` +var eZ = ` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -15995,7 +16071,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `; -var T7 = ` +var tZ = ` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -16004,7 +16080,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `; -var _7 = ` +var rZ = ` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -16014,7 +16090,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `; -var $7 = ` +var oZ = ` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -16026,16 +16102,16 @@ var $7 = ` return modCoord == 0. ? frag.r : frag.g; } `; -function sR() { +function yR() { return ` int getOutputCoords() { return 0; } `; } -function E7(r, e, t10) { +function nZ(r, e, t8) { let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)]; - return o[0] === 1 ? t10 ? ` + return o[0] === 1 ? t8 ? ` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -16043,7 +16119,7 @@ function E7(r, e, t10) { int getOutputCoords() { return 2 * int(resultUV.x * ${o[1]}.0); } - ` : o[1] === 1 ? t10 ? ` + ` : o[1] === 1 ? t8 ? ` int getOutputCoords() { return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); } @@ -16051,7 +16127,7 @@ function E7(r, e, t10) { int getOutputCoords() { return 2 * int(resultUV.y * ${o[0]}.0); } - ` : t10 ? ` + ` : t8 ? ` int getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -16066,8 +16142,8 @@ function E7(r, e, t10) { } `; } -function R7(r, e, t10) { - return e[0] === 1 ? t10 ? ` +function sZ(r, e, t8) { + return e[0] === 1 ? t8 ? ` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -16075,7 +16151,7 @@ function R7(r, e, t10) { int getOutputCoords() { return int(resultUV.x * ${e[1]}.0); } - ` : e[1] === 1 ? t10 ? ` + ` : e[1] === 1 ? t8 ? ` int getOutputCoords() { return int(resultUV.y * float(outTexShape[0])); } @@ -16083,7 +16159,7 @@ function R7(r, e, t10) { int getOutputCoords() { return int(resultUV.y * ${e[0]}.0); } - ` : t10 ? ` + ` : t8 ? ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -16097,8 +16173,8 @@ function R7(r, e, t10) { } `; } -function D7(r, e, t10) { - if (t10) +function aZ(r, e, t8) { + if (t8) return ` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); @@ -16134,18 +16210,18 @@ function D7(r, e, t10) { } `; } -function A7(r, e, t10) { - if (t10) +function iZ(r, e, t8) { + if (t8) return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${hp(["r", "c", "d"], r)} + ${bp(["r", "c", "d"], r)} return ivec3(r, c, d); } `; - let o = Os(["r", "c", "d"], r); + let o = zs(["r", "c", "d"], r); return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -16156,8 +16232,8 @@ function A7(r, e, t10) { } `; } -function F7(r, e, t10) { - if (t10) +function uZ(r, e, t8) { + if (t8) return ` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); @@ -16205,18 +16281,18 @@ function F7(r, e, t10) { } `; } -function P7(r, e, t10) { - if (t10) +function pZ(r, e, t8) { + if (t8) return ` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${hp(["r", "c", "d", "d2"], r)} + ${bp(["r", "c", "d", "d2"], r)} return ivec4(r, c, d, d2); } `; - let o = Os(["r", "c", "d", "d2"], r); + let o = zs(["r", "c", "d", "d2"], r); return ` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -16227,8 +16303,8 @@ function P7(r, e, t10) { } `; } -function O7(r, e) { - let t10 = Os(["r", "c", "d", "d2", "d3"], r); +function cZ(r, e) { + let t8 = zs(["r", "c", "d", "d2", "d3"], r); return ` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, @@ -16236,32 +16312,32 @@ function O7(r, e) { int index = resTexRC.x * ${e[1]} + resTexRC.y; - ${t10} + ${t8} ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } `; } -function M7(r, e) { - let t10 = Os(["r", "c", "d", "d2", "d3", "d4"], r); +function lZ(r, e) { + let t8 = zs(["r", "c", "d", "d2", "d3", "d4"], r); return ` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); int index = resTexRC.x * ${e[1]} + resTexRC.y; - ${t10} + ${t8} ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } `; } -function L7(r, e, t10) { +function mZ(r, e, t8) { let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)]; if (y.arraysEqual(r, e)) - return t10 ? ` + return t8 ? ` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -16272,7 +16348,7 @@ function L7(r, e, t10) { } `; let n = Math.ceil(r[1] / 2); - return t10 ? ` + return t8 ? ` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0)); @@ -16298,8 +16374,8 @@ function L7(r, e, t10) { } `; } -function B7(r, e, t10) { - return y.arraysEqual(r, e) ? t10 ? ` +function dZ(r, e, t8) { + return y.arraysEqual(r, e) ? t8 ? ` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -16307,7 +16383,7 @@ function B7(r, e, t10) { ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); } - ` : r[1] === 1 ? t10 ? ` + ` : r[1] === 1 ? t8 ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -16321,7 +16397,7 @@ function B7(r, e, t10) { int index = resTexRC.x * ${e[1]} + resTexRC.y; return ivec2(index, 0); } - ` : r[0] === 1 ? t10 ? ` + ` : r[0] === 1 ? t8 ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -16335,7 +16411,7 @@ function B7(r, e, t10) { int index = resTexRC.x * ${e[1]} + resTexRC.y; return ivec2(0, index); } - ` : t10 ? ` + ` : t8 ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -16355,53 +16431,53 @@ function B7(r, e, t10) { } `; } -function gp(r) { +function Cp(r) { return `offset${r}`; } -function z7(r) { - let e = r.name, t10 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = It(); +function fZ(r) { + let e = r.name, t8 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = vt(); return ` - vec4 ${t10}() { + vec4 ${t8}() { return ${o.texture2D}(${e}, halfCR); } `; } -function V7(r, e) { - let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1); +function hZ(r, e) { + let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1); if (r.shapeInfo.isUniform) - return `float ${o}() {return ${t10};}`; + return `float ${o}() {return ${t8};}`; let [n, s] = r.shapeInfo.texShape; if (n === 1 && s === 1) return ` float ${o}() { - return sampleTexture(${t10}, halfCR); + return sampleTexture(${t8}, halfCR); } `; - let a = gp(t10); + let a = Cp(t8); if (e) return ` float ${o}() { - vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], ${a}); - return sampleTexture(${t10}, uv); + vec2 uv = uvFromFlat(${t8}TexShape[0], ${t8}TexShape[1], ${a}); + return sampleTexture(${t8}, uv); } `; let [i, p] = r.shapeInfo.texShape; return ` float ${o}() { vec2 uv = uvFromFlat(${i}, ${p}, ${a}); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; } -function W7(r, e) { - let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = r.shapeInfo.texShape, s = It(); +function gZ(r, e) { + let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), n = r.shapeInfo.texShape, s = vt(); if (e) return ` vec4 ${o}(int index) { - ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}TexShape[1]) / 2.0)); + ivec2 packedTexShape = ivec2(ceil(float(${t8}TexShape[0]) / 2.0), ceil(float(${t8}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( packedTexShape[0], packedTexShape[1], index); - return ${s.texture2D}(${t10}, uv); + return ${s.texture2D}(${t8}, uv); } `; let a = [Math.ceil(n[0] / 2), Math.ceil(n[1] / 2)]; @@ -16409,61 +16485,61 @@ function W7(r, e) { vec4 ${o}(int index) { vec2 uv = packedUVfrom1D( ${a[0]}, ${a[1]}, index); - return ${s.texture2D}(${t10}, uv); + return ${s.texture2D}(${t8}, uv); } `; } -function U7(r, e) { - let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1); +function xZ(r, e) { + let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1); if (r.shapeInfo.isUniform) return ` float ${o}(int index) { - ${Ac(r)} + ${Fc(r)} } `; let n = r.shapeInfo.texShape, s = n[0], a = n[1]; if (a === 1 && s === 1) return ` float ${o}(int index) { - return sampleTexture(${t10}, halfCR); + return sampleTexture(${t8}, halfCR); } `; - let i = gp(t10); + let i = Cp(t8); return a === 1 ? e ? ` float ${o}(int index) { - vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t10}TexShape[0])); - return sampleTexture(${t10}, uv); + vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t8}TexShape[0])); + return sampleTexture(${t8}, uv); } ` : ` float ${o}(int index) { vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } ` : s === 1 ? e ? ` float ${o}(int index) { - vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t10}TexShape[1]), 0.5); - return sampleTexture(${t10}, uv); + vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t8}TexShape[1]), 0.5); + return sampleTexture(${t8}, uv); } ` : ` float ${o}(int index) { vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } ` : e ? ` float ${o}(int index) { - vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], index + ${i}); - return sampleTexture(${t10}, uv); + vec2 uv = uvFromFlat(${t8}TexShape[0], ${t8}TexShape[1], index + ${i}); + return sampleTexture(${t8}, uv); } ` : ` float ${o}(int index) { vec2 uv = uvFromFlat(${s}, ${a}, index + ${i}); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; } -function G7(r, e) { - let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = It(); - if (s != null && y.arraysEqual(t10, s)) +function yZ(r, e) { + let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = vt(); + if (s != null && y.arraysEqual(t8, s)) return e ? ` vec4 ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); @@ -16486,7 +16562,7 @@ function G7(r, e) { return ${p.texture2D}(${o}, uv); } `; - let u = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)], c = Math.ceil(t10[1] / 2); + let u = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)], c = Math.ceil(t8[1] / 2); return ` vec4 ${n}(int row, int col) { vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col); @@ -16494,9 +16570,9 @@ function G7(r, e) { } `; } -function H7(r, e) { - let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape; - if (s != null && y.arraysEqual(t10, s)) { +function bZ(r, e) { + let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape; + if (s != null && y.arraysEqual(t8, s)) { if (e) return ` float ${n}(int row, int col) { @@ -16512,24 +16588,24 @@ function H7(r, e) { } `; } - let { newShape: a, keptDims: i } = y.squeezeShape(t10), p = a; - if (p.length < t10.length) { - let m = Fc(r, p), d = ["row", "col"]; + let { newShape: a, keptDims: i } = y.squeezeShape(t8), p = a; + if (p.length < t8.length) { + let m = Pc(r, p), d = ["row", "col"]; return ` - ${Dc(m, e)} + ${Ac(m, e)} float ${n}(int row, int col) { - return ${n}(${Pc(d, i)}); + return ${n}(${Oc(d, i)}); } `; } if (r.shapeInfo.isUniform) return ` float ${n}(int row, int col) { - int index = round(dot(vec2(row, col), vec2(${t10[1]}, 1))); - ${Ac(r)} + int index = round(dot(vec2(row, col), vec2(${t8[1]}, 1))); + ${Fc(r)} } `; - let u = s[0], c = s[1], l = gp(o); + let u = s[0], c = s[1], l = Cp(o); return c === 1 ? e ? ` float ${n}(int row, int col) { float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1)); @@ -16538,7 +16614,7 @@ function H7(r, e) { } ` : ` float ${n}(int row, int col) { - float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1)); + float index = dot(vec3(row, col, ${l}), vec3(${t8[1]}, 1, 1)); vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0); return sampleTexture(${o}, uv); } @@ -16550,7 +16626,7 @@ function H7(r, e) { } ` : ` float ${n}(int row, int col) { - float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1)); + float index = dot(vec3(row, col, ${l}), vec3(${t8[1]}, 1, 1)); vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5); return sampleTexture(${o}, uv); } @@ -16564,24 +16640,24 @@ function H7(r, e) { ` : ` float ${n}(int row, int col) { // Explicitly use integer operations as dot() only works on floats. - int index = row * ${t10[1]} + col + ${l}; + int index = row * ${t8[1]} + col + ${l}; vec2 uv = uvFromFlat(${u}, ${c}, index); return sampleTexture(${o}, uv); } `; } -function K7(r, e) { - let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)]; - if (t10[0] === 1) { - let m = t10.slice(1), d = [1, 2], f = Fc(r, m), h = ["b", "row", "col"]; +function CZ(r, e) { + let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)]; + if (t8[0] === 1) { + let m = t8.slice(1), d = [1, 2], f = Pc(r, m), h = ["b", "row", "col"]; return ` - ${nR(f, e)} + ${xR(f, e)} vec4 ${n}(int b, int row, int col) { - return ${n}(${Pc(h, d)}); + return ${n}(${Oc(h, d)}); } `; } - let i = It(); + let i = vt(); if (e) return ` vec4 ${n}(int b, int row, int col) { @@ -16593,7 +16669,7 @@ function K7(r, e) { return ${i.texture2D}(${o}, uv); } `; - let p = a[0], u = a[1], c = Math.ceil(t10[2] / 2), l = c * Math.ceil(t10[1] / 2); + let p = a[0], u = a[1], c = Math.ceil(t8[2] / 2), l = c * Math.ceil(t8[1] / 2); return ` vec4 ${n}(int b, int row, int col) { vec2 uv = packedUVfrom3D( @@ -16602,14 +16678,14 @@ function K7(r, e) { } `; } -function q7(r, e) { - let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[1] * t10[2], a = t10[2], { newShape: i, keptDims: p } = y.squeezeShape(t10), u = i; - if (u.length < t10.length) { - let h = Fc(r, u), g = ["row", "col", "depth"]; +function wZ(r, e) { + let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t8[1] * t8[2], a = t8[2], { newShape: i, keptDims: p } = y.squeezeShape(t8), u = i; + if (u.length < t8.length) { + let h = Pc(r, u), g = ["row", "col", "depth"]; return ` - ${Dc(h, e)} + ${Ac(h, e)} float ${n}(int row, int col, int depth) { - return ${n}(${Pc(g, p)}); + return ${n}(${Oc(g, p)}); } `; } @@ -16618,7 +16694,7 @@ function q7(r, e) { float ${n}(int row, int col, int depth) { int index = round(dot(vec3(row, col, depth), vec3(${s}, ${a}, 1))); - ${Ac(r)} + ${Fc(r)} } `; let c = r.shapeInfo.texShape, l = c[0], m = c[1], d = r.shapeInfo.flatOffset; @@ -16651,13 +16727,13 @@ function q7(r, e) { } ` : ` float ${n}(int row, int col, int depth) { - float texR = dot(vec2(row, col), vec2(${t10[1]}, 1)); + float texR = dot(vec2(row, col), vec2(${t8[1]}, 1)); float texC = float(depth); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0); return sampleTexture(${o}, uv); } `; - let f = gp(o); + let f = Cp(o); return e ? ` float ${n}(int row, int col, int depth) { // Explicitly use integer operations as dot() only works on floats. @@ -16676,20 +16752,20 @@ function q7(r, e) { } `; } -function j7(r, e) { - let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = It(); +function SZ(r, e) { + let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), n = vt(); if (e) return ` vec4 ${o}(int b2, int b, int row, int col) { - int valuesPerRow = int(ceil(float(${t10}Shape[3]) / 2.0)); - int texelsInBatch = valuesPerRow * int(ceil(float(${t10}Shape[2]) / 2.0)); + int valuesPerRow = int(ceil(float(${t8}Shape[3]) / 2.0)); + int texelsInBatch = valuesPerRow * int(ceil(float(${t8}Shape[2]) / 2.0)); int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2); - texelsInBatch *= ${t10}Shape[1]; + texelsInBatch *= ${t8}Shape[1]; index = b2 * texelsInBatch + index; - ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}TexShape[1]) / 2.0)); + ivec2 packedTexShape = ivec2(ceil(float(${t8}TexShape[0]) / 2.0), ceil(float(${t8}TexShape[1]) / 2.0)); int texR = index / packedTexShape[1]; int texC = index - texR * packedTexShape[1]; - vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${n.texture2D}(${t10}, uv); + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${n.texture2D}(${t8}, uv); } `; let s = r.shapeInfo.logicalShape, a = s.length, i = r.shapeInfo.texShape, p = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], u = p[0], c = p[1], l = Math.ceil(s[a - 1] / 2), m = l * Math.ceil(s[a - 2] / 2), d = "int b, int row, int col", f = `b * ${m} + (row / 2) * ${l} + (col / 2)`; @@ -16701,18 +16777,18 @@ function j7(r, e) { int texR = index / ${c}; int texC = index - texR * ${c}; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u}); - return ${n.texture2D}(${t10}, uv); + return ${n.texture2D}(${t8}, uv); } `; } -function X7(r, e) { - let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[3], a = t10[2] * s, i = t10[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t10); - if (p.length < t10.length) { - let b = Fc(r, p), w = ["row", "col", "depth", "depth2"]; +function IZ(r, e) { + let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t8[3], a = t8[2] * s, i = t8[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t8); + if (p.length < t8.length) { + let b = Pc(r, p), C = ["row", "col", "depth", "depth2"]; return ` - ${Dc(b, e)} + ${Ac(b, e)} float ${n}(int row, int col, int depth, int depth2) { - return ${n}(${Pc(w, u)}); + return ${n}(${Oc(C, u)}); } `; } @@ -16721,7 +16797,7 @@ function X7(r, e) { float ${n}(int row, int col, int depth, int depth2) { int index = round(dot(vec4(row, col, depth, depth2), vec4(${i}, ${a}, ${s}, 1))); - ${Ac(r)} + ${Fc(r)} } `; let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1], f = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`; @@ -16762,14 +16838,14 @@ function X7(r, e) { ` : ` float ${n}(int row, int col, int depth, int depth2) { float texR = dot(vec3(row, col, depth), - vec3(${t10[1] * t10[2]}, ${t10[2]}, 1)); + vec3(${t8[1] * t8[2]}, ${t8[2]}, 1)); float texC = float(depth2); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${m}.0); return sampleTexture(${o}, uv); } `; - let x = gp(o); + let x = Cp(o); return e ? ` float ${n}(int row, int col, int depth, int depth2) { // Explicitly use integer operations as dot() only works on floats. @@ -16791,14 +16867,14 @@ function X7(r, e) { } `; } -function Y7(r) { - let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = e[4], s = e[3] * n, a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e); +function vZ(r) { + let e = r.shapeInfo.logicalShape, t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), n = e[4], s = e[3] * n, a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e); if (p.length < e.length) { - let h = Fc(r, p), g = ["row", "col", "depth", "depth2", "depth3"]; + let h = Pc(r, p), g = ["row", "col", "depth", "depth2", "depth3"]; return ` - ${Dc(h)} + ${Ac(h)} float ${o}(int row, int col, int depth, int depth2, int depth3) { - return ${o}(${Pc(g, u)}); + return ${o}(${Oc(g, u)}); } `; } @@ -16809,7 +16885,7 @@ function Y7(r) { vec4(row, col, depth, depth2), vec4(${i}, ${a}, ${s}, ${n})) + depth3; - ${Ac(r)} + ${Fc(r)} } `; let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1]; @@ -16821,7 +16897,7 @@ function Y7(r) { vec4(${a}, ${s}, ${n}, 1)); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${m}.0); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; if (d === n && c == null) @@ -16834,29 +16910,29 @@ function Y7(r) { int texC = depth3; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${m}.0); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; - let f = gp(t10); + let f = Cp(t8); return ` float ${o}(int row, int col, int depth, int depth2, int depth3) { // Explicitly use integer operations as dot() only works on floats. int index = row * ${i} + col * ${a} + depth * ${s} + depth2 * ${n} + depth3 + ${f}; vec2 uv = uvFromFlat(${m}, ${d}, index); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; } -function Q7(r) { - let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e); +function kZ(r) { + let e = r.shapeInfo.logicalShape, t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e); if (n.length < e.length) { - let g = Fc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"]; + let g = Pc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"]; return ` - ${Dc(g)} + ${Ac(g)} float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { - return ${o}(${Pc(x, s)}); + return ${o}(${Oc(x, s)}); } `; } @@ -16871,7 +16947,7 @@ function Q7(r) { dot( vec2(depth3, depth4), vec2(${a}, 1))); - ${Ac(r)} + ${Fc(r)} } `; let l = r.shapeInfo.flatOffset, m = r.shapeInfo.texShape, d = m[0], f = m[1]; @@ -16885,7 +16961,7 @@ function Q7(r) { float(depth4); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${f}.0, ${d}.0); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; if (f === a && l == null) @@ -16900,10 +16976,10 @@ function Q7(r) { int texC = depth4; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${f}.0, ${d}.0); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; - let h = gp(t10); + let h = Cp(t8); return ` float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { @@ -16911,26 +16987,26 @@ function Q7(r) { int index = row * ${c} + col * ${u} + depth * ${p} + depth2 * ${i} + depth3 * ${a} + depth4 + ${h}; vec2 uv = uvFromFlat(${d}, ${f}, index); - return sampleTexture(${t10}, uv); + return sampleTexture(${t8}, uv); } `; } -function Ac(r) { - let e = r.name, t10 = y.sizeFromShape(r.shapeInfo.logicalShape); - return t10 < 2 ? `return ${e};` : ` - for (int i = 0; i < ${t10}; i++) { +function Fc(r) { + let e = r.name, t8 = y.sizeFromShape(r.shapeInfo.logicalShape); + return t8 < 2 ? `return ${e};` : ` + for (int i = 0; i < ${t8}; i++) { if (i == index) { return ${e}[i]; } } `; } -function Z7(r, e) { - let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = rR(r.shapeInfo.logicalShape, e.logicalShape), p = Re(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"]; +function NZ(r, e) { + let t8 = r.name, o = t8.charAt(0).toUpperCase() + t8.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = hR(r.shapeInfo.logicalShape, e.logicalShape), p = Re(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"]; s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${l[b + u]} = 0;`).join(` `); let m = ""; - a < 2 && s > 0 ? m = "coords" : m = r.shapeInfo.logicalShape.map((b, w) => `coords.${l[w + u]}`).join(", "); + a < 2 && s > 0 ? m = "coords" : m = r.shapeInfo.logicalShape.map((b, C) => `coords.${l[C + u]}`).join(", "); let d = "return outputValue;", h = y.sizeFromShape(r.shapeInfo.logicalShape) === 1, x = y.sizeFromShape(e.logicalShape) === 1; if (s === 1 && !h && !x) d = ` @@ -16943,8 +17019,8 @@ function Z7(r, e) { return vec4(outputValue.x); `; else if (i.length) { - let b = s - 2, w = s - 1; - i.indexOf(b) > -1 && i.indexOf(w) > -1 ? d = "return vec4(outputValue.x);" : i.indexOf(b) > -1 ? d = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(w) > -1 && (d = "return vec4(outputValue.xx, outputValue.zz);"); + let b = s - 2, C = s - 1; + i.indexOf(b) > -1 && i.indexOf(C) > -1 ? d = "return vec4(outputValue.x);" : i.indexOf(b) > -1 ? d = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(C) > -1 && (d = "return vec4(outputValue.xx, outputValue.zz);"); } return ` vec4 ${n}() { @@ -16955,15 +17031,15 @@ function Z7(r, e) { } `; } -function J7(r, e) { - let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = e.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = e.logicalShape.length; +function TZ(r, e) { + let t8 = r.name, o = t8.charAt(0).toUpperCase() + t8.slice(1), n = "get" + o + "AtOutCoords", s = e.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = e.logicalShape.length; if (!r.shapeInfo.isUniform && i === p && r.shapeInfo.flatOffset == null && y.arraysEqual(a, s)) return ` float ${n}() { - return sampleTexture(${t10}, resultUV); + return sampleTexture(${t8}, resultUV); } `; - let u = Re(p), c = rR(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"]; + let u = Re(p), c = hR(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"]; i === 0 ? m = "" : p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((h) => `coords.${d[h + l]} = 0;`).join(` `); let f = ""; @@ -16990,59 +17066,59 @@ function Re(r) { return "ivec6"; throw Error(`GPU for rank ${r} is not yet supported`); } -function Xf(r, e, t10) { - let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t10) && o.length < s || a; +function Yf(r, e, t8) { + let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t8) && o.length < s || a; return { useSqueezeShape: p, uniformShape: p ? i : e, keptDims: n }; } -function Fc(r, e) { - let t10 = JSON.parse(JSON.stringify(r)); - return t10.shapeInfo.logicalShape = e, t10; -} function Pc(r, e) { - return e.map((t10) => r[t10]).join(", "); + let t8 = JSON.parse(JSON.stringify(r)); + return t8.shapeInfo.logicalShape = e, t8; } -function iR(r, e, t10, o) { - let n = t10.map((c, l) => { +function Oc(r, e) { + return e.map((t8) => r[t8]).join(", "); +} +function CR(r, e, t8, o) { + let n = t8.map((c, l) => { let m = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null }; return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (m.flatOffset = c.texData.slice.flatOffset), { name: e.variableNames[l], shapeInfo: m }; - }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = oR(n, a, e), p = mI(r.gl, i), u = r.createProgram(p); - return P().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, variablesLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : (r.buildVao(u), Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, EI(r, e, u))); + }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = gR(n, a, e), p = wI(r.gl, i), u = r.createProgram(p); + return A().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, variablesLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : (r.buildVao(u), Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, BI(r, e, u))); } -function EI(r, e, t10) { +function BI(r, e, t8) { let o = [], n = [], s, a, i, p = null, u = null; - u = r.getUniformLocation(t10, "NAN", false), P().getNumber("WEBGL_VERSION") === 1 && (p = r.getUniformLocation(t10, "INFINITY", false)); + u = r.getUniformLocation(t8, "NAN", false), A().getNumber("WEBGL_VERSION") === 1 && (p = r.getUniformLocation(t8, "INFINITY", false)); let c = false; for (let l of e.variableNames) { - let m = { name: l, uniform: r.getUniformLocation(t10, l, c), offset: r.getUniformLocation(t10, `offset${l}`, c) }; - e.enableShapeUniforms && (m.shape = r.getUniformLocation(t10, `${l}Shape`, c), m.texShape = r.getUniformLocation(t10, `${l}TexShape`, c)), o.push(m); + let m = { name: l, uniform: r.getUniformLocation(t8, l, c), offset: r.getUniformLocation(t8, `offset${l}`, c) }; + e.enableShapeUniforms && (m.shape = r.getUniformLocation(t8, `${l}Shape`, c), m.texShape = r.getUniformLocation(t8, `${l}TexShape`, c)), o.push(m); } - if (e.enableShapeUniforms && (s = r.getUniformLocation(t10, "outShape", c), i = r.getUniformLocation(t10, "outShapeStrides", c), a = r.getUniformLocation(t10, "outTexShape", c)), e.customUniforms) + if (e.enableShapeUniforms && (s = r.getUniformLocation(t8, "outShape", c), i = r.getUniformLocation(t8, "outShapeStrides", c), a = r.getUniformLocation(t8, "outTexShape", c)), e.customUniforms) for (let l of e.customUniforms) - n.push(r.getUniformLocation(t10, l.name, c)); + n.push(r.getUniformLocation(t8, l.name, c)); return { variablesLocations: o, customUniformLocations: n, infLoc: p, nanLoc: u, outShapeLocation: s, outShapeStridesLocation: i, outTexShapeLocation: a }; } -function aR(r, e) { +function bR(r, e) { if (r.length !== e.length) throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`); - r.forEach((t10, o) => { - let n = t10.logicalShape, s = e[o], a = s.shape; + r.forEach((t8, o) => { + let n = t8.logicalShape, s = e[o], a = s.shape; if (!y.arraysEqual(n, a)) throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`); - if (t10.isUniform && s.isUniform) + if (t8.isUniform && s.isUniform) return; - let i = t10.texShape, p = s.isUniform ? null : s.texData.texShape; + let i = t8.texShape, p = s.isUniform ? null : s.texData.texShape; if (!y.arraysEqual(i, p)) throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`); }); } -function uR(r, e, t10, o, n) { - e.program.enableShapeUniforms || (aR(e.inShapeInfos, t10), aR([e.outShapeInfo], [o])); +function wR(r, e, t8, o, n) { + e.program.enableShapeUniforms || (bR(e.inShapeInfos, t8), bR([e.outShapeInfo], [o])); let s = o.texData.texture, a = o.texData.texShape; - o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(e.webGLProgram), r.bindVertexArray(e.webGLProgram.vao), P().getNumber("WEBGL_VERSION") === 1 && e.infLoc !== null && r.gl.uniform1f(e.infLoc, 1 / 0), e.nanLoc !== null && r.gl.uniform1f(e.nanLoc, NaN); - for (let p = 0; p < t10.length; ++p) { - let u = t10[p], { uniform: c, offset: l, shape: m, texShape: d } = e.variablesLocations[p]; + o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(e.webGLProgram), r.bindVertexArray(e.webGLProgram.vao), A().getNumber("WEBGL_VERSION") === 1 && e.infLoc !== null && r.gl.uniform1f(e.infLoc, 1 / 0), e.nanLoc !== null && r.gl.uniform1f(e.nanLoc, NaN); + for (let p = 0; p < t8.length; ++p) { + let u = t8[p], { uniform: c, offset: l, shape: m, texShape: d } = e.variablesLocations[p]; if (m) { - let { uniformShape: f } = Xf(e.program.packedInputs, u.shape, u.texData.texShape); + let { uniformShape: f } = Yf(e.program.packedInputs, u.shape, u.texData.texShape); switch (f.length) { case 1: r.gl.uniform1iv(m, new Int32Array(f)); @@ -17131,12 +17207,12 @@ function uR(r, e, t10, o, n) { } r.executeProgram(); } -function pR(r, e, t10) { +function SR(r, e, t8) { let o = ""; - e.concat(t10).forEach((a) => { + e.concat(t8).forEach((a) => { let i = a.texData != null && a.texData.slice != null && a.texData.slice.flatOffset > 0; if (r.enableShapeUniforms && !a.isUniform) { - let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Xf(r.packedInputs, a.shape, p), m = "", d = "", f = ""; + let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Yf(r.packedInputs, a.shape, p), m = "", d = "", f = ""; if (c.length === 1 && r.packedInputs) { let k = [Math.ceil(p[0] / 2), Math.ceil(p[1] / 2)]; m = `${k[0] > 1}_${k[1] > 1}`; @@ -17146,26 +17222,26 @@ function pR(r, e, t10) { let k = y.computeStrides(c); f = `${k[0] === p[1]}_${k[k.length - 1] === p[1]}`; } - let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = C.getBroadcastDims(a.shape, t10.shape), w = !r.packedInputs && h === t10.shape.length && y.arraysEqual(p, t10.texData.texShape), S = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`; - o += `${h}_${w}_${u ? l : ""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`; + let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = w.getBroadcastDims(a.shape, t8.shape), C = !r.packedInputs && h === t8.shape.length && y.arraysEqual(p, t8.texData.texShape), S = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`; + o += `${h}_${C}_${u ? l : ""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`; } else { let p = a.isUniform ? "uniform" : a.texData.texShape; o += `${a.shape}_${p}_${i}`; } }); let n = r.userCode, s = r.constructor.name; - return s += "_" + o + "_" + n + `${P().getNumber("WEBGL_VERSION")}`, s; + return s += "_" + o + "_" + n + `${A().getNumber("WEBGL_VERSION")}`, s; } -function ut(r) { - return P().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4; +function pt(r) { + return A().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4; } -var Yf = class { +var Qf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = du.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let t10 = It(); - this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = xu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + let t8 = vt(); + this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms ? hp(["r", "c", "d"], e) : Os(["r", "c", "d"], e)} + ${this.enableShapeUniforms ? bp(["r", "c", "d"], e) : zs(["r", "c", "d"], e)} return ivec3(r, c, d); } @@ -17181,18 +17257,18 @@ var Yf = class { result[i] = getA(rc.x, rc.y, rc.z); } - ${t10.output} = result; + ${t8.output} = result; } `; } }; -var Qf = class { +var Zf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = du.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let t10 = It(); - this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = xu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + let t8 = vt(); + this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms ? hp(["r", "c", "d"], e) : Os(["r", "c", "d"], e)} + ${this.enableShapeUniforms ? bp(["r", "c", "d"], e) : zs(["r", "c", "d"], e)} return ivec3(r, c, d); } @@ -17208,58 +17284,58 @@ var Qf = class { result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z)); } - ${t10.output} = result; - } - `; - } -}; -var Zf = class { - constructor(e) { - this.variableNames = ["A"], this.outTexUsage = mr.DOWNLOAD; - let t10 = It(); - this.outputShape = e, this.userCode = ` - ${jf} - - void main() { - float x = getAAtOutCoords(); - ${t10.output} = encode_float(x); + ${t8.output} = result; } `; } }; var Jf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = mr.DOWNLOAD; - let t10 = It(); + this.variableNames = ["A"], this.outTexUsage = mr.DOWNLOAD; + let t8 = vt(); this.outputShape = e, this.userCode = ` - ${jf} + ${Xf} void main() { - ivec3 coords = getOutputCoords(); - float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); - ${t10.output} = encode_float(x); + float x = getAAtOutCoords(); + ${t8.output} = encode_float(x); } `; } }; -var rZ = { R: 0, G: 1, B: 2, A: 3 }; +var eh = class { + constructor(e) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = mr.DOWNLOAD; + let t8 = vt(); + this.outputShape = e, this.userCode = ` + ${Xf} + + void main() { + ivec3 coords = getOutputCoords(); + float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); + ${t8.output} = encode_float(x); + } + `; + } +}; +var EZ = { R: 0, G: 1, B: 2, A: 3 }; var Xl = class { - constructor(e, t10 = false, o = "RGBA") { + constructor(e, t8 = false, o = "RGBA") { this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let n = It(); - this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length); + let n = vt(); + this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length); let s = "result"; - t10 && (s = "floor(result * 255. + 0.5)"); + t8 && (s = "floor(result * 255. + 0.5)"); let a = ""; for (let i = 0; i < o.length; i++) { let p = o[i]; a += ` if(offset == ${i}) { - result = values[${rZ[p]}]; + result = values[${EZ[p]}]; }`; } this.userCode = ` - ${this.enableShapeUniforms ? Ec() : $c(e)} + ${this.enableShapeUniforms ? Rc() : Ec(e)} void main() { ivec3 coords = getOutputCoords(); @@ -17281,13 +17357,13 @@ var Xl = class { `; } }; -var eh = class { - constructor(e, t10 = false) { +var th = class { + constructor(e, t8 = false) { this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let o = It(); - this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length); + let o = vt(); + this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length); let n = "", s = "result"; - t10 && (s = "floor(result * 255. + 0.5)"); + t8 && (s = "floor(result * 255. + 0.5)"); for (let a = 0; a <= 1; a++) for (let i = 0; i <= 1; i++) { let p = a * 2 + i; @@ -17322,7 +17398,7 @@ var eh = class { `; } this.userCode = ` - ${this.enableShapeUniforms ? Ec() : $c(e)} + ${this.enableShapeUniforms ? Rc() : Ec(e)} void main() { ivec3 coords = getOutputCoords(); @@ -17340,10 +17416,10 @@ var eh = class { `; } }; -var qI = {}; -He(qI, { bindVertexProgramAttributeStreams: () => BI, createBufferFromOutputTexture: () => WI, createFloat16MatrixTexture: () => PI, createFloat16PackedMatrixTexture: () => LI, createFloat32MatrixTexture: () => FI, createIndexBuffer: () => AI, createPackedMatrixTexture: () => MI, createUnsignedBytesMatrixTexture: () => OI, createVertexBuffer: () => DI, createVertexShader: () => RI, downloadByteEncodedFloatMatrixFromOutputTexture: () => GI, downloadFloat32MatrixFromBuffer: () => UI, downloadMatrixFromPackedOutputTexture: () => KI, downloadPackedMatrixFromBuffer: () => HI, getInternalFormatForFloat16MatrixTexture: () => rh, getInternalFormatForFloat16PackedMatrixTexture: () => sh, getInternalFormatForFloat32MatrixTexture: () => th, getInternalFormatForPackedMatrixTexture: () => nh, getInternalFormatForUnsignedBytesMatrixTexture: () => oh, uploadDenseMatrixToTexture: () => zI, uploadPixelDataToTexture: () => VI }); -function RI(r) { - let e = It(), t10 = `${e.version} +var rv = {}; +Ke(rv, { bindVertexProgramAttributeStreams: () => jI, createBufferFromOutputTexture: () => QI, createFloat16MatrixTexture: () => GI, createFloat16PackedMatrixTexture: () => qI, createFloat32MatrixTexture: () => UI, createIndexBuffer: () => WI, createPackedMatrixTexture: () => KI, createUnsignedBytesMatrixTexture: () => HI, createVertexBuffer: () => VI, createVertexShader: () => zI, downloadByteEncodedFloatMatrixFromOutputTexture: () => JI, downloadFloat32MatrixFromBuffer: () => ZI, downloadMatrixFromPackedOutputTexture: () => tv, downloadPackedMatrixFromBuffer: () => ev, getInternalFormatForFloat16MatrixTexture: () => oh, getInternalFormatForFloat16PackedMatrixTexture: () => ah, getInternalFormatForFloat32MatrixTexture: () => rh, getInternalFormatForPackedMatrixTexture: () => sh, getInternalFormatForUnsignedBytesMatrixTexture: () => nh, uploadDenseMatrixToTexture: () => XI, uploadPixelDataToTexture: () => YI }); +function zI(r) { + let e = vt(), t8 = `${e.version} precision highp float; ${e.attribute} vec3 clipSpacePos; ${e.attribute} vec2 uv; @@ -17353,94 +17429,94 @@ function RI(r) { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; }`; - return lI(r, t10); + return CI(r, t8); } -function DI(r) { +function VI(r) { let e = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]); - return hI(r, e); + return vI(r, e); } -function AI(r) { +function WI(r) { let e = new Uint16Array([0, 1, 2, 2, 1, 3]); - return gI(r, e); + return kI(r, e); } -function Yl(r, e, t10, o, n, s) { - yI(e, t10); - let a = xI(r), i = r.TEXTURE_2D; - return ce(r, () => r.bindTexture(i, a)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), ce(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), P().getNumber("WEBGL_VERSION") === 1 ? ce(r, () => r.texImage2D(i, 0, o, e, t10, 0, n, s, null)) : ce(r, () => r.texStorage2D(i, 1, o, e, t10)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t10, e] }; -} -function th(r) { - return r.internalFormatFloat; -} -function FI(r, e, t10, o) { - let [n, s] = fp(e, t10); - return Yl(r, n, s, th(o), o.textureFormatFloat, r.FLOAT); +function Yl(r, e, t8, o, n, s) { + TI(e, t8); + let a = NI(r), i = r.TEXTURE_2D; + return ce(r, () => r.bindTexture(i, a)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), ce(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), A().getNumber("WEBGL_VERSION") === 1 ? ce(r, () => r.texImage2D(i, 0, o, e, t8, 0, n, s, null)) : ce(r, () => r.texStorage2D(i, 1, o, e, t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t8, e] }; } function rh(r) { - return r.internalFormatHalfFloat; + return r.internalFormatFloat; } -function PI(r, e, t10, o) { - let [n, s] = fp(e, t10); - return Yl(r, n, s, rh(o), o.textureFormatFloat, o.textureTypeHalfFloat); +function UI(r, e, t8, o) { + let [n, s] = yp(e, t8); + return Yl(r, n, s, rh(o), o.textureFormatFloat, r.FLOAT); } function oh(r) { - return r.downloadTextureFormat; + return r.internalFormatHalfFloat; } -function OI(r, e, t10, o) { - let [n, s] = fp(e, t10); - return Yl(r, n, s, oh(o), r.RGBA, r.UNSIGNED_BYTE); +function GI(r, e, t8, o) { + let [n, s] = yp(e, t8); + return Yl(r, n, s, oh(o), o.textureFormatFloat, o.textureTypeHalfFloat); } function nh(r) { - return r.internalFormatPackedFloat; + return r.downloadTextureFormat; } -function MI(r, e, t10, o) { - let [n, s] = Ea(e, t10); - return Yl(r, n, s, nh(o), r.RGBA, r.FLOAT); +function HI(r, e, t8, o) { + let [n, s] = yp(e, t8); + return Yl(r, n, s, nh(o), r.RGBA, r.UNSIGNED_BYTE); } function sh(r) { + return r.internalFormatPackedFloat; +} +function KI(r, e, t8, o) { + let [n, s] = Pa(e, t8); + return Yl(r, n, s, sh(o), r.RGBA, r.FLOAT); +} +function ah(r) { return r.internalFormatPackedHalfFloat; } -function LI(r, e, t10, o) { - let [n, s] = Ea(e, t10); - return Yl(r, n, s, sh(o), r.RGBA, o.textureTypeHalfFloat); +function qI(r, e, t8, o) { + let [n, s] = Pa(e, t8); + return Yl(r, n, s, ah(o), r.RGBA, o.textureTypeHalfFloat); } -function BI(r, e, t10) { - return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), Hf(r, e, "clipSpacePos", t10, 3, 20, 0) && Hf(r, e, "uv", t10, 2, 20, 12); +function jI(r, e, t8) { + return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t8)), Kf(r, e, "clipSpacePos", t8, 3, 20, 0) && Kf(r, e, "uv", t8, 2, 20, 12); } -function zI(r, e, t10, o, n, s) { +function XI(r, e, t8, o, n, s) { ce(r, () => r.bindTexture(r.TEXTURE_2D, e)); let a, i, p; - n instanceof Uint8Array ? (a = new Uint8Array(t10 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t10 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), P().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10, o, r.RGBA, i, a)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t10, o, 0, r.RGBA, i, a)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); + n instanceof Uint8Array ? (a = new Uint8Array(t8 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t8 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t8, o, r.RGBA, i, a)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t8, o, 0, r.RGBA, i, a)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); } -function VI(r, e, t10) { - ce(r, () => r.bindTexture(r.TEXTURE_2D, e)), t10.data instanceof Uint8Array ? P().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10.width, t10.height, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t10.width, t10.height, 0, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : P().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t10)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t10)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); +function YI(r, e, t8) { + ce(r, () => r.bindTexture(r.TEXTURE_2D, e)), t8.data instanceof Uint8Array ? A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t8.width, t8.height, r.RGBA, r.UNSIGNED_BYTE, t8.data)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t8.width, t8.height, 0, r.RGBA, r.UNSIGNED_BYTE, t8.data)) : A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t8)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); } -function WI(r, e, t10, o) { +function QI(r, e, t8, o) { let n = r.createBuffer(); ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n)); - let i = 4 * 4 * e * t10; - return ce(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), ce(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, 0)), ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n; + let i = 4 * 4 * e * t8; + return ce(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), ce(r, () => r.readPixels(0, 0, t8, e, r.RGBA, r.FLOAT, 0)), ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n; } -function UI(r, e, t10) { - let o = r, n = new Float32Array(t10); +function ZI(r, e, t8) { + let o = r, n = new Float32Array(t8); return o.bindBuffer(o.PIXEL_PACK_BUFFER, e), o.getBufferSubData(o.PIXEL_PACK_BUFFER, 0, n), o.bindBuffer(o.PIXEL_PACK_BUFFER, null), n; } -function GI(r, e, t10, o) { - let [n, s] = fp(e, t10), a = 4, i = new Uint8Array(XE(e * t10, a)); +function JI(r, e, t8, o) { + let [n, s] = yp(e, t8), a = 4, i = new Uint8Array(uR(e * t8, a)); return ce(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer); } -function HI(r, e, t10, o, n, s, a, i) { - let p = r, u = new Float32Array(YE(s, a)); +function ev(r, e, t8, o, n, s, a, i) { + let p = r, u = new Float32Array(pR(s, a)); return p.bindBuffer(p.PIXEL_PACK_BUFFER, e), p.getBufferSubData(p.PIXEL_PACK_BUFFER, 0, u), p.bindBuffer(p.PIXEL_PACK_BUFFER, null), u; } -function KI(r, e, t10) { - let o = new Float32Array(e * t10 * 4); - return ce(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, o)), o; +function tv(r, e, t8) { + let o = new Float32Array(e * t8 * 4); + return ce(r, () => r.readPixels(0, 0, t8, e, r.RGBA, r.FLOAT, o)), o; } -var xp = class { +var wp = class { constructor(e) { this.outputTexture = null, this.program = null, this.disposed = false, this.itemsToPoll = []; - let t10 = P().getNumber("WEBGL_VERSION"); - if (e != null ? (this.gl = e, iI(t10, e)) : this.gl = Gr(t10), e = this.gl, P().getNumber("WEBGL_VERSION") === 2) { + let t8 = A().getNumber("WEBGL_VERSION"); + if (e != null ? (this.gl = e, gI(t8, e)) : this.gl = Kr(t8), e = this.gl, A().getNumber("WEBGL_VERSION") === 2) { let s = e; this.createVertexArray = () => ce(s, () => s.createVertexArray()), this.bindVertexArray = (a) => ce(s, () => s.bindVertexArray(a)), this.deleteVertexArray = (a) => ce(s, () => s.deleteVertexArray(a)), this.getVertexArray = () => ce(s, () => s.getParameter(s.VERTEX_ARRAY_BINDING)); } else if (e != null) { @@ -17450,26 +17526,26 @@ var xp = class { this.createVertexArray = () => ce(e, () => s.createVertexArrayOES()), this.bindVertexArray = (a) => ce(e, () => s.bindVertexArrayOES(a)), this.deleteVertexArray = (a) => ce(e, () => s.deleteVertexArrayOES(a)), this.getVertexArray = () => ce(e, () => e.getParameter(s.VERTEX_ARRAY_BINDING_OES)); } let o = "WEBGL_color_buffer_float", n = "EXT_color_buffer_half_float"; - if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), P().getNumber("WEBGL_VERSION") === 1) { + if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), A().getNumber("WEBGL_VERSION") === 1) { let s = "OES_texture_float", a = "OES_texture_half_float"; - if (this.textureFloatExtension = kc(this.gl, s), Hr(this.gl, a)) - this.textureHalfFloatExtension = kc(this.gl, a); - else if (P().get("WEBGL_FORCE_F16_TEXTURES")) + if (this.textureFloatExtension = Nc(this.gl, s), qr(this.gl, a)) + this.textureHalfFloatExtension = Nc(this.gl, a); + else if (A().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true."); - if (this.colorBufferFloatExtension = this.gl.getExtension(o), Hr(this.gl, n)) - this.colorBufferHalfFloatExtension = kc(this.gl, n); - else if (P().get("WEBGL_FORCE_F16_TEXTURES")) + if (this.colorBufferFloatExtension = this.gl.getExtension(o), qr(this.gl, n)) + this.colorBufferHalfFloatExtension = Nc(this.gl, n); + else if (A().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true."); - } else if (o = "EXT_color_buffer_float", Hr(this.gl, o)) + } else if (o = "EXT_color_buffer_float", qr(this.gl, o)) this.colorBufferFloatExtension = this.gl.getExtension(o); - else if (Hr(this.gl, n)) + else if (qr(this.gl, n)) this.colorBufferHalfFloatExtension = this.gl.getExtension(n); else throw new Error("GL context does not support color renderable floats"); - this.vertexBuffer = DI(this.gl), this.indexBuffer = AI(this.gl), this.framebuffer = bI(this.gl), this.textureConfig = Kl(this.gl, this.textureHalfFloatExtension); + this.vertexBuffer = VI(this.gl), this.indexBuffer = WI(this.gl), this.framebuffer = _I(this.gl), this.textureConfig = Kl(this.gl, this.textureHalfFloatExtension); } get debug() { - return P().getBool("DEBUG"); + return A().getBool("DEBUG"); } dispose() { if (this.disposed) @@ -17478,42 +17554,42 @@ var xp = class { let e = this.gl; ce(e, () => e.finish()), ce(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), ce(e, () => e.deleteFramebuffer(this.framebuffer)), ce(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), ce(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), ce(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true; } - createFloat32MatrixTexture(e, t10) { - return this.throwIfDisposed(), FI(this.gl, e, t10, this.textureConfig); + createFloat32MatrixTexture(e, t8) { + return this.throwIfDisposed(), UI(this.gl, e, t8, this.textureConfig); } - createFloat16MatrixTexture(e, t10) { - return this.throwIfDisposed(), PI(this.gl, e, t10, this.textureConfig); + createFloat16MatrixTexture(e, t8) { + return this.throwIfDisposed(), GI(this.gl, e, t8, this.textureConfig); } - createUnsignedBytesMatrixTexture(e, t10) { - return this.throwIfDisposed(), OI(this.gl, e, t10, this.textureConfig); + createUnsignedBytesMatrixTexture(e, t8) { + return this.throwIfDisposed(), HI(this.gl, e, t8, this.textureConfig); } - uploadPixelDataToTexture(e, t10) { - this.throwIfDisposed(), VI(this.gl, e, t10); + uploadPixelDataToTexture(e, t8) { + this.throwIfDisposed(), YI(this.gl, e, t8); } - uploadDenseMatrixToTexture(e, t10, o, n) { - this.throwIfDisposed(), zI(this.gl, e, t10, o, n, this.textureConfig); + uploadDenseMatrixToTexture(e, t8, o, n) { + this.throwIfDisposed(), XI(this.gl, e, t8, o, n, this.textureConfig); } - createFloat16PackedMatrixTexture(e, t10) { - return this.throwIfDisposed(), LI(this.gl, e, t10, this.textureConfig); + createFloat16PackedMatrixTexture(e, t8) { + return this.throwIfDisposed(), qI(this.gl, e, t8, this.textureConfig); } - createPackedMatrixTexture(e, t10) { - return this.throwIfDisposed(), MI(this.gl, e, t10, this.textureConfig); + createPackedMatrixTexture(e, t8) { + return this.throwIfDisposed(), KI(this.gl, e, t8, this.textureConfig); } deleteMatrixTexture(e) { - this.throwIfDisposed(), this.outputTexture === e && (Kf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(e)); + this.throwIfDisposed(), this.outputTexture === e && (qf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(e)); } - downloadByteEncodedFloatMatrixFromOutputTexture(e, t10, o) { - return this.downloadMatrixDriver(e, () => GI(this.gl, t10, o, this.textureConfig)); + downloadByteEncodedFloatMatrixFromOutputTexture(e, t8, o) { + return this.downloadMatrixDriver(e, () => JI(this.gl, t8, o, this.textureConfig)); } - downloadPackedMatrixFromBuffer(e, t10, o, n, s, a) { - return HI(this.gl, e, t10, o, n, s, a, this.textureConfig); + downloadPackedMatrixFromBuffer(e, t8, o, n, s, a) { + return ev(this.gl, e, t8, o, n, s, a, this.textureConfig); } - downloadFloat32MatrixFromBuffer(e, t10) { - return UI(this.gl, e, t10); + downloadFloat32MatrixFromBuffer(e, t8) { + return ZI(this.gl, e, t8); } - createBufferFromTexture(e, t10, o) { + createBufferFromTexture(e, t8, o) { this.bindTextureToFrameBuffer(e); - let n = WI(this.gl, t10, o, this.textureConfig); + let n = QI(this.gl, t8, o, this.textureConfig); return this.unbindTextureToFrameBuffer(), n; } createAndWaitForFence() { @@ -17521,33 +17597,33 @@ var xp = class { return this.pollFence(e); } createFence(e) { - let t10, o; - if (P().getBool("WEBGL_FENCE_API_ENABLED")) { + let t8, o; + if (A().getBool("WEBGL_FENCE_API_ENABLED")) { let n = e, s = n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE, 0); e.flush(), o = () => { let a = n.clientWaitSync(s, 0, 0); return a === n.ALREADY_SIGNALED || a === n.CONDITION_SATISFIED; - }, t10 = s; + }, t8 = s; } else - P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t10 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t10, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true; - return { query: t10, isFencePassed: o }; + A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t8 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t8, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true; + return { query: t8, isFencePassed: o }; } - downloadMatrixFromPackedTexture(e, t10, o) { - return this.downloadMatrixDriver(e, () => KI(this.gl, t10, o)); + downloadMatrixFromPackedTexture(e, t8, o) { + return this.downloadMatrixDriver(e, () => tv(this.gl, t8, o)); } createProgram(e) { this.throwIfDisposed(); - let t10 = this.gl; - this.vertexShader == null && (this.vertexShader = RI(t10)); - let o = dI(t10); - ce(t10, () => t10.attachShader(o, this.vertexShader)), ce(t10, () => t10.attachShader(o, e)), fI(t10, o); + let t8 = this.gl; + this.vertexShader == null && (this.vertexShader = zI(t8)); + let o = SI(t8); + ce(t8, () => t8.attachShader(o, this.vertexShader)), ce(t8, () => t8.attachShader(o, e)), II(t8, o); let n = Object.assign(o, { vao: this.createVertexArray() }); - return this.debug && ql(t10, n), n; + return this.debug && ql(t8, n), n; } buildVao(e) { this.setProgram(e), this.bindVertexArray(e.vao); - let t10 = this.gl; - ce(t10, () => t10.bindBuffer(t10.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), BI(t10, e, this.vertexBuffer); + let t8 = this.gl; + ce(t8, () => t8.bindBuffer(t8.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), jI(t8, e, this.vertexBuffer); } deleteProgram(e) { this.throwIfDisposed(), e === this.program && (this.program = null), e != null && (ce(this.gl, () => this.gl.deleteProgram(e)), this.deleteVertexArray(e.vao)); @@ -17555,41 +17631,41 @@ var xp = class { setProgram(e) { this.throwIfDisposed(), this.program = e, this.program != null && this.debug && ql(this.gl, this.program), ce(this.gl, () => this.gl.useProgram(e)); } - getUniformLocation(e, t10, o = true) { - return this.throwIfDisposed(), o ? CI(this.gl, e, t10) : wI(this.gl, e, t10); + getUniformLocation(e, t8, o = true) { + return this.throwIfDisposed(), o ? $I(this.gl, e, t8) : EI(this.gl, e, t8); } - getAttributeLocation(e, t10) { - return this.throwIfDisposed(), ce(this.gl, () => this.gl.getAttribLocation(e, t10)); + getAttributeLocation(e, t8) { + return this.throwIfDisposed(), ce(this.gl, () => this.gl.getAttribLocation(e, t8)); } - getUniformLocationNoThrow(e, t10) { - return this.throwIfDisposed(), this.gl.getUniformLocation(e, t10); + getUniformLocationNoThrow(e, t8) { + return this.throwIfDisposed(), this.gl.getUniformLocation(e, t8); } - setInputMatrixTexture(e, t10, o) { - this.throwIfDisposed(), this.throwIfNoProgram(), SI(this.gl, e, t10, o); + setInputMatrixTexture(e, t8, o) { + this.throwIfDisposed(), this.throwIfNoProgram(), RI(this.gl, e, t8, o); } - setOutputMatrixTexture(e, t10, o) { - this.setOutputMatrixTextureDriver(e, o, t10); + setOutputMatrixTexture(e, t8, o) { + this.setOutputMatrixTextureDriver(e, o, t8); } - setOutputPackedMatrixTexture(e, t10, o) { + setOutputPackedMatrixTexture(e, t8, o) { this.throwIfDisposed(); - let [n, s] = Ea(t10, o); + let [n, s] = Pa(t8, o); this.setOutputMatrixTextureDriver(e, n, s); } - setOutputMatrixWriteRegion(e, t10, o, n) { - this.setOutputMatrixWriteRegionDriver(o, e, n, t10); + setOutputMatrixWriteRegion(e, t8, o, n) { + this.setOutputMatrixWriteRegionDriver(o, e, n, t8); } - setOutputPackedMatrixWriteRegion(e, t10, o, n) { + setOutputPackedMatrixWriteRegion(e, t8, o, n) { throw new Error("setOutputPackedMatrixWriteRegion not implemented."); } debugValidate() { - this.program != null && ql(this.gl, this.program), Nc(this.gl); + this.program != null && ql(this.gl, this.program), Tc(this.gl); } executeProgram() { this.throwIfDisposed(), this.throwIfNoProgram(); let e = this.gl; if (this.debug) { - let t10 = this.getVertexArray(); - console.assert(t10 === this.program.vao, "VAO changed between setProgram and executeProgram!"), this.debugValidate(); + let t8 = this.getVertexArray(); + console.assert(t8 === this.program.vao, "VAO changed between setProgram and executeProgram!"), this.debugValidate(); } ce(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0)); } @@ -17597,7 +17673,7 @@ var xp = class { this.throwIfDisposed(), ce(this.gl, () => this.gl.finish()); } getQueryTimerExtension() { - return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = kc(this.gl, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension; + return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Nc(this.gl, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension; } getQueryTimerExtensionWebGL2() { return this.getQueryTimerExtension(); @@ -17606,29 +17682,29 @@ var xp = class { return this.getQueryTimerExtension(); } beginQuery() { - if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { + if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.createQuery(); return o.beginQuery(n.TIME_ELAPSED_EXT, s), s; } - let e = this.getQueryTimerExtensionWebGL1(), t10 = e.createQueryEXT(); - return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t10), t10; + let e = this.getQueryTimerExtensionWebGL1(), t8 = e.createQueryEXT(); + return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t8), t8; } endQuery() { - if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { - let t10 = this.gl, o = this.getQueryTimerExtensionWebGL2(); - t10.endQuery(o.TIME_ELAPSED_EXT); + if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { + let t8 = this.gl, o = this.getQueryTimerExtensionWebGL2(); + t8.endQuery(o.TIME_ELAPSED_EXT); return; } let e = this.getQueryTimerExtensionWebGL1(); e.endQueryEXT(e.TIME_ELAPSED_EXT); } async waitForQueryAndGetTime(e) { - return await y.repeatedTry(() => this.disposed || this.isQueryAvailable(e, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); + return await y.repeatedTry(() => this.disposed || this.isQueryAvailable(e, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); } - getQueryTime(e, t10) { - if (t10 === 0) + getQueryTime(e, t8) { + if (t8 === 0) return null; - if (t10 === 2) { + if (t8 === 2) { let o = this.gl; return o.getQueryParameter(e, o.QUERY_RESULT) / 1e6; } else { @@ -17636,10 +17712,10 @@ var xp = class { return o.getQueryObjectEXT(e, o.QUERY_RESULT_EXT) / 1e6; } } - isQueryAvailable(e, t10) { - if (t10 === 0) + isQueryAvailable(e, t8) { + if (t8 === 0) return true; - if (t10 === 2) { + if (t8 === 2) { let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.getQueryParameter(e, o.QUERY_RESULT_AVAILABLE); return this.disjoint == null && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), s && !this.disjoint; } else { @@ -17648,42 +17724,42 @@ var xp = class { } } pollFence(e) { - return new Promise((t10) => { - this.addItemToPoll(() => e.isFencePassed(), () => t10()); + return new Promise((t8) => { + this.addItemToPoll(() => e.isFencePassed(), () => t8()); }); } pollItems() { - let e = oZ(this.itemsToPoll.map((t10) => t10.isDoneFn)); - for (let t10 = 0; t10 <= e; ++t10) { - let { resolveFn: o } = this.itemsToPoll[t10]; + let e = RZ(this.itemsToPoll.map((t8) => t8.isDoneFn)); + for (let t8 = 0; t8 <= e; ++t8) { + let { resolveFn: o } = this.itemsToPoll[t8]; o(); } this.itemsToPoll = this.itemsToPoll.slice(e + 1); } - addItemToPoll(e, t10) { - if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t10 }), this.itemsToPoll.length > 1) + addItemToPoll(e, t8) { + if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t8 }), this.itemsToPoll.length > 1) return; let o; - "setTimeoutCustom" in P().platform && (o = P().platform.setTimeoutCustom.bind(P().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o); + "setTimeoutCustom" in A().platform && (o = A().platform.setTimeoutCustom.bind(A().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o); } bindTextureToFrameBuffer(e) { - this.throwIfDisposed(), jl(this.gl, e, this.framebuffer), this.debug && Nc(this.gl); + this.throwIfDisposed(), jl(this.gl, e, this.framebuffer), this.debug && Tc(this.gl); } unbindTextureToFrameBuffer() { - this.outputTexture != null ? (jl(this.gl, this.outputTexture, this.framebuffer), this.debug && Nc(this.gl)) : Kf(this.gl, this.framebuffer); + this.outputTexture != null ? (jl(this.gl, this.outputTexture, this.framebuffer), this.debug && Tc(this.gl)) : qf(this.gl, this.framebuffer); } - downloadMatrixDriver(e, t10) { + downloadMatrixDriver(e, t8) { this.bindTextureToFrameBuffer(e); - let o = t10(); + let o = t8(); return this.unbindTextureToFrameBuffer(), o; } - setOutputMatrixTextureDriver(e, t10, o) { + setOutputMatrixTextureDriver(e, t8, o) { this.throwIfDisposed(); let n = this.gl; - jl(n, e, this.framebuffer), this.debug && Nc(n), this.outputTexture = e, ce(n, () => n.viewport(0, 0, t10, o)), ce(n, () => n.scissor(0, 0, t10, o)); + jl(n, e, this.framebuffer), this.debug && Tc(n), this.outputTexture = e, ce(n, () => n.viewport(0, 0, t8, o)), ce(n, () => n.scissor(0, 0, t8, o)); } - setOutputMatrixWriteRegionDriver(e, t10, o, n) { - this.throwIfDisposed(), ce(this.gl, () => this.gl.scissor(e, t10, o, n)); + setOutputMatrixWriteRegionDriver(e, t8, o, n) { + this.throwIfDisposed(), ce(this.gl, () => this.gl.scissor(e, t8, o, n)); } throwIfDisposed() { if (this.disposed) @@ -17694,37 +17770,37 @@ var xp = class { throw new Error("No GPU program is currently set."); } }; -function oZ(r) { +function RZ(r) { let e = 0; for (; e < r.length && r[e](); ++e) ; return e - 1; } -var { addImpl: cR, bincountImpl: ah, bincountReduceImpl: lR, castImpl: mR, ceilImpl: dR, concatImpl: fR, equalImpl: hR, expImpl: gR, expm1Impl: xR, floorImpl: yR, gatherNdImpl: bR, gatherV2Impl: CR, greaterImpl: wR, greaterEqualImpl: SR, lessImpl: IR, lessEqualImpl: vR, linSpaceImpl: kR, logImpl: NR, maxImpl: TR, maximumImpl: _R, minimumImpl: $R, multiplyImpl: ER, negImpl: RR, notEqualImpl: DR, prodImpl: AR, raggedGatherImpl: FR, raggedRangeImpl: PR, raggedTensorToTensorImpl: OR, rangeImpl: MR, rsqrtImpl: LR, scatterImpl: BR, sigmoidImpl: zR, simpleAbsImpl: ih, sliceImpl: VR, sparseFillEmptyRowsImpl: WR, sparseReshapeImpl: UR, sparseSegmentReductionImpl: uh, sqrtImpl: GR, staticRegexReplaceImpl: HR, stridedSliceImpl: KR, stringNGramsImpl: qR, stringSplitImpl: jR, stringToHashBucketFastImpl: XR, subImpl: YR, tileImpl: QR, topKImpl: ZR, transposeImpl: yp, uniqueImpl: JR } = Sc; -function jI(r, e) { - return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t10) => `${r}.${t10}`); +var { addImpl: IR, bincountImpl: ih, bincountReduceImpl: vR, bitwiseAndImpl: kR, castImpl: NR, ceilImpl: TR, concatImpl: _R, equalImpl: $R, expImpl: ER, expm1Impl: RR, floorImpl: DR, gatherNdImpl: AR, gatherV2Impl: FR, greaterImpl: PR, greaterEqualImpl: OR, lessImpl: MR, lessEqualImpl: LR, linSpaceImpl: BR, logImpl: zR, maxImpl: VR, maximumImpl: WR, minimumImpl: UR, multiplyImpl: GR, negImpl: HR, notEqualImpl: KR, prodImpl: qR, raggedGatherImpl: jR, raggedRangeImpl: XR, raggedTensorToTensorImpl: YR, rangeImpl: QR, rsqrtImpl: ZR, scatterImpl: JR, sigmoidImpl: eD, simpleAbsImpl: uh, sliceImpl: tD, sparseFillEmptyRowsImpl: rD, sparseReshapeImpl: oD, sparseSegmentReductionImpl: ph, sqrtImpl: nD, staticRegexReplaceImpl: sD, stridedSliceImpl: aD, stringNGramsImpl: iD, stringSplitImpl: uD, stringToHashBucketFastImpl: pD, subImpl: cD, tileImpl: lD, topKImpl: mD, transposeImpl: Sp, uniqueImpl: dD } = Ic; +function ov(r, e) { + return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t8) => `${r}.${t8}`); } function Rt(r, e) { - return e === 1 ? [r] : jI(r, e); + return e === 1 ? [r] : ov(r, e); } -function eD(r, e) { +function fD(r, e) { if (r === 1) return "rc"; - let t10 = ""; + let t8 = ""; for (let o = 0; o < r; o++) - t10 += e[o], o < r - 1 && (t10 += ","); - return t10; + t8 += e[o], o < r - 1 && (t8 += ","); + return t8; } -var ph = class { +var ch = class { constructor(e) { - if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = ut(this.outputShape.length), this.rank === 0) + if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = pt(this.outputShape.length), this.rank === 0) this.userCode = ` void main() { setOutput(vec4(getA(), 0., 0., 0.)); } `; else { - let t10 = Rt("rc", this.rank), o = Re(this.rank), n = this.getOutOfBoundsCondition(t10), s = this.getSetup(t10), a = this.getOutput(t10); + let t8 = Rt("rc", this.rank), o = Re(this.rank), n = this.getOutOfBoundsCondition(t8), s = this.getSetup(t8), a = this.getOutput(t8); this.userCode = ` void main() { ${o} rc = getOutputCoords(); @@ -17741,31 +17817,31 @@ var ph = class { } } getSourceCoordsArr(e) { - let t10 = []; + let t8 = []; for (let o = 0; o <= 1; o++) for (let n = 0; n <= 1; n++) { let s = `${o === 0 ? "r" : "rp1"}, ${n === 0 ? "c" : "cp1"}`; for (let a = 2; a < this.rank; a++) s = `${e[e.length - 1 - a]},` + s; - t10.push(s); + t8.push(s); } - return t10; + return t8; } getOutOfBoundsCondition(e) { if (this.rank === 1) return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`; - let t10 = ""; + let t8 = ""; for (let o = this.rank - 2; o < this.rank; o++) - t10 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t10 += "||"); - return t10; + t8 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t8 += "||"); + return t8; } getSetup(e) { if (this.rank === 1) return ""; - let t10 = e.slice(-2), o = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1], n = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; + let t8 = e.slice(-2), o = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1], n = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; return ` - int r = ${t10[0]}; - int c = ${t10[1]}; + int r = ${t8[0]}; + int c = ${t8[1]}; int rp1 = r + 1; int cp1 = c + 1; @@ -17774,16 +17850,16 @@ var ph = class { `; } getOutput(e) { - let t10 = this.getSourceCoordsArr(e); - return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t10[0]}), - cEdge ? 0. : getA(${t10[1]}), - rEdge ? 0. : getA(${t10[2]}), - rEdge || cEdge ? 0. : getA(${t10[3]})`; + let t8 = this.getSourceCoordsArr(e); + return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t8[0]}), + cEdge ? 0. : getA(${t8[1]}), + rEdge ? 0. : getA(${t8[2]}), + rEdge || cEdge ? 0. : getA(${t8[3]})`; } }; -var Oc = class { - constructor(e, t10) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length); +var Mc = class { + constructor(e, t8) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length); let o = ""; for (let n = 0; n < 4; n++) { let s = "thisRC = rc;"; @@ -17801,8 +17877,8 @@ var Oc = class { `; } this.userCode = ` - ${nZ(t10, this.enableShapeUniforms)} - ${this.enableShapeUniforms ? Ec() : $c(e)} + ${DZ(t8, this.enableShapeUniforms)} + ${this.enableShapeUniforms ? Rc() : Ec(e)} void main() { ivec3 rc = getOutputCoords(); @@ -17820,36 +17896,36 @@ var Oc = class { `; } }; -function nZ(r, e) { +function DZ(r, e) { return ` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${e ? tR(["r", "c", "d"], "inputShape") : Os(["r", "c", "d"], r)} + ${e ? fR(["r", "c", "d"], "inputShape") : zs(["r", "c", "d"], r)} return ivec3(r, c, d); } `; } -var ch = class { +var lh = class { constructor(e) { this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.usedTextures = {}, this.logEnabled = false; } - acquireTexture(e, t10, o) { - let n = rD(t10, o), s = oD(e, n, o); + acquireTexture(e, t8, o) { + let n = gD(t8, o), s = xD(e, n, o); s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []); - let a = tD(e, n, this.gpgpu.gl, this.gpgpu.textureConfig, o); + let a = hD(e, n, this.gpgpu.gl, this.gpgpu.textureConfig, o); if (this.freeTextures[s].length > 0) { this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log(); let p = this.freeTextures[s].pop(); return this.usedTextures[s].push(p), p; } let i; - return n === er.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === er.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === er.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === er.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === er.PACKED_4X1_UNSIGNED_BYTE && (i = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[s].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i; + return n === tr.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === tr.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === tr.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === tr.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === tr.PACKED_4X1_UNSIGNED_BYTE && (i = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[s].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i; } - releaseTexture(e, t10, o, n) { + releaseTexture(e, t8, o, n) { if (this.freeTextures == null) return; - let s = rD(o, n), a = oD(t10, s, n); + let s = gD(o, n), a = xD(t8, s, n); a in this.freeTextures || (this.freeTextures[a] = []); - let i = tD(t10, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = P().get("WEBGL_DELETE_TEXTURE_THRESHOLD"); + let i = hD(t8, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = A().get("WEBGL_DELETE_TEXTURE_THRESHOLD"); p !== -1 && this._numBytesAllocated > p ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--; let u = this.usedTextures[a], c = u && u.indexOf(e); if (c == null || c < 0) @@ -17861,8 +17937,8 @@ var ch = class { return; let e = this.numFreeTextures + this.numUsedTextures; console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${e})`); - let t10 = this._numBytesFree / this._numBytesAllocated; - console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t10)}%)`); + let t8 = this._numBytesFree / this._numBytesAllocated; + console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t8)}%)`); } get numBytesAllocated() { return this._numBytesAllocated; @@ -17879,81 +17955,81 @@ var ch = class { dispose() { if (this.freeTextures != null) { for (let e in this.freeTextures) - this.freeTextures[e].forEach((t10) => { - this.gpgpu.deleteMatrixTexture(t10.texture); + this.freeTextures[e].forEach((t8) => { + this.gpgpu.deleteMatrixTexture(t8.texture); }); for (let e in this.usedTextures) - this.usedTextures[e].forEach((t10) => { - this.gpgpu.deleteMatrixTexture(t10.texture); + this.usedTextures[e].forEach((t8) => { + this.gpgpu.deleteMatrixTexture(t8.texture); }); this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0; } } }; -function sZ(r, e) { - let t10 = r; - if (e === t10.R32F) +function AZ(r, e) { + let t8 = r; + if (e === t8.R32F) return 4; - if (e === t10.R16F) + if (e === t8.R16F) return 2; - if (e === t10.RGBA32F) + if (e === t8.RGBA32F) return 16; if (e === r.RGBA) return 16; - if (e === t10.RGBA16F) + if (e === t8.RGBA16F) return 8; - if (e === t10.RGBA8) + if (e === t8.RGBA8) return 4; throw new Error(`Unknown internal format ${e}`); } -function tD(r, e, t10, o, n) { - let s = aZ(e, o), a; +function hD(r, e, t8, o, n) { + let s = FZ(e, o), a; if (n) { - let [p, u] = Ea(r[0], r[1]); + let [p, u] = Pa(r[0], r[1]); a = p * u; } else { - let [p, u] = fp(r[0], r[1]); + let [p, u] = yp(r[0], r[1]); a = p * u; } - let i = sZ(t10, s); + let i = AZ(t8, s); return a * i; } -function aZ(r, e) { +function FZ(r, e) { switch (r) { - case er.PACKED_2X2_FLOAT32: - return nh(e); - case er.PACKED_2X2_FLOAT16: + case tr.PACKED_2X2_FLOAT32: return sh(e); - case er.UNPACKED_FLOAT32: - return th(e); - case er.UNPACKED_FLOAT16: + case tr.PACKED_2X2_FLOAT16: + return ah(e); + case tr.UNPACKED_FLOAT32: return rh(e); - case er.PACKED_4X1_UNSIGNED_BYTE: + case tr.UNPACKED_FLOAT16: return oh(e); + case tr.PACKED_4X1_UNSIGNED_BYTE: + return nh(e); default: throw new Error(`Unknown physical texture type ${r}`); } } -function iZ(r) { - return P().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? er.PACKED_2X2_FLOAT32 : er.UNPACKED_FLOAT32 : r ? er.PACKED_2X2_FLOAT16 : er.UNPACKED_FLOAT16; +function PZ(r) { + return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? tr.PACKED_2X2_FLOAT32 : tr.UNPACKED_FLOAT32 : r ? tr.PACKED_2X2_FLOAT16 : tr.UNPACKED_FLOAT16; } -function rD(r, e) { +function gD(r, e) { if (r === mr.UPLOAD) - return er.PACKED_2X2_FLOAT32; + return tr.PACKED_2X2_FLOAT32; if (r === mr.RENDER || r == null) - return iZ(e); + return PZ(e); if (r === mr.DOWNLOAD || r === mr.PIXELS) - return er.PACKED_4X1_UNSIGNED_BYTE; + return tr.PACKED_4X1_UNSIGNED_BYTE; throw new Error(`Unknown logical texture type ${r}`); } -function oD(r, e, t10) { - return `${r[0]}_${r[1]}_${e}_${t10}`; +function xD(r, e, t8) { + return `${r[0]}_${r[1]}_${e}_${t8}`; } -var tr = class { - constructor(e, t10) { - this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` +var rr = class { + constructor(e, t8) { + this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` float unaryOperation(float x) { - ${t10} + ${t8} } void main() { @@ -17966,19 +18042,19 @@ var tr = class { } }; var Ut = "if (isnan(x)) return x;"; -var nD = "return x;"; -var XI = "return abs(x);"; -var sD = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; -var aD = Ut + ` +var yD = "return x;"; +var nv = "return abs(x);"; +var bD = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; +var CD = Ut + ` return (x < 0.0) ? 0.0 : x; `; -var iD = Ut + ` +var wD = Ut + ` return (x < 0.0) ? 0.0 : min(6.0, x); `; -var Ra = "return x;"; -var uD = "return 1.0 / (1.0 + exp(-1.0 * x));"; -var cD = "return x;"; -var lD = ` +var Oa = "return x;"; +var SD = "return 1.0 / (1.0 + exp(-1.0 * x));"; +var vD = "return x;"; +var kD = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -17988,7 +18064,7 @@ var lD = ` return result; `; -var mD = ` +var ND = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -17999,7 +18075,7 @@ var mD = ` return result; `; -var dD = ` +var TD = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -18010,12 +18086,12 @@ var dD = ` return result; `; -var fD = "return 1.0 / (1.0 + exp(-1.0 * x));"; -var Ar = class { - constructor(e, t10) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` +var _D = "return 1.0 / (1.0 + exp(-1.0 * x));"; +var Fr = class { + constructor(e, t8) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` vec4 unaryOperation(vec4 x) { - ${t10} + ${t8} } void main() { @@ -18027,10 +18103,10 @@ var Ar = class { `; } }; -var lh = class { +var mh = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length); - let t10 = e.length, o = Rt("rc", t10), n = Re(t10), s = eD(t10, o), a = o.slice(-2), i = t10 <= 1 ? "rc" : `vec2(${a.join(",")})`; + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length); + let t8 = e.length, o = Rt("rc", t8), n = Re(t8), s = fD(t8, o), a = o.slice(-2), i = t8 <= 1 ? "rc" : `vec2(${a.join(",")})`; this.userCode = ` void main() { ${n} rc = getOutputCoords(); @@ -18041,81 +18117,81 @@ var lh = class { `; } }; -var pZ = Wt.whereImpl; -var cZ = 1e-7; -var lZ = 1e-4; -var mh = {}; -function mZ(r) { - return r in mh || (mh[r] = {}), mh[r]; +var MZ = Wt.whereImpl; +var LZ = 1e-7; +var BZ = 1e-4; +var dh = {}; +function zZ(r) { + return r in dh || (dh[r] = {}), dh[r]; } -var dZ = P().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); -var fZ = 600; -function hZ() { - return P().global.screen == null ? 1024 : P().global.screen.height * P().global.screen.width * window.devicePixelRatio * fZ / 1024 / 1024; +var VZ = A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); +var WZ = 600; +function UZ() { + return A().global.screen == null ? 1024 : A().global.screen.height * A().global.screen.width * window.devicePixelRatio * WZ / 1024 / 1024; } -var hu = class extends ro { +var bu = class extends so { nextDataId() { - return hu.nextDataId++; + return bu.nextDataId++; } constructor(e) { - if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !P().getBool("HAS_WEBGL")) + if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !A().getBool("HAS_WEBGL")) throw new Error("WebGL is not supported on this device"); - let t10; + let t8; if (e != null) { - if (e instanceof xp) - t10 = e; + if (e instanceof wp) + t8 = e; else { - let o = Gr(P().getNumber("WEBGL_VERSION"), e); - t10 = new xp(o); + let o = Kr(A().getNumber("WEBGL_VERSION"), e); + t8 = new wp(o); } this.binaryCache = {}, this.gpgpuCreatedLocally = false; } else { - let o = Gr(P().getNumber("WEBGL_VERSION")); - t10 = new xp(o), this.binaryCache = mZ(P().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true; + let o = Kr(A().getNumber("WEBGL_VERSION")); + t8 = new wp(o), this.binaryCache = zZ(A().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true; } - this.gpgpu = t10, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new ch(this.gpgpu), this.numMBBeforeWarning = hZ(), this.texData = new Lo(this, ur()); + this.gpgpu = t8, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new lh(this.gpgpu), this.numMBBeforeWarning = UZ(), this.texData = new Bo(this, ur()); } numDataIds() { return this.texData.numDataIds() - this.pendingDeletes; } - writeTexture(e, t10, o, n, s, a) { - let i = this.makeTensorInfo(t10, o), p = this.texData.get(i.dataId); + writeTexture(e, t8, o, n, s, a) { + let i = this.makeTensorInfo(t8, o), p = this.texData.get(i.dataId); p.isPacked = false, p.texture = { texture: e, texShape: [n, s] }, p.texShape = [n, s]; - let u = Tc(t10), c = new Xl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]); - return l.shape = t10, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId; + let u = _c(t8), c = new Xl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]); + return l.shape = t8, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId; } - write(e, t10, o) { - if ((P().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || P().getBool("DEBUG")) && this.checkNumericalProblems(e), o === "complex64" && e != null) + write(e, t8, o) { + if ((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || A().getBool("DEBUG")) && this.checkNumericalProblems(e), o === "complex64" && e != null) throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); let n = { id: this.nextDataId() }; - return this.texData.set(n, { shape: t10, dtype: o, values: e, usage: mr.UPLOAD, refCount: 1 }), n; + return this.texData.set(n, { shape: t8, dtype: o, values: e, usage: mr.UPLOAD, refCount: 1 }), n; } refCount(e) { return this.texData.has(e) ? this.texData.get(e).refCount : 0; } incRef(e) { - let t10 = this.texData.get(e); - t10.refCount++; + let t8 = this.texData.get(e); + t8.refCount++; } decRef(e) { if (this.texData.has(e)) { - let t10 = this.texData.get(e); - t10.refCount--; + let t8 = this.texData.get(e); + t8.refCount--; } } - move(e, t10, o, n, s) { - if (P().getBool("DEBUG") && this.checkNumericalProblems(t10), n === "complex64") + move(e, t8, o, n, s) { + if (A().getBool("DEBUG") && this.checkNumericalProblems(t8), n === "complex64") throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); - this.texData.set(e, { shape: o, dtype: n, values: t10, usage: mr.UPLOAD, refCount: s }); + this.texData.set(e, { shape: o, dtype: n, values: t8, usage: mr.UPLOAD, refCount: s }); } disposeIntermediateTensorInfo(e) { this.disposeData(e.dataId); } readSync(e) { - let t10 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t10; + let t8 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t8; if (a != null) { let m; - p ? m = new Ar(i, Ra) : m = new tr(i, Ra); + p ? m = new Fr(i, Oa) : m = new rr(i, Oa); let d = this.runWebGLProgram(m, [{ dataId: e, shape: i, dtype: n }], n), f = this.readSync(d.dataId); return this.disposeIntermediateTensorInfo(d), f; } @@ -18128,7 +18204,7 @@ var hu = class extends ro { let l; if (n === "complex64") { let m = this.readSync(s.real.dataId), d = this.readSync(s.imag.dataId); - l = C.mergeRealAndImagArrays(m, d); + l = w.mergeRealAndImagArrays(m, d); } else l = this.getValuesFromTexture(e); return u && (this.downloadWaitMs += y.now() - c), this.convertAndCacheOnCPU(e, l); @@ -18138,19 +18214,19 @@ var hu = class extends ro { let f = this.pendingRead.get(e); return new Promise((h) => f.push(h)); } - let t10 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t10; + let t8 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t8; if (s != null) { let f; - p ? f = new Ar(n, Ra) : f = new tr(n, Ra); + p ? f = new Fr(n, Oa) : f = new rr(n, Oa); let h = this.runWebGLProgram(f, [{ dataId: e, shape: n, dtype: a }], a), g = this.read(h.dataId); return this.disposeIntermediateTensorInfo(h), g; } if (o != null) return this.convertAndCacheOnCPU(e); - if (P().getBool("DEBUG") && !P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && P().getNumber("WEBGL_VERSION") === 2) + if (A().getBool("DEBUG") && !A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && A().getNumber("WEBGL_VERSION") === 2) throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported."); let u = null, c; - if (a !== "complex64" && P().get("WEBGL_BUFFER_SUPPORTED")) { + if (a !== "complex64" && A().get("WEBGL_BUFFER_SUPPORTED")) { c = this.decode(e); let f = this.texData.get(c.dataId); u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...Hl(n)); @@ -18159,7 +18235,7 @@ var hu = class extends ro { let l; if (a === "complex64") { let f = await Promise.all([this.read(i.real.dataId), this.read(i.imag.dataId)]), h = f[0], g = f[1]; - l = C.mergeRealAndImagArrays(h, g); + l = w.mergeRealAndImagArrays(h, g); } else if (u == null) l = this.getValuesFromTexture(e); else { @@ -18173,60 +18249,60 @@ var hu = class extends ro { let m = this.convertAndCacheOnCPU(e, l), d = this.pendingRead.get(e); return this.pendingRead.delete(e), d.forEach((f) => f(m)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && ur().removeDataId(e, this), this.pendingDeletes--), m; } - readToGPU(e, t10 = {}) { + readToGPU(e, t8 = {}) { let o = this.texData.get(e), { values: n, shape: s, slice: a, dtype: i, isPacked: p, texture: u } = o; if (i === "complex64") throw new Error("Does not support reading texture for complex64 dtype."); if (a != null) { let d; - p ? d = new Ar(s, Ra) : d = new tr(s, Ra); - let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t10); + p ? d = new Fr(s, Oa) : d = new rr(s, Oa); + let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t8); return this.disposeIntermediateTensorInfo(f), h; } if (u == null) throw n != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU."); - let c = this.decode(e, t10.customTexShape), l = ur().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId); + let c = this.decode(e, t8.customTexShape), l = ur().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId); return Object.assign({ tensorRef: l }, m.texture); } bufferSync(e) { - let t10 = this.readSync(e.dataId); + let t8 = this.readSync(e.dataId); if (e.dtype === "string") try { - let o = t10.map((n) => y.decodeString(n)); + let o = t8.map((n) => y.decodeString(n)); return me(e.shape, e.dtype, o); } catch (o) { throw new Error("Failed to decode encoded string bytes into utf-8"); } - return me(e.shape, e.dtype, t10); + return me(e.shape, e.dtype, t8); } checkNumericalProblems(e) { if (e != null) - for (let t10 = 0; t10 < e.length; t10++) { - let o = e[t10]; - if (!cI(o)) - throw P().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${o} cannot be represented on this device.`); + for (let t8 = 0; t8 < e.length; t8++) { + let o = e[t8]; + if (!bI(o)) + throw A().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${o} cannot be represented on this device.`); } } getValuesFromTexture(e) { - let { shape: t10, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t10); - if (P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) { - let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Hl(t10)).subarray(0, s); + let { shape: t8, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t8); + if (A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) { + let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Hl(t8)).subarray(0, s); return this.disposeIntermediateTensorInfo(m), f; } - let a = P().getBool("WEBGL_PACK") && n === true, i = a ? Tc(t10) : t10, p = a ? new Jf(i) : new Zf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s); + let a = A().getBool("WEBGL_PACK") && n === true, i = a ? _c(t8) : t8, p = a ? new eh(i) : new Jf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s); return this.disposeIntermediateTensorInfo(u), l; } timerAvailable() { - return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0; + return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0; } time(e) { - let t10 = this.activeTimers, o = [], n = false; + let t8 = this.activeTimers, o = [], n = false; this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e(); let s = y.flatten(this.activeTimers.map((p) => p.query)).filter((p) => p != null), a = y.flatten(this.activeTimers.map((p) => p.name)).filter((p) => p != null); - this.activeTimers = t10, n && (this.programTimersStack = null); + this.activeTimers = t8, n && (this.programTimersStack = null); let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }; return (async () => { - if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { let p = await Promise.all(s); i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "); } else @@ -18238,33 +18314,33 @@ var hu = class extends ro { return { unreliable: false, numBytesInGPU: this.numBytesInGPU, numBytesInGPUAllocated: this.textureManager.numBytesAllocated, numBytesInGPUFree: this.textureManager.numBytesFree }; } startTimer() { - return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: y.now(), endMs: null }; + return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: y.now(), endMs: null }; } endTimer(e) { - return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = y.now(), e); + return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = y.now(), e); } async getQueryTime(e) { - if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) + if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) return this.gpgpu.waitForQueryAndGetTime(e); - let t10 = e; - return t10.endMs - t10.startMs; + let t8 = e; + return t8.endMs - t8.startMs; } - disposeData(e, t10 = false) { + disposeData(e, t8 = false) { if (this.pendingDisposal.has(e)) return false; if (!this.texData.has(e)) return true; - if (t10 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t10 && this.texData.get(e).refCount > 0) + if (t8 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t8 && this.texData.get(e).refCount > 0) return false; if (this.pendingRead.has(e)) return this.pendingDisposal.add(e), this.pendingDeletes++, false; this.releaseGPUData(e); let { complexTensorInfos: o } = this.texData.get(e); - return o != null && (this.disposeData(o.real.dataId, t10), this.disposeData(o.imag.dataId, t10)), this.texData.delete(e), true; + return o != null && (this.disposeData(o.real.dataId, t8), this.disposeData(o.imag.dataId, t8)), this.texData.delete(e), true; } releaseGPUData(e) { - let { texture: t10, dtype: o, texShape: n, usage: s, isPacked: a, slice: i } = this.texData.get(e), p = i && i.origDataId || e, u = this.dataRefCount.get(p); - u > 1 ? this.dataRefCount.set(p, u - 1) : (this.dataRefCount.delete(p), t10 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t10, n, s, a))); + let { texture: t8, dtype: o, texShape: n, usage: s, isPacked: a, slice: i } = this.texData.get(e), p = i && i.origDataId || e, u = this.dataRefCount.get(p); + u > 1 ? this.dataRefCount.set(p, u - 1) : (this.dataRefCount.delete(p), t8 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t8, n, s, a))); let c = this.texData.get(e); c.texture = null, c.texShape = null, c.isPacked = false, c.slice = null; } @@ -18274,166 +18350,166 @@ var hu = class extends ro { getDataInfo(e) { return this.texData.get(e); } - shouldExecuteOnCPU(e, t10 = dZ) { - return P().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t10); + shouldExecuteOnCPU(e, t8 = VZ) { + return A().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t8); } getGPGPUContext() { return this.gpgpu; } where(e) { - C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead"); - let t10 = e.dataSync(); - return pZ(e.shape, t10); + w.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead"); + let t8 = e.dataSync(); + return MZ(e.shape, t8); } - packedUnaryOp(e, t10, o) { - let n = new Ar(e.shape, t10), s = this.compileAndRun(n, [e], o); + packedUnaryOp(e, t8, o) { + let n = new Fr(e.shape, t8), s = this.compileAndRun(n, [e], o); return ur().makeTensorFromTensorInfo(s); } abs(e) { if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") { - let n = ih(this.texData.get(e.dataId).values); + let n = uh(this.texData.get(e.dataId).values); return this.makeOutput(e.shape, e.dtype, n); } - if (P().getBool("WEBGL_PACK_UNARY_OPERATIONS")) - return this.packedUnaryOp(e, XI, e.dtype); - let t10 = new tr(e.shape, XI), o = this.compileAndRun(t10, [e]); + if (A().getBool("WEBGL_PACK_UNARY_OPERATIONS")) + return this.packedUnaryOp(e, nv, e.dtype); + let t8 = new rr(e.shape, nv), o = this.compileAndRun(t8, [e]); return ur().makeTensorFromTensorInfo(o); } - makeTensorInfo(e, t10, o) { + makeTensorInfo(e, t8, o) { let n; - if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) { + if (t8 === "string" && o != null && o.length > 0 && y.isString(o[0])) { let s = o.map((a) => y.encodeString(a)); - n = this.write(s, e, t10); + n = this.write(s, e, t8); } else - n = this.write(o, e, t10); - return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t10 }; + n = this.write(o, e, t8); + return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t8 }; } - makeOutput(e, t10, o) { - return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e, t10, o), this); + makeOutput(e, t8, o) { + return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e, t8, o), this); } unpackTensor(e) { - let t10 = new lh(e.shape); - return this.runWebGLProgram(t10, [e], e.dtype); + let t8 = new mh(e.shape); + return this.runWebGLProgram(t8, [e], e.dtype); } packTensor(e) { - let t10 = new ph(e.shape), o = true; - return this.runWebGLProgram(t10, [e], e.dtype, null, o); + let t8 = new ch(e.shape), o = true; + return this.runWebGLProgram(t8, [e], e.dtype, null, o); } - packedReshape(e, t10) { - let o = [di(e.shape), ...fi(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [di(t10), ...fi(t10)], a = new Oc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i); - return { dataId: u.dataId, shape: t10, dtype: u.dtype }; + packedReshape(e, t8) { + let o = [gi(e.shape), ...xi(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [gi(t8), ...xi(t8)], a = new Mc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i); + return { dataId: u.dataId, shape: t8, dtype: u.dtype }; } - decode(e, t10) { + decode(e, t8) { let o = this.texData.get(e), { isPacked: n, shape: s, dtype: a } = o; - if (t10 != null) { - let m = y.sizeFromShape(s), d = t10[0] * t10[1] * 4; + if (t8 != null) { + let m = y.sizeFromShape(s), d = t8[0] * t8[1] * 4; y.assert(m <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."); } - let i = Tc(s), p; - n ? p = new Qf(i) : p = new Yf(i); - let u = true, c = [t10 != null ? t10 : Hl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t10); + let i = _c(s), p; + n ? p = new Zf(i) : p = new Qf(i); + let u = true, c = [t8 != null ? t8 : Hl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t8); return { dtype: a, shape: s, dataId: l.dataId }; } - runWebGLProgram(e, t10, o, n, s = false, a) { + runWebGLProgram(e, t8, o, n, s = false, a) { let i = this.makeTensorInfo(e.outputShape, o), p = this.texData.get(i.dataId); - if (e.packedOutput && (p.isPacked = true), e.outPackingScheme === du.DENSE) { + if (e.packedOutput && (p.isPacked = true), e.outPackingScheme === xu.DENSE) { let x = a != null ? a : Hl(e.outputShape); p.texShape = x.map((b) => b * 2); } if (e.outTexUsage != null && (p.usage = e.outTexUsage), y.sizeFromShape(i.shape) === 0) return p.values = y.getTypedArrayFromDType(i.dtype, 0), i; - let u = [], c = t10.map((x) => { + let u = [], c = t8.map((x) => { if (x.dtype === "complex64") throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts."); let b = this.texData.get(x.dataId); if (b.texture == null) { - if (!e.packedInputs && y.sizeFromShape(x.shape) <= P().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) + if (!e.packedInputs && y.sizeFromShape(x.shape) <= A().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) return { shape: x.shape, texData: null, isUniform: true, uniformValues: b.values }; e.packedInputs && (b.isPacked = true, b.shape = x.shape); } if (this.uploadToGPU(x.dataId), !!b.isPacked != !!e.packedInputs) x = b.isPacked ? this.unpackTensor(x) : this.packTensor(x), u.push(x), b = this.texData.get(x.dataId); - else if (b.isPacked && !fu(b.shape, x.shape)) { - let w = x, S = x.shape; - x.shape = b.shape, x = this.packedReshape(x, S), u.push(x), b = this.texData.get(x.dataId), w.shape = S; + else if (b.isPacked && !yu(b.shape, x.shape)) { + let C = x, S = x.shape; + x.shape = b.shape, x = this.packedReshape(x, S), u.push(x), b = this.texData.get(x.dataId), C.shape = S; } return { shape: x.shape, texData: b, isUniform: false }; }); this.uploadToGPU(i.dataId); - let l = { shape: i.shape, texData: p, isUniform: false }, m = pR(e, c, l), d = this.getAndSaveBinary(m, () => iR(this.gpgpu, e, c, l)), f = this.activeTimers != null, h; - f && (h = this.startTimer()), P().get("ENGINE_COMPILE_ONLY") || uR(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) })); - let g = P().get("WEBGL_FLUSH_THRESHOLD"); + let l = { shape: i.shape, texData: p, isUniform: false }, m = SR(e, c, l), d = this.getAndSaveBinary(m, () => CR(this.gpgpu, e, c, l)), f = this.activeTimers != null, h; + f && (h = this.startTimer()), A().get("ENGINE_COMPILE_ONLY") || wR(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) })); + let g = A().get("WEBGL_FLUSH_THRESHOLD"); if (g > 0) { let x = y.now(); x - this.lastGlFlushTime > g && (this.gpgpu.gl.flush(), this.lastGlFlushTime = x); } - if (!P().getBool("WEBGL_LAZILY_UNPACK") && p.isPacked && s === false) { + if (!A().getBool("WEBGL_LAZILY_UNPACK") && p.isPacked && s === false) { let x = this.unpackTensor(i); return this.disposeIntermediateTensorInfo(i), x; } return i; } - compileAndRun(e, t10, o, n, s = false) { - return o = o || t10[0].dtype, this.runWebGLProgram(e, t10, o, n, s); + compileAndRun(e, t8, o, n, s = false) { + return o = o || t8[0].dtype, this.runWebGLProgram(e, t8, o, n, s); } - getAndSaveBinary(e, t10) { - return e in this.binaryCache || (this.binaryCache[e] = t10()), this.binaryCache[e]; + getAndSaveBinary(e, t8) { + return e in this.binaryCache || (this.binaryCache[e] = t8()), this.binaryCache[e]; } getTextureManager() { return this.textureManager; } dispose() { - this.disposed || (P().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t10) => { - this.gpgpu.deleteProgram(this.binaryCache[t10].webGLProgram), delete this.binaryCache[t10]; + this.disposed || (A().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t8) => { + this.gpgpu.deleteProgram(this.binaryCache[t8].webGLProgram), delete this.binaryCache[t8]; }), this.textureManager.dispose(), this.canvas != null && typeof HTMLCanvasElement != "undefined" && this.canvas instanceof HTMLCanvasElement ? this.canvas.remove() : this.canvas = null, this.gpgpuCreatedLocally && (this.gpgpu.program = null, this.gpgpu.dispose()), this.disposed = true); } floatPrecision() { return this.floatPrecisionValue == null && (this.floatPrecisionValue = De(() => { - if (!P().get("WEBGL_RENDER_FLOAT32_ENABLED")) { - let e = P().getBool("DEBUG"); - P().set("DEBUG", false); - let t10 = this.abs(ke(1e-8)).dataSync()[0]; - if (P().set("DEBUG", e), t10 > 0) + if (!A().get("WEBGL_RENDER_FLOAT32_ENABLED")) { + let e = A().getBool("DEBUG"); + A().set("DEBUG", false); + let t8 = this.abs(ke(1e-8)).dataSync()[0]; + if (A().set("DEBUG", e), t8 > 0) return 32; } return 16; })), this.floatPrecisionValue; } epsilon() { - return this.floatPrecision() === 32 ? cZ : lZ; + return this.floatPrecision() === 32 ? LZ : BZ; } uploadToGPU(e) { - let t10 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t10; + let t8 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t8; if (a != null) return; let u = this.activeTimers != null, c; u && (c = y.now()); - let l = t10.texShape; - if (l == null && (l = II(o, p), t10.texShape = l), s != null) { - let m = Tc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray; - (p || !g) && ([f, h] = Ea(l[0], l[1])), p ? d = new eh(m, g) : d = new Xl(m, g); - let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), w = this.texData.get(b.dataId); - g ? w.usage = mr.PIXELS : w.usage = mr.UPLOAD, w.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s); + let l = t8.texShape; + if (l == null && (l = DI(o, p), t8.texShape = l), s != null) { + let m = _c(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray; + (p || !g) && ([f, h] = Pa(l[0], l[1])), p ? d = new th(m, g) : d = new Xl(m, g); + let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), C = this.texData.get(b.dataId); + g ? C.usage = mr.PIXELS : C.usage = mr.UPLOAD, C.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s); let S = [[h, f]], k = true, _ = this.runWebGLProgram(d, [b], n, S, k), E = this.texData.get(_.dataId); - t10.texShape = E.texShape, t10.isPacked = E.isPacked, t10.usage = E.usage, P().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t10.texture = E.texture, t10.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c); + t8.texShape = E.texShape, t8.isPacked = E.isPacked, t8.usage = E.usage, A().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t8.texture = E.texture, t8.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c); } else { let m = this.acquireTexture(l, i, n, p); - t10.texture = m; + t8.texture = m; } } - convertAndCacheOnCPU(e, t10) { + convertAndCacheOnCPU(e, t8) { let o = this.texData.get(e), { dtype: n } = o; - return t10 != null && (o.values = gZ(t10, n)), o.values; + return t8 != null && (o.values = GZ(t8, n)), o.values; } - acquireTexture(e, t10, o, n) { + acquireTexture(e, t8, o, n) { if (this.numBytesInGPU += this.computeBytes(e, o), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) { let s = (this.numBytesInGPU / 1024 / 1024).toFixed(2); this.warnedAboutMemory = true, console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`); } - return this.textureManager.acquireTexture(e, t10, n); + return this.textureManager.acquireTexture(e, t8, n); } - computeBytes(e, t10) { - return e[0] * e[1] * y.bytesPerElement(t10); + computeBytes(e, t8) { + return e[0] * e[1] * y.bytesPerElement(t8); } checkCompileCompletion() { for (let [, e] of Object.entries(this.binaryCache)) @@ -18442,14 +18518,14 @@ var hu = class extends ro { async checkCompileCompletionAsync() { let e = []; if (this.gpgpu.parallelCompilationExtension) { - for (let [, t10] of Object.entries(this.binaryCache)) - e.push(this.checkCompletionAsync_(t10)); + for (let [, t8] of Object.entries(this.binaryCache)) + e.push(this.checkCompletionAsync_(t8)); return Promise.all(e); } else { - for (let [, t10] of Object.entries(this.binaryCache)) { + for (let [, t8] of Object.entries(this.binaryCache)) { let o = new Promise((n) => { try { - this.checkCompletion_(t10), n(true); + this.checkCompletion_(t8), n(true); } catch (s) { throw s; } @@ -18460,54 +18536,54 @@ var hu = class extends ro { } } async checkCompletionAsync_(e) { - return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await Kw(), this.checkCompletionAsync_(e)); + return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await tS(), this.checkCompletionAsync_(e)); } checkCompletion_(e) { if (this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) - throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Gf(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders."); + throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Hf(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders."); return true; } getUniformLocations() { for (let e of Object.values(this.binaryCache)) { this.gpgpu.buildVao(e.webGLProgram); - let { variablesLocations: t10, customUniformLocations: o, infLoc: n, nanLoc: s, outShapeLocation: a, outShapeStridesLocation: i, outTexShapeLocation: p } = EI(this.gpgpu, e.program, e.webGLProgram); - e.variablesLocations = t10, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.outShapeLocation = a, e.outShapeStridesLocation = i, e.outTexShapeLocation = p; + let { variablesLocations: t8, customUniformLocations: o, infLoc: n, nanLoc: s, outShapeLocation: a, outShapeStridesLocation: i, outTexShapeLocation: p } = BI(this.gpgpu, e.program, e.webGLProgram); + e.variablesLocations = t8, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.outShapeLocation = a, e.outShapeStridesLocation = i, e.outTexShapeLocation = p; } } - createTensorFromGPUData(e, t10, o) { + createTensorFromGPUData(e, t8, o) { e.channels = e.channels || "RGBA"; let { texture: n, height: s, width: a, channels: i } = e, p = ur().backend; if (!p.gpgpu.gl.isTexture(n)) throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'."); - let u = p.writeTexture(n, t10, o, s, a, i); - return ur().makeTensorFromDataId(u, t10, o, p); + let u = p.writeTexture(n, t8, o, s, a, i); + return ur().makeTensorFromDataId(u, t8, o, p); } }; -hu.nextDataId = 0; -function gZ(r, e) { +bu.nextDataId = 0; +function GZ(r, e) { if (e === "float32" || e === "complex64") return r; if (e === "int32" || e === "bool") { - let t10 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length); - for (let o = 0; o < t10.length; ++o) - t10[o] = Math.round(r[o]); - return t10; + let t8 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length); + for (let o = 0; o < t8.length; ++o) + t8[o] = Math.round(r[o]); + return t8; } else throw new Error(`Unknown dtype ${e}`); } -var xZ = "4.5.0"; -function hD() { - P().set("WEBGL_FORCE_F16_TEXTURES", true); +var HZ = "4.7.0"; +function $D() { + A().set("WEBGL_FORCE_F16_TEXTURES", true); } -Zi.isBrowser() && eu("webgl", () => new hu(), 2); -var cst = { forceHalfFloat: hD }; -var Mc = ` +ru.isBrowser() && nu("webgl", () => new bu(), 2); +var tat = { forceHalfFloat: $D }; +var Lc = ` if (isnan(a)) return a; if (isnan(b)) return b; `; -var lo = class { - constructor(e, t10, o) { - this.variableNames = ["A", "B"], this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` +var Pr = class { + constructor(e, t8, o) { + this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` float binaryOperation(float a, float b) { ${e} } @@ -18520,17 +18596,17 @@ var lo = class { `; } }; -var Kr = ` +var Xr = ` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `; -var Ro = class { - constructor(e, t10, o, n = false) { - this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = C.assertAndGetBroadcastShape(t10, o); +var jr = class { + constructor(e, t8, o, n = false) { + this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = w.assertAndGetBroadcastShape(t8, o); let s = this.outputShape.length; - this.enableShapeUniforms = ut(s); + this.enableShapeUniforms = pt(s); let a = ""; if (n) if (s === 0 || y.sizeFromShape(this.outputShape) === 1) @@ -18589,86 +18665,86 @@ var Ro = class { } }; function Dt(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; - return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; + let { inputs: e, backend: t8 } = r, { x: o } = e; + return t8.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -var gD = { kernelName: xo, backendName: "webgl", kernelFunc: Dt }; -function Fr(r) { - let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.texData.get(s.dataId), i = Dt({ inputs: { x: o }, backend: t10 }), p = Dt({ inputs: { x: n }, backend: t10 }); +var ED = { kernelName: Co, backendName: "webgl", kernelFunc: Dt }; +function Or(r) { + let { inputs: e, backend: t8 } = r, { real: o, imag: n } = e, s = t8.makeTensorInfo(o.shape, "complex64"), a = t8.texData.get(s.dataId), i = Dt({ inputs: { x: o }, backend: t8 }), p = Dt({ inputs: { x: n }, backend: t8 }); return a.complexTensorInfos = { real: i, imag: p }, s; } -var xD = { kernelName: Ti, backendName: "webgl", kernelFunc: Fr }; -var YI = "return (a < 0.) ? b * a : a;"; -var QI = ` +var RD = { kernelName: Ri, backendName: "webgl", kernelFunc: Or }; +var sv = "return (a < 0.) ? b * a : a;"; +var av = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -function yZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t10.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ro(QI, n.shape, a.shape) : new lo(YI, n.shape, a.shape), p = t10.runWebGLProgram(i, [n, a], "float32"); - return t10.disposeIntermediateTensorInfo(a), p; +function KZ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t8.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(av, n.shape, a.shape) : new Pr(sv, n.shape, a.shape), p = t8.runWebGLProgram(i, [n, a], "float32"); + return t8.disposeIntermediateTensorInfo(a), p; } -var yD = { kernelName: _n, backendName: "webgl", kernelFunc: yZ }; -var ZI = "return (a < 0.) ? b * a : a;"; -var JI = ` +var DD = { kernelName: En, backendName: "webgl", kernelFunc: KZ }; +var iv = "return (a < 0.) ? b * a : a;"; +var uv = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -function bZ(r) { - let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ro(JI, o.shape, n.shape) : new lo(ZI, o.shape, n.shape); - return t10.runWebGLProgram(s, [o, n], "float32"); +function qZ(r) { + let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(uv, o.shape, n.shape) : new Pr(iv, o.shape, n.shape); + return t8.runWebGLProgram(s, [o, n], "float32"); } -var bD = { kernelName: Jn, backendName: "webgl", kernelFunc: bZ }; -var Do = "if (isnan(x)) return x;"; -function ge({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t10, dtype: o }) { +var AD = { kernelName: rs, backendName: "webgl", kernelFunc: qZ }; +var Fo = "if (isnan(x)) return x;"; +function xe({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t8, dtype: o }) { return ({ inputs: n, backend: s }) => { let { x: a } = n, i = s, p = o || a.dtype; - if (i.shouldExecuteOnCPU([a]) && t10 != null) { - let l = i.texData.get(a.dataId), m = t10(l.values, p); + if (i.shouldExecuteOnCPU([a]) && t8 != null) { + let l = i.texData.get(a.dataId), m = t8(l.values, p); return i.makeTensorInfo(a.shape, p, m); } - let u = P().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c; - return u ? c = new Ar(a.shape, e) : c = new tr(a.shape, r), i.runWebGLProgram(c, [a], p); + let u = A().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c; + return u ? c = new Fr(a.shape, e) : c = new rr(a.shape, r), i.runWebGLProgram(c, [a], p); }; } -function nt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t10 = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) { +function nt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t8 = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) { return ({ inputs: a, backend: i }) => { let { a: p, b: u } = a, c = i; if (o && p.dtype === "complex64") { - let f = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, x] = [[f.complexTensorInfos.real, h.complexTensorInfos.real], [f.complexTensorInfos.imag, h.complexTensorInfos.imag]].map((w) => { - let [S, k] = w, _ = { dataId: S.dataId, dtype: S.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, R = new lo(r, p.shape, u.shape); + let f = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, x] = [[f.complexTensorInfos.real, h.complexTensorInfos.real], [f.complexTensorInfos.imag, h.complexTensorInfos.imag]].map((C) => { + let [S, k] = C, _ = { dataId: S.dataId, dtype: S.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, R = new Pr(r, p.shape, u.shape); return c.runWebGLProgram(R, [_, E], dt(S.dtype, k.dtype)); - }), b = Fr({ inputs: { real: g, imag: x }, backend: c }); + }), b = Or({ inputs: { real: g, imag: x }, backend: c }); return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(x), b; } let l = s || dt(p.dtype, u.dtype); if ((p.dtype === "string" || u.dtype === "string" || c.shouldExecuteOnCPU([p, u])) && n != null) { - let f = c.texData.get(p.dataId).values, h = c.texData.get(u.dataId).values, g = p.dtype === "string" ? C.fromUint8ToStringArray(f) : f, x = p.dtype === "string" ? C.fromUint8ToStringArray(h) : h, [b, w] = n(p.shape, u.shape, g, x, l), S = c.makeTensorInfo(w, l), k = c.texData.get(S.dataId); + let f = c.texData.get(p.dataId).values, h = c.texData.get(u.dataId).values, g = p.dtype === "string" ? w.fromUint8ToStringArray(f) : f, x = p.dtype === "string" ? w.fromUint8ToStringArray(h) : h, [b, C] = n(p.shape, u.shape, g, x, l), S = c.makeTensorInfo(C, l), k = c.texData.get(S.dataId); return k.values = b, S; } - let m = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, d; - return m ? d = new Ro(e, p.shape, u.shape, t10) : d = new lo(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l); + let m = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, d; + return m ? d = new jr(e, p.shape, u.shape, t8) : d = new Pr(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l); }; } -function hi(r, e = false) { +function yi(r, e = false) { if (r === "linear") - return e ? cD : nD; + return e ? vD : yD; if (r === "relu") - return e ? mD : aD; + return e ? ND : CD; if (r === "elu") - return e ? lD : sD; + return e ? kD : bD; if (r === "relu6") - return e ? dD : iD; + return e ? TD : wD; if (r === "prelu") - return e ? JI : ZI; + return e ? uv : iv; if (r === "leakyrelu") - return e ? QI : YI; + return e ? av : sv; if (r === "sigmoid") - return e ? fD : uD; + return e ? _D : SD; throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`); } -var Lc = class { - constructor(e, t10, o, n = false, s = false, a = false, i = null, p = false, u = false) { - this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = ut(this.outputShape.length); +var Bc = class { + constructor(e, t8, o, n = false, s = false, a = false, i = null, p = false, u = false) { + this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = pt(this.outputShape.length); let c = n ? e[1] : e[2], l = Math.ceil(c / 2), m = n ? "i * 2, rc.y" : "rc.y, i * 2", d = s ? "rc.z, i * 2" : "i * 2, rc.z", f = n ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], h = s ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], g = "", x = ""; i && (p ? g = `vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); @@ -18681,15 +18757,15 @@ var Lc = class { }`, x = "result = activation(result);"); let b = a ? "result += getBiasAtOutCoords();" : ""; a && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha"); - let w = "rc.x", S = "rc.x"; - e[0] < t10[0] ? w = `imod(rc.x, ${e[0]})` : t10[0] < e[0] && (S = `imod(rc.x, ${t10[0]})`), this.userCode = ` + let C = "rc.x", S = "rc.x"; + e[0] < t8[0] ? C = `imod(rc.x, ${e[0]})` : t8[0] < e[0] && (S = `imod(rc.x, ${t8[0]})`), this.userCode = ` ${g} // Don't use uniform for sharedDimensionPacked for performance. const float sharedDimension = ${l}.0; vec4 dot2x2ARowBCol(ivec3 rc) { vec4 result = vec4(0); - int batchA = ${w}; + int batchA = ${C}; int batchB = ${S}; for (int i = 0; i < ${l}; i++) { vec4 a = getMatrixA(batchA, ${m}); @@ -18716,10 +18792,10 @@ var Lc = class { `; } }; -var ev = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" }; +var pv = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" }; var Ql = class { - constructor(e, t10, o) { - this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.userCode = ` + constructor(e, t8, o) { + this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.userCode = ` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -18735,40 +18811,40 @@ var Ql = class { `; } }; -var CD = "return a * b;"; +var FD = "return a * b;"; function Zl(r) { - let { inputs: e, backend: t10 } = r, { a: o, b: n } = e, s = C.upcastType(o.dtype, n.dtype); + let { inputs: e, backend: t8 } = r, { a: o, b: n } = e, s = w.upcastType(o.dtype, n.dtype); if (o.dtype === "complex64") { - let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), u = new Ql(ev.REAL, o.shape, n.shape), c = new Ql(ev.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Fr({ inputs: { real: m, imag: d }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f; + let i = t8.texData.get(o.dataId), p = t8.texData.get(n.dataId), u = new Ql(pv.REAL, o.shape, n.shape), c = new Ql(pv.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = t8.runWebGLProgram(u, l, "float32"), d = t8.runWebGLProgram(c, l, "float32"), f = Or({ inputs: { real: m, imag: d }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), f; } - if (t10.shouldExecuteOnCPU([o, n])) { - let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), [u, c] = ER(o.shape, n.shape, i.values, p.values, s), l = t10.makeTensorInfo(c, s), m = t10.texData.get(l.dataId); + if (t8.shouldExecuteOnCPU([o, n])) { + let i = t8.texData.get(o.dataId), p = t8.texData.get(n.dataId), [u, c] = GR(o.shape, n.shape, i.values, p.values, s), l = t8.makeTensorInfo(c, s), m = t8.texData.get(l.dataId); return m.values = u, l; } let a; - return P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new Ro(CD, o.shape, n.shape) : a = new lo(CD, o.shape, n.shape), t10.runWebGLProgram(a, [o, n], s); + return A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new jr(FD, o.shape, n.shape) : a = new Pr(FD, o.shape, n.shape), t8.runWebGLProgram(a, [o, n], s); } -var wD = { kernelName: Kn, backendName: "webgl", kernelFunc: Zl }; -function SD(r, e, t10) { - let o = [di(r.shape), ...fi(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [di(e), ...fi(e)], a = new Oc(s, o), i = true, p = [o], u = t10.runWebGLProgram(a, [n], r.dtype, p, i); +var PD = { kernelName: Xn, backendName: "webgl", kernelFunc: Zl }; +function OD(r, e, t8) { + let o = [gi(r.shape), ...xi(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [gi(e), ...xi(e)], a = new Mc(s, o), i = true, p = [o], u = t8.runWebGLProgram(a, [n], r.dtype, p, i); return { dataId: u.dataId, shape: e, dtype: u.dtype }; } function te(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t10, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p); + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t8, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p); y.assert(i === u, () => `The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`); let c = a.texData.get(n.dataId); - return c.isPacked && !fu(n.shape, p) && !(c.texture !== null && fu(c.shape, p)) ? SD(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype }); + return c.isPacked && !yu(n.shape, p) && !(c.texture !== null && yu(c.shape, p)) ? OD(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype }); } -var ID = { kernelName: ia, backendName: "webgl", kernelFunc: te }; +var MD = { kernelName: ma, backendName: "webgl", kernelFunc: te }; var Jl = class { - constructor(e, t10) { + constructor(e, t8) { this.variableNames = ["x"]; let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e; this.outputShape = [n, a]; let i = Math.floor(o / 4) * 4, p = o % 4, u = "sumValue += dot(values, ones);"; - if (t10 != null) { - let l = 1 / t10; + if (t8 != null) { + let l = 1 / t8; u = `sumValue += dot(values * ${y.isInt(l) ? l.toPrecision(2) : l}, ones);`; } let c = ""; @@ -18828,24 +18904,24 @@ var Jl = class { `; } }; -var dh = class { - constructor(e, t10) { +var fh = class { + constructor(e, t8) { this.variableNames = ["x"]; let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e; this.outputShape = [n, a]; let i = "0.0", p = ""; - t10 === "prod" ? i = "1.0" : t10 === "min" ? (i = "1.0 / 1e-20", p = "min") : t10 === "max" && (i = "-1.0 / 1e-20", p = "max"); - let u = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; - t10 === "sum" ? u = "sumValue" : t10 === "prod" ? u = "prodValue" : t10 === "all" ? u = "allValue" : t10 === "any" && (u = "anyValue"); + t8 === "prod" ? i = "1.0" : t8 === "min" ? (i = "1.0 / 1e-20", p = "min") : t8 === "max" && (i = "-1.0 / 1e-20", p = "max"); + let u = `${t8}(${t8}(${t8}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + t8 === "sum" ? u = "sumValue" : t8 === "prod" ? u = "prodValue" : t8 === "all" ? u = "allValue" : t8 === "any" && (u = "anyValue"); let c = Math.floor(o / 4) * 4, l = o % 4, m = ` - if (${t10 === "sum"}) { + if (${t8 === "sum"}) { sumValue += dot(values, ones); - } else if (${t10 === "prod"}) { + } else if (${t8 === "prod"}) { vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); prodValue *= tmp[0] * tmp[1]; } else { minMaxValue = ${p}(values, minMaxValue); - if (${t10 === "min"} || ${t10 === "max"}) { + if (${t8 === "min"} || ${t8 === "max"}) { minMaxValue = ${p}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { @@ -18854,11 +18930,11 @@ var dh = class { } } `, d = "vec4"; - t10 === "all" ? (i = "1.0", m = ` + t8 === "all" ? (i = "1.0", m = ` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); - `, d = "bvec4") : t10 === "any" && (i = "0.0", m = ` + `, d = "bvec4") : t8 === "any" && (i = "0.0", m = ` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); @@ -18935,30 +19011,30 @@ var dh = class { `; } }; -function wZ(r) { +function XZ(r) { let e = []; for (; e.length === 0 || e[e.length - 1].outSize !== 1; ) { - let t10 = e.length ? e[e.length - 1].outSize : r[1], o = C.computeOptimalWindowSize(t10); - e.push({ inSize: t10, windowSize: o, outSize: Math.ceil(t10 / o) }); + let t8 = e.length ? e[e.length - 1].outSize : r[1], o = w.computeOptimalWindowSize(t8); + e.push({ inSize: t8, windowSize: o, outSize: Math.ceil(t8 / o) }); } return e; } -function qr(r, e, t10, o) { - let n = wZ(r.shape), s = r; +function Yr(r, e, t8, o) { + let n = XZ(r.shape), s = r; for (let a = 0; a < n.length; a++) { let { inSize: i, windowSize: p, outSize: u } = n[a], c, l; - t10 === "mean" ? c = a === 0 ? new Jl({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new Jl({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new dh({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t10), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l); + t8 === "mean" ? c = a === 0 ? new Jl({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new Jl({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new fh({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t8), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l); } return s; } -var fh = class { - constructor(e, t10) { +var hh = class { + constructor(e, t8) { this.variableNames = ["A"]; let o = new Array(e.length); for (let a = 0; a < o.length; a++) - o[a] = e[t10[a]]; + o[a] = e[t8[a]]; this.outputShape = o, this.rank = o.length; - let n = Re(this.rank), s = SZ(t10); + let n = Re(this.rank), s = YZ(t8); this.userCode = ` void main() { ${n} resRC = getOutputCoords(); @@ -18967,26 +19043,26 @@ var fh = class { `; } }; -function SZ(r) { +function YZ(r) { let e = r.length; if (e > 6) throw Error(`Transpose for rank ${e} is not yet supported`); - let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(e); + let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(e); for (let n = 0; n < r.length; n++) - o[r[n]] = t10[n]; + o[r[n]] = t8[n]; return o.join(); } -var hh = class { - constructor(e, t10) { +var gh = class { + constructor(e, t8) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true; let o = new Array(e.length); for (let c = 0; c < o.length; c++) - o[c] = e[t10[c]]; + o[c] = e[t8[c]]; if (this.outputShape = o, this.rank = o.length, this.rank > 6) throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`); - let n = Re(this.rank), s = jI("rc", this.rank), a = new Array(this.rank); - for (let c = 0; c < t10.length; c++) - a[t10[c]] = s[c]; + let n = Re(this.rank), s = ov("rc", this.rank), a = new Array(this.rank); + for (let c = 0; c < t8.length; c++) + a[t8[c]] = s[c]; let i = `vec2(${a.slice(-2).join()})`, p = `++${s[this.rank - 1]} < ${o[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`; this.userCode = ` void main() { @@ -19008,101 +19084,101 @@ var hh = class { `; } }; -function gu(r, e, t10) { - let o = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new hh(r.shape, e) : new fh(r.shape, e); - return t10.runWebGLProgram(o, [r], r.dtype); +function Cu(r, e, t8) { + let o = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new gh(r.shape, e) : new hh(r.shape, e); + return t8.runWebGLProgram(o, [r], r.dtype); } -function vD(r, e, t10, o) { - let n = e, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = C.getAxesPermutation(i, s), u = p != null, c = r; - u && (c = gu(r, p, o), i = C.getInnerMostAxes(i.length, s)), C.assertAxesAreInnerMostDims("sum", i, s); - let [l, m] = C.computeOutAndReduceShapes(c.shape, i), d = l; - t10 && (d = C.expandShapeToKeepDim(l, a)); - let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = te({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = Za(r.dtype), w = qr(x, b, "sum", o), S = te({ inputs: { x: w }, attrs: { shape: d }, backend: o }); - return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(w), u && o.disposeIntermediateTensorInfo(c), S; +function LD(r, e, t8, o) { + let n = e, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = w.getAxesPermutation(i, s), u = p != null, c = r; + u && (c = Cu(r, p, o), i = w.getInnerMostAxes(i.length, s)), w.assertAxesAreInnerMostDims("sum", i, s); + let [l, m] = w.computeOutAndReduceShapes(c.shape, i), d = l; + t8 && (d = w.expandShapeToKeepDim(l, a)); + let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = te({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = ti(r.dtype), C = Yr(x, b, "sum", o), S = te({ inputs: { x: C }, attrs: { shape: d }, backend: o }); + return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(C), u && o.disposeIntermediateTensorInfo(c), S; } -function bp(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return vD(n, s, a, t10); +function Ip(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return LD(n, s, a, t8); } -var kD = { kernelName: ys, backendName: "webgl", kernelFunc: bp }; -function bt(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i); +var BD = { kernelName: Ss, backendName: "webgl", kernelFunc: Ip }; +function Ct(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t8, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; let u; if (a.shouldExecuteOnCPU([n])) { - let l = a.texData.get(n.dataId).values, m = yp(l, n.shape, n.dtype, s, p); + let l = a.texData.get(n.dataId).values, m = Sp(l, n.shape, n.dtype, s, p); u = a.makeTensorInfo(p, n.dtype); let d = a.texData.get(u.dataId); d.values = m; } else - u = gu(n, s, a); + u = Cu(n, s, a); return u; } -var ND = { kernelName: ao, backendName: "webgl", kernelFunc: bt }; -var tv = 1e3; -function Cp({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { - let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Sr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); - y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`); - let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = te({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = te({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), D = [E, R], F = Math.max(x, b), O = t10 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, B = p === "leakyrelu", z = p != null ? hi(p, true) : null, U = M || L || B || z != null, j; - if ((d === 1 || f === 1) && O > tv && U === false) { - let X = E, J = R; - t10 && (X = bt({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(X)), o && (J = bt({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(J)); - let re = f !== 1, ne = f === 1, ee = X; - re && (ee = te({ inputs: { x: X }, backend: n, attrs: { shape: [F, O, 1] } }), D.push(ee)); +var zD = { kernelName: po, backendName: "webgl", kernelFunc: Ct }; +var cv = 1e3; +function vp({ a: r, b: e, transposeA: t8, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { + let u = r.shape.length, c = e.shape.length, l = t8 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t8 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Ir.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); + y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t8} and transposeB=${o} must match.`); + let k = t8 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = te({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = te({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), D = [E, R], P = Math.max(x, b), O = t8 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, B = p === "leakyrelu", z = p != null ? yi(p, true) : null, U = M || L || B || z != null, j; + if ((d === 1 || f === 1) && O > cv && U === false) { + let Y = E, J = R; + t8 && (Y = Ct({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(Y)), o && (J = Ct({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(J)); + let re = f !== 1, ne = f === 1, ee = Y; + re && (ee = te({ inputs: { x: Y }, backend: n, attrs: { shape: [P, O, 1] } }), D.push(ee)); let oe = f === 1 ? 2 : 1, ie = J; - ne && (ie = te({ inputs: { x: J }, backend: n, attrs: { shape: [F, 1, O] } }), D.push(ie)); + ne && (ie = te({ inputs: { x: J }, backend: n, attrs: { shape: [P, 1, O] } }), D.push(ie)); let le = Zl({ inputs: { a: ee, b: ie }, backend: n }); - j = bp({ inputs: { x: le }, backend: n, attrs: { axis: oe, keepDims: true } }), D.push(le); + j = Ip({ inputs: { x: le }, backend: n, attrs: { axis: oe, keepDims: true } }), D.push(le); } else { - let X = dt(r.dtype, e.dtype), J = new Lc(k, _, [F, d, f], t10, o, M, z, L, B), re = [E, R]; + let Y = dt(r.dtype, e.dtype), J = new Bc(k, _, [P, d, f], t8, o, M, z, L, B), re = [E, R]; if (s != null && re.push(s), L && re.push(a), B) { let ne = n.makeTensorInfo([], "float32", y.createScalarValue(i, "float32")); re.push(ne), D.push(ne); } - j = n.runWebGLProgram(J, re, X); + j = n.runWebGLProgram(J, re, Y); } - let H = te({ inputs: { x: j }, backend: n, attrs: { shape: S } }); + let q = te({ inputs: { x: j }, backend: n, attrs: { shape: S } }); D.push(j); - for (let X of D) - n.disposeIntermediateTensorInfo(X); - return H; + for (let Y of D) + n.disposeIntermediateTensorInfo(Y); + return q; } -function IZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; - return Cp({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); +function QZ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; + return vp({ a: n, b: s, transposeA: p, transposeB: u, backend: t8, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); } -var TD = { kernelName: bo, backendName: "webgl", kernelFunc: IZ }; -var _D = "return abs(x);"; -function vZ(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; - if (t10.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") { - let s = t10.texData.get(o.dataId), a = ih(s.values); - return t10.makeTensorInfo(o.shape, o.dtype, a); +var VD = { kernelName: So, backendName: "webgl", kernelFunc: QZ }; +var WD = "return abs(x);"; +function ZZ(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; + if (t8.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") { + let s = t8.texData.get(o.dataId), a = uh(s.values); + return t8.makeTensorInfo(o.shape, o.dtype, a); } let n; - return P().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, _D) : n = new tr(o.shape, _D), t10.runWebGLProgram(n, [o], o.dtype); + return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, WD) : n = new rr(o.shape, WD), t8.runWebGLProgram(n, [o], o.dtype); } -var $D = { kernelName: Gs, backendName: "webgl", kernelFunc: vZ }; -var kZ = Ut + ` +var UD = { kernelName: js, backendName: "webgl", kernelFunc: ZZ }; +var JZ = Ut + ` if (abs(x) > 1.) { return NAN; } return acos(x); `; -var NZ = ge({ opSnippet: kZ }); -var ED = { kernelName: zo, backendName: "webgl", kernelFunc: NZ }; -var TZ = Ut + ` +var e9 = xe({ opSnippet: JZ }); +var GD = { kernelName: Vo, backendName: "webgl", kernelFunc: e9 }; +var t9 = Ut + ` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`; -var _Z = ge({ opSnippet: TZ }); -var RD = { kernelName: Vo, backendName: "webgl", kernelFunc: _Z }; -var DD = "return a + b;"; -var $Z = nt({ opSnippet: DD, packedOpSnippet: DD, supportsComplex: true, cpuKernelImpl: cR }); -var AD = { kernelName: no, backendName: "webgl", kernelFunc: $Z }; -var gh = class { - constructor(e, t10) { - this.outputShape = [], this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`); +var r9 = xe({ opSnippet: t9 }); +var HD = { kernelName: Wo, backendName: "webgl", kernelFunc: r9 }; +var KD = "return a + b;"; +var o9 = nt({ opSnippet: KD, packedOpSnippet: KD, supportsComplex: true, cpuKernelImpl: IR }); +var qD = { kernelName: io, backendName: "webgl", kernelFunc: o9 }; +var xh = class { + constructor(e, t8) { + this.outputShape = [], this.outputShape = e, this.variableNames = t8.map((s, a) => `T${a}`); let o = []; this.variableNames.forEach((s) => { o.push(`float v${s} = get${s}AtOutCoords();`); @@ -19119,9 +19195,9 @@ var gh = class { `; } }; -var xh = class { - constructor(e, t10) { - this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`); +var yh = class { + constructor(e, t8) { + this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t8.map((s, a) => `T${a}`); let o = []; this.variableNames.forEach((s) => { o.push(`vec4 v${s} = get${s}AtOutCoords();`); @@ -19138,48 +19214,48 @@ var xh = class { `; } }; -function yh(r) { - let { inputs: e, backend: t10 } = r, o = e; +function bh(r) { + let { inputs: e, backend: t8 } = r, o = e; if (o.length === 1) - return Dt({ inputs: { x: o[0] }, backend: t10 }); - if (o.length > P().get("WEBGL_MAX_TEXTURES_IN_SHADER")) { - let p = Math.floor(o.length / 2), u = yh({ inputs: o.slice(0, p), backend: t10 }), c = yh({ inputs: o.slice(p), backend: t10 }); - return yh({ inputs: [u, c], backend: t10 }); + return Dt({ inputs: { x: o[0] }, backend: t8 }); + if (o.length > A().get("WEBGL_MAX_TEXTURES_IN_SHADER")) { + let p = Math.floor(o.length / 2), u = bh({ inputs: o.slice(0, p), backend: t8 }), c = bh({ inputs: o.slice(p), backend: t8 }); + return bh({ inputs: [u, c], backend: t8 }); } - let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = P().getBool("WEBGL_PACK") ? new xh(o[0].shape, s) : new gh(o[0].shape, s); - return t10.runWebGLProgram(i, o, n); + let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = A().getBool("WEBGL_PACK") ? new yh(o[0].shape, s) : new xh(o[0].shape, s); + return t8.runWebGLProgram(i, o, n); } -var FD = { kernelName: Wo, backendName: "webgl", kernelFunc: yh }; -function EZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = n; - c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = C.getInnerMostAxes(u.length, i)), C.assertAxesAreInnerMostDims("all", u, i); - let [m, d] = C.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = qr(h, h.dtype, "all", t10), x; +var jD = { kernelName: Uo, backendName: "webgl", kernelFunc: bh }; +function n9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; + c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("all", u, i); + let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "all", t8), x; if (a) { - let b = C.expandShapeToKeepDim(m, p); - x = te({ inputs: { x: g }, backend: t10, attrs: { shape: b } }); + let b = w.expandShapeToKeepDim(m, p); + x = te({ inputs: { x: g }, backend: t8, attrs: { shape: b } }); } else - x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); - return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; + x = te({ inputs: { x: g }, backend: t8, attrs: { shape: m } }); + return t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), c != null && t8.disposeIntermediateTensorInfo(l), x; } -var PD = { kernelName: Uo, backendName: "webgl", kernelFunc: EZ }; -function RZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = n; - c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = C.getInnerMostAxes(u.length, i)), C.assertAxesAreInnerMostDims("any", u, i); - let [m, d] = C.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = qr(h, h.dtype, "any", t10), x; +var XD = { kernelName: Go, backendName: "webgl", kernelFunc: n9 }; +function s9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; + c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("any", u, i); + let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "any", t8), x; if (a) { - let b = C.expandShapeToKeepDim(m, p); - x = te({ inputs: { x: g }, backend: t10, attrs: { shape: b } }); + let b = w.expandShapeToKeepDim(m, p); + x = te({ inputs: { x: g }, backend: t8, attrs: { shape: b } }); } else - x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); - return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; + x = te({ inputs: { x: g }, backend: t8, attrs: { shape: m } }); + return t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), c != null && t8.disposeIntermediateTensorInfo(l), x; } -var OD = { kernelName: Go, backendName: "webgl", kernelFunc: RZ }; -var bh = class { - constructor(e, t10, o) { +var YD = { kernelName: Ho, backendName: "webgl", kernelFunc: s9 }; +var Ch = class { + constructor(e, t8, o) { this.variableNames = ["A"]; let { windowSize: n, batchSize: s, outSize: a } = e; o || this.variableNames.push("bestIndicesA"), this.outputShape = [s, a]; - let i = t10 === "max" ? ">" : "<", p = o ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));"; + let i = t8 === "max" ? ">" : "<", p = o ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));"; this.userCode = ` void main() { ivec2 coords = getOutputCoords(); @@ -19203,10 +19279,10 @@ var bh = class { `; } }; -var Ch = class { - constructor(e, t10, o, n) { +var wh = class { + constructor(e, t8, o, n) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, y.assert(e.length > 2, () => `Packed arg${o.charAt(0).toUpperCase() + o.slice(1)} supports only inputs with rank above 2.`); - let s = e[e.length - 1], a = Math.ceil(s / t10); + let s = e[e.length - 1], a = Math.ceil(s / t8); this.outputShape = e.slice(0, -1), a > 1 && this.outputShape.push(a), n || this.variableNames.push("bestIndicesA"); let i = this.outputShape, p = i.length, u = Re(p), c = Rt("coords", p), l, m; if (a === 1) { @@ -19231,15 +19307,15 @@ var Ch = class { --${c[p - 1]}; ${u} sourceLocB = coords; --${c[p - 2]};`; - let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((R) => "int " + R), g = Rt("sourceLocR", m - 1).concat("inIdx.r"), x = Rt("sourceLocG", m - 1).concat("inIdx.g"), b = Rt("sourceLocB", m - 1).concat("inIdx.b"), w = Rt("sourceLocA", m - 1).concat("inIdx.a"), S = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : ` + let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((R) => "int " + R), g = Rt("sourceLocR", m - 1).concat("inIdx.r"), x = Rt("sourceLocG", m - 1).concat("inIdx.g"), b = Rt("sourceLocB", m - 1).concat("inIdx.b"), C = Rt("sourceLocA", m - 1).concat("inIdx.a"), S = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : ` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), - getBestIndicesAChannel(${w.join()})));`, _ = `vec4( + getBestIndicesAChannel(${C.join()})));`, _ = `vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`, E = n ? "" : ` + hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`, E = n ? "" : ` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); @@ -19256,12 +19332,12 @@ var Ch = class { bool hasNextRow = ${c[p - 2]} < ${i[p - 2] - 1}; ${l} ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f}, - sourceLocB${f}, sourceLocA${f}) * ${t10}; + sourceLocB${f}, sourceLocA${f}) * ${t8}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${_}; - for (int i = 0; i < ${t10}; i++) { + for (int i = 0; i < ${t8}; i++) { inIdx = srcIdx; ${k} vec4 candidate = ${_}; @@ -19281,94 +19357,94 @@ var Ch = class { `; } }; -function MD(r, e, t10, o = null) { +function QD(r, e, t8, o = null) { let n = e.shape[0], s = e.shape[1]; o != null && (n = o.shape[0], s = o.shape[1]); - let a = C.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new bh(i, t10, o == null), u = [e]; + let a = w.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new Ch(i, t8, o == null), u = [e]; o != null && u.push(o); let c = r.runWebGLProgram(p, u, "int32"); if (c.shape[1] === 1) return c; - let l = MD(r, e, t10, c); + let l = QD(r, e, t8, c); return r.disposeIntermediateTensorInfo(c), l; } -function LD(r, e, t10, o = null) { - let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = C.computeOptimalWindowSize(s), i = new Ch(n, a, t10, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32"); +function ZD(r, e, t8, o = null) { + let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = w.computeOptimalWindowSize(s), i = new wh(n, a, t8, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32"); if (u.shape.length === e.shape.length) { - let c = LD(r, e, t10, u); + let c = ZD(r, e, t8, u); return r.disposeIntermediateTensorInfo(u), c; } return u; } -function wh(r, e, t10, o) { - let n = [t10]; - if (C.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !P().getBool("WEBGL_PACK_REDUCE") || e.shape.length <= 2) { +function Sh(r, e, t8, o) { + let n = [t8]; + if (w.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !A().getBool("WEBGL_PACK_REDUCE") || e.shape.length <= 2) { let s = [], a = r.texData.get(e.dataId), i = a !== null && a.isPacked, p = e; i && (p = r.unpackTensor(e), s.push(p)); - let [u, c] = C.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } }); + let [u, c] = w.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } }); s.push(m); - let d = MD(r, m, o); + let d = QD(r, m, o); s.push(d); let f = te({ inputs: { x: d }, backend: r, attrs: { shape: u } }); return s.forEach((h) => r.disposeIntermediateTensorInfo(h)), f; } - return LD(r, e, o); + return ZD(r, e, o); } -function DZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = bt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); - let c = wh(t10, p, a[0], "max"); - return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c; +function a9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); + let c = Sh(t8, p, a[0], "max"); + return u.forEach((l) => t8.disposeIntermediateTensorInfo(l)), c; } -var BD = { kernelName: Hs, backendName: "webgl", kernelFunc: DZ }; -function AZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = bt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); - let c = wh(t10, p, a[0], "min"); - return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c; +var JD = { kernelName: Xs, backendName: "webgl", kernelFunc: a9 }; +function i9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); + let c = Sh(t8, p, a[0], "min"); + return u.forEach((l) => t8.disposeIntermediateTensorInfo(l)), c; } -var zD = { kernelName: Ks, backendName: "webgl", kernelFunc: AZ }; -var FZ = Ut + ` +var eA = { kernelName: Ys, backendName: "webgl", kernelFunc: i9 }; +var u9 = Ut + ` if (abs(x) > 1.) { return NAN; } return asin(x); `; -var PZ = ge({ opSnippet: FZ }); -var VD = { kernelName: Ho, backendName: "webgl", kernelFunc: PZ }; -var OZ = Ut + "return log(x + sqrt(x * x + 1.0));"; -var MZ = ge({ opSnippet: OZ }); -var WD = { kernelName: Ko, backendName: "webgl", kernelFunc: MZ }; -var LZ = Ut + ` +var p9 = xe({ opSnippet: u9 }); +var tA = { kernelName: Ko, backendName: "webgl", kernelFunc: p9 }; +var c9 = Ut + "return log(x + sqrt(x * x + 1.0));"; +var l9 = xe({ opSnippet: c9 }); +var rA = { kernelName: qo, backendName: "webgl", kernelFunc: l9 }; +var m9 = Ut + ` return atan(x); `; -var BZ = ge({ opSnippet: LZ }); -var UD = { kernelName: qo, backendName: "webgl", kernelFunc: BZ }; -var zZ = Mc + ` +var d9 = xe({ opSnippet: m9 }); +var oA = { kernelName: jo, backendName: "webgl", kernelFunc: d9 }; +var f9 = Lc + ` return atan(a, b); `; -var VZ = ` +var h9 = ` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Kr + ` + ` + Xr + ` return result; `; -var WZ = nt({ opSnippet: zZ, packedOpSnippet: VZ }); -var GD = { kernelName: Xo, backendName: "webgl", kernelFunc: WZ }; -var UZ = Ut + ` +var g9 = nt({ opSnippet: f9, packedOpSnippet: h9 }); +var nA = { kernelName: Yo, backendName: "webgl", kernelFunc: g9 }; +var x9 = Ut + ` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`; -var GZ = ge({ opSnippet: UZ }); -var HD = { kernelName: jo, backendName: "webgl", kernelFunc: GZ }; -var Ms = class { - constructor(e, t10, o, n = false, s = false) { - if (this.variableNames = ["x"], t10 === "avg" && o) +var y9 = xe({ opSnippet: x9 }); +var sA = { kernelName: Xo, backendName: "webgl", kernelFunc: y9 }; +var Vs = class { + constructor(e, t8, o, n = false, s = false) { + if (this.variableNames = ["x"], t8 === "avg" && o) throw new Error("Cannot compute positions for average pool."); let a = e.filterWidth, i = e.strideHeight, p = e.strideWidth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterHeight, m = e.effectiveFilterWidth, d = e.padInfo.top, f = e.padInfo.left; this.outputShape = e.outShape; - let h = t10 === "avg", g = `((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0"; + let h = t8 === "avg", g = `((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0"; if (h || (b = "-1.0 / 1e-20"), o) { let R = ">="; this.userCode = ` @@ -19425,13 +19501,13 @@ var Ms = class { `; return; } - let w = "max", S = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; - t10 === "avg" && (S = "avgValue / max(count, 1.0)"); + let C = "max", S = `${t8}(${t8}(${t8}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + t8 === "avg" && (S = "avgValue / max(count, 1.0)"); let k = Math.floor(a / 4) * 4, _ = a % 4, E = ` if (${h}) { avgValue += dot(values, ones); } else { - minMaxValue = ${w}(values, minMaxValue); + minMaxValue = ${C}(values, minMaxValue); } `; this.userCode = ` @@ -19521,15 +19597,15 @@ var Ms = class { `; } }; -var xu = class { - constructor(e, t10, o, n = false, s = false) { - if (this.variableNames = ["x"], t10 === "avg" && o) +var wu = class { + constructor(e, t8, o, n = false, s = false) { + if (this.variableNames = ["x"], t8 === "avg" && o) throw new Error("Cannot compute positions for average pool."); let a = e.filterWidth, i = e.strideDepth, p = e.strideHeight, u = e.strideWidth, c = e.dilationDepth, l = e.dilationHeight, m = e.dilationWidth, d = e.effectiveFilterDepth, f = e.effectiveFilterHeight, h = e.effectiveFilterWidth, g = e.padInfo.front, x = e.padInfo.top, b = e.padInfo.left; this.outputShape = e.outShape; - let w = t10 === "avg", S = "0.0"; - if (w || (S = "-1.0 / 1e-20"), o) { - let F = ">="; + let C = t8 === "avg", S = "0.0"; + if (C || (S = "-1.0 / 1e-20"), o) { + let P = ">="; this.userCode = ` const ivec3 strides = ivec3(${i}, ${p}, ${u}); @@ -19581,7 +19657,7 @@ var xu = class { // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${F} currMinMaxValue) { + if (value ${P} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n ? s ? `(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `wD * ${f} * ${h} + @@ -19595,10 +19671,10 @@ var xu = class { `; return; } - let k = "max", _ = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; - t10 === "avg" && (_ = "avgValue / max(count, 1.0)"); + let k = "max", _ = `${t8}(${t8}(${t8}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + t8 === "avg" && (_ = "avgValue / max(count, 1.0)"); let E = Math.floor(a / 4) * 4, R = a % 4, D = ` - if (${w}) { + if (${C}) { avgValue += dot(values, ones); } else { minMaxValue = ${k}(values, minMaxValue); @@ -19702,27 +19778,27 @@ var xu = class { `; } }; -function HZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; - Ps(n, "avgPool"); +function b9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e; + Bs(n, "avgPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; - y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); - let c = C.computePool2DInfo(n.shape, s, a, u, i, p); + y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); + let c = w.computePool2DInfo(n.shape, s, a, u, i, p); if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - return Dt({ inputs: { x: n }, backend: t10 }); - let l = new Ms(c, "avg", false); - return t10.runWebGLProgram(l, [n], "float32"); + return Dt({ inputs: { x: n }, backend: t8 }); + let l = new Vs(c, "avg", false); + return t8.runWebGLProgram(l, [n], "float32"); } -var KD = { kernelName: Yo, backendName: "webgl", kernelFunc: HZ }; -function KZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new xu(l, "avg", false); - return t10.runWebGLProgram(m, [n], "float32"); +var aA = { kernelName: Qo, backendName: "webgl", kernelFunc: b9 }; +function C9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new wu(l, "avg", false); + return t8.runWebGLProgram(m, [n], "float32"); } -var qD = { kernelName: qs, backendName: "webgl", kernelFunc: KZ }; -var Sh = class { +var iA = { kernelName: Qs, backendName: "webgl", kernelFunc: C9 }; +var Ih = class { constructor(e) { this.variableNames = ["dy"], this.outputShape = e.inShape; - let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterHeight, u = e.effectiveFilterWidth, c = p - 1 - e.padInfo.top, l = u - 1 - e.padInfo.left, m = 1 / (t10 * o); + let t8 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterHeight, u = e.effectiveFilterWidth, c = p - 1 - e.padInfo.top, l = u - 1 - e.padInfo.left, m = 1 / (t8 * o); this.userCode = ` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${m}); @@ -19768,10 +19844,10 @@ var Sh = class { `; } }; -var Ih = class { +var vh = class { constructor(e) { this.variableNames = ["dy"], this.outputShape = e.inShape; - let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t10 * o * n); + let t8 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t8 * o * n); this.userCode = ` const ivec3 pads = ivec3(${f}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -19831,30 +19907,30 @@ var Ih = class { `; } }; -function qZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = C.computePool3DInfo(a.shape, i, p, l, u, c), d = new Ih(m); - return t10.runWebGLProgram(d, [n], a.dtype); +function w9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new vh(m); + return t8.runWebGLProgram(d, [n], a.dtype); } -var jD = { kernelName: Ni, backendName: "webgl", kernelFunc: qZ }; -function jZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s; - Ps([n, s], "avgPoolGrad"); - let { filterSize: i, strides: p, pad: u } = o, c = C.computePool2DInfo(a.shape, i, p, 1, u), l = new Sh(c); - return t10.runWebGLProgram(l, [n], a.dtype); +var uA = { kernelName: Ei, backendName: "webgl", kernelFunc: w9 }; +function S9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s; + Bs([n, s], "avgPoolGrad"); + let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new Ih(c); + return t8.runWebGLProgram(l, [n], a.dtype); } -var XD = { kernelName: Gp, backendName: "webgl", kernelFunc: jZ }; -function XZ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; - return Cp({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 }); +var pA = { kernelName: $i, backendName: "webgl", kernelFunc: S9 }; +function I9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; + return vp({ a: n, b: s, transposeA: a, transposeB: i, backend: t8 }); } -var YD = { kernelName: Qo, backendName: "webgl", kernelFunc: XZ }; -var vh = class { - constructor(e, t10, o, n, s, a) { - this.outputShape = [], this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t10), C.assertAndGetBroadcastShape(e, o); +var cA = { kernelName: Zo, backendName: "webgl", kernelFunc: I9 }; +var kh = class { + constructor(e, t8, o, n, s, a) { + this.outputShape = [], this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(e, t8), w.assertAndGetBroadcastShape(e, o); let i = "0.0"; - n != null && (C.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()"); + n != null && (w.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()"); let p = "1.0"; - s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = ` + s != null && (w.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = ` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -19867,13 +19943,13 @@ var vh = class { `; } }; -var kh = class { - constructor(e, t10, o, n, s, a) { - this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t10), C.assertAndGetBroadcastShape(e, o); +var Nh = class { + constructor(e, t8, o, n, s, a) { + this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(e, t8), w.assertAndGetBroadcastShape(e, o); let i = "vec4(0.0)"; - n != null && (C.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()"); + n != null && (w.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()"); let p = "vec4(1.0)"; - s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = ` + s != null && (w.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = ` void main() { vec4 offset = ${i}; vec4 scale = ${p}; @@ -19889,28 +19965,28 @@ var kh = class { `; } }; -var YZ = ({ inputs: r, backend: e, attrs: t10 }) => { +var v9 = ({ inputs: r, backend: e, attrs: t8 }) => { let { x: o, mean: n, variance: s, offset: a, scale: i } = r; y.assert(n.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || n.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(i == null || n.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - let { varianceEpsilon: p } = t10; + let { varianceEpsilon: p } = t8; p == null && (p = 1e-3); let u = [o, n, s], c = null; a != null && (c = a.shape, u.push(a)); let l = null; i != null && (l = i.shape, u.push(i)); - let m = P().getBool("WEBGL_PACK_NORMALIZATION") ? new kh(o.shape, n.shape, s.shape, c, l, p) : new vh(o.shape, n.shape, s.shape, c, l, p); + let m = A().getBool("WEBGL_PACK_NORMALIZATION") ? new Nh(o.shape, n.shape, s.shape, c, l, p) : new kh(o.shape, n.shape, s.shape, c, l, p); return e.runWebGLProgram(m, u, u[0].dtype); }; -var QD = { kernelName: wn, backendName: "webgl", kernelFunc: YZ }; -var Nh = class { +var lA = { kernelName: In, backendName: "webgl", kernelFunc: v9 }; +var Th = class { constructor(e) { this.variableNames = ["source"], this.outputShape = e, this.rank = e.length; - let t10 = Re(this.rank); + let t8 = Re(this.rank); this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; - let o = QZ(this.rank), n, s = e.map((a, i) => `sourceLoc.${rv[i]} = start[${i}] + coords.${rv[i]};`); + let o = k9(this.rank), n, s = e.map((a, i) => `sourceLoc.${lv[i]} = start[${i}] + coords.${lv[i]};`); n = ` - ${t10} sourceLoc; - ${t10} coords = getOutputCoords(); + ${t8} sourceLoc; + ${t8} coords = getOutputCoords(); ${s.join(` `)} `, this.userCode = ` @@ -19921,18 +19997,18 @@ var Nh = class { `; } }; -var rv = ["x", "y", "z", "w", "u", "v"]; -function QZ(r) { +var lv = ["x", "y", "z", "w", "u", "v"]; +function k9(r) { if (r === 1) return "sourceLoc"; if (r <= 6) - return rv.slice(0, r).map((e) => "sourceLoc." + e).join(","); + return lv.slice(0, r).map((e) => "sourceLoc." + e).join(","); throw Error(`Slicing for rank ${r} is not yet supported`); } -var Th = class { +var _h = class { constructor(e) { this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; - let t10 = Re(this.rank), o = Rt("coords", this.rank), n = Rt("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = ` + let t8 = Re(this.rank), o = Rt("coords", this.rank), n = Rt("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = ` result.x = ${a}; if (++${o[this.rank - 1]} < ${e[this.rank - 1]}) { ++${n[this.rank - 1]}; @@ -19950,12 +20026,12 @@ var Th = class { } } `, u = this.rank <= 4 ? `sourceLoc = coords + - ${t10}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(` + ${t8}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(` `); this.userCode = ` void main() { - ${t10} coords = getOutputCoords(); - ${t10} sourceLoc; + ${t8} coords = getOutputCoords(); + ${t8} sourceLoc; ${u} vec4 result = vec4(0.); ${i} @@ -19965,93 +20041,113 @@ var Th = class { `; } }; -function ZZ(r, e, t10, o) { - let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t10, r.dtype), a = o.texData.get(s.dataId); - Object.assign(a, n), a.refCount = 1, a.shape = t10, a.dtype = r.dtype; +function N9(r, e, t8, o) { + let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t8, r.dtype), a = o.texData.get(s.dataId); + Object.assign(a, n), a.refCount = 1, a.shape = t8, a.dtype = r.dtype; let i = ct.computeFlatOffset(e, y.computeStrides(r.shape)); n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r.dataId }; let p = o.dataRefCount.get(a.slice.origDataId) || 1; return o.dataRefCount.set(a.slice.origDataId, p + 1), s; } -function Ls(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a); +function Ws(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a); if (ct.assertParamsValid(n, i, p), y.sizeFromShape(p) === 0) - return t10.makeTensorInfo(p, n.dtype, []); - if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string") { - let l = t10.texData.get(n.dataId), m = VR(l.values, i, p, n.shape, n.dtype); - return t10.makeTensorInfo(p, n.dtype, m); + return t8.makeTensorInfo(p, n.dtype, []); + if (t8.shouldExecuteOnCPU([n]) || n.dtype === "string") { + let l = t8.texData.get(n.dataId), m = tD(l.values, i, p, n.shape, n.dtype); + return t8.makeTensorInfo(p, n.dtype, m); } - let { isPacked: u } = t10.texData.get(n.dataId), c = ct.isSliceContinous(n.shape, i, p); + let { isPacked: u } = t8.texData.get(n.dataId), c = ct.isSliceContinous(n.shape, i, p); if (u || !c) { - let l = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Th(p) : new Nh(p), m = [i]; - return t10.runWebGLProgram(l, [n], n.dtype, m); + let l = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new _h(p) : new Th(p), m = [i]; + return t8.runWebGLProgram(l, [n], n.dtype, m); } - return t10.uploadToGPU(n.dataId), ZZ(n, i, p, t10); + return t8.uploadToGPU(n.dataId), N9(n, i, p, t8); } -var ZD = { kernelName: pa, backendName: "webgl", kernelFunc: Ls }; -var JZ = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; +var mA = { kernelName: fa, backendName: "webgl", kernelFunc: Ws }; +var T9 = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"); - let i = s.reduce((b, w) => b * w), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = bt({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = Ls({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } }); - return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x; + let i = s.reduce((b, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), h = Ct({ inputs: { x: f }, backend: t8, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: c } }), x = Ws({ inputs: { x: g }, backend: t8, attrs: { begin: l, size: m } }); + return d.push(f), d.push(h), d.push(g), d.forEach((b) => t8.disposeIntermediateTensorInfo(b)), x; }; -var JD = { kernelName: js, backendName: "webgl", kernelFunc: JZ }; -function e9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), u = ah(i, p, s.dtype, s.shape, a); - return t10.makeTensorInfo([a], s.dtype, u); +var dA = { kernelName: Zs, backendName: "webgl", kernelFunc: T9 }; +function _9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), u = ih(i, p, s.dtype, s.shape, a); + return t8.makeTensorInfo([a], s.dtype, u); } -var eA = { kernelName: Zo, backendName: "webgl", kernelFunc: e9 }; -function t9(r) { - let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.readSync(o.dataId), a = t10.readSync(n.dataId), i = C.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); - return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i)); +var fA = { kernelName: Jo, backendName: "webgl", kernelFunc: _9 }; +var $9 = ` + int r = int(a.r) & int(b.r); + int g = int(a.g) & int(b.g); + int rb = int(a.b) & int(b.b); + int ra = int(a.a) & int(b.a); + return vec4(r, g, rb, ra); +`; +var E9 = ` + return float(int(a.r) & int(b.r)); +`; +function R9(r) { + let { inputs: e, backend: t8 } = r, { a: o, b: n } = e, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS"), a = A().getNumber("WEBGL_VERSION"); + if (t8.shouldExecuteOnCPU([o, n]) || a === 1) { + let p = t8.texData.get(o.dataId).values, u = t8.texData.get(n.dataId).values, [c, l] = kR(o.shape, n.shape, p, u, o.dtype), m = t8.makeTensorInfo(l, o.dtype), d = t8.texData.get(m.dataId); + return d.values = c, m; + } + let i; + return s ? i = new jr($9, o.shape, n.shape, false) : i = new Pr(E9, o.shape, n.shape), t8.runWebGLProgram(i, [o, n], o.dtype); } -var tA = { kernelName: Xs, backendName: "webgl", kernelFunc: t9 }; -var r9 = "return float(a != b);"; -var ov = nt({ opSnippet: r9, cpuKernelImpl: DR, dtype: "bool" }); -var rA = { kernelName: qn, backendName: "webgl", kernelFunc: ov }; -function gi(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId); - return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: t10 }); +var hA = { kernelName: Ha, backendName: "webgl", kernelFunc: R9 }; +function D9(r) { + let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e, s = t8.readSync(o.dataId), a = t8.readSync(n.dataId), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); + return t8.makeTensorInfo([i.length], "int32", Int32Array.from(i)); } -var oA = { kernelName: zi, backendName: "webgl", kernelFunc: gi }; -var o9 = "return float(int(x));"; -function nA(r, e) { - let t10 = new tr(r.shape, o9), o = e.runWebGLProgram(t10, [r], "int32"); +var gA = { kernelName: Js, backendName: "webgl", kernelFunc: D9 }; +var A9 = "return float(a != b);"; +var mv = nt({ opSnippet: A9, cpuKernelImpl: KR, dtype: "bool" }); +var xA = { kernelName: Yn, backendName: "webgl", kernelFunc: mv }; +function bi(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.texData.get(o.dataId); + return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: t8 }); +} +var yA = { kernelName: Gi, backendName: "webgl", kernelFunc: bi }; +var F9 = "return float(int(x));"; +function bA(r, e) { + let t8 = new rr(r.shape, F9), o = e.runWebGLProgram(t8, [r], "int32"); return { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -function nv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o; +function dv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return Dt({ inputs: { x: n }, backend: t10 }); - let a = Wr(n.shape), i = nv({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = Fr({ inputs: { real: i, imag: a }, backend: t10 }); - return a.dispose(), t10.disposeIntermediateTensorInfo(i), p; + return Dt({ inputs: { x: n }, backend: t8 }); + let a = Gr(n.shape), i = dv({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }), p = Or({ inputs: { real: i, imag: a }, backend: t8 }); + return a.dispose(), t8.disposeIntermediateTensorInfo(i), p; } if (n.dtype === "complex64") { - let a = gi({ inputs: { input: n }, backend: t10 }), i = nv({ inputs: { x: a }, backend: t10, attrs: { dtype: s } }); - return t10.disposeIntermediateTensorInfo(a), i; + let a = bi({ inputs: { input: n }, backend: t8 }), i = dv({ inputs: { x: a }, backend: t8, attrs: { dtype: s } }); + return t8.disposeIntermediateTensorInfo(a), i; } if (!y.hasEncodingLoss(n.dtype, s)) { - let a = Dt({ inputs: { x: n }, backend: t10 }); + let a = Dt({ inputs: { x: n }, backend: t8 }); return { dataId: a.dataId, shape: a.shape, dtype: s }; } - if (t10.shouldExecuteOnCPU([n])) { - let a = t10.texData.get(n.dataId).values, [i, p, u] = mR(a, n.shape, n.dtype, s); - return t10.makeTensorInfo(i, p, u); + if (t8.shouldExecuteOnCPU([n])) { + let a = t8.texData.get(n.dataId).values, [i, p, u] = NR(a, n.shape, n.dtype, s); + return t8.makeTensorInfo(i, p, u); } if (s === "int32") - return nA(n, t10); + return bA(n, t8); if (s === "bool") { - let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = ov({ inputs: { a: n, b: a }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(a), p; + let a = t8.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = mv({ inputs: { a: n, b: a }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(a), p; } throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`); } -var sA = { kernelName: ho, backendName: "webgl", kernelFunc: nv }; -var aA = "return ceil(x);"; -var n9 = ge({ opSnippet: aA, packedOpSnippet: aA, cpuKernelImpl: dR }); -var iA = { kernelName: Jo, backendName: "webgl", kernelFunc: n9 }; -var _h = class { +var CA = { kernelName: yo, backendName: "webgl", kernelFunc: dv }; +var wA = "return ceil(x);"; +var P9 = xe({ opSnippet: wA, packedOpSnippet: wA, cpuKernelImpl: TR }); +var SA = { kernelName: en, backendName: "webgl", kernelFunc: P9 }; +var $h = class { constructor(e) { this.variableNames = ["A"], this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = ` @@ -20067,7 +20163,7 @@ var _h = class { `; } }; -var $h = class { +var Eh = class { constructor(e) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = ` void main() { @@ -20083,14 +20179,14 @@ var $h = class { `; } }; -function s9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i; - P().getBool("WEBGL_PACK_CLIP") ? i = new $h(n.shape) : i = new _h(n.shape); +function O9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i; + A().getBool("WEBGL_PACK_CLIP") ? i = new Eh(n.shape) : i = new $h(n.shape); let p = [[s], [a]]; - return t10.runWebGLProgram(i, [n], n.dtype, p); + return t8.runWebGLProgram(i, [n], n.dtype, p); } -var uA = { kernelName: go, backendName: "webgl", kernelFunc: s9 }; -var Eh = class { +var IA = { kernelName: bo, backendName: "webgl", kernelFunc: O9 }; +var Rh = class { constructor(e) { this.variableNames = ["real", "imag"], this.outputShape = e, this.userCode = ` void main() { @@ -20108,27 +20204,27 @@ var Eh = class { `; } }; -function pA(r, e) { +function vA(r, e) { return { dataId: e.dataId, dtype: e.dtype, shape: r.shape }; } -function a9(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.texData.get(o.dataId), s = new Eh(o.shape), a = [pA(o, n.complexTensorInfos.real), pA(o, n.complexTensorInfos.imag)]; - return t10.runWebGLProgram(s, a, a[0].dtype); +function M9(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e, n = t8.texData.get(o.dataId), s = new Rh(o.shape), a = [vA(o, n.complexTensorInfos.real), vA(o, n.complexTensorInfos.imag)]; + return t8.runWebGLProgram(s, a, a[0].dtype); } -var cA = { kernelName: _i, backendName: "webgl", kernelFunc: a9 }; -var Rh = class { +var kA = { kernelName: Di, backendName: "webgl", kernelFunc: M9 }; +var Dh = class { constructor(e) { - this.outputShape = [], this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`); - let t10 = new Array(e.length - 1); - t10[0] = e[0][1]; - for (let a = 1; a < t10.length; a++) - t10[a] = t10[a - 1] + e[a][1]; - let o = [`if (yC < ${t10[0]}) setOutput(getT0(yR, yC));`]; - for (let a = 1; a < t10.length; a++) { - let i = t10[a - 1]; - o.push(`else if (yC < ${t10[a]}) setOutput(getT${a}(yR, yC-${i}));`); + this.outputShape = [], this.outputShape = w.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`); + let t8 = new Array(e.length - 1); + t8[0] = e[0][1]; + for (let a = 1; a < t8.length; a++) + t8[a] = t8[a - 1] + e[a][1]; + let o = [`if (yC < ${t8[0]}) setOutput(getT0(yR, yC));`]; + for (let a = 1; a < t8.length; a++) { + let i = t8[a - 1]; + o.push(`else if (yC < ${t8[a]}) setOutput(getT${a}(yR, yC-${i}));`); } - let n = t10.length, s = t10[t10.length - 1]; + let n = t8.length, s = t8[t8.length - 1]; o.push(`else setOutput(getT${n}(yR, yC-${s}));`), this.userCode = ` void main() { ivec2 coords = getOutputCoords(); @@ -20141,16 +20237,16 @@ var Rh = class { `; } }; -var Ah = class { - constructor(e, t10) { - this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = C.computeOutShape(e, t10); +var Fh = class { + constructor(e, t8) { + this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = w.computeOutShape(e, t8); let o = this.outputShape, n = o.length, s = Re(n), a = Rt("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n); this.variableNames = e.map((h, g) => `T${g}`); let p = new Array(e.length - 1); - p[0] = e[0][t10]; + p[0] = e[0][t8]; for (let h = 1; h < p.length; h++) - p[h] = p[h - 1] + e[h][t10]; - let u = i[t10], c = i.slice(-2), l = i.join(), m = `if (${u} < ${p[0]}) { + p[h] = p[h - 1] + e[h][t8]; + let u = i[t8], c = i.slice(-2), l = i.join(), m = `if (${u} < ${p[0]}) { return getChannel( getT0(${l}), vec2(${c.join()})); }`; @@ -20159,15 +20255,15 @@ var Ah = class { m += ` if (${u} < ${p[h]} && ${u} >= ${p[h - 1]}) { return getChannel( - getT${h}(${Dh(i, u, g)}), - vec2(${Dh(c, u, g)})); + getT${h}(${Ah(i, u, g)}), + vec2(${Ah(c, u, g)})); }`; } let d = p.length, f = p[p.length - 1]; m += ` return getChannel( - getT${d}(${Dh(i, u, f)}), - vec2(${Dh(c, u, f)}));`, this.userCode = ` + getT${d}(${Ah(i, u, f)}), + vec2(${Ah(c, u, f)}));`, this.userCode = ` float getValue(${i.map((h) => "int " + h)}) { ${m} } @@ -20196,73 +20292,73 @@ var Ah = class { `; } }; -function Dh(r, e, t10) { +function Ah(r, e, t8) { let o = r.indexOf(e); - return r.map((s, a) => a === o ? `${s} - ${t10}` : s).join(); + return r.map((s, a) => a === o ? `${s} - ${t8}` : s).join(); } -function wp(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId); - return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 }); +function kp(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.texData.get(o.dataId); + return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: t8 }); } -var lA = { kernelName: Mi, backendName: "webgl", kernelFunc: wp }; -function Bc(r, e, t10) { +var NA = { kernelName: Vi, backendName: "webgl", kernelFunc: kp }; +function zc(r, e, t8) { let o = r[0].dtype; if (o === "complex64") { - let d = r.map((b) => gi({ inputs: { input: b }, backend: t10 })), f = r.map((b) => wp({ inputs: { input: b }, backend: t10 })), h = Bc(d, e, t10), g = Bc(f, e, t10), x = Fr({ inputs: { real: h, imag: g }, backend: t10 }); - return d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), f.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), x; + let d = r.map((b) => bi({ inputs: { input: b }, backend: t8 })), f = r.map((b) => kp({ inputs: { input: b }, backend: t8 })), h = zc(d, e, t8), g = zc(f, e, t8), x = Or({ inputs: { real: h, imag: g }, backend: t8 }); + return d.forEach((b) => t8.disposeIntermediateTensorInfo(b)), f.forEach((b) => t8.disposeIntermediateTensorInfo(b)), t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), x; } - let n = t10.shouldExecuteOnCPU(r); + let n = t8.shouldExecuteOnCPU(r); if (o === "string" && (n = true), n) { let d = r.map((S) => { let _ = [-1, y.sizeFromShape(S.shape.slice(e))]; - return te({ inputs: { x: S }, backend: t10, attrs: { shape: _ } }); - }), f = d.map((S) => ({ vals: t10.readSync(S.dataId), shape: S.shape })), h = C.computeOutShape(d.map((S) => S.shape), 1), g = d[0].shape[0] === 1, x = fR(f, h, o, g), b = C.computeOutShape(r.map((S) => S.shape), e), w = t10.makeTensorInfo(b, o, x); - return d.forEach((S) => t10.disposeIntermediateTensorInfo(S)), w; + return te({ inputs: { x: S }, backend: t8, attrs: { shape: _ } }); + }), f = d.map((S) => ({ vals: t8.readSync(S.dataId), shape: S.shape })), h = w.computeOutShape(d.map((S) => S.shape), 1), g = d[0].shape[0] === 1, x = _R(f, h, o, g), b = w.computeOutShape(r.map((S) => S.shape), e), C = t8.makeTensorInfo(b, o, x); + return d.forEach((S) => t8.disposeIntermediateTensorInfo(S)), C; } - let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1; + let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1; if (s.length === 1) { - let d = a ? new tr(r[0].shape, Ra) : new Ar(r[0].shape, Ra); - return t10.runWebGLProgram(d, r, o); + let d = a ? new rr(r[0].shape, Oa) : new Fr(r[0].shape, Oa); + return t8.runWebGLProgram(d, r, o); } - let i = P().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER"); + let i = A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER"); if (s.length > i) { let d = []; for (let h = 0; h < s.length; h += i) { let g = s.slice(h, h + i); - d.push(Bc(g, e, t10)); + d.push(zc(g, e, t8)); } - let f = Bc(d, e, t10); + let f = zc(d, e, t8); for (let h of d) - t10.disposeIntermediateTensorInfo(h); + t8.disposeIntermediateTensorInfo(h); return f; } if (a) { - let d = new Ah(s.map((f) => f.shape), e); - return t10.runWebGLProgram(d, s, o); + let d = new Fh(s.map((f) => f.shape), e); + return t8.runWebGLProgram(d, s, o); } - let { tensors2D: p, outShape: u } = i9(s, e, t10), c = new Rh(p.map((d) => d.shape)), l = t10.runWebGLProgram(c, p, o); - p.forEach((d) => t10.disposeIntermediateTensorInfo(d)); - let m = te({ inputs: { x: l }, attrs: { shape: u }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(l), m; + let { tensors2D: p, outShape: u } = L9(s, e, t8), c = new Dh(p.map((d) => d.shape)), l = t8.runWebGLProgram(c, p, o); + p.forEach((d) => t8.disposeIntermediateTensorInfo(d)); + let m = te({ inputs: { x: l }, attrs: { shape: u }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(l), m; } -function i9(r, e, t10) { - let o = C.computeOutShape(r.map((s) => s.shape), e); - return { tensors2D: r.map((s) => te({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(e))] }, backend: t10 })), outShape: o }; +function L9(r, e, t8) { + let o = w.computeOutShape(r.map((s) => s.shape), e); + return { tensors2D: r.map((s) => te({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(e))] }, backend: t8 })), outShape: o }; } -function sv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); - C.assertParamsConsistent(a, s); - let i = C.computeOutShape(e.map((u) => u.shape), s); +function fv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); + w.assertParamsConsistent(a, s); + let i = w.computeOutShape(e.map((u) => u.shape), s); if (y.sizeFromShape(i) === 0) - return t10.makeTensorInfo(i, e[0].dtype, []); + return t8.makeTensorInfo(i, e[0].dtype, []); let p = e.filter((u) => y.sizeFromShape(u.shape) > 0); - return p.length === 1 ? Dt({ inputs: { x: p[0] }, backend: t10 }) : Bc(p, s, t10); + return p.length === 1 ? Dt({ inputs: { x: p[0] }, backend: t8 }) : zc(p, s, t8); } -var mA = { kernelName: Ys, backendName: "webgl", kernelFunc: sv }; -var zc = class { - constructor(e, t10 = false, o = null, n = false, s = false) { +var TA = { kernelName: ea, backendName: "webgl", kernelFunc: fv }; +var Vc = class { + constructor(e, t8 = false, o = null, n = false, s = false) { this.variableNames = ["x", "W"], this.outputShape = e.outShape; - let a = e.padInfo.top, i = e.padInfo.left, p = e.strideHeight, u = e.strideWidth, c = e.dilationHeight, l = e.dilationWidth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4, g = e.dataFormat === "channelsLast", x = g ? 1 : 2, b = g ? 2 : 3, w = g ? 3 : 1, S = "", k = ""; + let a = e.padInfo.top, i = e.padInfo.left, p = e.strideHeight, u = e.strideWidth, c = e.dilationHeight, l = e.dilationWidth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4, g = e.dataFormat === "channelsLast", x = g ? 1 : 2, b = g ? 2 : 3, C = g ? 3 : 1, S = "", k = ""; o && (n ? S = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} @@ -20274,8 +20370,8 @@ var zc = class { ${o} } `, k = "result = activation(result);"); - let _ = t10 ? "result += getBiasAtOutCoords();" : ""; - t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let _ = t8 ? "result += getBiasAtOutCoords();" : ""; + t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${S} const ivec2 strides = ivec2(${p}, ${u}); @@ -20284,7 +20380,7 @@ var zc = class { void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d2 = coords[${w}]; + int d2 = coords[${C}]; ivec2 xRCCorner = ivec2(coords[${x}], coords[${b}]) * strides - pads; @@ -20402,13 +20498,13 @@ var zc = class { `; } }; -var Fh = class { +var Ph = class { constructor(e) { this.variableNames = ["x", "W"], this.outputShape = e.outShape; - let t10 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4; + let t8 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4; this.userCode = ` const ivec3 strides = ivec3(${s}, ${a}, ${i}); - const ivec3 pads = ivec3(${t10}, ${o}, ${n}); + const ivec3 pads = ivec3(${t8}, ${o}, ${n}); void main() { ivec5 coords = getOutputCoords(); @@ -20497,9 +20593,9 @@ var Fh = class { `; } }; -var Vc = class { - constructor(e, t10 = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ut(this.outputShape.length); +var Wc = class { + constructor(e, t8 = false, o = null, n = false, s = false) { + this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = pt(this.outputShape.length); let a = e.padInfo.left, i = e.strideWidth, p = e.dilationWidth, u = e.filterHeight, c = e.filterWidth, l = c, m = ` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`; @@ -20696,8 +20792,8 @@ var Vc = class { }` : d = `vec4 activation(vec4 x) { ${o} }`, f = "result = activation(result);"); - let h = t10 ? "result += getBiasAtOutCoords();" : ""; - t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let h = t8 ? "result += getBiasAtOutCoords();" : ""; + t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${d} void main() { @@ -20721,10 +20817,10 @@ var Vc = class { `; } }; -var Ph = class { - constructor(e, t10) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length); - let { dataFormat: o } = t10, n = It(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = ""; +var Oh = class { + constructor(e, t8) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length); + let { dataFormat: o } = t8, n = vt(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = ""; for (let c = 0; c <= 1; c++) for (let l = 0; l <= 1; l++) u += ` @@ -20778,81 +20874,81 @@ var Ph = class { `; } }; -function Oh(r, e) { - let t10 = r.length; - return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null; +function Mh(r, e) { + let t8 = r.length; + return t8 >= 3 ? e ? [...r.slice(0, -3), r[t8 - 3] * r[t8 - 2], r[t8 - 1]] : [...r.slice(0, -3), r[t8 - 3], r[t8 - 2] * r[t8 - 1]] : !e && t8 === 1 && r[0] > 1 ? [r[0], 1] : null; } -function Mh({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = r.shape, u = o.texData.get(r.dataId), c = t10.inChannels, l = p[0] * p[1] * p[2], m = t10.outChannels, d = t10.dataFormat === "channelsLast", f = false, h = false, g, x = []; +function Lh({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = r.shape, u = o.texData.get(r.dataId), c = t8.inChannels, l = p[0] * p[1] * p[2], m = t8.outChannels, d = t8.dataFormat === "channelsLast", f = false, h = false, g, x = []; if (s != null) { - let S = Oh(s.shape, d); + let S = Mh(s.shape, d); S != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: S } }), x.push(s)); } if (n != null) { - let S = Oh(n.shape, d); + let S = Mh(n.shape, d); S != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: S } }), x.push(n)); } - if (!((l === 1 || m === 1) && c > tv) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) { - let S = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, S, t10.inChannels], dtype: r.dtype }, _ = u.shape; - u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(fu(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`); - let E = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }); + if (!((l === 1 || m === 1) && c > cv) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) { + let S = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, S, t8.inChannels], dtype: r.dtype }, _ = u.shape; + u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(yu(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`); + let E = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t8.inChannels, t8.outChannels] } }); x.push(E); - let R = Cp({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), D = o.texData.get(R.dataId); - y.assert(D.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, D.shape = t10.outShape, g = Dt({ inputs: { x: R }, backend: o }), g.shape = t10.outShape, x.push(R); + let R = vp({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), D = o.texData.get(R.dataId); + y.assert(D.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, D.shape = t8.outShape, g = Dt({ inputs: { x: R }, backend: o }), g.shape = t8.outShape, x.push(R); } else { - let S = t10.outHeight * t10.outWidth, k = te({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t10.batchSize, S, t10.inChannels] : [t10.batchSize, t10.inChannels, S] } }), _ = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }), E = Cp({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); - g = te({ inputs: { x: E }, backend: o, attrs: { shape: t10.outShape } }), x.push(k), x.push(_), x.push(E); + let S = t8.outHeight * t8.outWidth, k = te({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t8.batchSize, S, t8.inChannels] : [t8.batchSize, t8.inChannels, S] } }), _ = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t8.inChannels, t8.outChannels] } }), E = vp({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); + g = te({ inputs: { x: E }, backend: o, attrs: { shape: t8.outShape } }), x.push(k), x.push(_), x.push(E); } for (let S of x) o.disposeIntermediateTensorInfo(S); return g; } -function Lh({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = t10, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t10.batchSize, h, g], b = true, w = false, S = []; +function Bh({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = t8, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t8.batchSize, h, g], b = true, C = false, S = []; if (s != null) { - let H = Oh(s.shape, f); - H != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: H } }), S.push(s)); + let q = Mh(s.shape, f); + q != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: q } }), S.push(s)); } if (n != null) { - let H = Oh(n.shape, f); - H != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: H } }), S.push(n)); + let q = Mh(n.shape, f); + q != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: q } }), S.push(n)); } let k = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, h, y.sizeFromShape(e.shape) / h] } }); S.push(k); - let _ = new Ph(x, t10), E = [r.shape, [t10.padInfo.top, t10.padInfo.left], [t10.strideHeight, t10.strideWidth], [t10.dilationHeight, t10.dilationWidth], [t10.inChannels], [t10.filterWidth * t10.inChannels], [t10.outWidth]], R = o.runWebGLProgram(_, [r], "float32", E), D = te({ inputs: { x: R }, backend: o, attrs: { shape: x } }); + let _ = new Oh(x, t8), E = [r.shape, [t8.padInfo.top, t8.padInfo.left], [t8.strideHeight, t8.strideWidth], [t8.dilationHeight, t8.dilationWidth], [t8.inChannels], [t8.filterWidth * t8.inChannels], [t8.outWidth]], R = o.runWebGLProgram(_, [r], "float32", E), D = te({ inputs: { x: R }, backend: o, attrs: { shape: x } }); S.push(R), S.push(D); - let F = n != null, O = s != null, M = i === "leakyrelu", L = i ? hi(i, true) : null, B = new Lc(f ? D.shape : k.shape, f ? k.shape : D.shape, f ? [t10.batchSize, g, t10.outChannels] : [t10.batchSize, t10.outChannels, g], b, w, F, L, O, M), z = f ? [D, k] : [k, D]; + let P = n != null, O = s != null, M = i === "leakyrelu", L = i ? yi(i, true) : null, B = new Bc(f ? D.shape : k.shape, f ? k.shape : D.shape, f ? [t8.batchSize, g, t8.outChannels] : [t8.batchSize, t8.outChannels, g], b, C, P, L, O, M), z = f ? [D, k] : [k, D]; if (n && z.push(n), O && z.push(s), M) { - let H = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32")); - z.push(H), S.push(H); + let q = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32")); + z.push(q), S.push(q); } - let U = o.runWebGLProgram(B, z, "float32"), j = te({ inputs: { x: U }, backend: o, attrs: { shape: t10.outShape } }); + let U = o.runWebGLProgram(B, z, "float32"), j = te({ inputs: { x: U }, backend: o, attrs: { shape: t8.outShape } }); S.push(U); - for (let H of S) - o.disposeIntermediateTensorInfo(H); + for (let q of S) + o.disposeIntermediateTensorInfo(q); return j; } -function u9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d; +function B9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d; if (m.filterHeight === 1 && m.filterWidth === 1 && m.dilationHeight === 1 && m.dilationWidth === 1 && m.strideHeight === 1 && m.strideWidth === 1 && (m.padInfo.type === "SAME" || m.padInfo.type === "VALID")) - d = Mh({ x: n, filter: s, convInfo: m, backend: t10 }); - else if (m.strideWidth <= 2 && l === "channelsLast" && P().getBool("WEBGL_EXP_CONV")) { - let h = new Vc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]]; - d = t10.runWebGLProgram(h, [n, s], "float32", g); - } else if (P().getBool("WEBGL_CONV_IM2COL")) - d = Lh({ x: n, filter: s, convInfo: m, backend: t10 }); + d = Lh({ x: n, filter: s, convInfo: m, backend: t8 }); + else if (m.strideWidth <= 2 && l === "channelsLast" && A().getBool("WEBGL_EXP_CONV")) { + let h = new Wc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]]; + d = t8.runWebGLProgram(h, [n, s], "float32", g); + } else if (A().getBool("WEBGL_CONV_IM2COL")) + d = Bh({ x: n, filter: s, convInfo: m, backend: t8 }); else { - let h = new zc(m); - d = t10.runWebGLProgram(h, [n, s], "float32"); + let h = new Vc(m); + d = t8.runWebGLProgram(h, [n, s], "float32"); } - let f = te({ inputs: { x: d }, backend: t10, attrs: { shape: m.outShape } }); - return t10.disposeIntermediateTensorInfo(d), f; + let f = te({ inputs: { x: d }, backend: t8, attrs: { shape: m.outShape } }); + return t8.disposeIntermediateTensorInfo(d), f; } -var dA = { kernelName: en, backendName: "webgl", kernelFunc: u9 }; -var Bh = class { +var _A = { kernelName: tn, backendName: "webgl", kernelFunc: B9 }; +var zh = class { constructor(e) { this.variableNames = ["x", "dy"], this.outputShape = e.filterShape; - let t10 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.dataFormat === "channelsLast"; + let t8 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.dataFormat === "channelsLast"; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -20867,7 +20963,7 @@ var Bh = class { for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t10} - ${n}; + int xR = wR + yR * ${t8} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; @@ -20893,10 +20989,10 @@ var Bh = class { `; } }; -var zh = class { +var Vh = class { constructor(e) { this.variableNames = ["dy", "W"], this.outputShape = e.inShape; - let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t10 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1; + let t8 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t8 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1; this.userCode = ` const ivec2 pads = ivec2(${i}, ${p}); @@ -20912,7 +21008,7 @@ var zh = class { // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${t10}; wR++) { + for (int wR = 0; wR < ${t8}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -20920,7 +21016,7 @@ var zh = class { } int idyR = int(dyR); - int wRPerm = ${t10} - 1 - wR; + int wRPerm = ${t8} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; @@ -20953,10 +21049,10 @@ var zh = class { `; } }; -var Vh = class { +var Wh = class { constructor(e) { this.variableNames = ["x", "dy"], this.outputShape = e.filterShape; - let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left; + let t8 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left; this.userCode = ` void main() { ivec5 coords = getOutputCoords(); @@ -20970,7 +21066,7 @@ var Vh = class { for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { - int xF = wF + yF * ${t10} - ${s}; + int xF = wF + yF * ${t8} - ${s}; if (xF < 0 || xF >= ${e.inDepth}) { continue; @@ -21002,10 +21098,10 @@ var Vh = class { `; } }; -var Wh = class { +var Uh = class { constructor(e) { this.variableNames = ["dy", "W"], this.outputShape = e.inShape; - let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t10 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left; + let t8 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t8 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left; this.userCode = ` const ivec3 pads = ivec3(${p}, ${u}, ${c}); @@ -21021,7 +21117,7 @@ var Wh = class { int dyCCorner = dyCorner.z; float dotProd = 0.0; - for (int wF = 0; wF < ${t10}; wF++) { + for (int wF = 0; wF < ${t8}; wF++) { float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { @@ -21029,7 +21125,7 @@ var Wh = class { } int idyF = int(dyF); - int wFPerm = ${t10} - 1 - wF; + int wFPerm = ${t8} - 1 - wF; for (int wR = 0; wR < ${o}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; @@ -21066,15 +21162,15 @@ var Wh = class { `; } }; -function p9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Bh(m); - return t10.runWebGLProgram(d, [n, s], "float32"); +function z9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new zh(m); + return t8.runWebGLProgram(d, [n, s], "float32"); } -var fA = { kernelName: $i, backendName: "webgl", kernelFunc: p9 }; -var Uh = class { +var $A = { kernelName: Ai, backendName: "webgl", kernelFunc: z9 }; +var Gh = class { constructor(e) { - this.variableNames = ["dy", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "strides", type: "vec2" }], this.outputShape = e.inShape, this.enableShapeUniforms = ut(this.outputShape.length); - let t10 = e.filterHeight, o = e.filterWidth, n = t10 - 1 - e.padInfo.top, s = o - 1 - e.padInfo.left; + this.variableNames = ["dy", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "strides", type: "vec2" }], this.outputShape = e.inShape, this.enableShapeUniforms = pt(this.outputShape.length); + let t8 = e.filterHeight, o = e.filterWidth, n = t8 - 1 - e.padInfo.top, s = o - 1 - e.padInfo.left; this.userCode = ` const ivec2 pads = ivec2(${n}, ${s}); @@ -21088,13 +21184,13 @@ var Uh = class { int dyCCorner = dyCorner.y; vec4 result = vec4(0.); - for (int wR = 0; wR < ${t10}; wR++) { + for (int wR = 0; wR < ${t8}; wR++) { float dyR = float(dyRCorner + wR) / strides[0]; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${t10} - 1 - wR; + int wRPerm = ${t8} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { int wCPerm = ${o} - 1 - wC; @@ -21152,58 +21248,58 @@ var Uh = class { `; } }; -function c9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(u), m = C.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l); - if (P().getBool("WEBGL_PACK") && l === "channelsLast") { - let d = [[m.strideHeight, m.strideWidth]], f = new Uh(m); - return t10.runWebGLProgram(f, [n, s], "float32", d); +function V9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l); + if (A().getBool("WEBGL_PACK") && l === "channelsLast") { + let d = [[m.strideHeight, m.strideWidth]], f = new Gh(m); + return t8.runWebGLProgram(f, [n, s], "float32", d); } else { - let d = new zh(m); - return t10.runWebGLProgram(d, [n, s], "float32"); + let d = new Vh(m); + return t8.runWebGLProgram(d, [n, s], "float32"); } } -var hA = { kernelName: tn, backendName: "webgl", kernelFunc: c9 }; -function l9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Fh(u); - return t10.runWebGLProgram(c, [n, s], "float32"); +var EA = { kernelName: rn, backendName: "webgl", kernelFunc: V9 }; +function W9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Ph(u); + return t8.runWebGLProgram(c, [n, s], "float32"); } -var gA = { kernelName: rn, backendName: "webgl", kernelFunc: l9 }; -function m9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = C.computeConv3DInfo(n.shape, p, a, 1, i), c = new Vh(u); - return t10.runWebGLProgram(c, [n, s], "float32"); +var RA = { kernelName: on, backendName: "webgl", kernelFunc: W9 }; +function U9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Wh(u); + return t8.runWebGLProgram(c, [n, s], "float32"); } -var xA = { kernelName: za, backendName: "webgl", kernelFunc: m9 }; -function d9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = C.computeConv3DInfo(p, s.shape, i, 1, a), c = new Wh(u); - return t10.runWebGLProgram(c, [n, s], "float32"); +var DA = { kernelName: Ka, backendName: "webgl", kernelFunc: U9 }; +function G9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, i, 1, a), c = new Uh(u); + return t8.runWebGLProgram(c, [n, s], "float32"); } -var yA = { kernelName: on, backendName: "webgl", kernelFunc: d9 }; -var f9 = Do + ` +var AA = { kernelName: nn, backendName: "webgl", kernelFunc: G9 }; +var H9 = Fo + ` return cos(x); `; -var h9 = ` +var K9 = ` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Kr} + ${Xr} return result; `; -var g9 = ge({ opSnippet: f9, packedOpSnippet: h9 }); -var bA = { kernelName: nn, backendName: "webgl", kernelFunc: g9 }; -var x9 = ` +var q9 = xe({ opSnippet: H9, packedOpSnippet: K9 }); +var FA = { kernelName: sn, backendName: "webgl", kernelFunc: q9 }; +var j9 = ` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `; -var y9 = ge({ opSnippet: x9 }); -var CA = { kernelName: sn, backendName: "webgl", kernelFunc: y9 }; -var Gh = class { - constructor(e, t10, o, n, s) { +var X9 = xe({ opSnippet: j9 }); +var PA = { kernelName: an, backendName: "webgl", kernelFunc: X9 }; +var Hh = class { + constructor(e, t8, o, n, s) { this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = []; - let [a, i, p, u] = e, [c] = t10, [l, m] = o; + let [a, i, p, u] = e, [c] = t8, [l, m] = o; this.outputShape = [c, l, m, u]; - let d = n === "bilinear" ? 1 : 0, [f, h] = [`${i - 1}.0`, `${p - 1}.0`], [g, x, b] = l > 1 ? [`${(i - 1) / (l - 1)}`, "(y2-y1) * height_ratio", `y1*${f} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${f}`], [w, S, k] = m > 1 ? [`${(p - 1) / (m - 1)}`, "(x2-x1) * width_ratio", `x1*${h} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${h}`]; + let d = n === "bilinear" ? 1 : 0, [f, h] = [`${i - 1}.0`, `${p - 1}.0`], [g, x, b] = l > 1 ? [`${(i - 1) / (l - 1)}`, "(y2-y1) * height_ratio", `y1*${f} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${f}`], [C, S, k] = m > 1 ? [`${(p - 1) / (m - 1)}`, "(x2-x1) * width_ratio", `x1*${h} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${h}`]; this.userCode = ` const float height_ratio = float(${g}); - const float width_ratio = float(${w}); + const float width_ratio = float(${C}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -21265,36 +21361,36 @@ var Gh = class { `; } }; -var b9 = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Gh(n.shape, s.shape, i, p, u); - return t10.runWebGLProgram(c, [n, s, a], "float32"); +var Y9 = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Hh(n.shape, s.shape, i, p, u); + return t8.runWebGLProgram(c, [n, s, a], "float32"); }; -var wA = { kernelName: pn, backendName: "webgl", kernelFunc: b9 }; -var Sp; +var OA = { kernelName: cn, backendName: "webgl", kernelFunc: Y9 }; +var Np; (function(r) { r.Prod = "*", r.Sum = "+"; -})(Sp || (Sp = {})); +})(Np || (Np = {})); var em = class { - constructor(e, t10, o, n) { - this.op = e, this.outputShape = t10, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }]; - let s = this.outputShape.length, a = this.op === Sp.Prod ? "1.0" : "0.0", i = o ? a : `getX(${SA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = ""; + constructor(e, t8, o, n) { + this.op = e, this.outputShape = t8, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }]; + let s = this.outputShape.length, a = this.op === Np.Prod ? "1.0" : "0.0", i = o ? a : `getX(${MA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = ""; o ? (u = n ? `end != ${p - 1}` : "end != 0", c = n ? "end + 1" : "end - 1") : (u = n ? `end + pow2 < ${p}` : "end >= pow2", c = n ? "end + pow2" : "end - pow2"), this.userCode = ` void main() { ${Re(s)} coords = getOutputCoords(); - int end = ${IA(s, "coords", this.op)}; + int end = ${LA(s, "coords", this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; - ${IA(s, "coords", this.op)} = idx; - val ${this.op}= getX(${SA(s, "coords", this.op)}); + ${LA(s, "coords", this.op)} = idx; + val ${this.op}= getX(${MA(s, "coords", this.op)}); } setOutput(val); } `; } }; -function SA(r, e, t10) { +function MA(r, e, t8) { if (r === 1) return `${e}`; if (r === 2) @@ -21303,9 +21399,9 @@ function SA(r, e, t10) { return `${e}.x, ${e}.y, ${e}.z`; if (r === 4) return `${e}.x, ${e}.y, ${e}.z, ${e}.w`; - throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`); + throw new Error(`Cumulative ${t8} for rank ${r} is not yet supported`); } -function IA(r, e, t10) { +function LA(r, e, t8) { if (r === 1) return `${e}`; if (r === 2) @@ -21314,54 +21410,54 @@ function IA(r, e, t10) { return `${e}.z`; if (r === 4) return `${e}.w`; - throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`); + throw new Error(`Cumulative ${t8} for rank ${r} is not yet supported`); } -function Hh(r, e, t10, o, n, s) { - let a = e.shape.length, i = C.getAxesPermutation([o], a), p = e; - i != null && (p = bt({ inputs: { x: e }, backend: t10, attrs: { perm: i } })); - let u = C.getInnerMostAxes(1, a)[0]; +function Kh(r, e, t8, o, n, s) { + let a = e.shape.length, i = w.getAxesPermutation([o], a), p = e; + i != null && (p = Ct({ inputs: { x: e }, backend: t8, attrs: { perm: i } })); + let u = w.getInnerMostAxes(1, a)[0]; if (u !== a - 1) throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`); - let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: t10 }); + let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: t8 }); for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) { let d = new em(r, p.shape, false, s), f = [[m]], h = l; - l = t10.runWebGLProgram(d, [l], l.dtype, f), t10.disposeIntermediateTensorInfo(h); + l = t8.runWebGLProgram(d, [l], l.dtype, f), t8.disposeIntermediateTensorInfo(h); } if (n) { let m = new em(r, p.shape, n, s), d = l; - l = t10.runWebGLProgram(m, [l], l.dtype), t10.disposeIntermediateTensorInfo(d); + l = t8.runWebGLProgram(m, [l], l.dtype), t8.disposeIntermediateTensorInfo(d); } if (i != null) { - let m = C.getUndoAxesPermutation(i), d = bt({ inputs: { x: l }, backend: t10, attrs: { perm: m } }); - return t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(p), d; + let m = w.getUndoAxesPermutation(i), d = Ct({ inputs: { x: l }, backend: t8, attrs: { perm: m } }); + return t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(p), d; } return l; } -function C9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return Hh(Sp.Prod, n, t10, s, a, i); +function Q9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return Kh(Np.Prod, n, t8, s, a, i); } -var vA = { kernelName: an, backendName: "webgl", kernelFunc: C9 }; -function w9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return Hh(Sp.Sum, n, t10, s, a, i); +var BA = { kernelName: un, backendName: "webgl", kernelFunc: Q9 }; +function Z9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return Kh(Np.Sum, n, t8, s, a, i); } -var kA = { kernelName: un, backendName: "webgl", kernelFunc: w9 }; -function S9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; +var zA = { kernelName: pn, backendName: "webgl", kernelFunc: Z9 }; +function J9(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; if (n.shape.length === 1) { - let p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = ah(p, u, s.dtype, s.shape, a); - return t10.makeTensorInfo([a], s.dtype, c); + let p = t8.readSync(n.dataId), u = t8.readSync(s.dataId), c = ih(p, u, s.dtype, s.shape, a); + return t8.makeTensorInfo([a], s.dtype, c); } else if (n.shape.length === 2) { - let p = t10.bufferSync(n), u = t10.bufferSync(s), c = lR(p, u, a, i); - return t10.makeTensorInfo(c.shape, s.dtype, c.values); + let p = t8.bufferSync(n), u = t8.bufferSync(s), c = vR(p, u, a, i); + return t8.makeTensorInfo(c.shape, s.dtype, c.values); } throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`); } -var NA = { kernelName: Qs, backendName: "webgl", kernelFunc: S9 }; -var Kh = class { - constructor(e, t10, o) { - this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t10, this.dataFormat = o, this.userCode = ` +var VA = { kernelName: ta, backendName: "webgl", kernelFunc: J9 }; +var qh = class { + constructor(e, t8, o) { + this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t8, this.dataFormat = o, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -21369,11 +21465,11 @@ var Kh = class { int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; - int in_h = h / ${t10}; - int offset_h = imod(h, ${t10}); - int in_w = w / ${t10}; - int offset_w = imod(w, ${t10}); - int offset_d = (offset_h * ${t10} + offset_w) * + int in_h = h / ${t8}; + int offset_h = imod(h, ${t8}); + int in_w = w / ${t8}; + int offset_w = imod(w, ${t8}); + int offset_d = (offset_h * ${t8} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; @@ -21398,14 +21494,14 @@ var Kh = class { return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)"; } }; -function I9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new Kh(f, s, a); - return t10.runWebGLProgram(h, [n], n.dtype); +function eJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new qh(f, s, a); + return t8.runWebGLProgram(h, [n], n.dtype); } -var TA = { kernelName: cn, backendName: "webgl", kernelFunc: I9 }; -var Wc = class { - constructor(e, t10 = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ut(this.outputShape.length); +var WA = { kernelName: ln, backendName: "webgl", kernelFunc: eJ }; +var Uc = class { + constructor(e, t8 = false, o = null, n = false, s = false) { + this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = pt(this.outputShape.length); let a = e.filterHeight, i = e.filterWidth, p = e.outChannels / e.inChannels, u = "", c = ""; o && (n ? u = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); @@ -21418,8 +21514,8 @@ var Wc = class { ${o} } `, c = "result = activation(result);"); - let l = t10 ? "result += getBiasAtOutCoords();" : ""; - t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let l = t8 ? "result += getBiasAtOutCoords();" : ""; + t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${u} void main() { @@ -21465,9 +21561,9 @@ var Wc = class { `; } }; -var Uc = class { - constructor(e, t10 = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ut(this.outputShape.length); +var Gc = class { + constructor(e, t8 = false, o = null, n = false, s = false) { + this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = pt(this.outputShape.length); let a = e.outChannels / e.inChannels, i = e.padInfo.left, p = e.strideWidth, u = e.dilationWidth, c = e.filterHeight, l = e.filterWidth, m = l, d = ` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`; @@ -21538,9 +21634,9 @@ var Uc = class { xC${b} = xTexelC${b}; `, b + 1 < l)) { - let w = i % 2 === 0 ? y.nearestLargerEven(u) : u; + let C = i % 2 === 0 ? y.nearestLargerEven(u) : u; u % 2 === 0 && i % 2 === 1 || u % 2 !== 0 && i % 2 !== 1 ? (d += ` - xCOffset = xC + imod(pads[1], 2) + ${w}; + xCOffset = xC + imod(pads[1], 2) + ${C}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) { xTexelC${b + 1} = getX(batch, xR, xCOffset, d1); @@ -21562,10 +21658,10 @@ var Uc = class { } ` : d += ` xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.xy); - `) : w === 1 ? d += ` + `) : C === 1 ? d += ` xC${b + 1} = xTexelC${b}; ` : d += ` - xCOffset = xC + ${w}; + xCOffset = xC + ${C}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) { xTexelC${b + 1} = getX(batch, xR, xCOffset, d1); @@ -21655,8 +21751,8 @@ var Uc = class { }` : f = `vec4 activation(vec4 x) { ${o} }`, h = "result = activation(result);"); - let g = t10 ? "result += getBiasAtOutCoords();" : ""; - t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let g = t8 ? "result += getBiasAtOutCoords();" : ""; + t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${f} void main() { @@ -21682,19 +21778,19 @@ var Uc = class { `; } }; -function v9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p; - c == null && (c = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`); - let l = C.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m; - P().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new Uc(l) : m = new Wc(l); +function tJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p; + c == null && (c = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`); + let l = w.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m; + A().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new Gc(l) : m = new Uc(l); let d = [[l.padInfo.top, l.padInfo.left], [l.strideHeight, l.strideWidth], [l.dilationHeight, l.dilationWidth], [l.inHeight, l.inWidth]]; - return t10.runWebGLProgram(m, [n, s], "float32", d); + return t8.runWebGLProgram(m, [n, s], "float32", d); } -var _A = { kernelName: ln, backendName: "webgl", kernelFunc: v9 }; -var qh = class { +var UA = { kernelName: mn, backendName: "webgl", kernelFunc: tJ }; +var jh = class { constructor(e) { this.variableNames = ["x", "dy"], this.outputShape = e.filterShape; - let t10 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.outChannels / e.inChannels; + let t8 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.outChannels / e.inChannels; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -21709,7 +21805,7 @@ var qh = class { // TO DO: Vec4 over the batch size for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t10} - ${n}; + int xR = wR + yR * ${t8} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; @@ -21733,10 +21829,10 @@ var qh = class { `; } }; -var jh = class { +var Xh = class { constructor(e) { this.variableNames = ["dy", "W"], this.outputShape = e.inShape; - let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t10 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels; + let t8 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t8 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels; this.userCode = ` const ivec2 pads = ivec2(${a}, ${i}); @@ -21750,7 +21846,7 @@ var jh = class { float dotProd = 0.0; - for (int wR = 0; wR < ${t10}; wR++) { + for (int wR = 0; wR < ${t8}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -21758,7 +21854,7 @@ var jh = class { } int idyR = int(dyR); - int wRPerm = ${t10} - 1 - wR; + int wRPerm = ${t8} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; @@ -21785,17 +21881,17 @@ var jh = class { `; } }; -function k9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = C.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new qh(l); - return t10.runWebGLProgram(m, [n, s], "float32"); +function rJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new jh(l); + return t8.runWebGLProgram(m, [n, s], "float32"); } -var $A = { kernelName: Ei, backendName: "webgl", kernelFunc: k9 }; -function N9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = C.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new jh(l); - return t10.runWebGLProgram(m, [n, s], "float32"); +var GA = { kernelName: Fi, backendName: "webgl", kernelFunc: rJ }; +function oJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Xh(l); + return t8.runWebGLProgram(m, [n, s], "float32"); } -var EA = { kernelName: Ri, backendName: "webgl", kernelFunc: N9 }; -var Xh = class { +var HA = { kernelName: Pi, backendName: "webgl", kernelFunc: oJ }; +var Yh = class { constructor(e) { this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = ` void main() { @@ -21806,15 +21902,15 @@ var Xh = class { `; } }; -function T9(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new Xh(s), p = t10.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: t10, attrs: { shape: n } }); - return t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(p), u; +function nJ(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: t8, attrs: { shape: [s] } }), i = new Yh(s), p = t8.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: t8, attrs: { shape: n } }); + return t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(p), u; } -var RA = { kernelName: Zs, backendName: "webgl", kernelFunc: T9 }; -var Yh = class { +var KA = { kernelName: ra, backendName: "webgl", kernelFunc: nJ }; +var Qh = class { constructor(e) { this.variableNames = ["x", "W"], this.outputShape = e.outShape; - let { inHeight: t10, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n; + let { inHeight: t8, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n; this.userCode = ` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${m}); @@ -21833,7 +21929,7 @@ var Yh = class { for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${u}; - if (hIn >= 0 && hIn < ${t10}) { + if (hIn >= 0 && hIn < ${t8}) { for (int w = 0; w < ${p}; w++) { int wIn = wBeg + w * ${c}; @@ -21856,35 +21952,35 @@ var Yh = class { `; } }; -function _9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Yh(u); - c = t10.runWebGLProgram(l, [n, s], "float32"); - let m = te({ inputs: { x: c }, backend: t10, attrs: { shape: u.outShape } }); - return t10.disposeIntermediateTensorInfo(c), m; +function sJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Qh(u); + c = t8.runWebGLProgram(l, [n, s], "float32"); + let m = te({ inputs: { x: c }, backend: t8, attrs: { shape: u.outShape } }); + return t8.disposeIntermediateTensorInfo(c), m; } -var DA = { kernelName: mn, backendName: "webgl", kernelFunc: _9 }; -function $9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = C.decodeEinsumEquation(n, s.length); - C.checkEinsumDimSizes(a.length, p, s); - let { path: u, steps: c } = C.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; +var qA = { kernelName: dn, backendName: "webgl", kernelFunc: sJ }; +function aJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); + w.checkEinsumDimSizes(a.length, p, s); + let { path: u, steps: c } = w.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { - let { permutationIndices: x, expandDims: b } = C.getEinsumPermutation(d, p[g]), w; - C.isIdentityPermutation(x) ? w = s[g] : (w = bt({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(w)); - let S = w.shape.slice(); + let { permutationIndices: x, expandDims: b } = w.getEinsumPermutation(d, p[g]), C; + w.isIdentityPermutation(x) ? C = s[g] : (C = Ct({ inputs: { x: s[g] }, backend: t8, attrs: { perm: x } }), f.push(C)); + let S = C.shape.slice(); for (let k = 0; k < b.length; ++k) S.splice(b[k], 0, 1); - y.arraysEqual(w.shape, S) || (w = te({ inputs: { x: w }, backend: t10, attrs: { shape: S } }), f.push(w)), m === null ? m = w : (m = Zl({ inputs: { a: w, b: m }, backend: t10 }), f.push(m)); + y.arraysEqual(C.shape, S) || (C = te({ inputs: { x: C }, backend: t8, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = Zl({ inputs: { a: C, b: m }, backend: t8 }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = bp({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = Ip({ inputs: { x: m }, backend: t8, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) - h !== m && t10.disposeIntermediateTensorInfo(h); + h !== m && t8.disposeIntermediateTensorInfo(h); return m; } -var AA = { kernelName: Fi, backendName: "webgl", kernelFunc: $9 }; -var E9 = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; -var R9 = ` +var jA = { kernelName: Li, backendName: "webgl", kernelFunc: aJ }; +var iJ = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; +var uJ = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -21894,46 +21990,46 @@ var R9 = ` return result; `; -var D9 = ge({ opSnippet: E9, packedOpSnippet: R9 }); -var FA = { kernelName: fn, backendName: "webgl", kernelFunc: D9 }; -var A9 = "return (b >= 0.0) ? a : a * (b + 1.0);"; -var F9 = ` +var pJ = xe({ opSnippet: iJ, packedOpSnippet: uJ }); +var XA = { kernelName: hn, backendName: "webgl", kernelFunc: pJ }; +var cJ = "return (b >= 0.0) ? a : a * (b + 1.0);"; +var lJ = ` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `; -var P9 = (r) => { - let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ro(F9, o.shape, n.shape) : new lo(A9, o.shape, n.shape); - return t10.runWebGLProgram(s, [o, n], o.dtype); +var mJ = (r) => { + let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(lJ, o.shape, n.shape) : new Pr(cJ, o.shape, n.shape); + return t8.runWebGLProgram(s, [o, n], o.dtype); }; -var PA = { kernelName: Va, backendName: "webgl", kernelFunc: P9 }; -var O9 = ` +var YA = { kernelName: qa, backendName: "webgl", kernelFunc: mJ }; +var dJ = ` return vec4(equal(a, b)); `; -var M9 = "return float(a == b);"; -var L9 = nt({ opSnippet: M9, packedOpSnippet: O9, dtype: "bool", cpuKernelImpl: hR }); -var OA = { kernelName: hn, backendName: "webgl", kernelFunc: L9 }; -var B9 = ` +var fJ = "return float(a == b);"; +var hJ = nt({ opSnippet: fJ, packedOpSnippet: dJ, dtype: "bool", cpuKernelImpl: $R }); +var QA = { kernelName: xn, backendName: "webgl", kernelFunc: hJ }; +var gJ = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - float p = ${C.ERF_P}; - float a1 = ${C.ERF_A1}; - float a2 = ${C.ERF_A2}; - float a3 = ${C.ERF_A3}; - float a4 = ${C.ERF_A4}; - float a5 = ${C.ERF_A5}; + float p = ${w.ERF_P}; + float a1 = ${w.ERF_A1}; + float a2 = ${w.ERF_A2}; + float a3 = ${w.ERF_A3}; + float a4 = ${w.ERF_A4}; + float a5 = ${w.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `; -var z9 = ge({ opSnippet: B9 }); -var MA = { kernelName: Wa, backendName: "webgl", kernelFunc: z9 }; -var V9 = Do + ` +var xJ = xe({ opSnippet: gJ }); +var ZA = { kernelName: gn, backendName: "webgl", kernelFunc: xJ }; +var yJ = Fo + ` return exp(x); `; -var W9 = ` +var bJ = ` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -21943,21 +22039,21 @@ var W9 = ` return result; `; -var av = ge({ opSnippet: V9, packedOpSnippet: W9, cpuKernelImpl: gR, dtype: "float32" }); -var LA = { kernelName: gn, backendName: "webgl", kernelFunc: av }; -function Qh(r) { - let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; +var hv = xe({ opSnippet: yJ, packedOpSnippet: bJ, cpuKernelImpl: ER, dtype: "float32" }); +var JA = { kernelName: yn, backendName: "webgl", kernelFunc: hv }; +function Zh(r) { + let { inputs: e, attrs: t8, backend: o } = r, { dim: n } = t8, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), te({ inputs: { x: s }, backend: o, attrs: { shape: i } }); } -var BA = { kernelName: Js, backendName: "webgl", kernelFunc: Qh }; -var zA = "return exp(x) - 1.0;"; -var U9 = ge({ opSnippet: zA, packedOpSnippet: zA, cpuKernelImpl: xR }); -var VA = { kernelName: xn, backendName: "webgl", kernelFunc: U9 }; +var eF = { kernelName: oa, backendName: "webgl", kernelFunc: Zh }; +var tF = "return exp(x) - 1.0;"; +var CJ = xe({ opSnippet: tF, packedOpSnippet: tF, cpuKernelImpl: RR }); +var rF = { kernelName: bn, backendName: "webgl", kernelFunc: CJ }; var tm = class { - constructor(e, t10, o) { + constructor(e, t8, o) { this.variableNames = ["real", "imag"]; - let n = t10[1]; - this.outputShape = t10; + let n = t8[1]; + this.outputShape = t8; let s = o ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = o ? `${n}.0` : "1.0", i; if (e === "real") i = "return real * expR - imag * expI;"; @@ -22001,19 +22097,19 @@ var tm = class { `; } }; -function Zh(r, e, t10) { - let o = t10.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = te({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }), p = i.shape, u = new tm("real", p, e), c = new tm("imag", p, e), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Fr({ inputs: { real: m, imag: d }, backend: t10 }); - t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d); - let h = te({ inputs: { x: f }, backend: t10, attrs: { shape: r.shape } }); - return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(f), h; +function Jh(r, e, t8) { + let o = t8.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = te({ inputs: { x: r }, backend: t8, attrs: { shape: [a, s] } }), p = i.shape, u = new tm("real", p, e), c = new tm("imag", p, e), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = t8.runWebGLProgram(u, l, "float32"), d = t8.runWebGLProgram(c, l, "float32"), f = Or({ inputs: { real: m, imag: d }, backend: t8 }); + t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d); + let h = te({ inputs: { x: f }, backend: t8, attrs: { shape: r.shape } }); + return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(f), h; } -function G9(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e; - return Zh(o, false, t10); +function wJ(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e; + return Jh(o, false, t8); } -var WA = { kernelName: Pi, backendName: "webgl", kernelFunc: G9 }; -var Jh = class { - constructor(e, t10) { +var oF = { kernelName: Bi, backendName: "webgl", kernelFunc: wJ }; +var eg = class { + constructor(e, t8) { this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = ` void main() { // Input can be obtained from uniform value. @@ -22022,29 +22118,29 @@ var Jh = class { `; } }; -function xi(r) { - let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10; +function Ci(r) { + let { backend: e, attrs: t8 } = r, { shape: o, value: n } = t8, { dtype: s } = t8; if (s = s || y.inferDtype(n), s === "string") { let a = y.getArrayFromDType(s, y.sizeFromShape(o)); return a.fill(n), e.makeTensorInfo(o, s, a); } else { - let a = new Jh(o, n), i = [[n]]; + let a = new eg(o, n), i = [[n]]; return e.runWebGLProgram(a, [], s, i); } } -var UA = { kernelName: ea, backendName: "webgl", kernelFunc: xi }; -var eg = class { +var nF = { kernelName: na, backendName: "webgl", kernelFunc: Ci }; +var tg = class { constructor(e) { this.variableNames = ["Image"], this.outputShape = []; - let t10 = e[2]; + let t8 = e[2]; this.outputShape = e, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; - int coordX = ${t10} - x - 1; + int coordX = ${t8} - x - 1; float outputValue; - if(coordX >= 0 && coordX < ${t10}) { + if(coordX >= 0 && coordX < ${t8}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); @@ -22054,14 +22150,14 @@ var eg = class { `; } }; -var GA = { kernelName: yn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => { - let { image: t10 } = r, o = e, n = new eg(t10.shape); - return o.runWebGLProgram(n, [t10], t10.dtype); +var sF = { kernelName: Cn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => { + let { image: t8 } = r, o = e, n = new tg(t8.shape); + return o.runWebGLProgram(n, [t8], t8.dtype); } }; -var HA = "return floor(x);"; -var H9 = ge({ opSnippet: HA, packedOpSnippet: HA, cpuKernelImpl: yR }); -var KA = { kernelName: bn, backendName: "webgl", kernelFunc: H9 }; -var K9 = ` +var aF = "return floor(x);"; +var SJ = xe({ opSnippet: aF, packedOpSnippet: aF, cpuKernelImpl: DR }); +var iF = { kernelName: wn, backendName: "webgl", kernelFunc: SJ }; +var IJ = ` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -22072,7 +22168,7 @@ var K9 = ` return NAN; } `; -var q9 = ` +var vJ = ` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -22094,12 +22190,12 @@ var q9 = ` } return vec4(result); `; -var j9 = nt({ opSnippet: K9, packedOpSnippet: q9, dtype: "int32" }); -var qA = { kernelName: Cn, backendName: "webgl", kernelFunc: j9 }; -var tg = class { +var kJ = nt({ opSnippet: IJ, packedOpSnippet: vJ, dtype: "int32" }); +var uF = { kernelName: Sn, backendName: "webgl", kernelFunc: kJ }; +var rg = class { constructor(e) { this.variableNames = ["A"]; - let t10 = It(), [o, n] = e; + let t8 = vt(), [o, n] = e; this.outputShape = e, this.userCode = ` void main() { ivec3 coords = getOutputCoords(); @@ -22108,7 +22204,7 @@ var tg = class { int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); - vec4 values = ${t10.texture2D}(A, uv); + vec4 values = ${t8.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -22125,10 +22221,10 @@ var tg = class { `; } }; -var rg = class { +var og = class { constructor(e) { this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true; - let t10 = It(), [o, n] = e; + let t8 = vt(), [o, n] = e; this.outputShape = e, this.userCode = ` void main() { ivec3 coords = getOutputCoords(); @@ -22145,7 +22241,7 @@ var rg = class { vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); - vec4 values = ${t10.texture2D}(A, uv); + vec4 values = ${t8.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -22161,72 +22257,72 @@ var rg = class { } } - ${t10.output} = result; + ${t8.output} = result; } `; } }; -var jA = { kernelName: $u, backendName: "webgl", kernelFunc: X9 }; -var Gc; -var iv = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); -function X9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s]; +var pF = { kernelName: Au, backendName: "webgl", kernelFunc: NJ }; +var Hc; +var gv = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); +function NJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s]; if (i || a) { - let h = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); - (Gc == null || h !== iv) && (iv = h, Gc = document.createElement("canvas").getContext("2d", { willReadFrequently: iv })), Gc.canvas.width = p, Gc.canvas.height = u, Gc.drawImage(n, 0, 0, p, u), n = Gc.canvas; + let h = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); + (Hc == null || h !== gv) && (gv = h, Hc = document.createElement("canvas").getContext("2d", { willReadFrequently: gv })), Hc.canvas.width = p, Hc.canvas.height = u, Hc.drawImage(n, 0, 0, p, u), n = Hc.canvas; } - let m = t10.makeTensorInfo(c, "int32"); - t10.texData.get(m.dataId).usage = mr.PIXELS, t10.gpgpu.uploadPixelDataToTexture(t10.getTexture(m.dataId), n); - let d = P().getBool("WEBGL_PACK") ? new rg(l) : new tg(l), f = t10.runWebGLProgram(d, [m], "int32"); - return t10.disposeData(m.dataId), f; + let m = t8.makeTensorInfo(c, "int32"); + t8.texData.get(m.dataId).usage = mr.PIXELS, t8.gpgpu.uploadPixelDataToTexture(t8.getTexture(m.dataId), n); + let d = A().getBool("WEBGL_PACK") ? new og(l) : new rg(l), f = t8.runWebGLProgram(d, [m], "int32"); + return t8.disposeData(m.dataId), f; } -function Y9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], w = a != null, S = i != null, k = d === "leakyrelu", _ = () => { - let R = [n, s], D = (F, O) => { - if (O === "NCHW" && F.shape.length === 1 && F.shape[0] !== 1) { - let M = te({ inputs: { x: F }, backend: t10, attrs: { shape: [F.shape[0], 1, 1] } }); +function TJ(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, S = i != null, k = d === "leakyrelu", _ = () => { + let R = [n, s], D = (P, O) => { + if (O === "NCHW" && P.shape.length === 1 && P.shape[0] !== 1) { + let M = te({ inputs: { x: P }, backend: t8, attrs: { shape: [P.shape[0], 1, 1] } }); return b.push(M), M; } - return F; + return P; }; - if (w && R.push(D(a, c)), S && R.push(D(i, c)), k) { - let F = t10.makeTensorInfo([], "float32", y.createScalarValue(f, "float32")); - R.push(F), b.push(F); + if (C && R.push(D(a, c)), S && R.push(D(i, c)), k) { + let P = t8.makeTensorInfo([], "float32", y.createScalarValue(f, "float32")); + R.push(P), b.push(P); } return R; }; if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID")) - x = Mh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); - else if (g.strideWidth <= 2 && h === "channelsLast" && P().getBool("WEBGL_EXP_CONV")) { - let R = d ? hi(d, true) : null, D = new Vc(g, w, R, S, k), F = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _(); - x = t10.runWebGLProgram(D, O, "float32", F); - } else if (P().getBool("WEBGL_CONV_IM2COL")) - x = Lh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); + x = Lh({ x: n, filter: s, convInfo: g, backend: t8, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); + else if (g.strideWidth <= 2 && h === "channelsLast" && A().getBool("WEBGL_EXP_CONV")) { + let R = d ? yi(d, true) : null, D = new Wc(g, C, R, S, k), P = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _(); + x = t8.runWebGLProgram(D, O, "float32", P); + } else if (A().getBool("WEBGL_CONV_IM2COL")) + x = Bh({ x: n, filter: s, convInfo: g, backend: t8, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); else { - let R = d ? hi(d, false) : null, D = new zc(g, w, R, S, k), F = _(); - x = t10.runWebGLProgram(D, F, "float32"); + let R = d ? yi(d, false) : null, D = new Vc(g, C, R, S, k), P = _(); + x = t8.runWebGLProgram(D, P, "float32"); } - let E = te({ inputs: { x }, backend: t10, attrs: { shape: g.outShape } }); - return b.push(x), b.forEach((R) => t10.disposeIntermediateTensorInfo(R)), E; + let E = te({ inputs: { x }, backend: t8, attrs: { shape: g.outShape } }); + return b.push(x), b.forEach((R) => t8.disposeIntermediateTensorInfo(R)), E; } -var XA = { kernelName: Co, backendName: "webgl", kernelFunc: Y9 }; -function Q9(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c; - h == null && (h = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`); - let g = C.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = P().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? hi(m, x) : null, w = [n, s], S = a != null, k = i != null, _ = m === "leakyrelu"; - if (S && w.push(a), k && w.push(i), _) { - let F = t10.makeTensorInfo([], "float32", y.createScalarValue(d, "float32")); - w.push(F), f.push(F); +var cF = { kernelName: Io, backendName: "webgl", kernelFunc: TJ }; +function _J(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c; + h == null && (h = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`); + let g = w.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = A().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? yi(m, x) : null, C = [n, s], S = a != null, k = i != null, _ = m === "leakyrelu"; + if (S && C.push(a), k && C.push(i), _) { + let P = t8.makeTensorInfo([], "float32", y.createScalarValue(d, "float32")); + C.push(P), f.push(P); } let E; - x ? E = new Uc(g, S, b, k, _) : E = new Wc(g, S, b, k, _); - let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], D = t10.runWebGLProgram(E, w, "float32", R); - return f.forEach((F) => t10.disposeIntermediateTensorInfo(F)), D; + x ? E = new Gc(g, S, b, k, _) : E = new Uc(g, S, b, k, _); + let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], D = t8.runWebGLProgram(E, C, "float32", R); + return f.forEach((P) => t8.disposeIntermediateTensorInfo(P)), D; } -var YA = { kernelName: wo, backendName: "webgl", kernelFunc: Q9 }; -var og = class { - constructor(e, t10, o, n) { - this.sliceDim = e, this.strides = t10, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o; +var lF = { kernelName: vo, backendName: "webgl", kernelFunc: _J }; +var ng = class { + constructor(e, t8, o, n) { + this.sliceDim = e, this.strides = t8, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o; let s = Re(o.length), a = ` int index;`; for (let i = 0; i < this.sliceDim; i++) @@ -22248,20 +22344,20 @@ var og = class { `; } }; -function Z9(r) { - let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = C.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); - if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { - let x = t10.readSync(n.dataId), b = t10.bufferSync(o), w = bR(x, b, o.dtype, u, a, c, l, o.shape, i); - return t10.makeTensorInfo(p, o.dtype, w.values); +function $J(r) { + let { inputs: e, backend: t8 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: t8, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: t8, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); + if (t8.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { + let x = t8.readSync(n.dataId), b = t8.bufferSync(o), C = AR(x, b, o.dtype, u, a, c, l, o.shape, i); + return t8.makeTensorInfo(p, o.dtype, C.values); } - let f = new og(a, l, [u, c], o.shape), h = t10.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: p } }); - return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), g; + let f = new ng(a, l, [u, c], o.shape), h = t8.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: p } }); + return t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(h), g; } -var QA = { kernelName: Sn, backendName: "webgl", kernelFunc: Z9 }; -var ng = class { - constructor(e, t10) { - this.variableNames = ["A", "indices"], this.outputShape = t10, this.rank = t10.length; - let o = Re(this.rank), n = J9(e, 2); +var mF = { kernelName: vn, backendName: "webgl", kernelFunc: $J }; +var sg = class { + constructor(e, t8) { + this.variableNames = ["A", "indices"], this.outputShape = t8, this.rank = t8.length; + let o = Re(this.rank), n = EJ(e, 2); this.userCode = ` void main() { ${o} resRC = getOutputCoords(); @@ -22272,81 +22368,81 @@ var ng = class { `; } }; -function J9(r, e) { - let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; +function EJ(r, e) { + let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; for (let n = 0; n < r.length; n++) - n === 2 ? o.push("index") : o.push(`${t10[n]}`); + n === 2 ? o.push("index") : o.push(`${t8[n]}`); return o.join(); } -function uv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0]; - if (P().get("DEBUG")) { - let b = t10.readSync(s.dataId), w = n.shape[p]; +function xv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0]; + if (A().get("DEBUG")) { + let b = t8.readSync(s.dataId), C = n.shape[p]; for (let S = 0; S < b.length; ++S) { let k = b[S]; - y.assert(k <= w - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${w - 1}]`); + y.assert(k <= C - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${C - 1}]`); } } - let u = C.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = te({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = te({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } }); + let u = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = te({ inputs: { x: n }, backend: t8, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = te({ inputs: { x: s }, backend: t8, attrs: { shape: [u.batchSize, c / u.batchSize] } }); l.push(m), l.push(d); let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize]; - if (t10.shouldExecuteOnCPU([n, s]) || n.dtype === "string") { - let b = t10.bufferSync(d), w = t10.bufferSync(m), S = CR(w, b, f); - return l.forEach((k) => t10.disposeIntermediateTensorInfo(k)), t10.makeTensorInfo(u.outputShape, S.dtype, S.values); + if (t8.shouldExecuteOnCPU([n, s]) || n.dtype === "string") { + let b = t8.bufferSync(d), C = t8.bufferSync(m), S = FR(C, b, f); + return l.forEach((k) => t8.disposeIntermediateTensorInfo(k)), t8.makeTensorInfo(u.outputShape, S.dtype, S.values); } - let h = new ng(m.shape, f), g = t10.runWebGLProgram(h, [m, d], m.dtype); + let h = new sg(m.shape, f), g = t8.runWebGLProgram(h, [m, d], m.dtype); l.push(g); - let x = te({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } }); - return l.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x; + let x = te({ inputs: { x: g }, backend: t8, attrs: { shape: u.outputShape } }); + return l.forEach((b) => t8.disposeIntermediateTensorInfo(b)), x; } -var ZA = { kernelName: ta, backendName: "webgl", kernelFunc: uv }; -var eJ = "return float(a > b);"; -var tJ = ` +var dF = { kernelName: sa, backendName: "webgl", kernelFunc: xv }; +var RJ = "return float(a > b);"; +var DJ = ` return vec4(greaterThan(a, b)); `; -var rJ = nt({ opSnippet: eJ, packedOpSnippet: tJ, cpuKernelImpl: wR, dtype: "bool" }); -var JA = { kernelName: In, backendName: "webgl", kernelFunc: rJ }; -var oJ = "return float(a >= b);"; -var nJ = ` +var AJ = nt({ opSnippet: RJ, packedOpSnippet: DJ, cpuKernelImpl: PR, dtype: "bool" }); +var fF = { kernelName: kn, backendName: "webgl", kernelFunc: AJ }; +var FJ = "return float(a >= b);"; +var PJ = ` return vec4(greaterThanEqual(a, b)); `; -var sJ = nt({ opSnippet: oJ, packedOpSnippet: nJ, dtype: "bool", cpuKernelImpl: SR }); -var eF = { kernelName: vn, backendName: "webgl", kernelFunc: sJ }; -function aJ(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e; - return Zh(o, true, t10); +var OJ = nt({ opSnippet: FJ, packedOpSnippet: PJ, dtype: "bool", cpuKernelImpl: OR }); +var hF = { kernelName: Nn, backendName: "webgl", kernelFunc: OJ }; +function MJ(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e; + return Jh(o, true, t8); } -var tF = { kernelName: Oi, backendName: "webgl", kernelFunc: aJ }; -var iJ = "return float(!isnan(x) && !isinf(x));"; -var uJ = ge({ opSnippet: iJ, dtype: "bool" }); -var rF = { kernelName: kn, backendName: "webgl", kernelFunc: uJ }; -var pJ = "return float(isinf(x));"; -var cJ = ge({ opSnippet: pJ, dtype: "bool" }); -var oF = { kernelName: Nn, backendName: "webgl", kernelFunc: cJ }; -var lJ = "return float(isnan(x));"; -var mJ = ge({ opSnippet: lJ, dtype: "bool" }); -var nF = { kernelName: Tn, backendName: "webgl", kernelFunc: mJ }; -var dJ = "return float(a < b);"; -var fJ = ` +var gF = { kernelName: zi, backendName: "webgl", kernelFunc: MJ }; +var LJ = "return float(!isnan(x) && !isinf(x));"; +var BJ = xe({ opSnippet: LJ, dtype: "bool" }); +var xF = { kernelName: Tn, backendName: "webgl", kernelFunc: BJ }; +var zJ = "return float(isinf(x));"; +var VJ = xe({ opSnippet: zJ, dtype: "bool" }); +var yF = { kernelName: _n, backendName: "webgl", kernelFunc: VJ }; +var WJ = "return float(isnan(x));"; +var UJ = xe({ opSnippet: WJ, dtype: "bool" }); +var bF = { kernelName: $n, backendName: "webgl", kernelFunc: UJ }; +var GJ = "return float(a < b);"; +var HJ = ` return vec4(lessThan(a, b)); `; -var hJ = nt({ opSnippet: dJ, packedOpSnippet: fJ, cpuKernelImpl: IR, dtype: "bool" }); -var sF = { kernelName: $n, backendName: "webgl", kernelFunc: hJ }; -var gJ = "return float(a <= b);"; -var xJ = ` +var KJ = nt({ opSnippet: GJ, packedOpSnippet: HJ, cpuKernelImpl: MR, dtype: "bool" }); +var CF = { kernelName: Rn, backendName: "webgl", kernelFunc: KJ }; +var qJ = "return float(a <= b);"; +var jJ = ` return vec4(lessThanEqual(a, b)); `; -var yJ = nt({ opSnippet: gJ, packedOpSnippet: xJ, cpuKernelImpl: vR, dtype: "bool" }); -var aF = { kernelName: En, backendName: "webgl", kernelFunc: yJ }; -function bJ(r) { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = kR(o, n, s); +var XJ = nt({ opSnippet: qJ, packedOpSnippet: jJ, cpuKernelImpl: LR, dtype: "bool" }); +var wF = { kernelName: Dn, backendName: "webgl", kernelFunc: XJ }; +function YJ(r) { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, num: s } = t8, a = BR(o, n, s); return e.makeTensorInfo([a.length], "float32", a); } -var iF = { kernelName: Rn, backendName: "webgl", kernelFunc: bJ }; -var CJ = Do + ` +var SF = { kernelName: An, backendName: "webgl", kernelFunc: YJ }; +var QJ = Fo + ` return x < 0.0 ? 0./0. : log(x); `; -var wJ = ` +var ZJ = ` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -22355,37 +22451,37 @@ var wJ = ` result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `; -var SJ = ge({ opSnippet: CJ, packedOpSnippet: wJ, cpuKernelImpl: NR }); -var uF = { kernelName: Dn, backendName: "webgl", kernelFunc: SJ }; -var IJ = Do + ` +var JJ = xe({ opSnippet: QJ, packedOpSnippet: ZJ, cpuKernelImpl: zR }); +var IF = { kernelName: Fn, backendName: "webgl", kernelFunc: JJ }; +var eee = Fo + ` return log(1.0 + x); `; -var vJ = ge({ opSnippet: IJ }); -var pF = { kernelName: An, backendName: "webgl", kernelFunc: vJ }; -var kJ = "return float(a >= 1.0 && b >= 1.0);"; -var NJ = ` +var tee = xe({ opSnippet: eee }); +var vF = { kernelName: Pn, backendName: "webgl", kernelFunc: tee }; +var ree = "return float(a >= 1.0 && b >= 1.0);"; +var oee = ` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `; -var TJ = nt({ opSnippet: kJ, packedOpSnippet: NJ, dtype: "bool" }); -var cF = { kernelName: Fn, backendName: "webgl", kernelFunc: TJ }; -var _J = "return float(!(x >= 1.0));"; -var $J = ge({ opSnippet: _J }); -var lF = { kernelName: Pn, backendName: "webgl", kernelFunc: $J }; -var EJ = "return float(a >= 1.0 || b >= 1.0);"; -var RJ = ` +var nee = nt({ opSnippet: ree, packedOpSnippet: oee, dtype: "bool" }); +var kF = { kernelName: On, backendName: "webgl", kernelFunc: nee }; +var see = "return float(!(x >= 1.0));"; +var aee = xe({ opSnippet: see }); +var NF = { kernelName: Mn, backendName: "webgl", kernelFunc: aee }; +var iee = "return float(a >= 1.0 || b >= 1.0);"; +var uee = ` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `; -var DJ = nt({ opSnippet: EJ, packedOpSnippet: RJ, dtype: "bool" }); -var mF = { kernelName: On, backendName: "webgl", kernelFunc: DJ }; -var sg = class { - constructor(e, t10, o, n, s) { +var pee = nt({ opSnippet: iee, packedOpSnippet: uee, dtype: "bool" }); +var TF = { kernelName: Ln, backendName: "webgl", kernelFunc: pee }; +var ag = class { + constructor(e, t8, o, n, s) { this.variableNames = ["x"], this.outputShape = []; - let a = t10, i = e[3] - 1; + let a = t8, i = e[3] - 1; this.outputShape = e; let p, u = `float(${o}) + float(${n}) * sum`; s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${u}) * float(-${s}));`, this.userCode = ` @@ -22410,10 +22506,10 @@ var sg = class { `; } }; -var ag = class { - constructor(e, t10, o, n, s) { +var ig = class { + constructor(e, t8, o, n, s) { this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true; - let a = t10, i = e[3] - 1; + let a = t8, i = e[3] - 1; this.outputShape = e; let p, u = `float(${o}) + float(${n}) * sum`; s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${u}) * float(-${s}));`, this.userCode = ` @@ -22481,14 +22577,14 @@ var ag = class { `; } }; -var AJ = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = P().getBool("WEBGL_PACK_NORMALIZATION") ? new ag(n.shape, s, a, i, p) : new sg(n.shape, s, a, i, p); - return t10.runWebGLProgram(u, [n], n.dtype); +var cee = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = A().getBool("WEBGL_PACK_NORMALIZATION") ? new ig(n.shape, s, a, i, p) : new ag(n.shape, s, a, i, p); + return t8.runWebGLProgram(u, [n], n.dtype); }; -var dF = { kernelName: Mn, backendName: "webgl", kernelFunc: AJ }; -var ig = class { - constructor(e, t10, o, n, s) { - this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t10, this.bias = o, this.alpha = n, this.beta = s, this.userCode = ` +var _F = { kernelName: Bn, backendName: "webgl", kernelFunc: cee }; +var ug = class { + constructor(e, t8, o, n, s) { + this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t8, this.bias = o, this.alpha = n, this.beta = s, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -22497,9 +22593,9 @@ var ig = class { float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { - int depthBegin = int(max(0.0, float(d - ${t10}))); + int depthBegin = int(max(0.0, float(d - ${t8}))); int depthEnd = int(min(float(${this.depth}), - float(d + ${t10} + 1))); + float(d + ${t8} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; @@ -22546,78 +22642,78 @@ var ig = class { `; } }; -var FJ = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new ig(n.shape, i, p, u, c); - return t10.runWebGLProgram(l, [n, s, a], n.dtype); +var lee = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new ug(n.shape, i, p, u, c); + return t8.runWebGLProgram(l, [n, s, a], n.dtype); }; -var fF = { kernelName: Ua, backendName: "webgl", kernelFunc: FJ }; -function hF(r, e, t10, o) { - let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = qr(i, r.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: t10 }, backend: o }); +var $F = { kernelName: ja, backendName: "webgl", kernelFunc: lee }; +function EF(r, e, t8, o) { + let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Yr(i, r.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: t8 }, backend: o }); return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u; } -function pv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = c != null, m = t10.shouldExecuteOnCPU([n]), d = n; +function yv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = t8.shouldExecuteOnCPU([n]), d = n; if (l) { if (m) { - let w = t10.texData.get(d.dataId).values, S = new Array(i); + let C = t8.texData.get(d.dataId).values, S = new Array(i); for (let E = 0; E < S.length; E++) S[E] = n.shape[c[E]]; - let k = yp(w, n.shape, n.dtype, c, S); - d = t10.makeTensorInfo(S, n.dtype); - let _ = t10.texData.get(d.dataId); + let k = Sp(C, n.shape, n.dtype, c, S); + d = t8.makeTensorInfo(S, n.dtype); + let _ = t8.texData.get(d.dataId); _.values = k; } else - d = gu(n, c, t10); - u = C.getInnerMostAxes(u.length, i); + d = Cu(n, c, t8); + u = w.getInnerMostAxes(u.length, i); } - C.assertAxesAreInnerMostDims("max", u, i); - let [f, h] = C.computeOutAndReduceShapes(d.shape, u), g = f; - a && (g = C.expandShapeToKeepDim(f, p)); + w.assertAxesAreInnerMostDims("max", u, i); + let [f, h] = w.computeOutAndReduceShapes(d.shape, u), g = f; + a && (g = w.expandShapeToKeepDim(f, p)); let x; if (m) { - let w = t10.texData.get(d.dataId).values, S = TR(w, y.sizeFromShape(h), g, n.dtype); - x = t10.makeTensorInfo(g, n.dtype); - let k = t10.texData.get(x.dataId); + let C = t8.texData.get(d.dataId).values, S = VR(C, y.sizeFromShape(h), g, n.dtype); + x = t8.makeTensorInfo(g, n.dtype); + let k = t8.texData.get(x.dataId); k.values = S; } else - x = hF(d, h, g, t10); - return l && t10.disposeIntermediateTensorInfo(d), x; + x = EF(d, h, g, t8); + return l && t8.disposeIntermediateTensorInfo(d), x; } -var gF = { kernelName: Ln, backendName: "webgl", kernelFunc: pv }; -var PJ = Mc + ` +var RF = { kernelName: zn, backendName: "webgl", kernelFunc: yv }; +var mee = Lc + ` return max(a, b); `; -var OJ = ` +var dee = ` vec4 result = vec4(max(a, b)); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Kr + ` + ` + Xr + ` return result; `; -var MJ = nt({ opSnippet: PJ, packedOpSnippet: OJ, cpuKernelImpl: _R }); -var xF = { kernelName: Bn, backendName: "webgl", kernelFunc: MJ }; -function LJ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; - Ps(n, "maxPool"); +var fee = nt({ opSnippet: mee, packedOpSnippet: dee, cpuKernelImpl: WR }); +var DF = { kernelName: Vn, backendName: "webgl", kernelFunc: fee }; +function hee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e; + Bs(n, "maxPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; - y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); - let c = C.computePool2DInfo(n.shape, s, a, u, i, p); + y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); + let c = w.computePool2DInfo(n.shape, s, a, u, i, p); if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - return Dt({ inputs: { x: n }, backend: t10 }); - let l = new Ms(c, "max", false); - return t10.runWebGLProgram(l, [n], n.dtype); + return Dt({ inputs: { x: n }, backend: t8 }); + let l = new Vs(c, "max", false); + return t8.runWebGLProgram(l, [n], n.dtype); } -var yF = { kernelName: zn, backendName: "webgl", kernelFunc: LJ }; -function BJ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new xu(l, "max", false); - return t10.runWebGLProgram(m, [n], n.dtype); +var AF = { kernelName: Wn, backendName: "webgl", kernelFunc: hee }; +function gee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "max", false); + return t8.runWebGLProgram(m, [n], n.dtype); } -var bF = { kernelName: ra, backendName: "webgl", kernelFunc: BJ }; -var ug = class { +var FF = { kernelName: aa, backendName: "webgl", kernelFunc: gee }; +var pg = class { constructor(e) { this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape; - let t10 = e.strideHeight, o = e.strideWidth, n = e.dilationHeight, s = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = s - 1 - e.padInfo.top, p = a - 1 - e.padInfo.left, u = s * a - 1; + let t8 = e.strideHeight, o = e.strideWidth, n = e.dilationHeight, s = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = s - 1 - e.padInfo.top, p = a - 1 - e.padInfo.left, u = s * a - 1; this.userCode = ` const ivec2 pads = ivec2(${i}, ${p}); @@ -22635,7 +22731,7 @@ var ug = class { float dotProd = 0.0; for (int wR = 0; wR < ${s}; wR += ${n}) { - float dyR = float(dyRCorner + wR) / ${t10}.0; + float dyR = float(dyRCorner + wR) / ${t8}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; @@ -22667,10 +22763,10 @@ var ug = class { `; } }; -var pg = class { +var cg = class { constructor(e) { this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape; - let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1; + let t8 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1; this.userCode = ` const ivec3 pads = ivec3(${l}, ${m}, ${d}); @@ -22691,7 +22787,7 @@ var pg = class { for (int wD = 0; wD < ${p}; wD += ${s}) { - float dyD = float(dyDCorner + wD) / ${t10}.0; + float dyD = float(dyDCorner + wD) / ${t8}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; @@ -22738,88 +22834,88 @@ var pg = class { `; } }; -function zJ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = C.computePool3DInfo(a.shape, i, p, l, u, c), d = new xu(m, "max", true), f = t10.runWebGLProgram(d, [a], a.dtype), h = new pg(m), g = t10.runWebGLProgram(h, [n, f], a.dtype); - return t10.disposeIntermediateTensorInfo(f), g; +function xee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new wu(m, "max", true), f = t8.runWebGLProgram(d, [a], a.dtype), h = new cg(m), g = t8.runWebGLProgram(h, [n, f], a.dtype); + return t8.disposeIntermediateTensorInfo(f), g; } -var CF = { kernelName: Li, backendName: "webgl", kernelFunc: zJ }; -function VJ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; - Ps([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = C.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new Ms(m, "max", d), h = t10.runWebGLProgram(f, [i], i.dtype), g = new ug(m), x = t10.runWebGLProgram(g, [n, h], i.dtype); - return t10.disposeIntermediateTensorInfo(h), x; +var PF = { kernelName: Ui, backendName: "webgl", kernelFunc: xee }; +function yee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; + Bs([s, a], "maxPoolGrad"); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new Vs(m, "max", d), h = t8.runWebGLProgram(f, [i], i.dtype), g = new pg(m), x = t8.runWebGLProgram(g, [n, h], i.dtype); + return t8.disposeIntermediateTensorInfo(h), x; } -var wF = { kernelName: Hp, backendName: "webgl", kernelFunc: VJ }; -function SF(r, e, t10, o) { - let n = new Ms(t10, "max", false), s = o.runWebGLProgram(n, [r], "float32"); - n = new Ms(t10, "max", true, true, e); +var OF = { kernelName: Wi, backendName: "webgl", kernelFunc: yee }; +function MF(r, e, t8, o) { + let n = new Vs(t8, "max", false), s = o.runWebGLProgram(n, [r], "float32"); + n = new Vs(t8, "max", true, true, e); let a = o.runWebGLProgram(n, [r], "float32"); return [s, a]; } -var IF = { kernelName: Bi, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10; +var LF = { kernelName: ia, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t8; y.assert(o.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`); let u = [1, 1]; - y.assert(C.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); - let c = C.computePool2DInfo(o.shape, n, s, u, a), [l, m] = SF(o, i, c, p); + y.assert(w.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); + let c = w.computePool2DInfo(o.shape, n, s, u, a), [l, m] = MF(o, i, c, p); return [l, m]; } }; -function vF(r, e, t10, o) { - let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = qr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: t10 }, backend: o }); +function BF(r, e, t8, o) { + let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Yr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: t8 }, backend: o }); return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u; } -var kF = { kernelName: Vn, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { x: o } = r, { keepDims: n, axis: s } = e, a = t10, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = C.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o; +var zF = { kernelName: Un, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { x: o } = r, { keepDims: n, axis: s } = e, a = t8, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o; if (l) { if (m) { let S = a.texData.get(f.dataId).values, k = new Array(i); for (let R = 0; R < k.length; R++) k[R] = o.shape[c[R]]; - let _ = yp(S, o.shape, o.dtype, c, k); + let _ = Sp(S, o.shape, o.dtype, c, k); f = a.makeTensorInfo(k, o.dtype); let E = a.texData.get(f.dataId); E.values = _; } else - f = gu(o, c, a); - d.push(f), u = C.getInnerMostAxes(u.length, i); + f = Cu(o, c, a); + d.push(f), u = w.getInnerMostAxes(u.length, i); } - C.assertAxesAreInnerMostDims("sum", u, i); - let [h, g] = C.computeOutAndReduceShapes(f.shape, u), x = h; - n && (x = C.expandShapeToKeepDim(h, p)); - let b = vF(f, g, x, a); - for (let w of d) - a.disposeIntermediateTensorInfo(w); + w.assertAxesAreInnerMostDims("sum", u, i); + let [h, g] = w.computeOutAndReduceShapes(f.shape, u), x = h; + n && (x = w.expandShapeToKeepDim(h, p)); + let b = BF(f, g, x, a); + for (let C of d) + a.disposeIntermediateTensorInfo(C); return b; } }; -function WJ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = n; - c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = C.getInnerMostAxes(u.length, n.shape.length)), C.assertAxesAreInnerMostDims("min", u, i); - let [m, d] = C.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = qr(h, h.dtype, "min", t10), x; +function bee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; + c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", u, i); + let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "min", t8), x; if (a) { - let b = C.expandShapeToKeepDim(m, p); - x = te({ inputs: { x: g }, backend: t10, attrs: { shape: b } }); + let b = w.expandShapeToKeepDim(m, p); + x = te({ inputs: { x: g }, backend: t8, attrs: { shape: b } }); } else - x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); - return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; + x = te({ inputs: { x: g }, backend: t8, attrs: { shape: m } }); + return t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), c != null && t8.disposeIntermediateTensorInfo(l), x; } -var NF = { kernelName: Wn, backendName: "webgl", kernelFunc: WJ }; -var UJ = Mc + ` +var VF = { kernelName: Gn, backendName: "webgl", kernelFunc: bee }; +var Cee = Lc + ` return min(a, b); `; -var GJ = ` +var wee = ` vec4 result = vec4(min(a, b)); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Kr + ` + ` + Xr + ` return result; `; -var HJ = nt({ opSnippet: UJ, packedOpSnippet: GJ, cpuKernelImpl: $R }); -var TF = { kernelName: Un, backendName: "webgl", kernelFunc: HJ }; -var cg = class { - constructor(e, t10, o) { - this.variableNames = ["x"], this.outputShape = t10.map((c, l) => c[0] + e[l] + c[1]); - let n = e.length, s = Re(n), a = t10.map((c) => c[0]).join(","), i = t10.map((c, l) => c[0] + e[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1; +var See = nt({ opSnippet: Cee, packedOpSnippet: wee, cpuKernelImpl: UR }); +var WF = { kernelName: Hn, backendName: "webgl", kernelFunc: See }; +var lg = class { + constructor(e, t8, o) { + this.variableNames = ["x"], this.outputShape = t8.map((c, l) => c[0] + e[l] + c[1]); + let n = e.length, s = Re(n), a = t8.map((c) => c[0]).join(","), i = t8.map((c, l) => c[0] + e[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1; if (n === 1) { this.userCode = ` int start = ${a}; @@ -22856,10 +22952,10 @@ var cg = class { `; } }; -var lg = class { - constructor(e, t10, o) { - this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t10.map((f, h) => f[0] + e[h] + f[1]); - let n = e.length, s = Re(n), a = t10.map((f) => f[0]).join(","), i = t10.map((f, h) => f[0] + e[h]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = ""; +var mg = class { + constructor(e, t8, o) { + this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t8.map((f, h) => f[0] + e[h] + f[1]); + let n = e.length, s = Re(n), a = t8.map((f) => f[0]).join(","), i = t8.map((f, h) => f[0] + e[h]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = ""; if (n === 1) { let f = ` ${s} source = rc; @@ -22926,23 +23022,23 @@ var lg = class { `; } }; -var KJ = ({ inputs: r, backend: e, attrs: t10 }) => { - let { x: o } = r, { paddings: n, mode: s } = t10, a = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new lg(o.shape, n, s) : new cg(o.shape, n, s); +var Iee = ({ inputs: r, backend: e, attrs: t8 }) => { + let { x: o } = r, { paddings: n, mode: s } = t8, a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new mg(o.shape, n, s) : new lg(o.shape, n, s); return e.runWebGLProgram(a, [o], o.dtype); }; -var _F = { kernelName: Gn, backendName: "webgl", kernelFunc: KJ }; -var qJ = `if (b == 0.0) return NAN; +var UF = { kernelName: Kn, backendName: "webgl", kernelFunc: Iee }; +var vee = `if (b == 0.0) return NAN; return mod(a, b);`; -var jJ = ` +var kee = ` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - ` + Kr + ` + ` + Xr + ` return result; `; -var XJ = nt({ opSnippet: qJ, packedOpSnippet: jJ }); -var $F = { kernelName: Ga, backendName: "webgl", kernelFunc: XJ }; -var mg = class { - constructor(e, t10, o) { +var Nee = nt({ opSnippet: vee, packedOpSnippet: kee }); +var GF = { kernelName: qn, backendName: "webgl", kernelFunc: Nee }; +var dg = class { + constructor(e, t8, o) { this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, o], this.userCode = ` void main() { ivec2 coords = getOutputCoords(); @@ -22951,7 +23047,7 @@ var mg = class { float r = random(seed); float cdf = 0.0; - for (int i = 0; i < ${t10 - 1}; i++) { + for (int i = 0; i < ${t8 - 1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { @@ -22961,17 +23057,17 @@ var mg = class { } // If no other event happened, last event happened. - setOutput(float(${t10 - 1})); + setOutput(float(${t8 - 1})); } `; } }; -var YJ = ` +var Tee = ` if (a == b) { return 1.0; }; return a / b;`; -var QJ = ` +var _ee = ` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -22990,25 +23086,25 @@ var QJ = ` return result; `; -var cv = nt({ opSnippet: YJ, packedOpSnippet: QJ, checkOutOfBounds: true }); -var EF = { kernelName: dn, backendName: "webgl", kernelFunc: cv }; -var RF = "return a - b;"; -var lv = nt({ opSnippet: RF, packedOpSnippet: RF, supportsComplex: true, cpuKernelImpl: YR }); -var DF = { kernelName: Is, backendName: "webgl", kernelFunc: lv }; -function mv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = pv({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = C.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = lv({ inputs: { a: n, b: u }, backend: t10 }), l = av({ inputs: { x: c }, backend: t10 }), m = bp({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = cv({ inputs: { a: l, b: d }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f; +var bv = nt({ opSnippet: Tee, packedOpSnippet: _ee, checkOutOfBounds: true }); +var HF = { kernelName: fn, backendName: "webgl", kernelFunc: bv }; +var KF = "return a - b;"; +var Cv = nt({ opSnippet: KF, packedOpSnippet: KF, supportsComplex: true, cpuKernelImpl: cD }); +var qF = { kernelName: Ts, backendName: "webgl", kernelFunc: Cv }; +function wv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = yv({ inputs: { x: n }, backend: t8, attrs: { reductionIndices: a, keepDims: false } }), p = w.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: t8, attrs: { shape: p } }), c = Cv({ inputs: { a: n, b: u }, backend: t8 }), l = hv({ inputs: { x: c }, backend: t8 }), m = Ip({ inputs: { x: l }, backend: t8, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: t8, attrs: { shape: p } }), f = bv({ inputs: { a: l, b: d }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(c), t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), f; } -var AF = { kernelName: bs, backendName: "webgl", kernelFunc: mv }; -function ZJ(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : mv({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new mg(u, c, s), m = [[a]], d = t10.runWebGLProgram(l, [p], "int32", m); - return i || t10.disposeIntermediateTensorInfo(p), d; +var jF = { kernelName: Is, backendName: "webgl", kernelFunc: wv }; +function $ee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : wv({ inputs: { logits: n }, backend: t8, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new dg(u, c, s), m = [[a]], d = t8.runWebGLProgram(l, [p], "int32", m); + return i || t8.disposeIntermediateTensorInfo(p), d; } -var FF = { kernelName: Hn, backendName: "webgl", kernelFunc: ZJ }; -var JJ = Ut + ` +var XF = { kernelName: jn, backendName: "webgl", kernelFunc: $ee }; +var Eee = Ut + ` return -x; `; -var eee = ` +var Ree = ` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -23019,40 +23115,40 @@ var eee = ` return result; `; -function tee(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; - if (t10.shouldExecuteOnCPU([o])) { - let s = t10.texData.get(o.dataId), [a, i] = RR(s.values, o.shape, o.dtype); - return t10.makeTensorInfo(i, o.dtype, a); +function Dee(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; + if (t8.shouldExecuteOnCPU([o])) { + let s = t8.texData.get(o.dataId), [a, i] = HR(s.values, o.shape, o.dtype); + return t8.makeTensorInfo(i, o.dtype, a); } let n; - return P().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, eee) : n = new tr(o.shape, JJ), t10.runWebGLProgram(n, [o], o.dtype); + return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, Ree) : n = new rr(o.shape, Eee), t8.runWebGLProgram(n, [o], o.dtype); } -var PF = { kernelName: oa, backendName: "webgl", kernelFunc: tee }; -var ree = Wt.nonMaxSuppressionV3Impl; -function oee(r) { - C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = ree(u, c, a, i, p); - return t10.makeTensorInfo([l.length], "int32", new Int32Array(l)); +var YF = { kernelName: ua, backendName: "webgl", kernelFunc: Dee }; +var Aee = Wt.nonMaxSuppressionV3Impl; +function Fee(r) { + w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t8.readSync(n.dataId), c = t8.readSync(s.dataId), { selectedIndices: l } = Aee(u, c, a, i, p); + return t8.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var OF = { kernelName: jn, backendName: "webgl", kernelFunc: oee }; -var nee = Wt.nonMaxSuppressionV4Impl; -function see(r) { - C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = nee(c, l, a, i, p, u); - return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))]; +var QF = { kernelName: Qn, backendName: "webgl", kernelFunc: Fee }; +var Pee = Wt.nonMaxSuppressionV4Impl; +function Oee(r) { + w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t8.readSync(n.dataId), l = t8.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = Pee(c, l, a, i, p, u); + return [t8.makeTensorInfo([m.length], "int32", new Int32Array(m)), t8.makeTensorInfo([], "int32", new Int32Array([d]))]; } -var MF = { kernelName: Ha, backendName: "webgl", kernelFunc: see }; -var aee = Wt.nonMaxSuppressionV5Impl; -function iee(r) { - C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = aee(c, l, m, d, f, h); - return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))]; +var ZF = { kernelName: Xa, backendName: "webgl", kernelFunc: Oee }; +var Mee = Wt.nonMaxSuppressionV5Impl; +function Lee(r) { + w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t8.readSync(n.dataId), l = t8.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Mee(c, l, m, d, f, h); + return [t8.makeTensorInfo([g.length], "int32", new Int32Array(g)), t8.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var LF = { kernelName: Xn, backendName: "webgl", kernelFunc: iee }; -var dg = class { - constructor(e, t10, o, n) { - this.variableNames = ["indices"], this.outputShape = [e, t10], this.userCode = ` +var JF = { kernelName: Zn, backendName: "webgl", kernelFunc: Lee }; +var fg = class { + constructor(e, t8, o, n) { + this.variableNames = ["indices"], this.outputShape = [e, t8], this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); @@ -23062,52 +23158,52 @@ var dg = class { `; } }; -var uee = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new dg(u, a, i, p), l = te({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = t10.runWebGLProgram(c, [l], s); - t10.disposeIntermediateTensorInfo(l); - let d = [...n.shape, a], f = te({ inputs: { x: m }, backend: t10, attrs: { shape: d } }); - return t10.disposeIntermediateTensorInfo(m), f; +var Bee = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new fg(u, a, i, p), l = te({ inputs: { x: n }, backend: t8, attrs: { shape: [u] } }), m = t8.runWebGLProgram(c, [l], s); + t8.disposeIntermediateTensorInfo(l); + let d = [...n.shape, a], f = te({ inputs: { x: m }, backend: t8, attrs: { shape: d } }); + return t8.disposeIntermediateTensorInfo(m), f; }; -var BF = { kernelName: Yn, backendName: "webgl", kernelFunc: uee }; +var e3 = { kernelName: Jn, backendName: "webgl", kernelFunc: Bee }; function rm(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; + let { inputs: e, backend: t8 } = r, { x: o } = e; if (o.dtype === "complex64") { - let n = gi({ inputs: { input: o }, backend: t10 }), s = rm({ inputs: { x: n }, backend: t10 }), a = wp({ inputs: { input: o }, backend: t10 }), i = rm({ inputs: { x: a }, backend: t10 }), p = Fr({ inputs: { real: s, imag: i }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; + let n = bi({ inputs: { input: o }, backend: t8 }), s = rm({ inputs: { x: n }, backend: t8 }), a = kp({ inputs: { input: o }, backend: t8 }), i = rm({ inputs: { x: a }, backend: t8 }), p = Or({ inputs: { real: s, imag: i }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p; } else - return xi({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 }); + return Ci({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t8 }); } -var zF = { kernelName: fa, backendName: "webgl", kernelFunc: rm }; -function VF(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; +var t3 = { kernelName: ba, backendName: "webgl", kernelFunc: rm }; +function r3(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("onesLike is not supported under string dtype"); if (o.dtype === "complex64") { - let n = gi({ inputs: { input: o }, backend: t10 }), s = VF({ inputs: { x: n }, backend: t10 }), a = wp({ inputs: { input: o }, backend: t10 }), i = rm({ inputs: { x: a }, backend: t10 }), p = Fr({ inputs: { real: s, imag: i }, backend: t10 }); - return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; + let n = bi({ inputs: { input: o }, backend: t8 }), s = r3({ inputs: { x: n }, backend: t8 }), a = kp({ inputs: { input: o }, backend: t8 }), i = rm({ inputs: { x: a }, backend: t8 }), p = Or({ inputs: { real: s, imag: i }, backend: t8 }); + return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p; } else - return xi({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 }); + return Ci({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t8 }); } -var WF = { kernelName: na, backendName: "webgl", kernelFunc: VF }; -function pee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; +var o3 = { kernelName: pa, backendName: "webgl", kernelFunc: r3 }; +function zee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return Qh({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); + return Zh({ inputs: { input: e[0] }, backend: t8, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = Qh({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); + let l = Zh({ inputs: { input: c }, backend: t8, attrs: { dim: n } }); return i.push(l), l; - }), u = sv({ inputs: p, backend: t10, attrs: { axis: n } }); - return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u; + }), u = fv({ inputs: p, backend: t8, attrs: { axis: n } }); + return i.forEach((c) => t8.disposeIntermediateTensorInfo(c)), u; } -var UF = { kernelName: sa, backendName: "webgl", kernelFunc: pee }; -var fg = class { - constructor(e, t10, o) { - this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((u, c) => u[0] + e[c] + u[1]); - let n = e.length, s = Re(n), a = t10.map((u) => u[0]).join(","), i = t10.map((u, c) => u[0] + e[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n); +var n3 = { kernelName: ca, backendName: "webgl", kernelFunc: zee }; +var hg = class { + constructor(e, t8, o) { + this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t8.map((u, c) => u[0] + e[c] + u[1]); + let n = e.length, s = Re(n), a = t8.map((u) => u[0]).join(","), i = t8.map((u, c) => u[0] + e[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n); if (n === 1) { this.userCode = ` int start = ${a}; @@ -23140,10 +23236,10 @@ var fg = class { `; } }; -var hg = class { - constructor(e, t10, o) { - this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((h, g) => h[0] + e[g] + h[1]); - let n = e.length, s = Re(n), a = t10.map((h) => h[0]).join(","), i = t10.map((h, g) => h[0] + e[g]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1; +var gg = class { + constructor(e, t8, o) { + this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t8.map((h, g) => h[0] + e[g] + h[1]); + let n = e.length, s = Re(n), a = t8.map((h) => h[0]).join(","), i = t8.map((h, g) => h[0] + e[g]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1; if(${c}) { `, n === 1 ? "" : `} rc = outputLoc; @@ -23173,17 +23269,17 @@ var hg = class { `; } }; -var dv = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; +var Sv = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; if (y.sizeFromShape(n.shape) === 0) { let u = s.map((c, l) => c[0] + n.shape[l] + c[1]); - return xi({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } }); + return Ci({ backend: t8, attrs: { shape: u, value: a, dtype: n.dtype } }); } - let i = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new hg(n.shape, s, a) : new fg(n.shape, s, a), p = [[a]]; - return t10.runWebGLProgram(i, [n], n.dtype, p); + let i = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new gg(n.shape, s, a) : new hg(n.shape, s, a), p = [[a]]; + return t8.runWebGLProgram(i, [n], n.dtype, p); }; -var GF = { kernelName: Qn, backendName: "webgl", kernelFunc: dv }; -var cee = ` +var s3 = { kernelName: es, backendName: "webgl", kernelFunc: Sv }; +var Vee = ` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -23193,7 +23289,7 @@ var cee = ` return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); `; -var lee = ` +var Wee = ` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -23209,57 +23305,57 @@ var lee = ` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - ` + Kr + ` + ` + Xr + ` return result; `; -var mee = nt({ opSnippet: cee, packedOpSnippet: lee }); -var HF = { kernelName: Zn, backendName: "webgl", kernelFunc: mee }; -function dee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = C.getAxesPermutation(c, i), m = n; - l != null && (m = bt({ inputs: { x: n }, backend: t10, attrs: { perm: l } }), c = C.getInnerMostAxes(c.length, i), p.push(m)), C.assertAxesAreInnerMostDims("prod", c, i); +var Uee = nt({ opSnippet: Vee, packedOpSnippet: Wee }); +var a3 = { kernelName: ts, backendName: "webgl", kernelFunc: Uee }; +function Gee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = w.getAxesPermutation(c, i), m = n; + l != null && (m = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: l } }), c = w.getInnerMostAxes(c.length, i), p.push(m)), w.assertAxesAreInnerMostDims("prod", c, i); let d; - if (t10.shouldExecuteOnCPU([m])) { - let f = t10.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = AR(m.shape, m.dtype, f, c); - d = t10.makeTensorInfo(g, x, h); + if (t8.shouldExecuteOnCPU([m])) { + let f = t8.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = qR(m.shape, m.dtype, f, c); + d = t8.makeTensorInfo(g, x, h); } else { - let [f, h] = C.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: t10, attrs: { shape: [-1, g] } }), b = Za(n.dtype), w = qr(x, b, "prod", t10); - d = te({ inputs: { x: w }, backend: t10, attrs: { shape: f } }), p.push(x), p.push(w); + let [f, h] = w.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: t8, attrs: { shape: [-1, g] } }), b = ti(n.dtype), C = Yr(x, b, "prod", t8); + d = te({ inputs: { x: C }, backend: t8, attrs: { shape: f } }), p.push(x), p.push(C); } if (a) { p.push(d); - let f = C.expandShapeToKeepDim(d.shape, u); - d = te({ inputs: { x: d }, backend: t10, attrs: { shape: f } }); + let f = w.expandShapeToKeepDim(d.shape, u); + d = te({ inputs: { x: d }, backend: t8, attrs: { shape: f } }); } - return p.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d; + return p.forEach((f) => t8.disposeIntermediateTensorInfo(f)), d; } -var KF = { kernelName: es, backendName: "webgl", kernelFunc: dee }; -function fee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.readSync(x.dataId)), u = n.map((x) => x.shape), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), [m, d, f] = FR(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d); +var i3 = { kernelName: os, backendName: "webgl", kernelFunc: Gee }; +function Hee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t8.readSync(x.dataId)), u = n.map((x) => x.shape), c = t8.readSync(s.dataId), l = t8.readSync(a.dataId), [m, d, f] = jR(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t8.makeTensorInfo([x.length], "int32", x)), g = t8.makeTensorInfo(f, s.dtype, d); return h.concat([g]); } -var qF = { kernelName: Kp, backendName: "webgl", kernelFunc: fee }; -function hee(r) { - let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = PR(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c); +var u3 = { kernelName: jp, backendName: "webgl", kernelFunc: Hee }; +function Kee(r) { + let { inputs: e, backend: t8 } = r, { starts: o, limits: n, deltas: s } = e, a = t8.readSync(o.dataId), i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c] = XR(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t8.makeTensorInfo([u.length], "int32", u), m = t8.makeTensorInfo([c.length], o.dtype, c); return [l, m]; } -var jF = { kernelName: qp, backendName: "webgl", kernelFunc: hee }; -function gee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), m = i.map((g) => t10.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = OR(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); - return t10.makeTensorInfo(f, s.dtype, h); +var p3 = { kernelName: Xp, backendName: "webgl", kernelFunc: Kee }; +function qee(r) { + let { inputs: e, backend: t8, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t8.readSync(n.dataId), c = t8.readSync(s.dataId), l = t8.readSync(a.dataId), m = i.map((g) => t8.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = YR(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); + return t8.makeTensorInfo(f, s.dtype, h); } -var XF = { kernelName: jp, backendName: "webgl", kernelFunc: gee }; -var fv = (r) => { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = MR(o, n, s, a); +var c3 = { kernelName: Yp, backendName: "webgl", kernelFunc: qee }; +var Iv = (r) => { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, step: s, dtype: a } = t8, i = QR(o, n, s, a); return e.makeTensorInfo([i.length], a, i); }; -var YF = { kernelName: aa, backendName: "webgl", kernelFunc: fv }; -var xee = "return 1.0 / x;"; -var yee = ge({ opSnippet: xee }); -var QF = { kernelName: ts, backendName: "webgl", kernelFunc: yee }; -var bee = Ut + ` +var l3 = { kernelName: la, backendName: "webgl", kernelFunc: Iv }; +var jee = "return 1.0 / x;"; +var Xee = xe({ opSnippet: jee }); +var m3 = { kernelName: ns, backendName: "webgl", kernelFunc: Xee }; +var Yee = Ut + ` return (x < 0.0) ? 0.0 : x; `; -var Cee = ` +var Qee = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -23270,12 +23366,12 @@ var Cee = ` return result; `; -var wee = ge({ opSnippet: bee, packedOpSnippet: Cee }); -var ZF = { kernelName: rs, backendName: "webgl", kernelFunc: wee }; -var See = Ut + ` +var Zee = xe({ opSnippet: Yee, packedOpSnippet: Qee }); +var d3 = { kernelName: ss, backendName: "webgl", kernelFunc: Zee }; +var Jee = Ut + ` return (x < 0.0) ? 0.0 : min(6.0, x); `; -var Iee = ` +var ete = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -23286,14 +23382,14 @@ var Iee = ` return result; `; -var vee = ge({ opSnippet: See, packedOpSnippet: Iee }); -var JF = { kernelName: ss, backendName: "webgl", kernelFunc: vee }; -var gg = class { - constructor(e, t10, o, n, s) { +var tte = xe({ opSnippet: Jee, packedOpSnippet: ete }); +var f3 = { kernelName: us, backendName: "webgl", kernelFunc: tte }; +var xg = class { + constructor(e, t8, o, n, s) { this.variableNames = ["A"], this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t10, o, u]; - let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m; + this.outputShape = [a, t8, o, u]; + let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, n && o > 1 ? o - 1 : o], m; s ? m = "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)" : m = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0] / l[0]}, @@ -23330,12 +23426,12 @@ var gg = class { `; } }; -var xg = class { - constructor(e, t10, o, n, s) { +var yg = class { + constructor(e, t8, o, n, s) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t10, o, u]; - let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m; + this.outputShape = [a, t8, o, u]; + let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, n && o > 1 ? o - 1 : o], m; s ? m = "(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)" : m = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0] / l[0]}, @@ -23416,15 +23512,15 @@ var xg = class { `; } }; -function kee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = P().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new xg(n.shape, p, u, s, a) : new gg(n.shape, p, u, s, a); - return t10.runWebGLProgram(c, [n], "float32"); +function rte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new yg(n.shape, p, u, s, a) : new xg(n.shape, p, u, s, a); + return t8.runWebGLProgram(c, [n], "float32"); } -var e3 = { kernelName: ns, backendName: "webgl", kernelFunc: kee }; -var yg = class { - constructor(e, t10, o) { - this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10; - let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; +var h3 = { kernelName: is, backendName: "webgl", kernelFunc: rte }; +var bg = class { + constructor(e, t8, o) { + this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t8; + let [, n, s] = t8, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -23509,17 +23605,17 @@ var yg = class { `; } }; -function Nee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new yg(s.shape, n.shape, a); - return t10.runWebGLProgram(i, [s], s.dtype); +function ote(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new bg(s.shape, n.shape, a); + return t8.runWebGLProgram(i, [s], s.dtype); } -var t3 = { kernelName: qa, backendName: "webgl", kernelFunc: Nee }; -var bg = class { - constructor(e, t10, o, n, s) { +var g3 = { kernelName: Qa, backendName: "webgl", kernelFunc: ote }; +var Cg = class { + constructor(e, t8, o, n, s) { this.variableNames = ["A"], this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t10, o, u]; - let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; + this.outputShape = [a, t8, o, u]; + let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; s ? d = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : d = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0] / l[0]}, @@ -23545,12 +23641,12 @@ var bg = class { `; } }; -var Cg = class { - constructor(e, t10, o, n, s) { +var wg = class { + constructor(e, t8, o, n, s) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t10, o, u]; - let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; + this.outputShape = [a, t8, o, u]; + let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; s ? d = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : d = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0] / l[0]}, @@ -23595,15 +23691,15 @@ var Cg = class { `; } }; -function Tee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = P().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Cg(n.shape, p, u, s, a) : new bg(n.shape, p, u, s, a); - return t10.runWebGLProgram(c, [n], n.dtype); +function nte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new wg(n.shape, p, u, s, a) : new Cg(n.shape, p, u, s, a); + return t8.runWebGLProgram(c, [n], n.dtype); } -var r3 = { kernelName: os, backendName: "webgl", kernelFunc: Tee }; -var wg = class { - constructor(e, t10, o) { - this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10; - let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; +var x3 = { kernelName: as, backendName: "webgl", kernelFunc: nte }; +var Sg = class { + constructor(e, t8, o) { + this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t8; + let [, n, s] = t8, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -23677,13 +23773,13 @@ var wg = class { `; } }; -function _ee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new wg(s.shape, n.shape, a); - return t10.runWebGLProgram(i, [s], s.dtype); +function ste(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new Sg(s.shape, n.shape, a); + return t8.runWebGLProgram(i, [s], s.dtype); } -var o3 = { kernelName: Ka, backendName: "webgl", kernelFunc: _ee }; -var Sg = class { - constructor(e, t10) { +var y3 = { kernelName: Ya, backendName: "webgl", kernelFunc: ste }; +var Ig = class { + constructor(e, t8) { this.variableNames = ["x"]; let o = e.length; if (o > 4) @@ -23697,7 +23793,7 @@ var Sg = class { `; return; } - let n = (i) => t10.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, s = e.map((i, p) => n(p)).join(","), a = Re(o); + let n = (i) => t8.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, s = e.map((i, p) => n(p)).join(","), a = Re(o); this.userCode = ` void main() { ${a} coords = getOutputCoords(); @@ -23706,8 +23802,8 @@ var Sg = class { `; } }; -var Ig = class { - constructor(e, t10) { +var vg = class { + constructor(e, t8) { this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true; let o = e.length; if (o > 4) @@ -23756,30 +23852,30 @@ var Ig = class { return f[o - 1] = "(" + f[o - 1] + " + 1)", f[o - 2] = "(" + f[o - 2] + " + 1)", m(f); } function m(f) { - let h = e.map((b, w) => d(w, f)), g = h.join(","), x = h.slice(-2).join(","); + let h = e.map((b, C) => d(C, f)), g = h.join(","), x = h.slice(-2).join(","); return `getChannel(getX(${g}), vec2(${x}))`; } function d(f, h) { - return t10.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`; + return t8.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`; } } }; -function $ee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape); +function ate(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape); if (a === 0) - return Dt({ inputs: { x: n }, backend: t10 }); - let p = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Ig(n.shape, i) : new Sg(n.shape, i); - return t10.runWebGLProgram(p, [n], n.dtype); + return Dt({ inputs: { x: n }, backend: t8 }); + let p = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new vg(n.shape, i) : new Ig(n.shape, i); + return t8.runWebGLProgram(p, [n], n.dtype); } -var n3 = { kernelName: as, backendName: "webgl", kernelFunc: $ee }; -var vg = class { - constructor(e, t10) { +var b3 = { kernelName: ps, backendName: "webgl", kernelFunc: ate }; +var kg = class { + constructor(e, t8) { this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }]; let o = e[1], n = e[2]; this.outputShape = e; let s = ""; - typeof t10 == "number" ? s = `float outputValue = ${t10.toFixed(2)};` : s = ` - vec3 fill = vec3(${t10.join(",")}); + typeof t8 == "number" ? s = `float outputValue = ${t8.toFixed(2)};` : s = ` + vec3 fill = vec3(${t8.join(",")}); float outputValue = fill[coords[3]];`, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -23800,11 +23896,11 @@ var vg = class { `; } }; -var s3 = { kernelName: _s, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new vg(o.shape, s), [u, c] = C.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]]; +var C3 = { kernelName: Ds, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t8, p = new kg(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]]; return i.runWebGLProgram(p, [o], o.dtype, l); } }; -var Eee = ` +var ite = ` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -23820,13 +23916,13 @@ var Eee = ` } } `; -var Ree = ge({ opSnippet: Eee }); -var a3 = { kernelName: is, backendName: "webgl", kernelFunc: Ree }; -var Dee = "return inversesqrt(x);"; -var Aee = ge({ opSnippet: Dee, cpuKernelImpl: LR }); -var i3 = { kernelName: us, backendName: "webgl", kernelFunc: Aee }; -var yu = class { - constructor(e, t10, o, n, s, a, i = true, p = false) { +var ute = xe({ opSnippet: ite }); +var w3 = { kernelName: cs, backendName: "webgl", kernelFunc: ute }; +var pte = "return inversesqrt(x);"; +var cte = xe({ opSnippet: pte, cpuKernelImpl: ZR }); +var S3 = { kernelName: ls, backendName: "webgl", kernelFunc: cte }; +var Su = class { + constructor(e, t8, o, n, s, a, i = true, p = false) { this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a; let u = Re(s.length), c = Re(a.length), l = ""; o === 1 ? l = "i" : o === 2 && (l = "i, j"); @@ -23834,7 +23930,7 @@ var yu = class { n === 1 ? d = "i" : n === 2 && (d = "i, coords[1]"); let f = `getUpdates(${d})`, h = ""; p && (h = "coords[0], coords[1]"); - let g = `getDefaultValue(${h})`, x = t10 > 1 ? "strides[j]" : "strides"; + let g = `getDefaultValue(${h})`, x = t8 > 1 ? "strides[j]" : "strides"; this.userCode = ` ${u} strides = ${u}(${s}); @@ -23844,7 +23940,7 @@ var yu = class { bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; - for (int j = 0; j < ${t10}; j++) { + for (int j = 0; j < ${t8}; j++) { int index = round(${m}); flattenedIndex += index * ${x}; } @@ -23858,8 +23954,8 @@ var yu = class { `; } }; -var kg = class { - constructor(e, t10, o, n, s, a, i = true, p = false) { +var Ng = class { + constructor(e, t8, o, n, s, a, i = true, p = false) { this.variableNames = ["updates", "indices", "defaultValue"], this.packedInputs = true, this.packedOutput = true, this.outputShape = a; let u = Re(s.length), c = Re(a.length), l = ""; o === 1 ? l = "i" : o === 2 && (l = "i, j"); @@ -23867,7 +23963,7 @@ var kg = class { n === 1 ? d = "i" : n === 2 && (d = "i, coords[1]"); let f = `getUpdates(${d})`, h = ""; p && (h = "coords[0], coords[1]"); - let g = `getDefaultValue(${h})`, x = t10 > 1 ? "strides[j]" : "strides", b = t10 > 1 ? "strides[j + 1]" : "strides"; + let g = `getDefaultValue(${h})`, x = t8 > 1 ? "strides[j]" : "strides", b = t8 > 1 ? "strides[j + 1]" : "strides"; this.userCode = ` ${u} strides = ${u}(${s}); @@ -23877,10 +23973,10 @@ var kg = class { vec4 found = vec4(0.); for (int i = 0; i < ${e}; i+=2) { ivec2 flattenedIndex = ivec2(0); - for (int j = 0; j < ${t10}; j+=2) { + for (int j = 0; j < ${t8}; j+=2) { ivec4 index = round(${m}); flattenedIndex += index.xz * ${x}; - if (j + 1 < ${t10}) { + if (j + 1 < ${t8}) { flattenedIndex += index.yw * ${b}; } } @@ -23908,20 +24004,20 @@ var kg = class { `; } }; -function Fee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(s, n, a), m = [l / u, u]; +function lte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u]; if (l === 0) - return t10.makeTensorInfo(a, n.dtype); - let d = te({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = te({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = t10.makeTensorInfo([], "float32", new Float32Array([0])), g; - P().getBool("WEBGL_PACK") ? g = new kg(p, i, d.shape.length, f.shape.length, c, m) : g = new yu(p, i, d.shape.length, f.shape.length, c, m); - let x = t10.runWebGLProgram(g, [f, d, h], f.dtype), b = te({ inputs: { x }, backend: t10, attrs: { shape: a } }); - return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(h), b; + return t8.makeTensorInfo(a, n.dtype); + let d = te({ inputs: { x: n }, backend: t8, attrs: { shape: [p, i] } }), f = te({ inputs: { x: s }, backend: t8, attrs: { shape: [p, u] } }), h = t8.makeTensorInfo([], "float32", new Float32Array([0])), g; + A().getBool("WEBGL_PACK") ? g = new Ng(p, i, d.shape.length, f.shape.length, c, m) : g = new Su(p, i, d.shape.length, f.shape.length, c, m); + let x = t8.runWebGLProgram(g, [f, d, h], f.dtype), b = te({ inputs: { x }, backend: t8, attrs: { shape: a } }); + return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(x), t8.disposeIntermediateTensorInfo(h), b; } -var u3 = { kernelName: ps, backendName: "webgl", kernelFunc: Fee }; -var Ng = class { - constructor(e, t10, o, n) { +var I3 = { kernelName: ms, backendName: "webgl", kernelFunc: lte }; +var Tg = class { + constructor(e, t8, o, n) { this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [e, o]; - let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t10 + 1))}; ++i) { if (left >= right) break;`, i = P().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<="; + let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t8 + 1))}; ++i) { if (left >= right) break;`, i = A().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<="; this.userCode = ` int findBound(int batch, float value) { int left = 0; @@ -23950,14 +24046,14 @@ var Ng = class { `; } }; -function Pee(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new Ng(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]]; - return t10.runWebGLProgram(i, [n, s], "int32", p); +function mte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new Tg(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]]; + return t8.runWebGLProgram(i, [n, s], "int32", p); } -var p3 = { kernelName: ls, backendName: "webgl", kernelFunc: Pee }; -var Tg = class { - constructor(e, t10, o) { - this.variableNames = ["c", "a", "b"], this.outputShape = t10; +var v3 = { kernelName: fs, backendName: "webgl", kernelFunc: mte }; +var _g = class { + constructor(e, t8, o) { + this.variableNames = ["c", "a", "b"], this.outputShape = t8; let n, s; if (o > 4) throw Error(`Where for rank ${o} is not yet supported`); @@ -23965,7 +24061,7 @@ var Tg = class { s = "resRC", n = "resRC"; else { let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], p = [], u = []; - for (let c = 0; c < t10.length; c++) + for (let c = 0; c < t8.length; c++) u.push(`${i[c]}`), c < e && p.push(`${i[c]}`); n = p.join(), s = u.join(); } @@ -23983,24 +24079,24 @@ var Tg = class { `; } }; -function Oee(r) { - let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new Tg(o.shape.length, n.shape, n.shape.length); - return t10.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype)); +function dte(r) { + let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e, a = new _g(o.shape.length, n.shape, n.shape.length); + return t8.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype)); } -var c3 = { kernelName: ua, backendName: "webgl", kernelFunc: Oee }; -var Mee = ` +var k3 = { kernelName: da, backendName: "webgl", kernelFunc: dte }; +var fte = ` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 - float scaleAlpha = ${C.SELU_SCALEALPHA}; - float scale = ${C.SELU_SCALE}; + float scaleAlpha = ${w.SELU_SCALEALPHA}; + float scale = ${w.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `; -var Lee = ge({ opSnippet: Mee }); -var l3 = { kernelName: ms, backendName: "webgl", kernelFunc: Lee }; -var Bee = Do + ` +var hte = xe({ opSnippet: fte }); +var N3 = { kernelName: hs, backendName: "webgl", kernelFunc: hte }; +var gte = Fo + ` return 1.0 / (1.0 + exp(-1.0 * x)); `; -var zee = ` +var xte = ` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -24011,32 +24107,32 @@ var zee = ` return result; `; -var Vee = ge({ opSnippet: Bee, packedOpSnippet: zee, cpuKernelImpl: zR }); -var m3 = { kernelName: hs, backendName: "webgl", kernelFunc: Vee }; -var Wee = ` +var yte = xe({ opSnippet: gte, packedOpSnippet: xte, cpuKernelImpl: eD }); +var T3 = { kernelName: bs, backendName: "webgl", kernelFunc: yte }; +var bte = ` if (isnan(x)) { return 0.0; } return sign(x); `; -var Uee = ge({ opSnippet: Wee }); -var d3 = { kernelName: fs, backendName: "webgl", kernelFunc: Uee }; -var Gee = Do + ` +var Cte = xe({ opSnippet: bte }); +var _3 = { kernelName: ys, backendName: "webgl", kernelFunc: Cte }; +var wte = Fo + ` return sin(x); `; -var Hee = ` +var Ste = ` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Kr} + ${Xr} return result; `; -var Kee = ge({ opSnippet: Gee, packedOpSnippet: Hee }); -var f3 = { kernelName: ds, backendName: "webgl", kernelFunc: Kee }; -var qee = ` +var Ite = xe({ opSnippet: wte, packedOpSnippet: Ste }); +var $3 = { kernelName: gs, backendName: "webgl", kernelFunc: Ite }; +var vte = ` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `; -var jee = ge({ opSnippet: qee }); -var h3 = { kernelName: ja, backendName: "webgl", kernelFunc: jee }; -var Xee = ` +var kte = xe({ opSnippet: vte }); +var E3 = { kernelName: xs, backendName: "webgl", kernelFunc: kte }; +var Nte = ` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -24057,21 +24153,21 @@ var Xee = ` } return result; `; -var Yee = ge({ opSnippet: Xee }); -var g3 = { kernelName: gs, backendName: "webgl", kernelFunc: Yee }; -var Qee = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; +var Tte = xe({ opSnippet: Nte }); +var R3 = { kernelName: Cs, backendName: "webgl", kernelFunc: Tte }; +var _te = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"); let i = s.reduce((x, b) => x * b), p = [[0, 0]]; p.push(...a); for (let x = 1 + s.length; x < n.shape.length; ++x) p.push([0, 0]); - let u = [], c = dv({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = C.getReshaped(c.shape, s, i, false), m = C.getPermuted(l.length, s.length, false), d = C.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = bt({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: d } }); - return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeIntermediateTensorInfo(x)), g; + let u = [], c = Sv({ inputs: { x: n }, backend: t8, attrs: { paddings: p, constantValue: 0 } }), l = w.getReshaped(c.shape, s, i, false), m = w.getPermuted(l.length, s.length, false), d = w.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: t8, attrs: { shape: l } }), h = Ct({ inputs: { x: f }, backend: t8, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: d } }); + return u.push(c), u.push(f), u.push(h), u.forEach((x) => t8.disposeIntermediateTensorInfo(x)), g; }; -var x3 = { kernelName: ca, backendName: "webgl", kernelFunc: Qee }; -function Zee(r) { - let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; +var D3 = { kernelName: ha, backendName: "webgl", kernelFunc: _te }; +function $te(r) { + let { inputs: e, backend: t8 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; if (s.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw: ${s.shape}`); @@ -24084,24 +24180,24 @@ function Zee(r) { if (a.shape.length !== 0) throw new Error(`Default value must be a scalar, saw: ${a.shape}`); - let i = t10.readSync(o.dataId), p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = t10.readSync(a.dataId)[0], [l, m, d, f, h] = WR(i, o.shape, o.dtype, p, n.dtype, u, c); - return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; + let i = t8.readSync(o.dataId), p = t8.readSync(n.dataId), u = t8.readSync(s.dataId), c = t8.readSync(a.dataId)[0], [l, m, d, f, h] = rD(i, o.shape, o.dtype, p, n.dtype, u, c); + return [t8.makeTensorInfo(m, o.dtype, l), t8.makeTensorInfo([m[0]], n.dtype, d), t8.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t8.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; } -var y3 = { kernelName: Vi, backendName: "webgl", kernelFunc: Zee }; -function Jee(r) { - let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; +var A3 = { kernelName: Hi, backendName: "webgl", kernelFunc: $te }; +function Ete(r) { + let { inputs: e, backend: t8 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); if (n.shape.length !== 1) throw new Error(`Input shape should be a vector but received shape ${n.shape}`); if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); - let a = Array.from(t10.readSync(n.dataId)), i = t10.readSync(o.dataId), p = Array.from(t10.readSync(s.dataId)), [u, c, l] = UR(i, o.shape, o.dtype, a, p); - return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; + let a = Array.from(t8.readSync(n.dataId)), i = t8.readSync(o.dataId), p = Array.from(t8.readSync(s.dataId)), [u, c, l] = oD(i, o.shape, o.dtype, a, p); + return [t8.makeTensorInfo(c, o.dtype, u), t8.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; } -var b3 = { kernelName: Xa, backendName: "webgl", kernelFunc: Jee }; -function ete(r) { - let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; +var F3 = { kernelName: Za, backendName: "webgl", kernelFunc: Ete }; +function Rte(r) { + let { inputs: e, backend: t8 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -24110,12 +24206,12 @@ function ete(r) { if (s.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape ${s.shape}`); - let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = uh(a, o.shape, o.dtype, i, p, true); - return t10.makeTensorInfo(c, o.dtype, u); + let a = t8.readSync(o.dataId), i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c] = ph(a, o.shape, o.dtype, i, p, true); + return t8.makeTensorInfo(c, o.dtype, u); } -var C3 = { kernelName: Wi, backendName: "webgl", kernelFunc: ete }; -function tte(r) { - let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; +var P3 = { kernelName: Ki, backendName: "webgl", kernelFunc: Rte }; +function Dte(r) { + let { inputs: e, backend: t8 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -24124,56 +24220,56 @@ function tte(r) { if (s.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape ${s.shape}`); - let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = uh(a, o.shape, o.dtype, i, p); - return t10.makeTensorInfo(c, o.dtype, u); + let a = t8.readSync(o.dataId), i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c] = ph(a, o.shape, o.dtype, i, p); + return t8.makeTensorInfo(c, o.dtype, u); } -var w3 = { kernelName: Ui, backendName: "webgl", kernelFunc: tte }; -function rte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = C.calculateShapes(s, n, i), d = false; +var O3 = { kernelName: qi, backendName: "webgl", kernelFunc: Dte }; +function Ate(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false; if (s.dtype === "string") { - let x = t10.bufferSync(n), b = t10.bufferSync(s), w = y.decodeString(t10.readSync(a.dataId)[0]), S = BR(x, b, i, m, c, u, p, l, w, d); - return t10.makeTensorInfo(i, S.dtype, S.values); + let x = t8.bufferSync(n), b = t8.bufferSync(s), C = y.decodeString(t8.readSync(a.dataId)[0]), S = JR(x, b, i, m, c, u, p, l, C, d); + return t8.makeTensorInfo(i, S.dtype, S.values); } - let f = new yu(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t10.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: i } }); - return t10.disposeIntermediateTensorInfo(h), g; + let f = new Su(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t8.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: i } }); + return t8.disposeIntermediateTensorInfo(h), g; } -var S3 = { kernelName: Cs, backendName: "webgl", kernelFunc: rte }; -function ote(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); +var M3 = { kernelName: vs, backendName: "webgl", kernelFunc: Ate }; +function Fte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); return p.map((m) => { let d = [...l]; d[i] = m; - let f = Ls({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } }); + let f = Ws({ inputs: { x: n }, backend: t8, attrs: { begin: c, size: d } }); return c[i] += m, f; }); } -var I3 = { kernelName: la, backendName: "webgl", kernelFunc: ote }; -var v3 = "return sqrt(x);"; -var nte = ge({ opSnippet: v3, packedOpSnippet: v3, cpuKernelImpl: GR }); -var k3 = { kernelName: xs, backendName: "webgl", kernelFunc: nte }; -var ste = "return x * x;"; -var ate = ge({ opSnippet: ste }); -var N3 = { kernelName: Gi, backendName: "webgl", kernelFunc: ate }; -var T3 = "return (a - b) * (a - b);"; -var ite = nt({ opSnippet: T3, packedOpSnippet: T3 }); -var _3 = { kernelName: ws, backendName: "webgl", kernelFunc: ite }; -function ute(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; +var L3 = { kernelName: ga, backendName: "webgl", kernelFunc: Fte }; +var B3 = "return sqrt(x);"; +var Pte = xe({ opSnippet: B3, packedOpSnippet: B3, cpuKernelImpl: nD }); +var z3 = { kernelName: ws, backendName: "webgl", kernelFunc: Pte }; +var Ote = "return x * x;"; +var Mte = xe({ opSnippet: Ote }); +var V3 = { kernelName: ji, backendName: "webgl", kernelFunc: Mte }; +var W3 = "return (a - b) * (a - b);"; +var Lte = nt({ opSnippet: W3, packedOpSnippet: W3 }); +var U3 = { kernelName: ks, backendName: "webgl", kernelFunc: Lte }; +function Bte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e; if (n.dtype !== "string") throw new Error("Input must be of datatype string"); - let s = t10.readSync(n.dataId), a = C.fromUint8ToStringArray(s), i = HR(a, "string", o); - return t10.makeTensorInfo(n.shape, "string", i); + let s = t8.readSync(n.dataId), a = w.fromUint8ToStringArray(s), i = sD(a, "string", o); + return t8.makeTensorInfo(n.shape, "string", i); } -var $3 = { kernelName: _u, backendName: "webgl", kernelFunc: ute }; -function pte({ inputs: r, attrs: e, backend: t10 }) { +var G3 = { kernelName: Du, backendName: "webgl", kernelFunc: Bte }; +function zte({ inputs: r, attrs: e, backend: t8 }) { let { x: o } = r, n = Ut + ` return x > 0.0 ? 1.0 : float(${e.alpha}); - `, s = new tr(o.shape, n); - return t10.runWebGLProgram(s, [o], o.dtype); + `, s = new rr(o.shape, n); + return t8.runWebGLProgram(s, [o], o.dtype); } -var E3 = { kernelName: yo, backendName: "webgl", kernelFunc: pte }; -var _g = class { - constructor(e, t10, o) { +var H3 = { kernelName: wo, backendName: "webgl", kernelFunc: zte }; +var $g = class { + constructor(e, t8, o) { this.variableNames = ["x"], this.outputShape = o; let n = o.length, s = Re(o.length), a = Re(o.length), i = ""; if (n === 1) @@ -24184,7 +24280,7 @@ var _g = class { } this.userCode = ` ${s} begin = ${s}(${e}); - ${s} strides = ${s}(${t10}); + ${s} strides = ${s}(${t8}); void main() { ${a} coords = getOutputCoords(); @@ -24193,77 +24289,77 @@ var _g = class { `; } }; -function cte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function Vte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = te({ inputs: { x: n }, backend: t10, attrs: { shape: f } }); + k = te({ inputs: { x: n }, backend: t8, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let E = ct.computeOutShape(b, w, S), R = Ls({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: E } }); - k = te({ inputs: { x: R }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(R); - } else if (t10.shouldExecuteOnCPU([n])) { - let R = t10.readSync(n.dataId), D = me(n.shape, n.dtype, R), F = KR(d, D, S, b); - k = t10.makeTensorInfo(f, n.dtype, F.values); + let E = ct.computeOutShape(b, C, S), R = Ws({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: E } }); + k = te({ inputs: { x: R }, backend: t8, attrs: { shape: f } }), t8.disposeIntermediateTensorInfo(R); + } else if (t8.shouldExecuteOnCPU([n])) { + let R = t8.readSync(n.dataId), D = me(n.shape, n.dtype, R), P = aD(d, D, S, b); + k = t8.makeTensorInfo(f, n.dtype, P.values); } else { - let R = new _g(b, S, d); - k = t10.runWebGLProgram(R, [n], n.dtype); + let R = new $g(b, S, d); + k = t8.runWebGLProgram(R, [n], n.dtype); } - let _ = te({ inputs: { x: k }, backend: t10, attrs: { shape: f } }); - return t10.disposeIntermediateTensorInfo(k), _; + let _ = te({ inputs: { x: k }, backend: t8, attrs: { shape: f } }); + return t8.disposeIntermediateTensorInfo(k), _; } -var R3 = { kernelName: Ss, backendName: "webgl", kernelFunc: cte }; -function lte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = qR(m, d, n, s, a, i, p, u); - return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)]; +var K3 = { kernelName: Ns, backendName: "webgl", kernelFunc: Vte }; +function Wte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t8.readSync(c.dataId), d = t8.readSync(l.dataId), [f, h] = iD(m, d, n, s, a, i, p, u); + return [t8.makeTensorInfo([f.length], "string", f), t8.makeTensorInfo(l.shape, "int32", h)]; } -var D3 = { kernelName: ma, backendName: "webgl", kernelFunc: lte }; -function mte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; +var q3 = { kernelName: xa, backendName: "webgl", kernelFunc: Wte }; +function Ute(r) { + let { inputs: e, backend: t8, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (s.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${s.shape}`); if (a.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`); - let i = t10.readSync(s.dataId), p = t10.readSync(a.dataId)[0], [u, c, l] = jR(i, p, n), m = c.length; - return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))]; + let i = t8.readSync(s.dataId), p = t8.readSync(a.dataId)[0], [u, c, l] = uD(i, p, n), m = c.length; + return [t8.makeTensorInfo([m, 2], "int32", u), t8.makeTensorInfo([m], "string", c), t8.makeTensorInfo([2], "int32", new Int32Array(l))]; } -var A3 = { kernelName: Hi, backendName: "webgl", kernelFunc: mte }; -function dte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; +var j3 = { kernelName: Xi, backendName: "webgl", kernelFunc: Ute }; +function Gte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (n <= 0) throw new Error("Number of buckets must be at least 1"); - let a = t10.readSync(s.dataId), i = XR(a, n); - return t10.makeTensorInfo(s.shape, "int32", i); + let a = t8.readSync(s.dataId), i = pD(a, n); + return t8.makeTensorInfo(s.shape, "int32", i); } -var F3 = { kernelName: Ki, backendName: "webgl", kernelFunc: dte }; -var fte = "return tan(x);"; -var hte = ge({ opSnippet: fte }); -var P3 = { kernelName: vs, backendName: "webgl", kernelFunc: hte }; -var gte = ` +var X3 = { kernelName: Yi, backendName: "webgl", kernelFunc: Gte }; +var Hte = "return tan(x);"; +var Kte = xe({ opSnippet: Hte }); +var Y3 = { kernelName: _s, backendName: "webgl", kernelFunc: Kte }; +var qte = ` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `; -var xte = ge({ opSnippet: gte }); -var O3 = { kernelName: ks, backendName: "webgl", kernelFunc: xte }; -function yte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(a, s, n.shape), m = [l / u, u]; +var jte = xe({ opSnippet: qte }); +var Q3 = { kernelName: $s, backendName: "webgl", kernelFunc: jte }; +function Xte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u]; if (l === 0) - return t10.makeTensorInfo(n.shape, s.dtype); - let d = te({ inputs: { x: s }, backend: t10, attrs: { shape: [p, i] } }), f = te({ inputs: { x: a }, backend: t10, attrs: { shape: [p, u] } }), h = te({ inputs: { x: n }, backend: t10, attrs: { shape: m } }), g = new yu(p, i, d.shape.length, f.shape.length, c, m, false, true), x = t10.runWebGLProgram(g, [f, d, h], h.dtype), b = te({ inputs: { x }, backend: t10, attrs: { shape: n.shape } }); - return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(x), b; + return t8.makeTensorInfo(n.shape, s.dtype); + let d = te({ inputs: { x: s }, backend: t8, attrs: { shape: [p, i] } }), f = te({ inputs: { x: a }, backend: t8, attrs: { shape: [p, u] } }), h = te({ inputs: { x: n }, backend: t8, attrs: { shape: m } }), g = new Su(p, i, d.shape.length, f.shape.length, c, m, false, true), x = t8.runWebGLProgram(g, [f, d, h], h.dtype), b = te({ inputs: { x }, backend: t8, attrs: { shape: n.shape } }); + return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(x), b; } -var M3 = { kernelName: cs, backendName: "webgl", kernelFunc: yte }; -var $g = class { - constructor(e, t10) { +var Z3 = { kernelName: ds, backendName: "webgl", kernelFunc: Xte }; +var Eg = class { + constructor(e, t8) { this.variableNames = ["A"]; let o = new Array(e.length); for (let a = 0; a < o.length; a++) - o[a] = e[a] * t10[a]; + o[a] = e[a] * t8[a]; this.outputShape = o, this.rank = o.length; - let n = Re(this.rank), s = bte(e); + let n = Re(this.rank), s = Yte(e); this.userCode = ` void main() { ${n} resRC = getOutputCoords(); @@ -24272,28 +24368,28 @@ var $g = class { `; } }; -function bte(r) { +function Yte(r) { let e = r.length; if (e > 5) throw Error(`Tile for rank ${e} is not yet supported`); if (e === 1) return `imod(resRC, ${r[0]})`; - let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = []; + let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = []; for (let n = 0; n < r.length; n++) - o.push(`imod(${t10[n]}, ${r[n]})`); + o.push(`imod(${t8[n]}, ${r[n]})`); return o.join(); } -function hv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o; +function vv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reps: s } = o; if (n.dtype === "string" || n.shape.length > 5) { - let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = QR(c, s); - return t10.makeTensorInfo(l.shape, l.dtype, l.values); + let p = t8.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = lD(c, s); + return t8.makeTensorInfo(l.shape, l.dtype, l.values); } - let a = new $g(n.shape, s); - return t10.runWebGLProgram(a, [n], n.dtype); + let a = new Eg(n.shape, s); + return t8.runWebGLProgram(a, [n], n.dtype); } -var L3 = { kernelName: so, backendName: "webgl", kernelFunc: hv }; -var Eg = class { +var J3 = { kernelName: uo, backendName: "webgl", kernelFunc: vv }; +var Rg = class { constructor(e) { this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "negativeInf", type: "float" }, { name: "dir", type: "int" }, { name: "inc", type: "int" }], this.outputShape = e, this.userCode = ` void main() { @@ -24338,7 +24434,7 @@ var Eg = class { `; } }; -var Rg = class { +var Dg = class { constructor(e) { this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "k", type: "int" }], this.outputShape = e, this.userCode = ` void main() { @@ -24377,55 +24473,55 @@ var Rg = class { `; } }; -function Ip(r, e) { +function Tp(r, e) { e !== null && r.disposeIntermediateTensorInfo(e); } -function B3(r) { +function eP(r) { let e = 1; for (; e < r; ) e *= 2; return e; } -function Cte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = P().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = P().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1]; - if (t10.shouldExecuteOnCPU([n]) || c < i || s > p) { - let F = t10.readSync(n.dataId), [O, M] = ZR(F, u, n.dtype, s, a); - return [t10.makeTensorInfo(O.shape, O.dtype, O.values), t10.makeTensorInfo(M.shape, M.dtype, M.values)]; +function Qte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = A().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = A().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1]; + if (t8.shouldExecuteOnCPU([n]) || c < i || s > p) { + let P = t8.readSync(n.dataId), [O, M] = mD(P, u, n.dtype, s, a); + return [t8.makeTensorInfo(O.shape, O.dtype, O.values), t8.makeTensorInfo(M.shape, M.dtype, M.values)]; } if (s === 0) - return u[u.length - 1] = 0, [t10.makeTensorInfo(u, n.dtype, []), t10.makeTensorInfo(u, "int32", [])]; + return u[u.length - 1] = 0, [t8.makeTensorInfo(u, n.dtype, []), t8.makeTensorInfo(u, "int32", [])]; if (c === 1) - return [n, xi({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t10 })]; - let l = t10.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t10.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = te({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t10 }); - m && Ip(t10, d); - let x = B3(s), b = B3(c), w = null, S = () => w === null ? [g, g] : [g, w], k = (F, O, M) => { - let L = S(), B = new Eg(M), U = [[c], [w === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [F], [O]], j = w; - w = t10.runWebGLProgram(B, L, "int32", U), Ip(t10, j); + return [n, Ci({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t8 })]; + let l = t8.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t8.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = te({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t8 }); + m && Tp(t8, d); + let x = eP(s), b = eP(c), C = null, S = () => C === null ? [g, g] : [g, C], k = (P, O, M) => { + let L = S(), B = new Rg(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [O]], j = C; + C = t8.runWebGLProgram(B, L, "int32", U), Tp(t8, j); }; - for (let F = 1; F < x; F *= 2) { - let O = F * 2; - for (let M = F; M >= 1; M /= 2) + for (let P = 1; P < x; P *= 2) { + let O = P * 2; + for (let M = P; M >= 1; M /= 2) k(O, M, [h, b]); } - for (let F = b; F > x; F /= 2) { - let O = S(), M = new Rg([h, F / 2]), B = [[c], [w === null ? 1 : 0], [x]], z = w; - w = t10.runWebGLProgram(M, O, "int32", B), Ip(t10, z); + for (let P = b; P > x; P /= 2) { + let O = S(), M = new Dg([h, P / 2]), B = [[c], [C === null ? 1 : 0], [x]], z = C; + C = t8.runWebGLProgram(M, O, "int32", B), Tp(t8, z); let U = x / 2, j = U * 2; - for (let H = U; H >= 1; H /= 2) - k(j, H, w.shape); + for (let q = U; q >= 1; q /= 2) + k(j, q, C.shape); } - let _ = w; - w = Ls({ inputs: { x: w }, backend: t10, attrs: { begin: 0, size: [h, s] } }), Ip(t10, _); - let E = uv({ inputs: { x: g, indices: w }, backend: t10, attrs: { axis: 1, batchDims: 1 } }); - Ip(t10, g); + let _ = C; + C = Ws({ inputs: { x: C }, backend: t8, attrs: { begin: 0, size: [h, s] } }), Tp(t8, _); + let E = xv({ inputs: { x: g, indices: C }, backend: t8, attrs: { axis: 1, batchDims: 1 } }); + Tp(t8, g); let R = u.slice(0, -1); - R.push(s), _ = w, w = te({ inputs: { x: w }, attrs: { shape: R }, backend: t10 }), Ip(t10, _); + R.push(s), _ = C, C = te({ inputs: { x: C }, attrs: { shape: R }, backend: t8 }), Tp(t8, _); let D = E; - return E = te({ inputs: { x: E }, attrs: { shape: R }, backend: t10 }), Ip(t10, D), [E, w]; + return E = te({ inputs: { x: E }, attrs: { shape: R }, backend: t8 }), Tp(t8, D), [E, C]; } -var z3 = { kernelName: Ns, backendName: "webgl", kernelFunc: Cte }; -var Dg = class { - constructor(e, t10, o, n, s, a) { +var tP = { kernelName: Es, backendName: "webgl", kernelFunc: Qte }; +var Ag = class { + constructor(e, t8, o, n, s, a) { this.variableNames = ["Image", "Transforms"], this.outputShape = a; let i = o === "nearest" ? 1 : 2, p; switch (n) { @@ -24499,7 +24595,7 @@ var Dg = class { float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; - if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t10}) { + if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t8}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${s}); @@ -24530,7 +24626,7 @@ var Dg = class { } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; - float mapX = mapCoord(inX, float(${t10})); + float mapX = mapCoord(inX, float(${t8})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { @@ -24560,20 +24656,20 @@ var Dg = class { `; } }; -function wte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Dg(l, m, a, i, p, g); - return t10.runWebGLProgram(x, [n, s], "float32"); +function Zte(r) { + let { inputs: e, backend: t8, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Ag(l, m, a, i, p, g); + return t8.runWebGLProgram(x, [n, s], "float32"); } -var V3 = { kernelName: Ts, backendName: "webgl", kernelFunc: wte }; -function Ste(r) { - let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e; - Ps(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); - let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = JR(a, n, s.shape, s.dtype); +var rP = { kernelName: Rs, backendName: "webgl", kernelFunc: Zte }; +function Jte(r) { + let { inputs: e, attrs: t8, backend: o } = r, { axis: n } = t8, { x: s } = e; + Bs(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); + let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = dD(a, n, s.shape, s.dtype); return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)]; } -var W3 = { kernelName: qi, backendName: "webgl", kernelFunc: Ste }; -function Ite(r) { - let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; +var oP = { kernelName: Qi, backendName: "webgl", kernelFunc: Jte }; +function ere(r) { + let { inputs: e, backend: t8, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0; for (let h = 0; h < i; h++) @@ -24583,14 +24679,14 @@ function Ite(r) { let f = new Array(p); for (let h = 0; h < f.length; h++) { m[s] = h; - let g = Ls({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = te({ inputs: { x: g }, backend: t10, attrs: { shape: u } }); + let g = Ws({ inputs: { x: a }, backend: t8, attrs: { begin: m, size: d } }), x = te({ inputs: { x: g }, backend: t8, attrs: { shape: u } }); f[h] = x, l.push(g); } - return l.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f; + return l.forEach((h) => t8.disposeIntermediateTensorInfo(h)), f; } -var U3 = { kernelName: da, backendName: "webgl", kernelFunc: Ite }; -var Ag = class { - constructor(e, t10) { +var nP = { kernelName: ya, backendName: "webgl", kernelFunc: ere }; +var Fg = class { + constructor(e, t8) { this.variableNames = ["x", "segmentIds"]; let o = e.windowSize, n = e.batchSize, s = e.inSize, a = e.numSegments, i = a * Math.ceil(s / o); this.outputShape = [n, i]; @@ -24706,80 +24802,80 @@ var Ag = class { `; } }; -function vte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = C.getAxesPermutation([u], i), l = n; - c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = C.getInnerMostAxes(1, i)[0]); - let m = C.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } }); +function tre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n; + c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]); + let m = w.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, d] } }); p.push(f); - let h = Za(n.dtype), g = (S, k, _, E, R) => { - let D = S.shape[0], F = S.shape[1], O = C.segment_util.segOpComputeOptimalWindowSize(F, R), M = { windowSize: O, inSize: F, batchSize: D, numSegments: R }, L = new Ag(M, k), B = t10.compileAndRun(L, [S, _], E); + let h = ti(n.dtype), g = (S, k, _, E, R) => { + let D = S.shape[0], P = S.shape[1], O = w.segment_util.segOpComputeOptimalWindowSize(P, R), M = { windowSize: O, inSize: P, batchSize: D, numSegments: R }, L = new Fg(M, k), B = t8.compileAndRun(L, [S, _], E); if (p.push(B), B.shape[1] === R) return B; - let z = fv({ backend: t10, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), U = hv({ inputs: { x: z }, backend: t10, attrs: { reps: [F / O] } }); + let z = Iv({ backend: t8, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), U = vv({ inputs: { x: z }, backend: t8, attrs: { reps: [P / O] } }); return p.push(z), p.push(U), g(B, k, U, E, R); - }, x = g(f, "unsortedSegmentSum", s, h, a), b = te({ inputs: { x }, backend: t10, attrs: { shape: m } }), w = b; + }, x = g(f, "unsortedSegmentSum", s, h, a), b = te({ inputs: { x }, backend: t8, attrs: { shape: m } }), C = b; if (c != null) { p.push(b); - let S = C.getUndoAxesPermutation(c); - w = bt({ inputs: { x: w }, backend: t10, attrs: { perm: S } }); + let S = w.getUndoAxesPermutation(c); + C = Ct({ inputs: { x: C }, backend: t8, attrs: { perm: S } }); } - return p.forEach((S) => t10.disposeIntermediateTensorInfo(S)), w; + return p.forEach((S) => t8.disposeIntermediateTensorInfo(S)), C; } -var G3 = { kernelName: ji, backendName: "webgl", kernelFunc: vte }; -var kte = [TD, $D, ED, RD, AD, FD, PD, OD, BD, zD, VD, WD, UD, GD, HD, KD, qD, jD, XD, YD, QD, JD, eA, tA, sA, iA, uA, xD, cA, mA, dA, fA, hA, gA, xA, yA, bA, CA, wA, vA, kA, NA, TA, _A, $A, EA, RA, DA, AA, FA, PA, OA, MA, LA, BA, VA, WA, UA, GA, KA, qA, jA, XA, YA, QA, ZA, JA, eF, gD, tF, lA, rF, oF, nF, yD, sF, aF, iF, uF, pF, cF, lF, mF, dF, fF, gF, xF, yF, bF, CF, wF, IF, kF, NF, TF, _F, $F, FF, wD, PF, OF, MF, LF, rA, BF, WF, UF, GF, HF, bD, KF, qF, jF, XF, YF, oA, EF, QF, ZF, JF, ID, e3, t3, r3, o3, n3, s3, a3, i3, u3, p3, c3, l3, m3, d3, f3, h3, ZD, AF, g3, x3, y3, b3, C3, w3, S3, I3, k3, N3, _3, $3, E3, R3, D3, A3, F3, DF, kD, P3, O3, M3, L3, z3, V3, ND, W3, U3, G3, zF]; -for (let r of kte) - Ya(r); +var sP = { kernelName: Zi, backendName: "webgl", kernelFunc: tre }; +var rre = [VD, UD, GD, HD, qD, jD, XD, YD, JD, eA, tA, rA, oA, nA, sA, aA, iA, uA, pA, cA, lA, dA, fA, hA, gA, CA, SA, IA, RD, kA, TA, _A, $A, EA, RA, DA, AA, FA, PA, OA, BA, zA, VA, WA, UA, GA, HA, KA, qA, jA, XA, YA, QA, ZA, JA, eF, rF, oF, nF, sF, iF, uF, pF, cF, lF, mF, dF, fF, hF, ED, gF, NA, xF, yF, bF, DD, CF, wF, SF, IF, vF, kF, NF, TF, _F, $F, RF, DF, AF, FF, PF, OF, LF, zF, VF, WF, UF, GF, XF, PD, YF, QF, ZF, JF, xA, e3, o3, n3, s3, a3, AD, i3, u3, p3, c3, l3, yA, HF, m3, d3, f3, MD, h3, g3, x3, y3, b3, C3, w3, S3, I3, v3, k3, N3, T3, _3, $3, E3, mA, jF, R3, D3, A3, F3, P3, O3, M3, L3, z3, V3, U3, G3, H3, K3, q3, j3, X3, qF, BD, Y3, Q3, Z3, J3, tP, rP, zD, oP, nP, sP, t3]; +for (let r of rre) + Ja(r); var we; (function(r) { r[r.float32 = 0] = "float32", r[r.int32 = 1] = "int32", r[r.bool = 2] = "bool", r[r.string = 3] = "string", r[r.complex64 = 4] = "complex64"; })(we || (we = {})); -var bu; +var Iu; (function(r) { r[r.linear = 0] = "linear", r[r.relu = 1] = "relu", r[r.relu6 = 2] = "relu6", r[r.prelu = 3] = "prelu", r[r.leakyrelu = 4] = "leakyrelu", r[r.sigmoid = 5] = "sigmoid", r[r.elu = 6] = "elu"; -})(bu || (bu = {})); -var H3; -function Nte(r) { - H3 = r.wasm.cwrap(bo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]); +})(Iu || (Iu = {})); +var aP; +function ore(r) { + aP = r.wasm.cwrap(So, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Tte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e; +function nre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e; if (n.dtype !== "float32" || s.dtype !== "float32") throw new Error("_FusedMatMul for non non-float32 tensors not yet supported."); - let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = 0; + let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = t8.dataIdMap.get(n.dataId).id, d = t8.dataIdMap.get(s.dataId).id, f = 0; if (a != null) { - let R = t10.dataIdMap.get(a.dataId); + let R = t8.dataIdMap.get(a.dataId); if (R.shape.length !== 1) throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`); f = R.id; } - let h = i == null ? 0 : t10.dataIdMap.get(i.dataId).id, g = bu[c]; + let h = i == null ? 0 : t8.dataIdMap.get(i.dataId).id, g = Iu[c]; if (g == null) throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`); - let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], w = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), S = t10.makeOutput([...w, x, b], n.dtype), k = t10.dataIdMap.get(S.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer); - return H3(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), S; + let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), S = t8.makeOutput([...C, x, b], n.dtype), k = t8.dataIdMap.get(S.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer); + return aP(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), S; } -var K3 = { kernelName: bo, backendName: "wasm", setupFunc: Nte, kernelFunc: Tte }; -function Ce(r, e) { - let t10; +var iP = { kernelName: So, backendName: "wasm", setupFunc: ore, kernelFunc: nre }; +function he(r, e) { + let t8; function o(s) { - t10 = s.wasm.cwrap(r, null, ["number", "number", "number"]); + t8 = s.wasm.cwrap(r, null, ["number", "number", "number"]); } function n(s) { let { backend: a, inputs: { x: i } } = s, p = a.dataIdMap.get(i.dataId).id, u = a.makeOutput(i.shape, e || i.dtype), c = a.dataIdMap.get(u.dataId).id; - return y.sizeFromShape(u.shape) === 0 || t10(p, we[i.dtype], c), u; + return y.sizeFromShape(u.shape) === 0 || t8(p, we[i.dtype], c), u; } return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n }; } -var q3 = Ce(Gs); -var j3 = Ce(zo); -var X3 = Ce(Vo); -function Je(r, e, t10) { +var uP = he(js); +var pP = he(Vo); +var cP = he(Wo); +function We(r, e, t8) { let o; function n(a) { o = a.wasm.cwrap(r, null, ["number", "array", "number", "number", "array", "number", "number", "number"]); } function s(a) { - let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = t10 != null ? t10 : u.dtype, f = C.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d); + let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = t8 != null ? t8 : u.dtype, f = w.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d); if (y.sizeFromShape(f) === 0) return h; let g = new Uint8Array(new Int32Array(u.shape).buffer), x = new Uint8Array(new Int32Array(c.shape).buffer), b = i.dataIdMap.get(h.dataId).id; @@ -24787,306 +24883,317 @@ function Je(r, e, t10) { } return { kernelName: r, backendName: "wasm", setupFunc: n, kernelFunc: s }; } -var _te = true; -var Y3 = Je(no, _te); -var Q3; -function $te(r) { - Q3 = r.wasm.cwrap(Wo, null, ["array", "number", "number", "number"]); +var sre = true; +var lP = We(io, sre); +var mP; +function are(r) { + mP = r.wasm.cwrap(Uo, null, ["array", "number", "number", "number"]); } -function Ete(r) { - let { inputs: e, backend: t10 } = r, o = t10.makeOutput(e[0].shape, e[0].dtype); +function ire(r) { + let { inputs: e, backend: t8 } = r, o = t8.makeOutput(e[0].shape, e[0].dtype); if (y.sizeFromShape(o.shape) === 0) return o; - let n = e.map((i) => t10.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t10.dataIdMap.get(o.dataId).id; - return Q3(s, n.length, we[o.dtype], a), o; + let n = e.map((i) => t8.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t8.dataIdMap.get(o.dataId).id; + return mP(s, n.length, we[o.dtype], a), o; } -var Z3 = { kernelName: Wo, backendName: "wasm", setupFunc: $te, kernelFunc: Ete }; -function vp(r) { - let { inputs: { x: e }, backend: t10 } = r; +var dP = { kernelName: Uo, backendName: "wasm", setupFunc: are, kernelFunc: ire }; +function _p(r) { + let { inputs: { x: e }, backend: t8 } = r; if (e.dtype === "string") - return ir(t10.readSync(e.dataId), e.shape, e.dtype); - let o = t10.makeOutput(e.shape, e.dtype), n = t10.typedArrayFromHeap(e); - return t10.typedArrayFromHeap(o).set(n), o; + return ir(t8.readSync(e.dataId), e.shape, e.dtype); + let o = t8.makeOutput(e.shape, e.dtype), n = t8.typedArrayFromHeap(e); + return t8.typedArrayFromHeap(o).set(n), o; } -var J3 = { kernelName: xo, backendName: "wasm", kernelFunc: vp }; -var eP; -function Rte(r) { - eP = r.wasm.cwrap(ao, null, ["number", "array", "number", "number", "number", "array", "number"]); +var fP = { kernelName: Co, backendName: "wasm", kernelFunc: _p }; +var hP; +function ure(r) { + hP = r.wasm.cwrap(po, null, ["number", "array", "number", "number", "number", "array", "number"]); } -function mo(r) { - let { inputs: e, backend: t10, attrs: o } = r, [n, s] = Ate(e.x.shape, o.perm), a = true; +function ho(r) { + let { inputs: e, backend: t8, attrs: o } = r, [n, s] = cre(e.x.shape, o.perm), a = true; for (let f = 0; f < s.length; f++) s[f] !== f && (a = false); - let i = Dte(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype }; + let i = pre(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype }; if (a) { - let f = vp({ inputs: e, backend: t10 }); + let f = _p({ inputs: e, backend: t8 }); return f.shape = i, f; } - let u = t10.makeOutput(i, p.dtype), c = t10.dataIdMap.get(p.dataId).id, l = t10.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer); - return eP(c, d, p.shape.length, we[p.dtype], l, m, s.length), u; + let u = t8.makeOutput(i, p.dtype), c = t8.dataIdMap.get(p.dataId).id, l = t8.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer); + return hP(c, d, p.shape.length, we[p.dtype], l, m, s.length), u; } -function Dte(r, e) { - let t10 = new Array(r.length); - for (let o = 0; o < t10.length; o++) - t10[o] = r[e[o]]; - return t10; +function pre(r, e) { + let t8 = new Array(r.length); + for (let o = 0; o < t8.length; o++) + t8[o] = r[e[o]]; + return t8; } -function Ate(r, e) { - let t10 = [], o = []; +function cre(r, e) { + let t8 = [], o = []; for (let n = 0; n < r.length; ++n) - r[n] !== 1 && t10.push(r[n]), r[e[n]] !== 1 && o.push(e[n]); + r[n] !== 1 && t8.push(r[n]), r[e[n]] !== 1 && o.push(e[n]); for (let n = 0; n < o.length; ++n) { let s = -1; for (let a = 0; a < o.length; ++a) o[a] >= n && (s === -1 || o[s] > o[a]) && (s = a); o[s] = n; } - return [t10, o]; + return [t8, o]; } -var tP = { kernelName: ao, backendName: "wasm", kernelFunc: mo, setupFunc: Rte }; -function Tr(r, e, t10) { - let o = r.shape, n = r.shape.length, s = y.parseAxisParam(e, o), a = s, i = C.getAxesPermutation(a, n), p = null, u = false; +var gP = { kernelName: po, backendName: "wasm", kernelFunc: ho, setupFunc: ure }; +function _r(r, e, t8) { + let o = r.shape, n = r.shape.length, s = y.parseAxisParam(e, o), a = s, i = w.getAxesPermutation(a, n), p = null, u = false; if (i != null) { let c = new Array(n); for (let d = 0; d < c.length; d++) c[d] = o[i[d]]; - a = C.getInnerMostAxes(a.length, n), p = mo({ inputs: { x: r }, attrs: { perm: i }, backend: t10 }); - let l = t10.dataIdMap.get(r.dataId).id; - t10.dataIdMap.get(p.dataId).id !== l && (u = true); + a = w.getInnerMostAxes(a.length, n), p = ho({ inputs: { x: r }, attrs: { perm: i }, backend: t8 }); + let l = t8.dataIdMap.get(r.dataId).id; + t8.dataIdMap.get(p.dataId).id !== l && (u = true); } return { transposed: p, originalAxes: s, axes: a, inputWasTransposed: u }; } -var rP; -function Fte(r) { - rP = r.wasm.cwrap(Uo, null, ["number, number, number"]); +var xP; +function lre(r) { + xP = r.wasm.cwrap(Go, null, ["number, number, number"]); } -function Pte(r) { - let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); +function mre(r) { + let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e); if (d) { - let w = e.dataIdMap.get(c.dataId).id; - u = c, p = w; + let C = e.dataIdMap.get(c.dataId).id; + u = c, p = C; } let f = u.shape.length; - C.assertAxesAreInnerMostDims("all", l, f); - let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); + w.assertAxesAreInnerMostDims("all", l, f); + let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { - let w = e.dataIdMap.get(b.dataId).id; - rP(p, x, w); + let C = e.dataIdMap.get(b.dataId).id; + xP(p, x, C); } if (d && e.disposeData(c.dataId), s) { - let w = C.expandShapeToKeepDim(b.shape, m); - b.shape = w; + let C = w.expandShapeToKeepDim(b.shape, m); + b.shape = C; } return b; } -var oP = { kernelName: Uo, backendName: "wasm", setupFunc: Fte, kernelFunc: Pte }; -var nP; -function Ote(r) { - nP = r.wasm.cwrap(Go, null, ["number, number, number"]); +var yP = { kernelName: Go, backendName: "wasm", setupFunc: lre, kernelFunc: mre }; +var bP; +function dre(r) { + bP = r.wasm.cwrap(Ho, null, ["number, number, number"]); } -function Mte(r) { - let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); +function fre(r) { + let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e); if (d) { - let w = e.dataIdMap.get(c.dataId).id; - u = c, p = w; + let C = e.dataIdMap.get(c.dataId).id; + u = c, p = C; } let f = u.shape.length; - C.assertAxesAreInnerMostDims("any", l, f); - let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); + w.assertAxesAreInnerMostDims("any", l, f); + let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { - let w = e.dataIdMap.get(b.dataId).id; - nP(p, x, w); + let C = e.dataIdMap.get(b.dataId).id; + bP(p, x, C); } if (d && e.disposeData(c.dataId), s) { - let w = C.expandShapeToKeepDim(b.shape, m); - b.shape = w; + let C = w.expandShapeToKeepDim(b.shape, m); + b.shape = C; } return b; } -var sP = { kernelName: Go, backendName: "wasm", setupFunc: Ote, kernelFunc: Mte }; -function Fg(r) { +var CP = { kernelName: Ho, backendName: "wasm", setupFunc: dre, kernelFunc: fre }; +function Pg(r) { let e; - function t10(n) { + function t8(n) { e = n.wasm.cwrap(r, null, ["number", "number", "number", "number", "number"]); } function o(n) { - let { backend: s, inputs: a, attrs: i } = n, { axis: p } = i, { x: u } = a, c = s.dataIdMap.get(u.dataId).id, l = c, m = u, { transposed: d, axes: f, inputWasTransposed: h } = Tr(u, p, s); + let { backend: s, inputs: a, attrs: i } = n, { axis: p } = i, { x: u } = a, c = s.dataIdMap.get(u.dataId).id, l = c, m = u, { transposed: d, axes: f, inputWasTransposed: h } = _r(u, p, s); if (h) { let k = s.dataIdMap.get(d.dataId).id; k !== c && (m = d, l = k); } - let g = m.shape.slice(0, -1), x = s.makeOutput(g, "int32"), b = s.dataIdMap.get(x.dataId).id, w = y.sizeFromShape(x.shape), S = m.shape[f[0]]; - return e(l, we[m.dtype], w, S, b), h && s.disposeData(d.dataId), x; + let g = m.shape.slice(0, -1), x = s.makeOutput(g, "int32"), b = s.dataIdMap.get(x.dataId).id, C = y.sizeFromShape(x.shape), S = m.shape[f[0]]; + return e(l, we[m.dtype], C, S, b), h && s.disposeData(d.dataId), x; } - return { kernelName: r, backendName: "wasm", setupFunc: t10, kernelFunc: o }; + return { kernelName: r, backendName: "wasm", setupFunc: t8, kernelFunc: o }; } -var aP = Fg(Hs); -var iP = Fg(Ks); -var uP = Ce(Ho); -var pP = Ce(Ko); -var cP = Ce(qo); -var lP = Je(Xo, false); -var mP = Ce(jo); -var dP; -function Lte(r) { - dP = r.wasm.cwrap(Yo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var wP = Pg(Xs); +var SP = Pg(Ys); +var IP = he(Ko); +var vP = he(qo); +var kP = he(jo); +var NP = We(Yo, false); +var TP = he(Xo); +var _P; +function hre(r) { + _P = r.wasm.cwrap(Qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Bte(r) { - let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = C.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, w = c.inChannels; +function gre(r) { + let { inputs: e, attrs: t8, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t8, c = w.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, C = c.inChannels; if (c.dataFormat !== "channelsLast") throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`); if (c.dilationWidth !== 1 || c.dilationHeight !== 1) throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`); let S = o.makeOutput(c.outShape, "float32"), k = o.dataIdMap.get(S.dataId).id; - return dP(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, w, k), S; + return _P(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), S; } -var fP = { kernelName: Yo, backendName: "wasm", setupFunc: Lte, kernelFunc: Bte }; -var hP; -function zte(r) { - hP = r.wasm.cwrap("AvgPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var $P = { kernelName: Qo, backendName: "wasm", setupFunc: hre, kernelFunc: gre }; +var EP; +function xre(r) { + EP = r.wasm.cwrap("AvgPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Vte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.makeOutput(c.outShape, n.dtype); - return hP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; +function yre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.makeOutput(c.outShape, n.dtype); + return EP(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; } -var gP = { kernelName: qs, backendName: "wasm", setupFunc: zte, kernelFunc: Vte }; -var xP; -function Wte(r) { - xP = r.wasm.cwrap("AvgPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var RP = { kernelName: Qs, backendName: "wasm", setupFunc: xre, kernelFunc: yre }; +var DP; +function bre(r) { + DP = r.wasm.cwrap("AvgPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Ute(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.makeOutput(s.shape, s.dtype); - return xP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left, c.filterDepth, c.filterHeight, c.filterWidth), l; +function Cre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = t8.makeOutput(s.shape, s.dtype); + return DP(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left, c.filterDepth, c.filterHeight, c.filterWidth), l; } -var yP = { kernelName: Ni, backendName: "wasm", setupFunc: Wte, kernelFunc: Ute }; +var AP = { kernelName: Ei, backendName: "wasm", setupFunc: bre, kernelFunc: Cre }; +var FP; +function wre(r) { + FP = r.wasm.cwrap("AvgPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +} +function Sre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p } = o, u = w.computePool2DInfo(s.shape, a, i, 1, p), c = t8.makeOutput(s.shape, s.dtype); + return FP(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(c.dataId).id, u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.effectiveFilterHeight, u.effectiveFilterWidth, u.padInfo.top, u.padInfo.left, u.filterHeight, u.filterWidth), c; +} +var PP = { kernelName: $i, backendName: "wasm", setupFunc: wre, kernelFunc: Sre }; function zt(r) { - let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s); + let { inputs: e, attrs: t8 } = r, { x: o } = e, { shape: n } = t8, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s); return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; } -var bP = { kernelName: ia, backendName: "wasm", kernelFunc: zt }; -var CP; -function Gte(r) { - CP = r.wasm.cwrap(Qo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]); +var OP = { kernelName: ma, backendName: "wasm", kernelFunc: zt }; +var MP; +function Ire(r) { + MP = r.wasm.cwrap(Zo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]); } -function Hte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; +function vre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; if (n.dtype !== "float32" || s.dtype !== "float32") throw new Error("BatchMatMul for non non-float32 tensors not yet supported."); - let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), w = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); + let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`); - let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = zt({ inputs: { x: n }, backend: t10, attrs: { shape: S } }), E = zt({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), R = t10.dataIdMap.get(_.dataId).id, D = t10.dataIdMap.get(E.dataId).id, F = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = t10.makeOutput([M, F, O], _.dtype), B = t10.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer); - return CP(R, z, _.shape.length, D, U, E.shape.length, a, i, B), t10.disposeData(_.dataId), t10.disposeData(E.dataId), L.shape = w, L; + let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = zt({ inputs: { x: n }, backend: t8, attrs: { shape: S } }), E = zt({ inputs: { x: s }, backend: t8, attrs: { shape: k } }), R = t8.dataIdMap.get(_.dataId).id, D = t8.dataIdMap.get(E.dataId).id, P = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = t8.makeOutput([M, P, O], _.dtype), B = t8.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer); + return MP(R, z, _.shape.length, D, U, E.shape.length, a, i, B), t8.disposeData(_.dataId), t8.disposeData(E.dataId), L.shape = C, L; } -var wP = { kernelName: Qo, backendName: "wasm", setupFunc: Gte, kernelFunc: Hte }; -function Ao(r) { - let { inputs: { x: e }, attrs: { begin: t10, size: o }, backend: n } = r, [s, a] = ct.parseSliceParams(e, t10, o), i = ct.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = y.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId); +var LP = { kernelName: Zo, backendName: "wasm", setupFunc: Ire, kernelFunc: vre }; +function Po(r) { + let { inputs: { x: e }, attrs: { begin: t8, size: o }, backend: n } = r, [s, a] = ct.parseSliceParams(e, t8, o), i = ct.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = y.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId); if (i) { let f = ct.computeFlatOffset(s, c); return e.dtype === "string" ? l.stringBytes = p.slice(f, f + y.sizeFromShape(a)) : n.typedArrayFromHeap(u).set(p.subarray(f, f + y.sizeFromShape(a))), u; } if (e.dtype === "string") { - let f = ip(p, s, a, e.shape, e.dtype); + let f = lp(p, s, a, e.shape, e.dtype); return l.stringBytes = f, u; } let m = n.typedArrayFromHeap(u), d = e.shape.length; if (d === 2) - Kte(p, c[0], m, s, a); + kre(p, c[0], m, s, a); else if (d === 3) - qte(p, c[0], c[1], m, s, a); + Nre(p, c[0], c[1], m, s, a); else if (d === 4) - jte(p, c[0], c[1], c[2], m, s, a); + Tre(p, c[0], c[1], c[2], m, s, a); else { - let f = ip(p, s, a, e.shape, e.dtype); + let f = lp(p, s, a, e.shape, e.dtype); m.set(f); } return u; } -function Kte(r, e, t10, o, n) { +function kre(r, e, t8, o, n) { let s = 0, a = o[0], i = o[1], p = a + n[0]; for (let u = a; u < p; u++) { let c = u * e + i; - t10.set(r.subarray(c, c + n[1]), s), s += n[1]; + t8.set(r.subarray(c, c + n[1]), s), s += n[1]; } } -function qte(r, e, t10, o, n, s) { +function Nre(r, e, t8, o, n, s) { let a = 0, i = n[0], p = n[1], u = n[2], c = i + s[0], l = p + s[1]; for (let m = i; m < c; m++) for (let d = p; d < l; d++) { - let f = m * e + d * t10 + u; + let f = m * e + d * t8 + u; o.set(r.subarray(f, f + s[2]), a), a += s[2]; } } -function jte(r, e, t10, o, n, s, a) { +function Tre(r, e, t8, o, n, s, a) { let i = 0, p = s[0], u = s[1], c = s[2], l = p + a[0], m = u + a[1], d = c + a[2], f = s[3]; for (let h = p; h < l; h++) for (let g = u; g < m; g++) for (let x = c; x < d; x++) { - let b = h * e + g * t10 + x * o + f; + let b = h * e + g * t8 + x * o + f; n.set(r.subarray(b, b + a[3]), i), i += a[3]; } } -var SP = { kernelName: pa, backendName: "wasm", kernelFunc: Ao }; -function Xte(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = zt({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = mo({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = zt({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = Ao({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } }); - return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(d.dataId), g; +var BP = { kernelName: fa, backendName: "wasm", kernelFunc: Po }; +function _re(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = zt({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), f = ho({ inputs: { x: d }, backend: t8, attrs: { perm: u } }), h = zt({ inputs: { x: f }, backend: t8, attrs: { shape: c } }), g = Po({ inputs: { x: h }, backend: t8, attrs: { begin: l, size: m } }); + return t8.disposeData(d.dataId), t8.disposeData(f.dataId), t8.disposeData(d.dataId), g; } -var IP = { kernelName: js, backendName: "wasm", kernelFunc: Xte }; -var vP; -function Yte(r) { - vP = r.wasm.cwrap(Zo, null, ["number", "number", "boolean", "number", "number", "number"]); +var zP = { kernelName: Zs, backendName: "wasm", kernelFunc: _re }; +var VP; +function $re(r) { + VP = r.wasm.cwrap(Jo, null, ["number", "number", "boolean", "number", "number", "number"]); } -function Qte(r) { - let { backend: e, inputs: t10, attrs: o } = r, { x: n, weights: s } = t10, { size: a } = o, i = s.shape.reduce((l, m) => l * m, 1) !== 0, p = n.shape.length === 1 ? [a] : [n.shape[0], a], u = e.makeOutput(p, s.dtype); +function Ere(r) { + let { backend: e, inputs: t8, attrs: o } = r, { x: n, weights: s } = t8, { size: a } = o, i = s.shape.reduce((l, m) => l * m, 1) !== 0, p = n.shape.length === 1 ? [a] : [n.shape[0], a], u = e.makeOutput(p, s.dtype); function c(l) { return e.dataIdMap.get(l.dataId).id; } - return vP(c(n), a, i, c(s), we[s.dtype], c(u)), u; + return VP(c(n), a, i, c(s), we[s.dtype], c(u)), u; } -var kP = { kernelName: Zo, backendName: "wasm", setupFunc: Yte, kernelFunc: Qte }; -function Zte(r) { - let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.typedArrayFromHeap(o), a = t10.typedArrayFromHeap(n), i = C.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); - return t10.makeOutput([i.length], "int32", void 0, new Int32Array(i)); +var WP = { kernelName: Jo, backendName: "wasm", setupFunc: $re, kernelFunc: Ere }; +var Rre = true; +var UP = We(Ha, Rre); +function Dre(r) { + let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e, s = t8.typedArrayFromHeap(o), a = t8.typedArrayFromHeap(n), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); + return t8.makeOutput([i.length], "int32", void 0, new Int32Array(i)); } -var NP = { kernelName: Xs, backendName: "wasm", kernelFunc: Zte }; -function Pr(r) { - let { inputs: { x: e }, attrs: { dtype: t10 }, backend: o } = r, n = o.makeOutput(e.shape, t10), s = o.typedArrayFromHeap(e); +var GP = { kernelName: Js, backendName: "wasm", kernelFunc: Dre }; +function Mr(r) { + let { inputs: { x: e }, attrs: { dtype: t8 }, backend: o } = r, n = o.makeOutput(e.shape, t8), s = o.typedArrayFromHeap(e); return o.typedArrayFromHeap(n).set(s), n; } -var TP = { kernelName: ho, backendName: "wasm", kernelFunc: Pr }; -var _P = Ce(Jo); -var $P; -function Jte(r) { - $P = r.wasm.cwrap(go, null, ["number", "number", "number", "number"]); +var HP = { kernelName: yo, backendName: "wasm", kernelFunc: Mr }; +var KP = he(en); +var qP; +function Are(r) { + qP = r.wasm.cwrap(bo, null, ["number", "number", "number", "number"]); } -function ere(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t10.dataIdMap.get(n.dataId).id, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(p.dataId).id; - return $P(i, s, a, u), p; +function Fre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t8.dataIdMap.get(n.dataId).id, p = t8.makeOutput(n.shape, n.dtype), u = t8.dataIdMap.get(p.dataId).id; + return qP(i, s, a, u), p; } -var EP = { kernelName: go, backendName: "wasm", setupFunc: Jte, kernelFunc: ere }; -function gv(r) { - let { inputs: e, backend: t10 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape); - C.assertParamsConsistent(n, o); - let s = C.computeOutShape(e.map((d) => d.shape), o), a = e.filter((d) => y.sizeFromShape(d.shape) > 0); +var jP = { kernelName: bo, backendName: "wasm", setupFunc: Are, kernelFunc: Fre }; +function kv(r) { + let { inputs: e, backend: t8 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape); + w.assertParamsConsistent(n, o); + let s = w.computeOutShape(e.map((d) => d.shape), o), a = e.filter((d) => y.sizeFromShape(d.shape) > 0); if (a.length === 1) - return vp({ inputs: { x: a[0] }, backend: t10 }); - let i = t10.makeOutput(s, e[0].dtype); + return _p({ inputs: { x: a[0] }, backend: t8 }); + let i = t8.makeOutput(s, e[0].dtype); if (y.sizeFromShape(s) === 0) return i; if (a[0].dtype === "string") { - let d = a.map((w) => { - let k = [-1, y.sizeFromShape(w.shape.slice(o))]; - return zt({ inputs: { x: w }, backend: t10, attrs: { shape: k } }); - }), f = d.map((w) => ({ vals: t10.readSync(w.dataId), shape: w.shape })); - s = C.computeOutShape(d.map((w) => w.shape), 1); - let h = d[0].shape[0] === 1, g = np(f, s, e[0].dtype, h), x = C.computeOutShape(a.map((w) => w.shape), o); + let d = a.map((C) => { + let k = [-1, y.sizeFromShape(C.shape.slice(o))]; + return zt({ inputs: { x: C }, backend: t8, attrs: { shape: k } }); + }), f = d.map((C) => ({ vals: t8.readSync(C.dataId), shape: C.shape })); + s = w.computeOutShape(d.map((C) => C.shape), 1); + let h = d[0].shape[0] === 1, g = up(f, s, e[0].dtype, h), x = w.computeOutShape(a.map((C) => C.shape), o); i.shape = x; - let b = t10.dataIdMap.get(i.dataId); - return b.stringBytes = C.fromStringArrayToUint8(g), d.forEach((w) => t10.disposeData(w.dataId)), i; + let b = t8.dataIdMap.get(i.dataId); + return b.stringBytes = w.fromStringArrayToUint8(g), d.forEach((C) => t8.disposeData(C.dataId)), i; } let p = y.sizeFromShape(a[0].shape.slice(0, o)), u = 0, c = a.map((d) => { let f = y.sizeFromShape(d.shape.slice(o)); return u += f, f; - }), l = a.map((d) => t10.typedArrayFromHeap(d)), m = t10.typedArrayFromHeap(i); + }), l = a.map((d) => t8.typedArrayFromHeap(d)), m = t8.typedArrayFromHeap(i); for (let d = 0; d < p; d++) { let f = d * u; for (let h = 0; h < l.length; h++) { @@ -25096,830 +25203,855 @@ function gv(r) { } return i; } -var RP = { kernelName: Ys, backendName: "wasm", kernelFunc: gv }; -var DP; -function tre(r) { - DP = r.wasm.cwrap(en, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var XP = { kernelName: ea, backendName: "wasm", kernelFunc: kv }; +var YP; +function Pre(r) { + YP = r.wasm.cwrap(tn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function rre(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = t10, d = C.convertConv2DDataFormat(m), f = C.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, w = f.padInfo.bottom, S = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, R = f.strideWidth, D = f.inChannels, F = f.outChannels, O = f.padInfo.type === "SAME" ? 1 : 0; +function Ore(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = t8, d = w.convertConv2DDataFormat(m), f = w.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, C = f.padInfo.bottom, S = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, R = f.strideWidth, D = f.inChannels, P = f.outChannels, O = f.padInfo.type === "SAME" ? 1 : 0; if (f.dataFormat !== "channelsLast") throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`); let M = o.makeOutput(f.outShape, "float32"), L = o.dataIdMap.get(M.dataId).id; - return DP(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, w, S, O, k, _, E, R, D, F, L), M; + return YP(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, S, O, k, _, E, R, D, P, L), M; } -var AP = { kernelName: en, backendName: "wasm", setupFunc: tre, kernelFunc: rre }; -var FP; -function ore(r) { - FP = r.wasm.cwrap(tn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var QP = { kernelName: tn, backendName: "wasm", setupFunc: Pre, kernelFunc: Ore }; +var ZP; +function Mre(r) { + ZP = r.wasm.cwrap(rn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function nre(r) { - let { backend: e, inputs: t10, attrs: o } = r, { dy: n, filter: s } = t10, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = C.convertConv2DDataFormat(p), d = C.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: w, outChannels: S, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = d, D = h - 1 - d.padInfo.top, F = g - 1 - d.padInfo.left, O = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [B, z, U] = y.computeStrides(s.shape), j = M[0], H = O ? M[1] : M[2], X = O ? M[2] : 1, J = O ? 1 : M[1], re = L[0], ne = O ? L[1] : L[2], ee = O ? L[2] : 1, oe = O ? 1 : L[1], ie = e.makeOutput(d.inShape, "float32"), le = e.dataIdMap.get(ie.dataId).id, ye = e.dataIdMap.get(n.dataId).id, _e = e.dataIdMap.get(s.dataId).id; - return FP(ye, _e, f, h, g, b, w, x, k, _, S, E, R, D, F, B, z, U, j, H, X, J, re, ne, ee, oe, le), ie; +function Lre(r) { + let { backend: e, inputs: t8, attrs: o } = r, { dy: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = w.convertConv2DDataFormat(p), d = w.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: S, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = d, D = h - 1 - d.padInfo.top, P = g - 1 - d.padInfo.left, O = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [B, z, U] = y.computeStrides(s.shape), j = M[0], q = O ? M[1] : M[2], Y = O ? M[2] : 1, J = O ? 1 : M[1], re = L[0], ne = O ? L[1] : L[2], ee = O ? L[2] : 1, oe = O ? 1 : L[1], ie = e.makeOutput(d.inShape, "float32"), le = e.dataIdMap.get(ie.dataId).id, be = e.dataIdMap.get(n.dataId).id, _e = e.dataIdMap.get(s.dataId).id; + return ZP(be, _e, f, h, g, b, C, x, k, _, S, E, R, D, P, B, z, U, j, q, Y, J, re, ne, ee, oe, le), ie; } -var PP = { kernelName: tn, backendName: "wasm", setupFunc: ore, kernelFunc: nre }; -var OP; -function sre(r) { - OP = r.wasm.cwrap(rn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var JP = { kernelName: rn, backendName: "wasm", setupFunc: Mre, kernelFunc: Lre }; +var eO; +function Bre(r) { + eO = r.wasm.cwrap(on, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function are(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; +function zre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; if (n.dtype !== "float32") throw new Error(`Tensor x must have dtype float32, got ${n.dtype}`); if (s.dtype !== "float32") throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`); - let u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), c = t10.makeOutput(u.outShape, n.dtype); - return OP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; + let u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = t8.makeOutput(u.outShape, n.dtype); + return eO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } -var MP = { kernelName: rn, backendName: "wasm", setupFunc: sre, kernelFunc: are }; -var LP; -function ire(r) { - LP = r.wasm.cwrap(za, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var tO = { kernelName: on, backendName: "wasm", setupFunc: Bre, kernelFunc: zre }; +var rO; +function Vre(r) { + rO = r.wasm.cwrap(Ka, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function ure(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o; +function Wre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o; if (n.dtype !== "float32") throw new Error(`Tensor dy must have dtype float32, got ${n.dtype}`); if (s.dtype !== "float32") throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`); - let u = C.computeConv3DInfo(n.shape, p, a, 1, i), c = t10.makeOutput(u.filterShape, s.dtype); - return LP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; + let u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = t8.makeOutput(u.filterShape, s.dtype); + return rO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } -var BP = { kernelName: za, backendName: "wasm", setupFunc: ire, kernelFunc: ure }; -var zP; -function pre(r) { - zP = r.wasm.cwrap(on, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var oO = { kernelName: Ka, backendName: "wasm", setupFunc: Vre, kernelFunc: Wre }; +var nO; +function Ure(r) { + nO = r.wasm.cwrap(nn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function cre(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o; +function Gre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o; if (n.dtype !== "float32") throw new Error(`Tensor dy must have dtype float32, got ${n.dtype}`); if (s.dtype !== "float32") throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`); - let u = C.computeConv3DInfo(p, s.shape, i, 1, a), c = t10.makeOutput(u.inShape, n.dtype); - return zP(t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; + let u = w.computeConv3DInfo(p, s.shape, i, 1, a), c = t8.makeOutput(u.inShape, n.dtype); + return nO(t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } -var VP = { kernelName: on, backendName: "wasm", setupFunc: pre, kernelFunc: cre }; -var WP = Ce(nn); -var UP = Ce(sn); -var xv; +var sO = { kernelName: nn, backendName: "wasm", setupFunc: Ure, kernelFunc: Gre }; +var aO = he(sn); +var iO = he(an); +var Nv; (function(r) { r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest"; -})(xv || (xv = {})); -var GP; -function lre(r) { - GP = r.wasm.cwrap(pn, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]); +})(Nv || (Nv = {})); +var uO; +function Hre(r) { + uO = r.wasm.cwrap(cn, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]); } -function mre(r) { - let { backend: e, inputs: t10, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t10, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = e.dataIdMap.get(i.dataId), h; - i.dtype !== "float32" && (h = Pr({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), f = e.dataIdMap.get(h.dataId)); - let g = f.id, x = e.dataIdMap.get(p.dataId).id, b = e.dataIdMap.get(u.dataId).id, w = e.makeOutput(d, "float32"), S = e.dataIdMap.get(w.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer); - return GP(g, x, b, c, k, l, m, xv[n], s, S), h != null && e.disposeData(h.dataId), w; +function Kre(r) { + let { backend: e, inputs: t8, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t8, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = e.dataIdMap.get(i.dataId), h; + i.dtype !== "float32" && (h = Mr({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), f = e.dataIdMap.get(h.dataId)); + let g = f.id, x = e.dataIdMap.get(p.dataId).id, b = e.dataIdMap.get(u.dataId).id, C = e.makeOutput(d, "float32"), S = e.dataIdMap.get(C.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer); + return uO(g, x, b, c, k, l, m, Nv[n], s, S), h != null && e.disposeData(h.dataId), C; } -var HP = { kernelName: pn, backendName: "wasm", setupFunc: lre, kernelFunc: mre }; -var KP; -function dre(r) { - KP = r.wasm.cwrap(an, null, ["number", "number", "number", "number", "number", "number"]); +var pO = { kernelName: cn, backendName: "wasm", setupFunc: Hre, kernelFunc: Kre }; +var cO; +function qre(r) { + cO = r.wasm.cwrap(un, null, ["number", "number", "number", "number", "number", "number"]); } -function fre(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; +function jre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumprod does not support ${n.dtype} tensors in the WASM backend`); - let u = C.getAxesPermutation([s], p), c = n; - u !== null && (c = mo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 })); - let l = C.getInnerMostAxes(1, p)[0]; - C.assertAxesAreInnerMostDims("cumprod", [l], p); - let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id; - KP(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); + let u = w.getAxesPermutation([s], p), c = n; + u !== null && (c = ho({ inputs: { x: n }, attrs: { perm: u }, backend: t8 })); + let l = w.getInnerMostAxes(1, p)[0]; + w.assertAxesAreInnerMostDims("cumprod", [l], p); + let m = t8.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t8.dataIdMap.get(c.dataId).id, h = t8.dataIdMap.get(m.dataId).id; + cO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); let g = m; if (u !== null) { - let x = C.getUndoAxesPermutation(u); - g = mo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId); + let x = w.getUndoAxesPermutation(u); + g = ho({ inputs: { x: m }, attrs: { perm: x }, backend: t8 }), t8.disposeData(c.dataId), t8.disposeData(m.dataId); } return g; } -var qP = { kernelName: an, backendName: "wasm", setupFunc: dre, kernelFunc: fre }; -var jP; -function hre(r) { - jP = r.wasm.cwrap(un, null, ["number", "number", "number", "number", "number", "number"]); +var lO = { kernelName: un, backendName: "wasm", setupFunc: qre, kernelFunc: jre }; +var mO; +function Xre(r) { + mO = r.wasm.cwrap(pn, null, ["number", "number", "number", "number", "number", "number"]); } -function gre(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; +function Yre(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumsum does not support ${n.dtype} tensors in the WASM backend`); - let u = C.getAxesPermutation([s], p), c = n; - u !== null && (c = mo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 })); - let l = C.getInnerMostAxes(1, p)[0]; - C.assertAxesAreInnerMostDims("cumsum", [l], p); - let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id; - jP(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); + let u = w.getAxesPermutation([s], p), c = n; + u !== null && (c = ho({ inputs: { x: n }, attrs: { perm: u }, backend: t8 })); + let l = w.getInnerMostAxes(1, p)[0]; + w.assertAxesAreInnerMostDims("cumsum", [l], p); + let m = t8.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t8.dataIdMap.get(c.dataId).id, h = t8.dataIdMap.get(m.dataId).id; + mO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); let g = m; if (u !== null) { - let x = C.getUndoAxesPermutation(u); - g = mo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId); + let x = w.getUndoAxesPermutation(u); + g = ho({ inputs: { x: m }, attrs: { perm: x }, backend: t8 }), t8.disposeData(c.dataId), t8.disposeData(m.dataId); } return g; } -var XP = { kernelName: un, backendName: "wasm", setupFunc: hre, kernelFunc: gre }; -var YP; -function xre(r) { - YP = r.wasm.cwrap("DenseBincount", null, ["number", "array", "number", "number", "boolean", "number", "number", "boolean", "number"]); +var dO = { kernelName: pn, backendName: "wasm", setupFunc: Xre, kernelFunc: Yre }; +var fO; +function Qre(r) { + fO = r.wasm.cwrap("DenseBincount", null, ["number", "array", "number", "number", "boolean", "number", "number", "boolean", "number"]); } -function yre(r) { - let { backend: e, inputs: t10, attrs: o } = r, { x: n, weights: s } = t10, { size: a, binaryOutput: i } = o, p = s.shape.reduce((m, d) => m * d, 1) !== 0, u = n.shape.length === 1 ? [a] : [n.shape[0], a], c = e.makeOutput(u, s.dtype); +function Zre(r) { + let { backend: e, inputs: t8, attrs: o } = r, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o, p = s.shape.reduce((m, d) => m * d, 1) !== 0, u = n.shape.length === 1 ? [a] : [n.shape[0], a], c = e.makeOutput(u, s.dtype); function l(m) { return e.dataIdMap.get(m.dataId).id; } - return YP(l(n), new Uint8Array(new Int32Array(n.shape).buffer), n.shape.length, a, p, l(s), we[s.dtype], i, l(c)), c; + return fO(l(n), new Uint8Array(new Int32Array(n.shape).buffer), n.shape.length, a, p, l(s), we[s.dtype], i, l(c)), c; } -var QP = { kernelName: Qs, backendName: "wasm", setupFunc: xre, kernelFunc: yre }; -var ZP; -function bre(r) { - ZP = r.wasm.cwrap(cn, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]); +var hO = { kernelName: ta, backendName: "wasm", setupFunc: Qre, kernelFunc: Zre }; +var gO; +function Jre(r) { + gO = r.wasm.cwrap(ln, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]); } -function Cre(r) { - let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = e.makeOutput(f, "float32"), x = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), w = new Uint8Array(new Int32Array(f).buffer), S = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id; - return ZP(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, w, S, f.length, k), h; +function eoe(r) { + let { backend: e, inputs: t8, attrs: o } = r, { x: n } = t8, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = e.makeOutput(f, "float32"), x = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(f).buffer), S = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id; + return gO(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, S, f.length, k), h; } -var JP = { kernelName: cn, backendName: "wasm", setupFunc: bre, kernelFunc: Cre }; -var eO; -function wre(r) { - eO = r.wasm.cwrap(ln, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var xO = { kernelName: ln, backendName: "wasm", setupFunc: Jre, kernelFunc: eoe }; +var yO; +function toe(r) { + yO = r.wasm.cwrap(mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Sre(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = t10, m = u == null ? [1, 1] : u, d = C.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, w = d.padInfo.left, S = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, R = d.inChannels, D = d.outChannels, F = d.padInfo.type === "SAME" ? 1 : 0; +function roe(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = t8, m = u == null ? [1, 1] : u, d = w.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, C = d.padInfo.left, S = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, R = d.inChannels, D = d.outChannels, P = d.padInfo.type === "SAME" ? 1 : 0; if (d.dataFormat !== "channelsLast") throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`); let O = o.makeOutput(d.outShape, "float32"), M = o.dataIdMap.get(O.dataId).id; - return eO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, w, F, S, k, _, E, R, D, M), O; + return yO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, P, S, k, _, E, R, D, M), O; } -var tO = { kernelName: ln, backendName: "wasm", setupFunc: wre, kernelFunc: Sre }; -var rO; -function Ire(r) { - rO = r.wasm.cwrap("Diag", null, ["number", "number", "number", "number"]); +var bO = { kernelName: mn, backendName: "wasm", setupFunc: toe, kernelFunc: roe }; +var CO; +function ooe(r) { + CO = r.wasm.cwrap("Diag", null, ["number", "number", "number", "number"]); } -function vre(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t10.makeOutput([...o.shape, ...o.shape], o.dtype); - return rO(t10.dataIdMap.get(o.dataId).id, we[o.dtype], n, t10.dataIdMap.get(s.dataId).id), s; +function noe(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t8.makeOutput([...o.shape, ...o.shape], o.dtype); + return CO(t8.dataIdMap.get(o.dataId).id, we[o.dtype], n, t8.dataIdMap.get(s.dataId).id), s; } -var oO = { kernelName: Zs, backendName: "wasm", setupFunc: Ire, kernelFunc: vre }; -var nO; -function kre(r) { - nO = r.wasm.cwrap(mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var wO = { kernelName: ra, backendName: "wasm", setupFunc: ooe, kernelFunc: noe }; +var SO; +function soe(r) { + SO = r.wasm.cwrap(dn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Nre(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; +function aoe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; if (n.dtype !== s.dtype) throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${n.dtype} and ${s.dtype}`); - let u = C.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = t10.makeOutput(u.outShape, n.dtype); - return nO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(c.dataId).id, we[n.dtype], u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.filterHeight, u.filterWidth, u.padInfo.top, u.padInfo.left), c; + let u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = t8.makeOutput(u.outShape, n.dtype); + return SO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(c.dataId).id, we[n.dtype], u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.filterHeight, u.filterWidth, u.padInfo.top, u.padInfo.left), c; } -var sO = { kernelName: mn, backendName: "wasm", setupFunc: kre, kernelFunc: Nre }; -var aO; -function Tre(r) { - aO = r.wasm.cwrap(Ai, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var IO = { kernelName: dn, backendName: "wasm", setupFunc: soe, kernelFunc: aoe }; +var vO; +function ioe(r) { + vO = r.wasm.cwrap(Mi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function _re(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o; +function uoe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o; if (n.dtype !== s.dtype || n.dtype !== a.dtype) throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`); - let c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t10.makeOutput(s.shape, s.dtype); - return aO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; + let c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t8.makeOutput(s.shape, s.dtype); + return vO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(a.dataId).id, t8.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; } -var iO = { kernelName: Ai, backendName: "wasm", setupFunc: Tre, kernelFunc: _re }; -var uO; -function $re(r) { - uO = r.wasm.cwrap(Di, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var kO = { kernelName: Mi, backendName: "wasm", setupFunc: ioe, kernelFunc: uoe }; +var NO; +function poe(r) { + NO = r.wasm.cwrap(Oi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Ere(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o; +function coe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o; if (n.dtype !== s.dtype || n.dtype !== a.dtype) throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`); - let c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t10.makeOutput(n.shape, n.dtype); - return uO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; + let c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t8.makeOutput(n.shape, n.dtype); + return NO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(a.dataId).id, t8.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; } -var pO = { kernelName: Di, backendName: "wasm", setupFunc: $re, kernelFunc: Ere }; -var cO = Ce(fn); -var lO; -function Rre(r) { - lO = r.wasm.cwrap(Va, null, ["number", "number", "number"]); +var TO = { kernelName: Oi, backendName: "wasm", setupFunc: poe, kernelFunc: coe }; +var _O = he(hn); +var $O; +function loe(r) { + $O = r.wasm.cwrap(qa, null, ["number", "number", "number"]); } -function Dre(r) { - let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = t10.makeOutput(n.shape, "float32"), a = (i) => t10.dataIdMap.get(i.dataId).id; - return lO(a(n), a(o), a(s)), s; +function moe(r) { + let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e, s = t8.makeOutput(n.shape, "float32"), a = (i) => t8.dataIdMap.get(i.dataId).id; + return $O(a(n), a(o), a(s)), s; } -var mO = { kernelName: Va, backendName: "wasm", setupFunc: Rre, kernelFunc: Dre }; -var Are = false; -var dO = Je(hn, Are, "bool"); -var fO = Ce(gn, "float32"); -function Pg(r) { - let { inputs: e, attrs: t10, backend: o } = r, { input: n } = e, { dim: s } = t10, a = n.shape.length, i = n.shape.slice(), p = s; +var EO = { kernelName: qa, backendName: "wasm", setupFunc: loe, kernelFunc: moe }; +var doe = false; +var RO = We(xn, doe, "bool"); +var DO = he(gn); +var AO = he(yn, "float32"); +function Og(r) { + let { inputs: e, attrs: t8, backend: o } = r, { input: n } = e, { dim: s } = t8, a = n.shape.length, i = n.shape.slice(), p = s; return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), zt({ inputs: { x: n }, backend: o, attrs: { shape: i } }); } -var hO = { kernelName: Js, backendName: "wasm", kernelFunc: Pg }; -var gO = Ce(xn, "float32"); -function yv(r) { - let { attrs: { shape: e, value: t10, dtype: o }, backend: n } = r, s = n.makeOutput(e, o); - return n.typedArrayFromHeap(s).fill(t10), s; +var FO = { kernelName: oa, backendName: "wasm", kernelFunc: Og }; +var PO = he(bn, "float32"); +function Tv(r) { + let { attrs: { shape: e, value: t8, dtype: o }, backend: n } = r, s = n.makeOutput(e, o); + return n.typedArrayFromHeap(s).fill(t8), s; } -var xO = { kernelName: ea, backendName: "wasm", kernelFunc: yv }; -var yO; -function Fre(r) { - yO = r.wasm.cwrap(yn, null, ["number", "number", "number", "number", "number", "number"]); +var OO = { kernelName: na, backendName: "wasm", kernelFunc: Tv }; +var MO; +function foe(r) { + MO = r.wasm.cwrap(Cn, null, ["number", "number", "number", "number", "number", "number"]); } -function Pre(r) { - let { inputs: e, backend: t10 } = r, { image: o } = e, n = t10.makeOutput(o.shape, o.dtype), s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape; - return yO(s, i, p, u, c, a), n; +function hoe(r) { + let { inputs: e, backend: t8 } = r, { image: o } = e, n = t8.makeOutput(o.shape, o.dtype), s = t8.dataIdMap.get(o.dataId).id, a = t8.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape; + return MO(s, i, p, u, c, a), n; } -var bO = { kernelName: yn, backendName: "wasm", kernelFunc: Pre, setupFunc: Fre }; -var CO = Ce(bn); -var Ore = false; -var wO = Je(Cn, Ore); -var SO; -function Mre(r) { - SO = r.wasm.cwrap(wn, null, ["number", "number", "number", "number", "number", "number", "number"]); +var LO = { kernelName: Cn, backendName: "wasm", kernelFunc: hoe, setupFunc: foe }; +var BO = he(wn); +var goe = false; +var zO = We(Sn, goe); +var VO; +function xoe(r) { + VO = r.wasm.cwrap(In, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function Lre(r) { - let { backend: e, inputs: t10, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t10, c = e.dataIdMap.get(s.dataId).id, l = e.dataIdMap.get(a.dataId).id, m = e.dataIdMap.get(i.dataId).id, d = p != null ? e.dataIdMap.get(p.dataId).id : 0, f = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype); +function yoe(r) { + let { backend: e, inputs: t8, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t8, c = e.dataIdMap.get(s.dataId).id, l = e.dataIdMap.get(a.dataId).id, m = e.dataIdMap.get(i.dataId).id, d = p != null ? e.dataIdMap.get(p.dataId).id : 0, f = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype); if (y.sizeFromShape(s.shape) === 0) return h; let g = e.dataIdMap.get(h.dataId).id; - return SO(c, l, m, d, f, n, g), h; + return VO(c, l, m, d, f, n, g), h; } -var IO = { kernelName: wn, backendName: "wasm", setupFunc: Mre, kernelFunc: Lre }; -var vO; -function Bre(r) { - vO = r.wasm.cwrap(Co, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var WO = { kernelName: In, backendName: "wasm", setupFunc: xoe, kernelFunc: yoe }; +var UO; +function boe(r) { + UO = r.wasm.cwrap(Io, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function zre(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = C.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = bu[d]; +function Coe(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t8, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = Iu[d]; if (g == null) throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`); - let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, w = h.outChannels, S = 0; + let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, S = 0; if (a != null) { let ee = o.dataIdMap.get(a.dataId); if (ee.shape.length !== 1) throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`); - if (ee.shape[0] !== w) - throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`); + if (ee.shape[0] !== C) + throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`); S = ee.id; } - let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, F = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, H = h.inHeight, X = h.inWidth; + let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, P = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, q = h.inHeight, Y = h.inWidth; if (l !== "NHWC") throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`); let J = o.makeOutput(h.outShape, "float32"), re = o.dataIdMap.get(J.dataId).id, ne = i == null ? 0 : o.dataIdMap.get(i.dataId).id; - return vO(x, j, H, X, b, k, _, S, E, R, D, F, U, O, M, L, B, z, w, g, ne, f || 0, re), J; + return UO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J; } -var kO = { kernelName: Co, backendName: "wasm", setupFunc: Bre, kernelFunc: zre }; -var NO; -function Vre(r) { - NO = r.wasm.cwrap(wo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var GO = { kernelName: Io, backendName: "wasm", setupFunc: boe, kernelFunc: Coe }; +var HO; +function woe(r) { + HO = r.wasm.cwrap(vo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Wre(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = C.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = bu[d]; +function Soe(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t8, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = Iu[d]; if (g == null) throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`); - let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, w = h.outChannels, S = 0; + let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, S = 0; if (a != null) { let ee = o.dataIdMap.get(a.dataId); if (ee.shape.length !== 1) throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`); - if (ee.shape[0] !== w) - throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`); + if (ee.shape[0] !== C) + throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`); S = ee.id; } - let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, F = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, H = h.inHeight, X = h.inWidth; + let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, P = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, q = h.inHeight, Y = h.inWidth; if (l !== "NHWC") throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`); let J = o.makeOutput(h.outShape, "float32"), re = o.dataIdMap.get(J.dataId).id, ne = i == null ? 0 : o.dataIdMap.get(i.dataId).id; - return NO(x, j, H, X, b, k, _, S, E, R, D, F, U, O, M, L, B, z, w, g, ne, f || 0, re), J; + return HO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J; } -var TO = { kernelName: wo, backendName: "wasm", setupFunc: Vre, kernelFunc: Wre }; -var _O; -function Ure(r) { - _O = r.wasm.cwrap(Sn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]); +var KO = { kernelName: vo, backendName: "wasm", setupFunc: woe, kernelFunc: Soe }; +var qO; +function Ioe(r) { + qO = r.wasm.cwrap(vn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]); } -function Gre(r) { - let { backend: e, inputs: t10 } = r, { params: o, indices: n } = t10, [s, a, i, p] = of.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype); +function voe(r) { + let { backend: e, inputs: t8 } = r, { params: o, indices: n } = t8, [s, a, i, p] = nf.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype); if (a === 0) return u; let c = n.shape, l = c[c.length - 1], d = e.dataIdMap.get(o.dataId).id, h = e.dataIdMap.get(n.dataId).id, g = new Uint8Array(new Int32Array(p).buffer), x = e.dataIdMap.get(u.dataId).id; - return _O(d, we[o.dtype], h, a, l, i, g, x), u; + return qO(d, we[o.dtype], h, a, l, i, g, x), u; } -var $O = { kernelName: Sn, backendName: "wasm", setupFunc: Ure, kernelFunc: Gre }; -var EO; -function Hre(r) { - EO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]); +var jO = { kernelName: vn, backendName: "wasm", setupFunc: Ioe, kernelFunc: voe }; +var XO; +function koe(r) { + XO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]); } -function Kre(r) { - let { backend: e, inputs: t10, attrs: o } = r, { x: n, indices: s } = t10, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p]; +function Noe(r) { + let { backend: e, inputs: t8, attrs: o } = r, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p]; for (let D = 0; D < u.length; ++D) { - let F = u[D]; - y.assert(F <= c - 1 && F >= 0, () => `GatherV2: the index value ${F} is not in [0, ${c - 1}]`); + let P = u[D]; + y.assert(P <= c - 1 && P >= 0, () => `GatherV2: the index value ${P} is not in [0, ${c - 1}]`); } - let l = C.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = zt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = zt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype); + let l = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = zt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = zt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype); if (y.sizeFromShape(n.shape) === 0) return g; - let x = m.shape.length - 1, w = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), R = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer); - return EO(w, we[n.dtype], E, x, k, l.batchSize, R, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g; + let x = m.shape.length - 1, C = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), R = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer); + return XO(C, we[n.dtype], E, x, k, l.batchSize, R, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g; } -var RO = { kernelName: ta, backendName: "wasm", setupFunc: Hre, kernelFunc: Kre }; -var qre = false; -var DO = Je(In, qre, "bool"); -var jre = false; -var AO = Je(vn, jre, "bool"); -var FO = Ce(kn, "bool"); -var PO = Ce(Nn, "bool"); -var OO = Ce(Tn, "bool"); -var MO; -function Xre(r) { - MO = r.wasm.cwrap(_n, null, ["number", "number", "number", "number"]); +var YO = { kernelName: sa, backendName: "wasm", setupFunc: koe, kernelFunc: Noe }; +var Toe = false; +var QO = We(kn, Toe, "bool"); +var _oe = false; +var ZO = We(Nn, _oe, "bool"); +var JO = he(Tn, "bool"); +var eM = he(_n, "bool"); +var tM = he($n, "bool"); +var rM; +function $oe(r) { + rM = r.wasm.cwrap(En, null, ["number", "number", "number", "number"]); } -function Yre(r) { - let { inputs: { x: e }, attrs: { alpha: t10 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32"); +function Eoe(r) { + let { inputs: { x: e }, attrs: { alpha: t8 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32"); if (y.sizeFromShape(e.shape) !== 0) { let a = o.dataIdMap.get(s.dataId).id; - MO(n, we[e.dtype], t10, a); + rM(n, we[e.dtype], t8, a); } return s; } -var LO = { kernelName: _n, backendName: "wasm", setupFunc: Xre, kernelFunc: Yre }; -var Qre = false; -var BO = Je($n, Qre, "bool"); -var Zre = false; -var zO = Je(En, Zre, "bool"); -var VO; -function Jre(r) { - VO = r.wasm.cwrap(Rn, null, ["number", "number", "number", "number"]); +var oM = { kernelName: En, backendName: "wasm", setupFunc: $oe, kernelFunc: Eoe }; +var Roe = false; +var nM = We(Rn, Roe, "bool"); +var Doe = false; +var sM = We(Dn, Doe, "bool"); +var aM; +function Aoe(r) { + aM = r.wasm.cwrap(An, null, ["number", "number", "number", "number"]); } -function eoe(r) { - let { attrs: e, backend: t10 } = r, { start: o, stop: n, num: s } = e, a = Math.floor(s), i = t10.makeOutput([a], "float32"); - return VO(t10.dataIdMap.get(i.dataId).id, o, n, a), i; +function Foe(r) { + let { attrs: e, backend: t8 } = r, { start: o, stop: n, num: s } = e, a = Math.floor(s), i = t8.makeOutput([a], "float32"); + return aM(t8.dataIdMap.get(i.dataId).id, o, n, a), i; } -var WO = { kernelName: Rn, backendName: "wasm", setupFunc: Jre, kernelFunc: eoe }; -var UO = Ce(Dn); -var GO = Ce(An); -var toe = false; -var HO = Je(Fn, toe, "bool"); -var KO = Ce(Pn); -var roe = false; -var qO = Je(On, roe, "bool"); -var ooe = false; -var jO = Je(m0, ooe, "bool"); -var XO; -function noe(r) { - XO = r.wasm.cwrap(Mn, null, ["number", "number", "number", "number", "number", "number", "number"]); +var iM = { kernelName: An, backendName: "wasm", setupFunc: Aoe, kernelFunc: Foe }; +var uM = he(Fn); +var pM = he(Pn); +var Poe = false; +var cM = We(On, Poe, "bool"); +var lM = he(Mn); +var Ooe = false; +var mM = We(Ln, Ooe, "bool"); +var Moe = false; +var dM = We(w0, Moe, "bool"); +var fM; +function Loe(r) { + fM = r.wasm.cwrap(Bn, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function soe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; +function Boe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; if (n.dtype !== "float32") throw new Error("LRN error: x must have dtype float32"); - let u = t10.makeOutput(n.shape, n.dtype); - return XO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u; + let u = t8.makeOutput(n.shape, n.dtype); + return fM(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u; } -var YO = { kernelName: Mn, backendName: "wasm", setupFunc: noe, kernelFunc: soe }; -var QO; -function aoe(r) { - QO = r.wasm.cwrap(Ua, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var hM = { kernelName: Bn, backendName: "wasm", setupFunc: Loe, kernelFunc: Boe }; +var gM; +function zoe(r) { + gM = r.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function ioe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; +function Voe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; if (n.dtype !== "float32" || s.dtype !== "float32" || a.dtype !== "float32") throw new Error("LRNGrad error: x, y, and dy must have dtype float32"); - let l = t10.makeOutput(n.shape, n.dtype); - return QO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, a.shape[3], i, p, u, c), l; + let l = t8.makeOutput(n.shape, n.dtype); + return gM(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(a.dataId).id, t8.dataIdMap.get(l.dataId).id, a.shape[3], i, p, u, c), l; } -var ZO = { kernelName: Ua, backendName: "wasm", setupFunc: aoe, kernelFunc: ioe }; -var JO; -function uoe(r) { - JO = r.wasm.cwrap(Ln, null, ["number", "number", "number", "number"]); +var xM = { kernelName: ja, backendName: "wasm", setupFunc: zoe, kernelFunc: Voe }; +var yM; +function Woe(r) { + yM = r.wasm.cwrap(zn, null, ["number", "number", "number", "number"]); } -function poe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); +function Uoe(r) { + let { backend: e, inputs: t8, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t8, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e); if (d) { - let w = e.dataIdMap.get(c.dataId).id; - u = c, p = w; + let C = e.dataIdMap.get(c.dataId).id; + u = c, p = C; } let f = u.shape.length; - C.assertAxesAreInnerMostDims("max", l, f); - let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); + w.assertAxesAreInnerMostDims("max", l, f); + let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { - let w = e.dataIdMap.get(b.dataId).id; - JO(p, we[a.dtype], x, w); + let C = e.dataIdMap.get(b.dataId).id; + yM(p, we[a.dtype], x, C); } if (d && e.disposeData(c.dataId), s) { - let w = C.expandShapeToKeepDim(b.shape, m); - b.shape = w; + let C = w.expandShapeToKeepDim(b.shape, m); + b.shape = C; } return b; } -var eM = { kernelName: Ln, backendName: "wasm", setupFunc: uoe, kernelFunc: poe }; -var coe = false; -var tM = Je(Bn, coe); -var rM; -function loe(r) { - rM = r.wasm.cwrap(zn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var bM = { kernelName: zn, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe }; +var Goe = false; +var CM = We(Vn, Goe); +var wM; +function Hoe(r) { + wM = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function moe(r) { - let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id; +function Koe(r) { + let { inputs: e, attrs: t8, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id; y.assert(n.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`); - let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = C.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, w = c.strideHeight, S = c.strideWidth, k = c.inChannels, _ = c.outChannels; + let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t8, c = w.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, S = c.strideWidth, k = c.inChannels, _ = c.outChannels; if (c.dataFormat !== "channelsLast") throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`); let E = o.makeOutput(c.outShape, "float32"), R = o.dataIdMap.get(E.dataId).id; - return rM(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, w, S, k, _, R), E; + return wM(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, S, k, _, R), E; } -var oM = { kernelName: zn, backendName: "wasm", setupFunc: loe, kernelFunc: moe }; -var nM; -function doe(r) { - nM = r.wasm.cwrap("MaxPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var SM = { kernelName: Wn, backendName: "wasm", setupFunc: Hoe, kernelFunc: Koe }; +var IM; +function qoe(r) { + IM = r.wasm.cwrap("MaxPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function foe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.makeOutput(c.outShape, n.dtype); - return nM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; +function joe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.makeOutput(c.outShape, n.dtype); + return IM(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; } -var sM = { kernelName: ra, backendName: "wasm", setupFunc: doe, kernelFunc: foe }; -var aM; -function hoe(r) { - aM = r.wasm.cwrap("MaxPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var vM = { kernelName: aa, backendName: "wasm", setupFunc: qoe, kernelFunc: joe }; +var kM; +function Xoe(r) { + kM = r.wasm.cwrap("MaxPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function goe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.makeOutput(s.shape, s.dtype); - return aM(t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; +function Yoe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = t8.makeOutput(s.shape, s.dtype); + return kM(t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; } -var iM = { kernelName: Li, backendName: "wasm", setupFunc: hoe, kernelFunc: goe }; -var uM; -function xoe(r) { - uM = r.wasm.cwrap(Vn, null, ["number, number, number"]); +var NM = { kernelName: Ui, backendName: "wasm", setupFunc: Xoe, kernelFunc: Yoe }; +var TM; +function Qoe(r) { + TM = r.wasm.cwrap("MaxPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function yoe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l; +function Zoe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool2DInfo(s.shape, a, i, 1, p, u), l = t8.makeOutput(s.shape, s.dtype); + return TM(t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), l; +} +var _M = { kernelName: Wi, backendName: "wasm", setupFunc: Qoe, kernelFunc: Zoe }; +var $M; +function Joe(r) { + $M = r.wasm.cwrap("MaxPoolWithArgmax", null, ["number", "number", "number", "number", "boolean", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +} +function ene(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, includeBatchInIndex: p } = o; + y.assert(n.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`); + let u = [1, 1]; + y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); + let c = w.computePool2DInfo(n.shape, s, a, [1, 1], i), l = t8.makeOutput(c.outShape, n.dtype), m = t8.makeOutput(c.outShape, "int32"); + return $M(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, t8.dataIdMap.get(m.dataId).id, we[n.dtype], p, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), [l, m]; +} +var EM = { kernelName: ia, backendName: "wasm", setupFunc: Joe, kernelFunc: ene }; +var RM; +function tne(r) { + RM = r.wasm.cwrap(Un, null, ["number, number, number"]); +} +function rne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e), f = l; if (d) { let S = e.dataIdMap.get(c.dataId).id; - S !== i && (u = c, p = S, f = C.getInnerMostAxes(f.length, u.shape.length)); + S !== i && (u = c, p = S, f = w.getInnerMostAxes(f.length, u.shape.length)); } - C.assertAxesAreInnerMostDims("mean", f, u.shape.length); - let [h, g] = C.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u; - u.dtype !== "float32" && (b = Pr({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id); - let w = e.makeOutput(h, "float32"); + w.assertAxesAreInnerMostDims("mean", f, u.shape.length); + let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u; + u.dtype !== "float32" && (b = Mr({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id); + let C = e.makeOutput(h, "float32"); if (y.sizeFromShape(u.shape) !== 0) { - let S = e.dataIdMap.get(w.dataId).id; - uM(p, x, S); + let S = e.dataIdMap.get(C.dataId).id; + RM(p, x, S); } if (d && e.disposeData(c.dataId), s) { - let S = C.expandShapeToKeepDim(w.shape, m); - w.shape = S; + let S = w.expandShapeToKeepDim(C.shape, m); + C.shape = S; } - return u.dtype !== "float32" && e.disposeData(b.dataId), w; + return u.dtype !== "float32" && e.disposeData(b.dataId), C; } -var pM = { kernelName: Vn, backendName: "wasm", setupFunc: xoe, kernelFunc: yoe }; -var cM; -function boe(r) { - cM = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number"]); +var DM = { kernelName: Un, backendName: "wasm", setupFunc: tne, kernelFunc: rne }; +var AM; +function one(r) { + AM = r.wasm.cwrap(Gn, null, ["number", "number", "number", "number"]); } -function Coe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); +function nne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e); if (d) { - let w = e.dataIdMap.get(c.dataId).id; - w !== i && (u = c, p = w); + let C = e.dataIdMap.get(c.dataId).id; + C !== i && (u = c, p = C); } let f = u.shape.length; - C.assertAxesAreInnerMostDims("min", l, f); - let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); + w.assertAxesAreInnerMostDims("min", l, f); + let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { - let w = e.dataIdMap.get(b.dataId).id; - cM(p, we[a.dtype], x, w); + let C = e.dataIdMap.get(b.dataId).id; + AM(p, we[a.dtype], x, C); } if (d && e.disposeData(c.dataId), s) { - let w = C.expandShapeToKeepDim(b.shape, m); - b.shape = w; + let C = w.expandShapeToKeepDim(b.shape, m); + b.shape = C; } return b; } -var lM = { kernelName: Wn, backendName: "wasm", setupFunc: boe, kernelFunc: Coe }; -var woe = false; -var mM = Je(Un, woe); -var bv; +var FM = { kernelName: Gn, backendName: "wasm", setupFunc: one, kernelFunc: nne }; +var sne = false; +var PM = We(Hn, sne); +var _v; (function(r) { r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric"; -})(bv || (bv = {})); -var dM; -function Soe(r) { - dM = r.wasm.cwrap(Gn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); +})(_v || (_v = {})); +var OM; +function ane(r) { + OM = r.wasm.cwrap(Kn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); } -function Ioe(r) { - let { inputs: { x: e }, backend: t10, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), p = t10.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(e.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer); - return dM(a, u, e.shape.length, we[e.dtype], m, d, bv[n], p), i; +function ine(r) { + let { inputs: { x: e }, backend: t8, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t8.dataIdMap.get(e.dataId).id, i = t8.makeOutput(s, e.dtype), p = t8.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(e.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer); + return OM(a, u, e.shape.length, we[e.dtype], m, d, _v[n], p), i; } -var fM = { kernelName: Gn, backendName: "wasm", kernelFunc: Ioe, setupFunc: Soe }; -var hM; -function voe(r) { - hM = r.wasm.cwrap(bs, null, ["number", "number", "number", "number"]); +var MM = { kernelName: Kn, backendName: "wasm", kernelFunc: ine, setupFunc: ane }; +var LM; +function une(r) { + LM = r.wasm.cwrap(Is, null, ["number", "number", "number", "number"]); } -function Cv(r) { - let { backend: e, inputs: { logits: t10 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t10.dataId).id, s = e.makeOutput(t10.shape, t10.dtype), a = e.dataIdMap.get(s.dataId).id, i = t10.shape[o], p = y.sizeFromShape(t10.shape) / i; - return y.sizeFromShape(s.shape) === 0 || hM(n, a, i, p), s; +function $v(r) { + let { backend: e, inputs: { logits: t8 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t8.dataId).id, s = e.makeOutput(t8.shape, t8.dtype), a = e.dataIdMap.get(s.dataId).id, i = t8.shape[o], p = y.sizeFromShape(t8.shape) / i; + return y.sizeFromShape(s.shape) === 0 || LM(n, a, i, p), s; } -var gM = { kernelName: bs, backendName: "wasm", setupFunc: voe, kernelFunc: Cv }; -var xM; -function koe(r) { - xM = r.wasm.cwrap(Hn, null, ["number", "number", "number", "number", "number", "number"]); +var BM = { kernelName: Is, backendName: "wasm", setupFunc: une, kernelFunc: $v }; +var zM; +function pne(r) { + zM = r.wasm.cwrap(jn, null, ["number", "number", "number", "number", "number", "number"]); } -function Noe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o; +function cne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o; if (n.dtype !== "float32") throw new Error(`Tensor logits must have dtype float32, got ${n.dtype}`); - let p = i ? n : Cv({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), [u, c] = p.shape, l = t10.makeOutput([u, s], "int32"); - return xM(t10.dataIdMap.get(p.dataId).id, u, c, s, a, t10.dataIdMap.get(l.dataId).id), i || t10.disposeData(p.dataId), l; + let p = i ? n : $v({ inputs: { logits: n }, backend: t8, attrs: { dim: n.shape.length - 1 } }), [u, c] = p.shape, l = t8.makeOutput([u, s], "int32"); + return zM(t8.dataIdMap.get(p.dataId).id, u, c, s, a, t8.dataIdMap.get(l.dataId).id), i || t8.disposeData(p.dataId), l; } -var yM = { kernelName: Hn, backendName: "wasm", setupFunc: koe, kernelFunc: Noe }; -var Toe = true; -var bM = Je(Kn, Toe); -var CM = Ce(oa); -function Hc(r, e) { - let t10 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t10[0], n = t10[1], s = t10[2], a = t10[3]; +var VM = { kernelName: jn, backendName: "wasm", setupFunc: pne, kernelFunc: cne }; +var WM = We(qn, true); +var lne = true; +var UM = We(Xn, lne); +var GM = he(ua); +function Kc(r, e) { + let t8 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t8[0], n = t8[1], s = t8[2], a = t8[3]; return r.wasm._free(e), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a }; } -var wM; -function _oe(r) { - wM = r.wasm.cwrap(jn, "number", ["number", "number", "number", "number", "number"]); +var HM; +function mne(r) { + HM = r.wasm.cwrap(Qn, "number", ["number", "number", "number", "number", "number"]); } -function $oe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t10, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = wM(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Hc(e, l); +function dne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t8, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = HM(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Kc(e, l); return e.wasm._free(f), e.wasm._free(h), e.makeOutput([d], "int32", m); } -var SM = { kernelName: jn, backendName: "wasm", setupFunc: _oe, kernelFunc: $oe }; -var IM; -function Eoe(r) { - IM = r.wasm.cwrap(Ha, "number", ["number", "number", "number", "number", "number", "bool"]); +var KM = { kernelName: Qn, backendName: "wasm", setupFunc: mne, kernelFunc: dne }; +var qM; +function fne(r) { + qM = r.wasm.cwrap(Xa, "number", ["number", "number", "number", "number", "number", "bool"]); } -function Roe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = IM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Hc(e, m); +function hne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t8, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = qM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Kc(e, m); e.wasm._free(h); let x = e.makeOutput([f], "int32", d), b = e.makeOutput([], "int32", g); return [x, b]; } -var vM = { kernelName: Ha, backendName: "wasm", setupFunc: Eoe, kernelFunc: Roe }; -var kM; -function Doe(r) { - kM = r.wasm.cwrap(Xn, "number", ["number", "number", "number", "number", "number", "number"]); +var jM = { kernelName: Xa, backendName: "wasm", setupFunc: fne, kernelFunc: hne }; +var XM; +function gne(r) { + XM = r.wasm.cwrap(Zn, "number", ["number", "number", "number", "number", "number", "number"]); } -function Aoe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = kM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Hc(e, m); +function xne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t8, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = XM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Kc(e, m); e.wasm._free(g); let x = e.makeOutput([f], "int32", d), b = e.makeOutput([f], "float32", h); return [x, b]; } -var NM = { kernelName: Xn, backendName: "wasm", setupFunc: Doe, kernelFunc: Aoe }; -var Foe = false; -var TM = Je(qn, Foe, "bool"); -var _M; -function Poe(r) { - _M = r.wasm.cwrap(Yn, null, ["number", "number", "number", "number", "number"]); +var YM = { kernelName: Zn, backendName: "wasm", setupFunc: gne, kernelFunc: xne }; +var yne = false; +var QM = We(Yn, yne, "bool"); +var ZM; +function bne(r) { + ZM = r.wasm.cwrap(Jn, null, ["number", "number", "number", "number", "number"]); } -function Ooe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t10.makeOutput([...n.shape, a], s), c = t10.dataIdMap.get(u.dataId).id, m = t10.dataIdMap.get(n.dataId).id; - return _M(m, a, i, p, c), u; +function Cne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t8.makeOutput([...n.shape, a], s), c = t8.dataIdMap.get(u.dataId).id, m = t8.dataIdMap.get(n.dataId).id; + return ZM(m, a, i, p, c), u; } -var $M = { kernelName: Yn, backendName: "wasm", setupFunc: Poe, kernelFunc: Ooe }; -function Moe(r) { - let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype); - return t10.typedArrayFromHeap(o).fill(1), o; +var JM = { kernelName: Jn, backendName: "wasm", setupFunc: bne, kernelFunc: Cne }; +function wne(r) { + let { inputs: { x: e }, backend: t8 } = r, o = t8.makeOutput(e.shape, e.dtype); + return t8.typedArrayFromHeap(o).fill(1), o; } -var EM = { kernelName: na, backendName: "wasm", kernelFunc: Moe }; -function Loe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; +var eL = { kernelName: pa, backendName: "wasm", kernelFunc: wne }; +function Sne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return Pg({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); + return Og({ inputs: { input: e[0] }, backend: t8, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = Pg({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); + let l = Og({ inputs: { input: c }, backend: t8, attrs: { dim: n } }); return i.push(l), l; - }), u = gv({ inputs: p, backend: t10, attrs: { axis: n } }); - return i.forEach((c) => t10.disposeData(c.dataId)), u; + }), u = kv({ inputs: p, backend: t8, attrs: { axis: n } }); + return i.forEach((c) => t8.disposeData(c.dataId)), u; } -var RM = { kernelName: sa, backendName: "wasm", kernelFunc: Loe }; -var DM; -function Boe(r) { - DM = r.wasm.cwrap(Qn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); +var tL = { kernelName: ca, backendName: "wasm", kernelFunc: Sne }; +var rL; +function Ine(r) { + rL = r.wasm.cwrap(es, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); } -function zoe(r) { - let { inputs: { x: e }, backend: t10, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + e.shape[g] + h[1]); +function vne(r) { + let { inputs: { x: e }, backend: t8, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + e.shape[g] + h[1]); if (y.sizeFromShape(e.shape) === 0) - return yv({ backend: t10, attrs: { shape: s, value: n, dtype: e.dtype } }); - let a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(e.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer); - return DM(a, c, e.shape.length, we[e.dtype], d, f, n, u), i; + return Tv({ backend: t8, attrs: { shape: s, value: n, dtype: e.dtype } }); + let a = t8.dataIdMap.get(e.dataId).id, i = t8.makeOutput(s, e.dtype), u = t8.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(e.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer); + return rL(a, c, e.shape.length, we[e.dtype], d, f, n, u), i; } -var Og = { kernelName: Qn, backendName: "wasm", kernelFunc: zoe, setupFunc: Boe }; -var Voe = false; -var AM = Je(Zn, Voe); -var FM; -function Woe(r) { - FM = r.wasm.cwrap(Jn, null, ["number", "number", "number"]); +var Mg = { kernelName: es, backendName: "wasm", kernelFunc: vne, setupFunc: Ine }; +var kne = false; +var oL = We(ts, kne); +var nL; +function Nne(r) { + nL = r.wasm.cwrap(rs, null, ["number", "number", "number"]); } -function Uoe(r) { - let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, i = s, p = o, u = p; - p.dtype !== "float32" && (u = Pr({ backend: t10, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t10.dataIdMap.get(u.dataId).id); - let c = t10.makeOutput(o.shape, "float32"), l = t10.dataIdMap.get(c.dataId).id; - return FM(i, a, l), p.dtype !== "float32" && t10.disposeData(u.dataId), c; +function Tne(r) { + let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e, s = t8.dataIdMap.get(o.dataId).id, a = t8.dataIdMap.get(n.dataId).id, i = s, p = o, u = p; + p.dtype !== "float32" && (u = Mr({ backend: t8, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t8.dataIdMap.get(u.dataId).id); + let c = t8.makeOutput(o.shape, "float32"), l = t8.dataIdMap.get(c.dataId).id; + return nL(i, a, l), p.dtype !== "float32" && t8.disposeData(u.dataId), c; } -var PM = { kernelName: Jn, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe }; -var OM; -function Goe(r) { - OM = r.wasm.cwrap(es, null, ["number", "number", "number", "number"]); +var sL = { kernelName: rs, backendName: "wasm", setupFunc: Nne, kernelFunc: Tne }; +var aL; +function _ne(r) { + aL = r.wasm.cwrap(os, null, ["number", "number", "number", "number"]); } -function Hoe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l; +function $ne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e), f = l; if (d) { - let w = e.dataIdMap.get(c.dataId).id; - w !== i && (u = c, p = w, f = C.getInnerMostAxes(f.length, u.shape.length)); + let C = e.dataIdMap.get(c.dataId).id; + C !== i && (u = c, p = C, f = w.getInnerMostAxes(f.length, u.shape.length)); } - C.assertAxesAreInnerMostDims("prod", f, u.shape.length); - let [h, g] = C.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); + w.assertAxesAreInnerMostDims("prod", f, u.shape.length); + let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { - let w = e.dataIdMap.get(b.dataId).id; - OM(p, x, we[b.dtype], w); + let C = e.dataIdMap.get(b.dataId).id; + aL(p, x, we[b.dtype], C); } if (d && e.disposeData(c.dataId), s) { - let w = C.expandShapeToKeepDim(b.shape, m); - b.shape = w; + let C = w.expandShapeToKeepDim(b.shape, m); + b.shape = C; } return b; } -var MM = { kernelName: es, backendName: "wasm", setupFunc: Goe, kernelFunc: Hoe }; -var Koe = (r) => { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = ap(o, n, s, a), p = e.makeOutput([i.length], a); +var iL = { kernelName: os, backendName: "wasm", setupFunc: _ne, kernelFunc: $ne }; +var Ene = (r) => { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, step: s, dtype: a } = t8, i = cp(o, n, s, a), p = e.makeOutput([i.length], a); return e.typedArrayFromHeap(p).set(i), p; }; -var LM = { kernelName: aa, backendName: "wasm", kernelFunc: Koe }; -var qoe = true; -var BM = Je(dn, qoe); -var zM = Ce(ts); -var VM = Ce(rs); -var WM = Ce(ss); -var UM; -function joe(r) { - UM = r.wasm.cwrap(ns, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var uL = { kernelName: la, backendName: "wasm", kernelFunc: Ene }; +var Rne = true; +var pL = We(fn, Rne); +var cL = he(ns); +var lL = he(ss); +var mL = he(us); +var dL; +function Dne(r) { + dL = r.wasm.cwrap(is, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Xoe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.dataIdMap.get(n.dataId), g; - h.dtype !== "float32" && (g = Pr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId)); +function Ane(r) { + let { backend: e, inputs: t8, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.dataIdMap.get(n.dataId), g; + h.dtype !== "float32" && (g = Mr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId)); let x = h.id, b = e.makeOutput(f, "float32"); if (y.sizeFromShape(n.shape) === 0) return b; - let w = e.dataIdMap.get(b.dataId).id; - return UM(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, w), g != null && e.disposeData(g.dataId), b; + let C = e.dataIdMap.get(b.dataId).id; + return dL(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && e.disposeData(g.dataId), b; } -var GM = { kernelName: ns, backendName: "wasm", setupFunc: joe, kernelFunc: Xoe }; -var HM; -function Yoe(r) { - HM = r.wasm.cwrap(qa, null, ["number", "number", "number", "array", "array", "boolean"]); +var fL = { kernelName: is, backendName: "wasm", setupFunc: Dne, kernelFunc: Ane }; +var hL; +function Fne(r) { + hL = r.wasm.cwrap(Qa, null, ["number", "number", "number", "array", "array", "boolean"]); } -function Qoe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t10.makeOutput(n.shape, "float32"), p = t10.dataIdMap.get(n.dataId), u; - return p.dtype !== "float32" && (u = Pr({ backend: t10, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t10.dataIdMap.get(u.dataId)), HM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t10.disposeData(u.dataId), i; +function Pne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t8.makeOutput(n.shape, "float32"), p = t8.dataIdMap.get(n.dataId), u; + return p.dtype !== "float32" && (u = Mr({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t8.dataIdMap.get(u.dataId)), hL(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t8.disposeData(u.dataId), i; } -var KM = { kernelName: qa, backendName: "wasm", setupFunc: Yoe, kernelFunc: Qoe }; -var qM; -function Zoe(r) { - qM = r.wasm.cwrap(os, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var gL = { kernelName: Qa, backendName: "wasm", setupFunc: Fne, kernelFunc: Pne }; +var xL; +function One(r) { + xL = r.wasm.cwrap(as, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Joe(r) { - let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.makeOutput(f, "float32"); +function Mne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.makeOutput(f, "float32"); if (y.sizeFromShape(n.shape) === 0) return h; let g = e.dataIdMap.get(n.dataId), x; - g.dtype !== "float32" && (x = Pr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId)); - let b = g.id, w = e.dataIdMap.get(h.dataId).id; - return qM(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, w), x != null && e.disposeData(x.dataId), h; + g.dtype !== "float32" && (x = Mr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId)); + let b = g.id, C = e.dataIdMap.get(h.dataId).id; + return xL(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && e.disposeData(x.dataId), h; } -var jM = { kernelName: os, backendName: "wasm", setupFunc: Zoe, kernelFunc: Joe }; -var XM; -function ene(r) { - XM = r.wasm.cwrap(Ka, null, ["number", "number", "number", "array", "array", "boolean"]); +var yL = { kernelName: as, backendName: "wasm", setupFunc: One, kernelFunc: Mne }; +var bL; +function Lne(r) { + bL = r.wasm.cwrap(Ya, null, ["number", "number", "number", "array", "array", "boolean"]); } -function tne(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t10.makeOutput(n.shape, "float32"), p = t10.dataIdMap.get(n.dataId), u; - return p.dtype !== "float32" && (u = Pr({ backend: t10, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t10.dataIdMap.get(u.dataId)), XM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t10.disposeData(u.dataId), i; +function Bne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t8.makeOutput(n.shape, "float32"), p = t8.dataIdMap.get(n.dataId), u; + return p.dtype !== "float32" && (u = Mr({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t8.dataIdMap.get(u.dataId)), bL(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t8.disposeData(u.dataId), i; } -var YM = { kernelName: Ka, backendName: "wasm", setupFunc: ene, kernelFunc: tne }; -var QM; -function rne(r) { - QM = r.wasm.cwrap(as, null, ["number", "array", "number", "array", "number", "number"]); +var CL = { kernelName: Ya, backendName: "wasm", setupFunc: Lne, kernelFunc: Bne }; +var wL; +function zne(r) { + wL = r.wasm.cwrap(ps, null, ["number", "array", "number", "array", "number", "number"]); } -function one(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape); +function Vne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape); if (n.shape.length === 0) - return vp({ inputs: { x: n }, backend: t10 }); - let i = t10.makeOutput(n.shape, n.dtype), p = t10.dataIdMap.get(n.dataId).id, u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer); - QM(p, c, a.length, l, n.shape.length, u); - let m = zt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t10 }); - return t10.disposeData(i.dataId), m; + return _p({ inputs: { x: n }, backend: t8 }); + let i = t8.makeOutput(n.shape, n.dtype), p = t8.dataIdMap.get(n.dataId).id, u = t8.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer); + wL(p, c, a.length, l, n.shape.length, u); + let m = zt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t8 }); + return t8.disposeData(i.dataId), m; } -var ZM = { kernelName: as, backendName: "wasm", kernelFunc: one, setupFunc: rne }; -var JM; -function nne(r) { - JM = r.wasm.cwrap(_s, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); +var SL = { kernelName: ps, backendName: "wasm", kernelFunc: Vne, setupFunc: zne }; +var IL; +function Wne(r) { + IL = r.wasm.cwrap(Ds, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); } -function sne(r) { - let { inputs: e, backend: t10, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(n.dataId).id, c = t10.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = C.getImageCenter(i, m, d), x = a === 0, b = 255, w = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], S = new Uint8Array(new Int32Array(w).buffer); - return JM(u, l, m, d, f, s, h, g, S, w.length, c), p; +function Une(r) { + let { inputs: e, backend: t8, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t8.makeOutput(n.shape, n.dtype), u = t8.dataIdMap.get(n.dataId).id, c = t8.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = w.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], S = new Uint8Array(new Int32Array(C).buffer); + return IL(u, l, m, d, f, s, h, g, S, C.length, c), p; } -var eL = { kernelName: _s, backendName: "wasm", kernelFunc: sne, setupFunc: nne }; -var tL = Ce(is); -var rL = Ce(us); -var oL; -function ane(r) { - oL = r.wasm.cwrap(ps, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]); +var vL = { kernelName: Ds, backendName: "wasm", kernelFunc: Une, setupFunc: Wne }; +var kL = he(cs); +var NL = he(ls); +var TL; +function Gne(r) { + TL = r.wasm.cwrap(ms, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]); } -function ine(r) { - let { backend: e, inputs: t10, attrs: o } = r, { indices: n, updates: s } = t10, { shape: a } = o, i = e.makeOutput(a, s.dtype); +function Hne(r) { + let { backend: e, inputs: t8, attrs: o } = r, { indices: n, updates: s } = t8, { shape: a } = o, i = e.makeOutput(a, s.dtype); if (y.sizeFromShape(a) === 0) return i; - let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = pu.calculateShapes(s, n, a), f = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id; - return oL(f, g, we[s.dtype], p, u, c, x, m, b), i; + let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = du.calculateShapes(s, n, a), f = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id; + return TL(f, g, we[s.dtype], p, u, c, x, m, b), i; } -var nL = { kernelName: ps, backendName: "wasm", setupFunc: ane, kernelFunc: ine }; -var sL; -function une(r) { - sL = r.wasm.cwrap(ls, null, ["number", "number", "number", "number", "number", "number", "bool", "number"]); +var _L = { kernelName: ms, backendName: "wasm", setupFunc: Gne, kernelFunc: Hne }; +var $L; +function Kne(r) { + $L = r.wasm.cwrap(fs, null, ["number", "number", "number", "number", "number", "number", "bool", "number"]); } -function pne(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o; +function qne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o; if (n.dtype !== s.dtype) throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${n.dtype} and ${s.dtype}`); - let i = t10.makeOutput(s.shape, "int32"); + let i = t8.makeOutput(s.shape, "int32"); function p(u) { - return t10.dataIdMap.get(u.dataId).id; + return t8.dataIdMap.get(u.dataId).id; } - return sL(p(n), p(s), n.shape[0], n.shape[1], s.shape[1], we[n.dtype], a === "left", p(i)), i; + return $L(p(n), p(s), n.shape[0], n.shape[1], s.shape[1], we[n.dtype], a === "left", p(i)), i; } -var aL = { kernelName: ls, backendName: "wasm", setupFunc: une, kernelFunc: pne }; -var iL; -function cne(r) { - iL = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]); +var EL = { kernelName: fs, backendName: "wasm", setupFunc: Kne, kernelFunc: qne }; +var RL; +function jne(r) { + RL = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]); } -function lne(r) { - let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = t10.dataIdMap.get(o.dataId).id, i = t10.dataIdMap.get(n.dataId).id, p = t10.dataIdMap.get(s.dataId).id, u = t10.makeOutput(n.shape, n.dtype), c = t10.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); - return iL(a, i, p, d, c), u; +function Xne(r) { + let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e, a = t8.dataIdMap.get(o.dataId).id, i = t8.dataIdMap.get(n.dataId).id, p = t8.dataIdMap.get(s.dataId).id, u = t8.makeOutput(n.shape, n.dtype), c = t8.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); + return RL(a, i, p, d, c), u; } -var uL = { kernelName: ua, backendName: "wasm", kernelFunc: lne, setupFunc: cne }; -var pL = Ce(ms); -var cL; -function mne(r) { - cL = r.wasm.cwrap(hs, null, ["number", "number"]); +var DL = { kernelName: da, backendName: "wasm", kernelFunc: Xne, setupFunc: jne }; +var AL = he(hs); +var FL; +function Yne(r) { + FL = r.wasm.cwrap(bs, null, ["number", "number"]); } -function dne(r) { - let { backend: e, inputs: { x: t10 } } = r, o = e.dataIdMap.get(t10.dataId).id, n = e.makeOutput(t10.shape, t10.dtype), s = e.dataIdMap.get(n.dataId).id; - return y.sizeFromShape(n.shape) === 0 || cL(o, s), n; +function Qne(r) { + let { backend: e, inputs: { x: t8 } } = r, o = e.dataIdMap.get(t8.dataId).id, n = e.makeOutput(t8.shape, t8.dtype), s = e.dataIdMap.get(n.dataId).id; + return y.sizeFromShape(n.shape) === 0 || FL(o, s), n; } -var lL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: mne, kernelFunc: dne }; -var mL = Ce(fs); -var dL = Ce(ds); -var fL = Ce(gs); -function fne(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]]; +var PL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: Yne, kernelFunc: Qne }; +var OL = he(ys); +var ML = he(gs); +var LL = he(xs); +var BL = he(Cs); +function Zne(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]]; p.push(...a); for (let _ = 1 + s.length; _ < n.shape.length; ++_) p.push([0, 0]); - let u = Og.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = C.getReshaped(u.shape, s, i, false), l = C.getPermuted(c.length, s.length, false), m = C.getReshapedPermuted(u.shape, s, i, false), h = zt({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = mo({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = zt({ inputs: { x: b }, backend: t10, attrs: { shape: m } }); - return t10.disposeData(u.dataId), t10.disposeData(h.dataId), t10.disposeData(b.dataId), k; + let u = Mg.kernelFunc({ inputs: { x: n }, backend: t8, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = zt({ inputs: { x: u }, backend: t8, attrs: { shape: c } }), b = ho({ inputs: { x: h }, backend: t8, attrs: { perm: l } }), k = zt({ inputs: { x: b }, backend: t8, attrs: { shape: m } }); + return t8.disposeData(u.dataId), t8.disposeData(h.dataId), t8.disposeData(b.dataId), k; } -var hL = { kernelName: ca, backendName: "wasm", kernelFunc: fne }; -var gL; -function hne(r) { - gL = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var zL = { kernelName: ha, backendName: "wasm", kernelFunc: Zne }; +var VL; +function Jne(r) { + VL = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function gne(r) { - let { backend: e, inputs: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t10, i = o.shape[0], p = o.shape[1], u = e.readSync(s.dataId)[0], c = [i + u, p], l = e.dataIdMap.get(o.dataId).id, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(a.dataId).id, f = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(f.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), x = e.dataIdMap.get(g.dataId).id, b = e.makeOutput([u], "bool"), w = e.dataIdMap.get(b.dataId).id, S = e.makeOutput([i], o.dtype), k = e.dataIdMap.get(S.dataId).id, _ = e.makeOutput([4], "int32"), E = e.dataIdMap.get(_.dataId).id, R = gL(l, m, we[n.dtype], i, u, p, d, h, x, w, k, E), D = e.readSync(_.dataId), F; +function ese(r) { + let { backend: e, inputs: t8 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t8, i = o.shape[0], p = o.shape[1], u = e.readSync(s.dataId)[0], c = [i + u, p], l = e.dataIdMap.get(o.dataId).id, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(a.dataId).id, f = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(f.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), x = e.dataIdMap.get(g.dataId).id, b = e.makeOutput([u], "bool"), C = e.dataIdMap.get(b.dataId).id, S = e.makeOutput([i], o.dtype), k = e.dataIdMap.get(S.dataId).id, _ = e.makeOutput([4], "int32"), E = e.dataIdMap.get(_.dataId).id, R = VL(l, m, we[n.dtype], i, u, p, d, h, x, C, k, E), D = e.readSync(_.dataId), P; switch (D[0]) { case 1: { - F = C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(D[1]); + P = w.getSparseFillEmptyRowsIndicesDenseShapeMismatch(D[1]); break; } case 2: { - F = C.getSparseFillEmptyRowsNegativeIndexErrorMessage(D[1], D[2]); + P = w.getSparseFillEmptyRowsNegativeIndexErrorMessage(D[1], D[2]); break; } case 3: - F = C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(D[1], D[2], D[3]); + P = w.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(D[1], D[2], D[3]); break; default: - F = ""; + P = ""; } - if (e.disposeData(_.dataId), F) - throw e.disposeData(f.dataId), e.disposeData(g.dataId), e.disposeData(b.dataId), e.disposeData(S.dataId), new Error(F); + if (e.disposeData(_.dataId), P) + throw e.disposeData(f.dataId), e.disposeData(g.dataId), e.disposeData(b.dataId), e.disposeData(S.dataId), new Error(P); let O = f, M = g; - return R !== c[0] && (O = Ao({ inputs: { x: f }, attrs: { begin: 0, size: [R, p] }, backend: e }), M = Ao({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: e }), e.disposeData(f.dataId), e.disposeData(g.dataId)), [O, M, b, S]; + return R !== c[0] && (O = Po({ inputs: { x: f }, attrs: { begin: 0, size: [R, p] }, backend: e }), M = Po({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: e }), e.disposeData(f.dataId), e.disposeData(g.dataId)), [O, M, b, S]; } -var xL = { kernelName: Vi, backendName: "wasm", setupFunc: hne, kernelFunc: gne }; -var yL; -function xne(r) { - yL = r.wasm.cwrap(Xa, null, ["number", "number", "number", "number", "number", "number", "number"]); +var WL = { kernelName: Hi, backendName: "wasm", setupFunc: Jne, kernelFunc: ese }; +var UL; +function tse(r) { + UL = r.wasm.cwrap(Za, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function yne(r) { - let { backend: e, inputs: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = t10; +function rse(r) { + let { backend: e, inputs: t8 } = r, { inputIndices: o, inputShape: n, newShape: s } = t8; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); @@ -25929,28 +26061,28 @@ function yne(r) { if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); let a = e.dataIdMap.get(o.dataId).id, i = e.dataIdMap.get(n.dataId).id, p = e.dataIdMap.get(s.dataId).id, u = o.shape[0], c = y.sizeFromShape(s.shape), l = e.makeOutput([u, c], o.dtype), m = e.dataIdMap.get(l.dataId).id, d = e.makeOutput([c], s.dtype), f = e.dataIdMap.get(d.dataId).id, h = e.makeOutput([3], "int32"), g = e.dataIdMap.get(h.dataId).id; - yL(a, i, p, u, m, f, g); + UL(a, i, p, u, m, f, g); let x = e.readSync(h.dataId), b; switch (x[0]) { case 0: { - b = C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1], x[2]); + b = w.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1], x[2]); break; } case 1: { - b = C.getSparseReshapeNegativeOutputDimErrorMessage(x[1], x[2]); + b = w.getSparseReshapeNegativeOutputDimErrorMessage(x[1], x[2]); break; } case 2: - b = C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage(); + b = w.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage(); break; case 3: { - let w = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId)); - b = C.getSparseReshapeInputOutputMultipleErrorMessage(w, S); + let C = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId)); + b = w.getSparseReshapeInputOutputMultipleErrorMessage(C, S); break; } case 4: { - let w = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId)); - b = C.getSparseReshapeInputOutputMismatchErrorMessage(w, S); + let C = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId)); + b = w.getSparseReshapeInputOutputMismatchErrorMessage(C, S); break; } default: @@ -25960,220 +26092,220 @@ function yne(r) { throw e.disposeData(l.dataId), e.disposeData(d.dataId), new Error(b); return [l, d]; } -var bL = { kernelName: Xa, backendName: "wasm", setupFunc: xne, kernelFunc: yne }; -var CL; -function Mg(r) { - CL = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var GL = { kernelName: Za, backendName: "wasm", setupFunc: tse, kernelFunc: rse }; +var HL; +function Lg(r) { + HL = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Lg(r, e) { - let { backend: t10, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t10.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0; +function Bg(r, e) { + let { backend: t8, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t8.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0; if (c < 0) - throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let l = n.shape.slice(); l[0] = c; - let m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = t10.dataIdMap.get(a.dataId).id, h = t10.makeOutput(l, n.dtype), g = t10.dataIdMap.get(h.dataId).id, x = t10.makeOutput([4], "int32"), b = t10.dataIdMap.get(x.dataId).id; - CL(m, we[n.dtype], n.shape[0], d, f, g, b, e, 0); - let w = t10.readSync(x.dataId), S; - switch (w[0]) { + let m = t8.dataIdMap.get(n.dataId).id, d = t8.dataIdMap.get(s.dataId).id, f = t8.dataIdMap.get(a.dataId).id, h = t8.makeOutput(l, n.dtype), g = t8.dataIdMap.get(h.dataId).id, x = t8.makeOutput([4], "int32"), b = t8.dataIdMap.get(x.dataId).id; + HL(m, we[n.dtype], n.shape[0], d, f, g, b, e, 0); + let C = t8.readSync(x.dataId), S; + switch (C[0]) { case 0: { - S = C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage(); + S = w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage(); break; } case 1: { - S = C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage(); + S = w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage(); break; } case 2: - S = C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(w[1], w[2]); + S = w.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1], C[2]); break; case 3: - S = C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w[1], w[2], w[3]); + S = w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1], C[2], C[3]); break; default: S = ""; } - if (t10.disposeData(x.dataId), S) - throw t10.disposeData(h.dataId), new Error(S); + if (t8.disposeData(x.dataId), S) + throw t8.disposeData(h.dataId), new Error(S); return h; } -function bne(r) { - return Lg(r, true); +function ose(r) { + return Bg(r, true); } -var wL = { kernelName: Wi, backendName: "wasm", setupFunc: Mg, kernelFunc: bne }; -function Cne(r) { - return Lg(r, false); +var KL = { kernelName: Ki, backendName: "wasm", setupFunc: Lg, kernelFunc: ose }; +function nse(r) { + return Bg(r, false); } -var SL = { kernelName: Ui, backendName: "wasm", setupFunc: Mg, kernelFunc: Cne }; -var IL; -function wne(r) { - IL = r.wasm.cwrap(Cs, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); +var qL = { kernelName: qi, backendName: "wasm", setupFunc: Lg, kernelFunc: nse }; +var jL; +function sse(r) { + jL = r.wasm.cwrap(vs, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); } -function Sne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t10, { outputShape: i } = o, p = e.makeOutput(i, a.dtype); +function ase(r) { + let { backend: e, inputs: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, p = e.makeOutput(i, a.dtype); if (y.sizeFromShape(i) === 0) return p; - let { sliceRank: u, numUpdates: c, sliceSize: l, strides: m, outputSize: d } = C.calculateShapes(s, n, i), f = e.dataIdMap.get(n.dataId).id, h = e.dataIdMap.get(s.dataId).id, g = e.dataIdMap.get(a.dataId).id, x = new Uint8Array(new Int32Array(m).buffer), b = e.dataIdMap.get(p.dataId).id; - return IL(f, h, s.shape.length, g, we[a.dtype], u, c, l, x, d, b), p; + let { sliceRank: u, numUpdates: c, sliceSize: l, strides: m, outputSize: d } = w.calculateShapes(s, n, i), f = e.dataIdMap.get(n.dataId).id, h = e.dataIdMap.get(s.dataId).id, g = e.dataIdMap.get(a.dataId).id, x = new Uint8Array(new Int32Array(m).buffer), b = e.dataIdMap.get(p.dataId).id; + return jL(f, h, s.shape.length, g, we[a.dtype], u, c, l, x, d, b), p; } -var vL = { kernelName: Cs, backendName: "wasm", setupFunc: wne, kernelFunc: Sne }; -function Ine(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t10, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); +var XL = { kernelName: vs, backendName: "wasm", setupFunc: sse, kernelFunc: ase }; +function ise(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t8, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); return p.map((l) => { let m = [...c]; m[i] = l; - let d = Ao({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o }); + let d = Po({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o }); return u[i] += l, d; }); } -var kL = { kernelName: la, backendName: "wasm", kernelFunc: Ine }; -var NL = Ce(xs); -var TL = Ce(Gi); -var vne = true; -var _L = Je(ws, vne); -var $L; -function kne(r) { - $L = r.wasm.cwrap(yo, null, ["number", "number", "number", "number"]); +var YL = { kernelName: ga, backendName: "wasm", kernelFunc: ise }; +var QL = he(ws); +var ZL = he(ji); +var use = true; +var JL = We(ks, use); +var eB; +function pse(r) { + eB = r.wasm.cwrap(wo, null, ["number", "number", "number", "number"]); } -function Nne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { alpha: n } = o, { x: s } = t10, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id; - return $L(a, n, we[s.dtype], p), i; +function cse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { alpha: n } = o, { x: s } = t8, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id; + return eB(a, n, we[s.dtype], p), i; } -var EL = { kernelName: yo, backendName: "wasm", setupFunc: kne, kernelFunc: Nne }; -var RL; -function Tne(r) { - RL = r.wasm.cwrap(Ss, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]); +var tB = { kernelName: wo, backendName: "wasm", setupFunc: pse, kernelFunc: cse }; +var rB; +function lse(r) { + rB = r.wasm.cwrap(Ns, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]); } -function _ne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function mse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) k = zt({ inputs: { x: n }, backend: e, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ct.computeOutShape(b, w, S), E = Ao({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); + let _ = ct.computeOutShape(b, C, S), E = Po({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); k = zt({ inputs: { x: E }, backend: e, attrs: { shape: f } }), e.disposeData(E.dataId); } else { - let _ = e.makeOutput(d, "float32"), E = e.dataIdMap.get(n.dataId).id, R = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), D = new Uint8Array(new Int32Array(b).buffer), F = new Uint8Array(new Int32Array(w).buffer), O = new Uint8Array(new Int32Array(S).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), B = e.dataIdMap.get(_.dataId).id; - RL(E, R, n.shape.length, D, F, O, M, L, d.length, B), k = zt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId); + let _ = e.makeOutput(d, "float32"), E = e.dataIdMap.get(n.dataId).id, R = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), D = new Uint8Array(new Int32Array(b).buffer), P = new Uint8Array(new Int32Array(C).buffer), O = new Uint8Array(new Int32Array(S).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), B = e.dataIdMap.get(_.dataId).id; + rB(E, R, n.shape.length, D, P, O, M, L, d.length, B), k = zt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId); } return k; } -var DL = { kernelName: Ss, backendName: "wasm", setupFunc: Tne, kernelFunc: _ne }; -function $ne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { data: n, dataSplits: s } = t10, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = e.readSync(n.dataId), d = e.readSync(s.dataId), [f, h] = up(m, d, a, i, p, u, c, l), g = e.makeOutput([f.length], "string"), x = e.dataIdMap.get(g.dataId); +var oB = { kernelName: Ns, backendName: "wasm", setupFunc: lse, kernelFunc: mse }; +function dse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { data: n, dataSplits: s } = t8, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = e.readSync(n.dataId), d = e.readSync(s.dataId), [f, h] = mp(m, d, a, i, p, u, c, l), g = e.makeOutput([f.length], "string"), x = e.dataIdMap.get(g.dataId); x.stringBytes = f; let b = e.makeOutput(s.shape, "int32"); return e.typedArrayFromHeap(b).set(h), [g, b]; } -var AL = { kernelName: ma, backendName: "wasm", kernelFunc: $ne }; -function Ene(r) { - let { backend: e, inputs: t10, attrs: o } = r, { input: n, delimiter: s } = t10, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = pp(i, p[0], a), m = c.length, d = e.makeOutput([m, 2], "int32"); +var nB = { kernelName: xa, backendName: "wasm", kernelFunc: dse }; +function fse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { input: n, delimiter: s } = t8, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = dp(i, p[0], a), m = c.length, d = e.makeOutput([m, 2], "int32"); e.typedArrayFromHeap(d).set(u); let h = e.makeOutput([m], "string"), g = e.dataIdMap.get(h.dataId); g.stringBytes = c; let x = e.makeOutput([2], "int32"); return e.typedArrayFromHeap(x).set(l), [d, h, x]; } -var FL = { kernelName: Hi, backendName: "wasm", kernelFunc: Ene }; -function Rne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { input: n } = t10, { numBuckets: s } = o, a = e.readSync(n.dataId), i = cp(a, s), p = e.makeOutput(n.shape, "int32"); +var sB = { kernelName: Xi, backendName: "wasm", kernelFunc: fse }; +function hse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { input: n } = t8, { numBuckets: s } = o, a = e.readSync(n.dataId), i = fp(a, s), p = e.makeOutput(n.shape, "int32"); return e.typedArrayFromHeap(p).set(i), p; } -var PL = { kernelName: Ki, backendName: "wasm", kernelFunc: Rne }; -var Dne = true; -var OL = Je(Is, Dne); -var ML; -function Ane(r) { - ML = r.wasm.cwrap(ys, null, ["number", "number", "number", "number"]); +var aB = { kernelName: Yi, backendName: "wasm", kernelFunc: hse }; +var gse = true; +var iB = We(Ts, gse); +var uB; +function xse(r) { + uB = r.wasm.cwrap(Ss, null, ["number", "number", "number", "number"]); } -function Fne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l; +function yse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e), f = l; if (d) { - let w = e.dataIdMap.get(c.dataId).id; - w !== i && (u = c, p = w, f = C.getInnerMostAxes(f.length, u.shape.length)); + let C = e.dataIdMap.get(c.dataId).id; + C !== i && (u = c, p = C, f = w.getInnerMostAxes(f.length, u.shape.length)); } - C.assertAxesAreInnerMostDims("sum", f, u.shape.length); - let [h, g] = C.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); + w.assertAxesAreInnerMostDims("sum", f, u.shape.length); + let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { - let w = e.dataIdMap.get(b.dataId).id; - ML(p, x, we[b.dtype], w); + let C = e.dataIdMap.get(b.dataId).id; + uB(p, x, we[b.dtype], C); } if (d && e.disposeData(c.dataId), s) { - let w = C.expandShapeToKeepDim(b.shape, m); - b.shape = w; + let C = w.expandShapeToKeepDim(b.shape, m); + b.shape = C; } return b; } -var LL = { kernelName: ys, backendName: "wasm", setupFunc: Ane, kernelFunc: Fne }; -var BL = Ce(vs); -var zL = Ce(ks); -var VL; -function Pne(r) { - VL = r.wasm.cwrap(cs, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number", "number"]); +var pB = { kernelName: Ss, backendName: "wasm", setupFunc: xse, kernelFunc: yse }; +var cB = he(_s); +var lB = he($s); +var mB; +function bse(r) { + mB = r.wasm.cwrap(ds, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number", "number"]); } -function One(r) { - let { backend: e, inputs: t10, attrs: o } = r, { tensor: n, indices: s, updates: a } = t10, {} = o, i = e.makeOutput(n.shape, n.dtype); +function Cse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { tensor: n, indices: s, updates: a } = t8, {} = o, i = e.makeOutput(n.shape, n.dtype); if (y.sizeFromShape(n.shape) === 0) return i; - let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = pu.calculateShapes(a, s, n.shape), f = e.dataIdMap.get(s.dataId).id, g = e.dataIdMap.get(a.dataId).id, b = e.dataIdMap.get(n.dataId).id, w = new Uint8Array(new Int32Array(l).buffer), S = e.dataIdMap.get(i.dataId).id; - return VL(f, g, we[a.dtype], p, u, c, w, m, S, b), i; + let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = du.calculateShapes(a, s, n.shape), f = e.dataIdMap.get(s.dataId).id, g = e.dataIdMap.get(a.dataId).id, b = e.dataIdMap.get(n.dataId).id, C = new Uint8Array(new Int32Array(l).buffer), S = e.dataIdMap.get(i.dataId).id; + return mB(f, g, we[a.dtype], p, u, c, C, m, S, b), i; } -var WL = { kernelName: cs, backendName: "wasm", setupFunc: Pne, kernelFunc: One }; -var UL; -function Mne(r) { - UL = r.wasm.cwrap(so, null, ["number", "array", "number", "array", "number", "number"]); +var dB = { kernelName: ds, backendName: "wasm", setupFunc: bse, kernelFunc: Cse }; +var fB; +function wse(r) { + fB = r.wasm.cwrap(uo, null, ["number", "array", "number", "array", "number", "number"]); } -function Lne(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, s = t10.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length); +function Sse(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, s = t8.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length); for (let m = 0; m < i.length; m++) i[m] = n.shape[m] * a[m]; - let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = t10.makeOutput(i, n.dtype), l = t10.dataIdMap.get(c.dataId).id; - return UL(s, p, n.shape.length, u, i.length, we[c.dtype], l), c; + let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = t8.makeOutput(i, n.dtype), l = t8.dataIdMap.get(c.dataId).id; + return fB(s, p, n.shape.length, u, i.length, we[c.dtype], l), c; } -var GL = { kernelName: so, backendName: "wasm", setupFunc: Mne, kernelFunc: Lne }; -var HL; -function Bne(r) { - HL = r.wasm.cwrap(Ns, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]); +var hB = { kernelName: uo, backendName: "wasm", setupFunc: wse, kernelFunc: Sse }; +var gB; +function Ise(r) { + gB = r.wasm.cwrap(Es, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]); } -var zne = ({ inputs: r, backend: e, attrs: t10 }) => { - let { x: o } = r, { k: n, sorted: s } = t10, a = e.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice(); +var vse = ({ inputs: r, backend: e, attrs: t8 }) => { + let { x: o } = r, { k: n, sorted: s } = t8, a = e.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice(); p[p.length - 1] = n; let u = e.makeOutput(p, o.dtype), c = e.dataIdMap.get(u.dataId).id, l = e.makeOutput(p, "int32"), m = e.dataIdMap.get(l.dataId).id; - return HL(a, i, o.shape.length, we[o.dtype], n, s, c, m), [u, l]; + return gB(a, i, o.shape.length, we[o.dtype], n, s, c, m), [u, l]; }; -var KL = { kernelName: Ns, backendName: "wasm", setupFunc: Bne, kernelFunc: zne }; -var qL; -function Vne(r) { - qL = r.wasm.cwrap(Ts, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]); +var xB = { kernelName: Es, backendName: "wasm", setupFunc: Ise, kernelFunc: vse }; +var yB; +function kse(r) { + yB = r.wasm.cwrap(Rs, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]); } -function Wne(r) { - let { backend: e, inputs: t10, attrs: o } = r, { image: n, transforms: s } = t10, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), w = e.makeOutput(g, n.dtype), S = e.dataIdMap.get(w.dataId).id, _ = e.dataIdMap.get(n.dataId).id, R = e.dataIdMap.get(s.dataId).id, D = a === "nearest" ? 1 : 2, F; +function Nse(r) { + let { backend: e, inputs: t8, attrs: o } = r, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), C = e.makeOutput(g, n.dtype), S = e.dataIdMap.get(C.dataId).id, _ = e.dataIdMap.get(n.dataId).id, R = e.dataIdMap.get(s.dataId).id, D = a === "nearest" ? 1 : 2, P; switch (i) { case "constant": - F = 1; + P = 1; break; case "reflect": - F = 2; + P = 2; break; case "wrap": - F = 3; + P = 3; break; case "nearest": - F = 4; + P = 4; break; default: - F = 1; + P = 1; break; } - return qL(_, R, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, D, F, p, S), w; + return yB(_, R, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, D, P, p, S), C; } -var jL = { kernelName: Ts, backendName: "wasm", setupFunc: Vne, kernelFunc: Wne }; -function Une(r) { - let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e, { outputValues: a, outputShape: i, indices: p } = lp(o.readSync(s.dataId), n, s.shape, s.dtype); +var bB = { kernelName: Rs, backendName: "wasm", setupFunc: kse, kernelFunc: Nse }; +function Tse(r) { + let { inputs: e, attrs: t8, backend: o } = r, { axis: n } = t8, { x: s } = e, { outputValues: a, outputShape: i, indices: p } = hp(o.readSync(s.dataId), n, s.shape, s.dtype); return [o.makeOutput(i, s.dtype, void 0, a), o.makeOutput([p.length], "int32", void 0, p)]; } -var XL = { kernelName: qi, backendName: "wasm", kernelFunc: Une }; -function Gne(r) { - let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; +var CB = { kernelName: Qi, backendName: "wasm", kernelFunc: Tse }; +function _se(r) { + let { inputs: e, backend: t8, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n.shape[s], i = n.shape.length, p = new Array(i - 1), u = 0; for (let d = 0; d < i; d++) @@ -26181,28 +26313,28 @@ function Gne(r) { let c = new Array(a), l = new Array(i).fill(0), m = n.shape.slice(); m[s] = 1; for (let d = 0; d < c.length; d++) - l[s] = d, c[d] = Ao({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t10 }); + l[s] = d, c[d] = Po({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t8 }); return c.map(({ dataId: d, dtype: f }) => ({ dataId: d, dtype: f, shape: p })); } -var YL = { kernelName: da, backendName: "wasm", kernelFunc: Gne }; -function Hne(r) { - let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype); - return t10.typedArrayFromHeap(o).fill(0), o; +var wB = { kernelName: ya, backendName: "wasm", kernelFunc: _se }; +function $se(r) { + let { inputs: { x: e }, backend: t8 } = r, o = t8.makeOutput(e.shape, e.dtype); + return t8.typedArrayFromHeap(o).fill(0), o; } -var QL = { kernelName: fa, backendName: "wasm", kernelFunc: Hne }; -var Kne = [K3, q3, j3, X3, Y3, Z3, oP, sP, aP, iP, uP, pP, cP, lP, mP, fP, gP, yP, wP, IP, kP, NP, TP, _P, EP, RP, AP, PP, MP, BP, VP, WP, UP, HP, qP, XP, QP, JP, tO, oO, sO, iO, pO, cO, mO, dO, fO, hO, gO, xO, bO, CO, wO, IO, kO, TO, $O, RO, DO, AO, J3, FO, PO, OO, LO, BO, zO, WO, GO, UO, HO, KO, qO, jO, YO, ZO, eM, tM, oM, sM, iM, pM, lM, mM, fM, yM, bM, CM, SM, vM, NM, TM, $M, EM, RM, Og, AM, PM, MM, LM, BM, zM, VM, WM, bP, GM, KM, jM, YM, ZM, eL, tL, rL, nL, aL, uL, pL, lL, mL, dL, SP, gM, fL, hL, xL, bL, wL, SL, vL, kL, NL, TL, _L, EL, DL, AL, FL, PL, OL, LL, BL, zL, WL, GL, KL, jL, tP, XL, YL, QL]; -for (let r of Kne) - Ya(r); -var wv = P(); -wv.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { +var SB = { kernelName: ba, backendName: "wasm", kernelFunc: $se }; +var Ese = [iP, uP, pP, cP, lP, dP, yP, CP, wP, SP, IP, vP, kP, NP, TP, $P, PP, RP, AP, LP, zP, WP, UP, GP, HP, KP, jP, XP, QP, JP, tO, oO, sO, aO, iO, pO, lO, dO, hO, xO, bO, wO, IO, kO, TO, _O, EO, RO, DO, AO, FO, PO, OO, LO, BO, zO, WO, GO, KO, jO, YO, QO, ZO, fP, JO, eM, tM, oM, nM, sM, iM, pM, uM, cM, lM, mM, dM, hM, xM, bM, CM, SM, vM, NM, _M, EM, DM, FM, PM, MM, VM, WM, UM, GM, KM, jM, YM, QM, JM, eL, tL, Mg, oL, sL, iL, uL, pL, cL, lL, mL, OP, fL, gL, yL, CL, SL, vL, kL, NL, _L, EL, DL, AL, PL, OL, ML, LL, BP, BM, BL, zL, WL, GL, KL, qL, XL, YL, QL, ZL, JL, tB, oB, nB, sB, aB, iB, pB, cB, lB, dB, hB, xB, bB, gP, CB, wB, SB]; +for (let r of Ese) + Ja(r); +var Ev = A(); +Ev.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { try { return WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])); } catch (r) { return false; } }); -wv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { - if (wv.get("IS_NODE")) +Ev.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { + if (Ev.get("IS_NODE")) return false; try { return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11])); @@ -26210,51 +26342,51 @@ wv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { return false; } }); -var $v = Bp(tB()); -var iB = Bp(oB()); -var Ev = Bp(nB()); -var sB = $v.default || $v; -var qne = Ev.default || Ev; -var am = class extends ro { +var Lv = Up(NB()); +var DB = Up(_B()); +var Bv = Up($B()); +var EB = Lv.default || Lv; +var Rse = Bv.default || Bv; +var am = class extends so { constructor(e) { - super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(pB), _v = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Lo(this, ur()); + super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(FB), Mv = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Bo(this, ur()); } - write(e, t10, o) { + write(e, t8, o) { let n = { id: this.dataIdNextNumber++ }; - return this.move(n, e, t10, o, 1), n; + return this.move(n, e, t8, o, 1), n; } numDataIds() { return this.dataIdMap.numDataIds(); } async time(e) { - let t10 = y.now(); - return e(), { kernelMs: y.now() - t10 }; + let t8 = y.now(); + return e(), { kernelMs: y.now() - t8 }; } - move(e, t10, o, n, s) { + move(e, t8, o, n, s) { let a = this.dataIdNextNumber++; if (n === "string") { - let c = t10; + let c = t8; this.dataIdMap.set(e, { id: a, stringBytes: c, shape: o, dtype: n, memoryOffset: null, refCount: s }); return; } let i = y.sizeFromShape(o), p = i * y.bytesPerElement(n), u = this.wasm._malloc(p) >>> 0; - this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: o, dtype: n, refCount: s }), this.wasm.tfjs.registerTensor(a, i, u), t10 != null && this.wasm.HEAPU8.set(new Uint8Array(t10.buffer, t10.byteOffset, p), u); + this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: o, dtype: n, refCount: s }), this.wasm.tfjs.registerTensor(a, i, u), t8 != null && this.wasm.HEAPU8.set(new Uint8Array(t8.buffer, t8.byteOffset, p), u); } async read(e) { return this.readSync(e); } - readSync(e, t10, o) { + readSync(e, t8, o) { let { memoryOffset: n, dtype: s, shape: a, stringBytes: i } = this.dataIdMap.get(e); if (s === "string") - return (t10 == null || t10 === 0) && (o == null || o >= i.length) ? i : i.slice(t10, o); - t10 = t10 || 0, o = o || y.sizeFromShape(a); - let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t10 * p, n + o * p); - return Xne(u.buffer, s); + return (t8 == null || t8 === 0) && (o == null || o >= i.length) ? i : i.slice(t8, o); + t8 = t8 || 0, o = o || y.sizeFromShape(a); + let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t8 * p, n + o * p); + return Ase(u.buffer, s); } - disposeData(e, t10 = false) { + disposeData(e, t8 = false) { if (this.dataIdMap.has(e)) { let o = this.dataIdMap.get(e); - if (o.refCount--, !t10 && o.refCount > 0) + if (o.refCount--, !t8 && o.refCount > 0) return false; this.wasm._free(o.memoryOffset), this.wasm.tfjs.disposeData(o.id), this.dataIdMap.delete(e); } @@ -26264,8 +26396,8 @@ var am = class extends ro { return this.dataIdMap.has(e) ? this.dataIdMap.get(e).refCount : 0; } incRef(e) { - let t10 = this.dataIdMap.get(e); - t10 != null && t10.refCount++; + let t8 = this.dataIdMap.get(e); + t8 != null && t8.refCount++; } floatPrecision() { return 32; @@ -26279,21 +26411,21 @@ var am = class extends ro { memory() { return { unreliable: false }; } - makeOutput(e, t10, o, n) { + makeOutput(e, t8, o, n) { let s; if (o == null) - s = this.write(n != null ? n : null, e, t10); + s = this.write(n != null ? n : null, e, t8); else { let a = this.dataIdNextNumber++; - s = { id: a }, this.dataIdMap.set(s, { id: a, memoryOffset: o, shape: e, dtype: t10, refCount: 1 }); + s = { id: a }, this.dataIdMap.set(s, { id: a, memoryOffset: o, shape: e, dtype: t8, refCount: 1 }); let i = y.sizeFromShape(e); this.wasm.tfjs.registerTensor(a, i, o); } - return { dataId: s, shape: e, dtype: t10 }; + return { dataId: s, shape: e, dtype: t8 }; } - typedArrayFromHeap({ shape: e, dtype: t10, dataId: o }) { + typedArrayFromHeap({ shape: e, dtype: t8, dataId: o }) { let n = this.wasm.HEAPU8.buffer, { memoryOffset: s } = this.dataIdMap.get(o), a = y.sizeFromShape(e); - switch (t10) { + switch (t8) { case "float32": return new Float32Array(n, s, a); case "int32": @@ -26301,36 +26433,36 @@ var am = class extends ro { case "bool": return new Uint8Array(n, s, a); default: - throw new Error(`Unknown dtype ${t10}`); + throw new Error(`Unknown dtype ${t8}`); } } }; -function jne(r) { - return (e, t10) => (y.fetch(r, { credentials: "same-origin" }).then((o) => { +function Dse(r) { + return (e, t8) => (y.fetch(r, { credentials: "same-origin" }).then((o) => { o.ok || e.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => { WebAssembly.instantiate(n, e).then((s) => { - t10(s.instance, s.module); + t8(s.instance, s.module); }); }); }), {}); } -function aB(r, e, t10) { - if (Vg != null) - return Vg; +function RB(r, e, t8) { + if (Wg != null) + return Wg; let o = "tfjs-backend-wasm.wasm"; - return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), nm != null && nm[o] != null ? nm[o] : t10 + o; + return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), nm != null && nm[o] != null ? nm[o] : t8 + o; } -async function uB() { - let [r, e] = await Promise.all([P().getAsync("WASM_HAS_SIMD_SUPPORT"), P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]); - return new Promise((t10, o) => { +async function AB() { + let [r, e] = await Promise.all([A().getAsync("WASM_HAS_SIMD_SUPPORT"), A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]); + return new Promise((t8, o) => { let n = {}; n.locateFile = (i, p) => { if (i.endsWith(".worker.js")) { - let u = iB.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" }); + let u = DB.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" }); return URL.createObjectURL(c); } - return i.endsWith(".wasm") ? aB(r, e, om != null ? om : p) : p + i; - }, Rv && (n.instantiateWasm = jne(aB(r, e, om != null ? om : ""))); + return i.endsWith(".wasm") ? RB(r, e, om != null ? om : p) : p + i; + }, zv && (n.instantiateWasm = Dse(RB(r, e, om != null ? om : ""))); let s = false; n.onAbort = () => { if (s || sm) @@ -26338,14 +26470,14 @@ async function uB() { sm = true, o({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" }); }; let a; - e && r && Vg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + sB.toString()], { type: "text/javascript" }), a = sB(n)) : a = qne(n), a.then((i) => { + e && r && Wg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + EB.toString()], { type: "text/javascript" }), a = EB(n)) : a = Rse(n), a.then((i) => { s = true, sm = false; let p = null; - i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, t10({ wasm: i }); + i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, t8({ wasm: i }); }).catch(o); }); } -function Xne(r, e) { +function Ase(r, e) { switch (e) { case "float32": return new Float32Array(r); @@ -26357,60 +26489,61 @@ function Xne(r, e) { throw new Error(`Unknown dtype ${e}`); } } -var Yne = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"]; -var Vg = null; +var Fse = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"]; +var Wg = null; var om = null; var nm = {}; var sm = false; -var Rv = false; -function Qne(r, e = false) { - if (bw("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), sm) +var zv = false; +function Pse(r, e = false) { + if (_w("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), sm) throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`"); - Vg = r, Rv = e; + Wg = r, zv = e; } -function Zne(r, e = false) { +function Ose(r, e = false) { if (sm) throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`"); if (typeof r == "string") om = r; else { nm = r; - let t10 = Yne.filter((o) => nm[o] == null); - if (t10.length > 0) - throw new Error(`There were no entries found for the following binaries: ${t10.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`); + let t8 = Fse.filter((o) => nm[o] == null); + if (t8.length > 0) + throw new Error(`There were no entries found for the following binaries: ${t8.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`); } - Rv = e; + zv = e; } -var pB = -1; -var _v = -1; -function Jne(r) { - pB = r; +var FB = -1; +var Mv = -1; +function Mse(r) { + FB = r; } -function ese() { - if (_v === -1) +function Lse() { + if (Mv === -1) throw new Error("WASM backend not initialized."); - return _v; + return Mv; } -var tse = "4.5.0"; -var rse = 2; -eu("wasm", async () => { - let { wasm: r } = await uB(); +var Bse = "4.7.0"; +var zse = 2; +nu("wasm", async () => { + let { wasm: r } = await AB(); return new am(r); -}, rse); -var Fo = P(); -Fo.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); -Fo.registerFlag("WEBGPU_CPU_FORWARD", () => true); -Fo.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1); -Fo.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => true); -Fo.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); -Fo.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); -Fo.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); -Fo.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true); -Fo.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false); -Fo.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0); -Fo.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); -Fo.registerFlag("WEBGPU_PRINT_SHADER", () => ""); -var Wg = class { +}, zse); +var go = A(); +go.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); +go.registerFlag("WEBGPU_CPU_FORWARD", () => true); +go.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1); +go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => true); +go.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); +go.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); +go.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); +go.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true); +go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false); +go.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0); +go.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); +go.registerFlag("WEBGPU_PRINT_SHADER", () => ""); +go.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY", () => false); +var Ug = class { constructor(e) { e && (this.vendor = e.vendor, this.architecture = e.architecture, this.intelGPUGeneration = this.getIntelGPUGeneration()); } @@ -26427,21 +26560,21 @@ var Wg = class { return this.vendor === "intel"; } }; -var Ug = class { +var Gg = class { constructor(e) { this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0; } - acquireBuffer(e, t10, o = false, n = true) { - let s, a = cB(e, t10); - return n ? (this.freeBuffers.has(a) || this.freeBuffers.set(a, []), this.freeBuffers.get(a).length > 0 ? (s = this.freeBuffers.get(a).pop(), this.numFreeBuffers--) : (s = this.device.createBuffer({ size: e, usage: t10, mappedAtCreation: o }), this.numBytesAllocated += e)) : (s = this.device.createBuffer({ size: e, usage: t10, mappedAtCreation: o }), this.numBytesAllocated += e), this.usedBuffers.has(a) || this.usedBuffers.set(a, []), this.usedBuffers.get(a).push(s), this.numUsedBuffers++, this.numBytesUsed += e, s; + acquireBuffer(e, t8, o = false, n = true) { + let s, a = PB(e, t8); + return n ? (this.freeBuffers.has(a) || this.freeBuffers.set(a, []), this.freeBuffers.get(a).length > 0 ? (s = this.freeBuffers.get(a).pop(), this.numFreeBuffers--) : (s = this.device.createBuffer({ size: e, usage: t8, mappedAtCreation: o }), this.numBytesAllocated += e)) : (s = this.device.createBuffer({ size: e, usage: t8, mappedAtCreation: o }), this.numBytesAllocated += e), this.usedBuffers.has(a) || this.usedBuffers.set(a, []), this.usedBuffers.get(a).push(s), this.numUsedBuffers++, this.numBytesUsed += e, s; } - releaseBuffer(e, t10, o, n = true) { + releaseBuffer(e, t8 = true) { if (this.freeBuffers.size === 0) return; - let s = cB(t10, o), a = this.usedBuffers.get(s), i = a.indexOf(e); + let o = e.size, n = e.usage, s = PB(o, n), a = this.usedBuffers.get(s), i = a.indexOf(e); if (i < 0) throw new Error("Cannot find the buffer in buffer manager"); - a[i] = a[a.length - 1], a.pop(), this.numUsedBuffers--, this.numBytesUsed -= t10, n ? (this.freeBuffers.get(s).push(e), this.numFreeBuffers++) : (e.destroy(), this.numBytesAllocated -= t10); + a[i] = a[a.length - 1], a.pop(), this.numUsedBuffers--, this.numBytesUsed -= o, t8 ? (this.freeBuffers.get(s).push(e), this.numFreeBuffers++) : (e.destroy(), this.numBytesAllocated -= o); } getNumUsedBuffers() { return this.numUsedBuffers; @@ -26450,45 +26583,45 @@ var Ug = class { return this.numFreeBuffers; } dispose() { - this.freeBuffers.forEach((e, t10) => { + this.freeBuffers.forEach((e, t8) => { e.forEach((o) => { o.destroy(); }); - }), this.usedBuffers.forEach((e, t10) => { + }), this.usedBuffers.forEach((e, t8) => { e.forEach((o) => { o.destroy(); }); }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0; } }; -function cB(r, e) { +function PB(r, e) { return `${r}_${e}`; } -var Gg = class { +var Hg = class { constructor(e) { this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0; } - acquireTexture(e, t10, o, n) { - let s = mB(o), a = e * t10 * s, i = lB(e, t10, o, n); + acquireTexture(e, t8, o, n) { + let s = MB(o), a = e * t8 * s, i = OB(e, t8, o, n); if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) { this.numFreeTextures--; let u = this.freeTextures.get(i).shift(); return this.usedTextures.get(i).push(u), u; } this.numBytesAllocated += a; - let p = this.device.createTexture({ size: [e, t10], format: o, usage: n }); + let p = this.device.createTexture({ size: [e, t8], format: o, usage: n }); return this.usedTextures.get(i).push(p), p; } - releaseTexture(e, t10, o, n, s) { + releaseTexture(e) { if (this.freeTextures.size === 0) return; - let a = lB(t10, o, n, s); + let t8 = e.width, o = e.height, n = e.format, s = e.usage, a = OB(t8, o, n, s); this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--; let i = this.usedTextures.get(a), p = i.indexOf(e); if (p < 0) throw new Error("Cannot release a texture that was never provided by this texture manager"); i.splice(p, 1); - let u = mB(n), c = t10 * o * u; + let u = MB(n), c = t8 * o * u; this.numBytesUsed -= c; } getNumUsedTextures() { @@ -26498,35 +26631,35 @@ var Gg = class { return this.numFreeTextures; } dispose() { - this.freeTextures.forEach((e, t10) => { + this.freeTextures.forEach((e, t8) => { e.forEach((o) => { o.destroy(); }); - }), this.usedTextures.forEach((e, t10) => { + }), this.usedTextures.forEach((e, t8) => { e.forEach((o) => { o.destroy(); }); }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0; } }; -function lB(r, e, t10, o) { - return `${r}_${e}_${t10}_${o}`; +function OB(r, e, t8, o) { + return `${r}_${e}_${t8}_${o}`; } -function mB(r) { +function MB(r) { if (r === "rgba8unorm") return 16; throw new Error(`${r} is not supported!`); } -function dB(r, e) { +function LB(r, e) { if (Math.max(...r) > 5) throw new Error("Cannot symbolically compute strides for rank > 6 tensor."); - let t10 = r.length, o = "xyzwuv", n = r.map((a) => `${e}.${o[a]}`), s = new Array(t10 - 1); - s[t10 - 2] = n[t10 - 1]; - for (let a = t10 - 3; a >= 0; --a) + let t8 = r.length, o = "xyzwuv", n = r.map((a) => `${e}.${o[a]}`), s = new Array(t8 - 1); + s[t8 - 2] = n[t8 - 1]; + for (let a = t8 - 3; a >= 0; --a) s[a] = `(${s[a + 1]} * ${n[a + 1]})`; return s; } -var Bs = (r, e, t10) => t10 === "int32" ? `atomicAdd(${r}, bitcast(${e}));` : ` +var Us = (r, e, t8) => t8 === "int32" ? `atomicAdd(${r}, bitcast(${e}));` : ` { var oldValue = 0; loop { @@ -26539,14 +26672,14 @@ var Bs = (r, e, t10) => t10 === "int32" ? `atomicAdd(${r}, bitcast(${e}));` oldValue = res.old_value; } }`; -var xB = (r, e, t10, o, n) => { - let s = { dtype: o.dtype, shape: o.shape }, a = nse(t10, s, e), i = r.createShaderModule({ code: a, label: e.constructor.name }), p = r.createComputePipeline({ compute: { module: i, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" }), u = P().get("WEBGPU_PRINT_SHADER"); - if (u !== "") { - u = u.toLowerCase(); - let c = u.split(","); - (u === "all" || c.some((l) => n.toLowerCase().includes(l))) && (console.group(n), console.debug(a), console.groupEnd()); +var WB = (r, e, t8, o, n) => { + let s = { dtype: o.dtype, shape: o.shape }, a = Wse(t8, s, e), i = r.createShaderModule({ code: a, label: e.constructor.name }), p = A().get("WEBGPU_PRINT_SHADER"); + if (p !== "") { + p = p.toLowerCase(); + let u = p.split(","); + (p === "all" || u.some((c) => e.shaderKey.toLowerCase().includes(c))) && (console.group(e.shaderKey), console.debug(a), console.groupEnd()); } - return p; + return n ? r.createComputePipelineAsync({ compute: { module: i, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" }) : r.createComputePipeline({ compute: { module: i, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" }); }; var Ae = (r, e = "f32") => { switch (r) { @@ -26562,7 +26695,7 @@ var Ae = (r, e = "f32") => { throw new Error(`${r}-component ${e} is not supported.`); } }; -function Nt(r) { +function ht(r) { if (r <= 1) return "i32"; if (r === 2) @@ -26577,7 +26710,7 @@ function Nt(r) { return "vec6"; throw Error(`GPU for rank ${r} is not yet supported`); } -function Po(r) { +function Oo(r) { if (r === 0) return "x"; if (r === 1) @@ -26592,7 +26725,7 @@ function Po(r) { return "v"; throw Error(`Index ${r} is not yet supported`); } -function K(...r) { +function H(...r) { let e; switch (r.length) { case 0: @@ -26610,10 +26743,10 @@ function K(...r) { } return e; } -function fB(r, e) { - let t10; - return t10 = ` - ${ose(e)} +function BB(r, e) { + let t8; + return t8 = ` + ${Vse(e)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @@ -26626,16 +26759,16 @@ function fB(r, e) { workgroupId = WorkgroupId; ${r ? "main(getGlobalIndex());" : "main();"}; } - `, t10; + `, t8; } -function ose(r) { +function Vse(r) { return ` @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]}) `; } -function nse(r, e, t10) { - let o = [], n = t10.workgroupSize[0] * t10.workgroupSize[1] * t10.workgroupSize[2]; - if (t10.outputComponent = t10.outputComponent ? t10.outputComponent : 1, o.push(` +function Wse(r, e, t8) { + let o = [], n = t8.workgroupSize[0] * t8.workgroupSize[1] * t8.workgroupSize[2]; + if (t8.outputComponent = t8.outputComponent ? t8.outputComponent : 1, o.push(` var localId: vec3; var localIndex: u32; @@ -26645,12 +26778,12 @@ function nse(r, e, t10) { // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${bB(t10) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${GB(t8) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } - `), t10.isFromPixels) { + `), t8.isFromPixels) { o.push(` struct Uniform { size : i32, @@ -26658,52 +26791,56 @@ function nse(r, e, t10) { outShapeStrides : vec2, }; - @group(0) @binding(0) var result: array<${kp(e.dtype, t10.outputComponent)}>; + @group(0) @binding(0) var result: array<${$p(e.dtype, t8.outputComponent)}>; @group(0) @binding(2) var uniforms: Uniform; `); - let f = gB(t10); - return [hB, o.join(` -`), Dv(e.shape), t10.getUserCode(), fB(f, t10)].join(` + let f = VB(t8); + return [zB, o.join(` +`), im(e.shape), t8.getUserCode(), BB(f, t8)].join(` `); } let s, a, i = "struct Uniforms { NAN : f32, INFINITY : f32, "; - t10.variableNames.forEach((f, h) => { - let g = Nt(r[h].shape.length); - i += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `, s = r[h].shape.length - 1, a = Nt(s), i += `${f.charAt(0).toLowerCase() + f.slice(1)}ShapeStrides: ${a}, `; + t8.variableNames.forEach((f, h) => { + let g = ht(r[h].shape.length); + i += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `, s = r[h].shape.length - 1, a = ht(s), i += `${f.charAt(0).toLowerCase() + f.slice(1)}ShapeStrides: ${a}, `; }); - let p = Nt(e.shape.length); - i += `outShape : ${p}, `, s = e.shape.length - 1, a = Nt(s), i += ` - outShapeStrides: ${a}, `, t10.size && (i += "size : i32, "), t10.uniforms && (i += t10.uniforms), i += "};", i = mse(i), o.push(i), t10.atomic ? o.push(` + let p = ht(e.shape.length); + i += `outShape : ${p}, `, s = e.shape.length - 1, a = ht(s), i += ` + outShapeStrides: ${a}, `, t8.size && (i += "size : i32, "), t8.uniforms && (i += t8.uniforms), i += "};", i = Yse(i), o.push(i), t8.atomic ? o.push(` @group(0) @binding(0) var result: array>; `) : o.push(` - @group(0) @binding(0) var result: array<${kp(e.dtype, t10.outputComponent)}>; - `), t10.variableNames.forEach((f, h) => { + @group(0) @binding(0) var result: array<${$p(e.dtype, t8.outputComponent)}>; + `), t8.variableNames.forEach((f, h) => { o.push(` - @group(0) @binding(${1 + h}) var ${f}: array<${t10.variableComponents ? kp(r[h].dtype, t10.variableComponents[h]) : kp(r[h].dtype, t10.outputComponent)}>; + @group(0) @binding(${1 + h}) var ${f}: array<${t8.variableComponents ? $p(r[h].dtype, t8.variableComponents[h]) : $p(r[h].dtype, t8.outputComponent)}>; `); }), i !== "" && o.push(` - @group(0) @binding(${1 + t10.variableNames.length}) var uniforms: Uniforms; + @group(0) @binding(${1 + t8.variableNames.length}) var uniforms: Uniforms; `); - let u = pse(e.shape, t10.dispatchLayout), c = [hB, o.join(` -`) + sse, Dv(e.shape), u, cse(e.shape.length)]; - t10.atomic || c.push(lse(e.shape, e.dtype, t10.outputComponent)), t10.variableNames.forEach((f, h) => { - c.push(`${Dv(r[h].shape, f)}`); + let u = qse(e.shape, t8.dispatchLayout), c = [zB, o.join(` +`) + Use, im(e.shape), u, jse(e.shape.length)]; + t8.atomic || c.push(Xse(e.shape, e.dtype, t8.outputComponent)), t8.variableNames.forEach((f, h) => { + c.push(`${im(r[h].shape, f)}`); }); - let l = r.map((f, h) => use(f, e.shape, t10.variableComponents ? t10.variableComponents[h] : t10.outputComponent, t10.dispatchLayout.x.length === e.shape.length)).join(` + let l = r.map((f, h) => Kse(f, e.shape, t8.variableComponents ? t8.variableComponents[h] : t8.outputComponent, t8.dispatchLayout.x.length === e.shape.length)).join(` `); - c.push(l), c.push(t10.getUserCode()); - let m = gB(t10); - return c.push(fB(m, t10)), c.join(` + c.push(l), c.push(t8.getUserCode()); + let m = VB(t8); + return c.push(BB(m, t8)), c.join(` `); } -function yB(r, e, t10, o) { - let n = r.shaderKey; +function UB(r, e, t8) { + let o = r.shaderKey; if (r.isFromPixels) - return n; - let s = t10.map((c) => c.dtype).concat(o.dtype), a = t10.map((c) => C.getBroadcastDims(c.shape, o.shape)), i = t10.map((c) => y.arraysEqual(c.shape, o.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = bB(r) ? "flatDispatch" : ""; - return n += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + e.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, n; + return o; + let n = [], s = []; + e.forEach((c) => { + n.push(c.shape), s.push(c.dtype); + }), n.push(t8.shape), s.push(t8.dtype); + let a = e.map((c) => w.getBroadcastDims(c.shape, t8.shape)), i = e.map((c) => y.arraysEqual(c.shape, t8.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = GB(r) ? "flatDispatch" : ""; + return o += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + n.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, o; } -var hB = ` +var zB = ` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -26763,17 +26900,17 @@ var hB = ` return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } `; -var sse = ` +var Use = ` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } `; -function Dv(r, e = "") { - let t10 = r.length, o = e !== "" ? `get${e.charAt(0).toUpperCase() + e.slice(1)}CoordsFromIndex` : "getCoordsFromIndex", n = e !== "" ? `${e.charAt(0).toLowerCase() + e.slice(1)}ShapeStrides` : "outShapeStrides"; - if (t10 <= 1) +function im(r, e = "") { + let t8 = r.length, o = e !== "" ? `get${e.charAt(0).toUpperCase() + e.slice(1)}CoordsFromIndex` : "getCoordsFromIndex", n = e !== "" ? `${e.charAt(0).toLowerCase() + e.slice(1)}ShapeStrides` : "outShapeStrides"; + if (t8 <= 1) return `fn ${o}(index : i32) -> i32 { return index; }`; - let s = y.computeStrides(r), a = Nt(t10), i = []; - for (let u = 0; u < t10; u++) + let s = y.computeStrides(r), a = ht(t8), i = []; + for (let u = 0; u < t8; u++) i.push(`d${u}`); if (s.length === 1) return ` fn ${o}(index : i32) -> vec2 { @@ -26782,7 +26919,7 @@ function Dv(r, e = "") { }`; let p; return p = "var index2 = index;" + s.map((u, c) => { - let l = `let ${i[c]} = index2 / uniforms.${n}.${Po(c)}`, m = c === s.length - 1 ? `let ${i[c + 1]} = index2 - ${i[c]} * uniforms.${n}.${Po(c)}` : `index2 = index2 - ${i[c]} * uniforms.${n}.${Po(c)}`; + let l = `let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`, m = c === s.length - 1 ? `let ${i[c + 1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}` : `index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`; return `${l}; ${m};`; }).join(""), ` fn ${o}(index : i32) -> ${a} { @@ -26791,92 +26928,92 @@ function Dv(r, e = "") { } `; } -function ase(r, e) { - let t10 = r.name, o = r.shape.length, n = Nt(o), s = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", "); +function Gse(r, e) { + let t8 = r.name, o = r.shape.length, n = ht(o), s = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", "); if (o < 1) return ` fn ${s}() -> ${Ae(e)} { - return ${Ae(e)}(${t10}[0]); + return ${Ae(e)}(${t8}[0]); } `; - let p = `uniforms.${t10.charAt(0).toLowerCase() + t10.slice(1)}Shape`, u = `${o}D`; + let p = `uniforms.${t8.charAt(0).toLowerCase() + t8.slice(1)}Shape`, u = `${o}D`; return o === 0 && (u = "1D"), ` fn ${s}(${i}) -> ${Ae(e)} { - return ${Ae(e)}(${t10}[getIndexFromCoords${u}(${n}(${a.join(",")}), + return ${Ae(e)}(${t8}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})${e === 1 ? "" : ` / ${e}`}]); } `; } -function ise(r, e, t10, o) { - let n = r.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r.shape.length, p = e.length, u = Nt(p); +function Hse(r, e, t8, o) { + let n = r.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r.shape.length, p = e.length, u = ht(p); if (y.arraysEqual(r.shape, e) && o) return ` - fn ${a}Index(globalIndex : i32) -> ${Ae(t10)} { - return ${Ae(t10)}(${n}[globalIndex]); + fn ${a}Index(globalIndex : i32) -> ${Ae(t8)} { + return ${Ae(t8)}(${n}[globalIndex]); } - fn ${a}Coords(coords : ${u}) -> ${Ae(t10)} { - return ${Ae(t10)}(${n}[${p > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}${t10 === 1 ? "" : ` / ${t10}`}]); + fn ${a}Coords(coords : ${u}) -> ${Ae(t8)} { + return ${Ae(t8)}(${n}[${p > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}${t8 === 1 ? "" : ` / ${t8}`}]); } `; - let c = C.getBroadcastDims(r.shape, e), l = p - i, m = ""; + let c = w.getBroadcastDims(r.shape, e), l = p - i, m = ""; if (i === 0) return ` - fn ${a}Index(globalIndex : i32) -> ${Ae(t10)}{ + fn ${a}Index(globalIndex : i32) -> ${Ae(t8)}{ return get${s}(); } - fn ${a}Coords(coords : ${u}) -> ${Ae(t10)}{ + fn ${a}Coords(coords : ${u}) -> ${Ae(t8)}{ return get${s}(); } `; - p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Po(g + l)} = 0;`).join(` + p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Oo(g + l)} = 0;`).join(` `); let d = ""; if (p < 2 && i > 0) d = "coords"; else if (p > 1) { - let g = Nt(i), x = r.shape.map((b, w) => `coords.${Po(w + l)}`).join(", "); + let g = ht(i), x = r.shape.map((b, C) => `coords.${Oo(C + l)}`).join(", "); d = `${g}(${x})`; } else d = "coords"; let f = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, h = `${i}D`; return ` - fn ${a}Index(globalIndex : i32) -> ${Ae(t10)} { + fn ${a}Index(globalIndex : i32) -> ${Ae(t8)} { var coords = getCoordsFromIndex(globalIndex); ${m} - return ${Ae(t10)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t10 === 1 ? "" : ` / ${t10}`}]); + return ${Ae(t8)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t8 === 1 ? "" : ` / ${t8}`}]); } - fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t10)} { + fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t8)} { var coords = coordsIn; ${m} - return ${Ae(t10)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t10 === 1 ? "" : ` / ${t10}`}]); + return ${Ae(t8)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t8 === 1 ? "" : ` / ${t8}`}]); } `; } -function use(r, e, t10, o) { - let n = ase(r, t10); - return r.shape.length <= e.length && (n += ise(r, e, t10, o)), n; +function Kse(r, e, t8, o) { + let n = Gse(r, t8); + return r.shape.length <= e.length && (n += Hse(r, e, t8, o)), n; } -function pse(r, e) { - let { x: t10, y: o = [], z: n = [] } = e, s = r.length, a = t10.length + o.length + n.length; +function qse(r, e) { + let { x: t8, y: o = [], z: n = [] } = e, s = r.length, a = t8.length + o.length + n.length; if (a !== s) return ""; - if (t10.length === s) - return `fn getOutputCoords() -> ${Nt(s)}{ + if (t8.length === s) + return `fn getOutputCoords() -> ${ht(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } `; - let i = "", p = [t10, o, n]; + let i = "", p = [t8, o, n]; for (let m = 0; m < p.length; m++) { let d = p[m]; if (d.length !== 0) if (d.length === 1) i += `let d${d[0]} = i32(globalId[${m}]);`; else { - let f = dB(d, "uniforms.outShape"); + let f = LB(d, "uniforms.outShape"); i += `var index${m} = i32(globalId[${m}]);`; for (let h = 0; h < f.length; h++) i += `let d${d[h]} = index${m} / ${f[h]};`, h === f.length - 1 ? i += `let d${d[h + 1]} = index${m} - d${d[h]} * ${f[h]};` : i += `index${m} = index${m} - d${d[h]} * ${f[h]};`; @@ -26885,12 +27022,12 @@ function pse(r, e) { let u = []; for (let m = 0; m < a; m++) u.push(`d${m}`); - let c = Nt(a), l = `fn getOutputCoords() -> ${c} { + let c = ht(a), l = `fn getOutputCoords() -> ${c} { ${i} `; return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l; } -function cse(r) { +function jse(r) { let e = ""; switch (r) { case 0: @@ -26952,229 +27089,223 @@ function cse(r) { } return e; } -function bB(r) { +function GB(r) { return r.dispatch[1] === 1 && r.dispatch[2] === 1; } -function kp(r, e = 1) { +function $p(r, e = 1) { if (r === "float32") return Ae(e, "f32"); if (r === "int32" || r === "bool") return Ae(e, "i32"); throw new Error(`type ${r} is not supported.`); } -function lse(r, e, t10) { - let o = r.length, n = kp(e, t10), s = `fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t10)}) { +function Xse(r, e, t8) { + let o = r.length, n = $p(e, t8), s = `fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t8)}) { result[flatIndex] = ${n}(value); } - fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t10, "i32")}) { + fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t8, "i32")}) { result[flatIndex] = ${n}(value); } `; if (o >= 2) { - let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = Nt(o); + let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = ht(o); s += ` - fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t10)}) { + fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t8)}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); - setOutputAtIndex(flatIndex${t10 === 1 ? "" : ` / ${t10}`}, value); + setOutputAtIndex(flatIndex${t8 === 1 ? "" : ` / ${t8}`}, value); } - fn setOutputAtCoordsI32(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t10, "i32")}) { + fn setOutputAtCoordsI32(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t8, "i32")}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); - setOutputAtIndexI32(flatIndex${t10 === 1 ? "" : ` / ${t10}`}, value); + setOutputAtIndexI32(flatIndex${t8 === 1 ? "" : ` / ${t8}`}, value); } `; } return s; } -function mse(r) { +function Yse(r) { let e = /(\w+)\s*:\s*vec(5|6)/g; r = r.replace(e, (o) => "@align(16) " + o); - let t10 = /vec(5|6)\s*,\s*(\w+)/g; - return r = r.replace(t10, (o, n, s) => `vec${n}, @align(16) ${s}`), r; + let t8 = /vec(5|6)\s*,\s*(\w+)/g; + return r = r.replace(t8, (o, n, s) => `vec${n}, @align(16) ${s}`), r; } -function gB(r) { +function VB(r) { return !(r.dispatchLayout.hasOwnProperty("y") && r.dispatchLayout.y.length !== 0 || r.dispatchLayout.hasOwnProperty("z") && r.dispatchLayout.z.length !== 0); } -var Fv = {}; -He(Fv, { GPUBytesPerElement: () => Hg, MatMulProgramType: () => Oo, assertNotComplex: () => cm, computeDispatch: () => q, computeWorkPerThreadForConv2d: () => um, computeWorkgroupInfoForMatMul: () => Av, computeWorkgroupSizeForConv2d: () => im, flatDispatchLayout: () => Z, isWebGPUSupported: () => pm, tilesFitEvenlyIntoShape: () => fse }); -var Np = (r) => { +var Wv = {}; +Ke(Wv, { GPUBytesPerElement: () => Kg, MatMulProgramType: () => Mo, assertNotComplex: () => lm, computeDispatch: () => K, computeWorkPerThreadForConv2d: () => pm, computeWorkgroupInfoForMatMul: () => Vv, computeWorkgroupSizeForConv2d: () => um, flatDispatchLayout: () => X, isWebGPUSupported: () => cm, tilesFitEvenlyIntoShape: () => Zse }); +var Ep = (r) => { let e = 1; - for (let t10 = 0; t10 < r.length; t10++) - e *= r[t10]; + for (let t8 = 0; t8 < r.length; t8++) + e *= r[t8]; return e; }; -function fse(r, e) { +function Zse(r, e) { if (r.length !== e.length) throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${e.length} shape - ranks must match.`); - return e.every((t10, o) => t10 % r[o] === 0); + return e.every((t8, o) => t8 % r[o] === 0); } -function q(r, e, t10 = [1, 1, 1], o = [1, 1, 1]) { - let [n, s, a] = [Math.ceil(Np(r.x.map((i) => e[i])) / (t10[0] * o[0])), r.y ? Math.ceil(Np(r.y.map((i) => e[i])) / (t10[1] * o[1])) : 1, r.z ? Math.ceil(Np(r.z.map((i) => e[i])) / (t10[2] * o[2])) : 1]; +function K(r, e, t8 = [1, 1, 1], o = [1, 1, 1]) { + let [n, s, a] = [Math.ceil(Ep(r.x.map((i) => e[i])) / (t8[0] * o[0])), r.y ? Math.ceil(Ep(r.y.map((i) => e[i])) / (t8[1] * o[1])) : 1, r.z ? Math.ceil(Ep(r.z.map((i) => e[i])) / (t8[2] * o[2])) : 1]; return [n, s, a]; } -function Av(r, e, t10, o = false) { +function Vv(r, e, t8, o = false) { let n = [8, 8, 1], s = [4, 4, 1]; - return o || (r <= 8 && (s[1] = 1), e <= 16 && t10 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s }; + return o || (r <= 8 && (s[1] = 1), e <= 16 && t8 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s }; } -function im(r, e, t10 = false) { - if (t10) +function um(r, e, t8 = false) { + if (t8) return [8, 8, 1]; - let o = Np(r.x.map((s) => e[s])), n = Np(r.y.map((s) => e[s])); + let o = Ep(r.x.map((s) => e[s])), n = Ep(r.y.map((s) => e[s])); return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1]; } -function um(r, e, t10 = false) { - if (t10) +function pm(r, e, t8 = false) { + if (t8) return [4, 4, 1]; - let o = Np(r.x.map((s) => e[s])), n = Np(r.y.map((s) => e[s])); + let o = Ep(r.x.map((s) => e[s])), n = Ep(r.y.map((s) => e[s])); return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1]; } -function Z(r) { - return { x: r.map((e, t10) => t10) }; +function X(r) { + return { x: r.map((e, t8) => t8) }; } -function Hg(r) { +function Kg(r) { if (r === "float32" || r === "int32" || r === "bool" || r === "string") return 4; if (r === "complex64") return 8; throw new Error(`Unknown dtype ${r}`); } -function pm() { +function cm() { return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu; } -function cm(r, e) { - Array.isArray(r) || (r = [r]), r.forEach((t10) => { - t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGPU backend.`); +function lm(r, e) { + Array.isArray(r) || (r = [r]), r.forEach((t8) => { + t8 != null && y.assert(t8.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGPU backend.`); }); } -var Oo; +var Mo; (function(r) { r[r.MatMulReduceProgram = 0] = "MatMulReduceProgram", r[r.MatMulSplitKProgram = 1] = "MatMulSplitKProgram", r[r.MatMulSmallOutputSizeProgram = 2] = "MatMulSmallOutputSizeProgram", r[r.MatMulPackedProgram = 3] = "MatMulPackedProgram", r[r.MatMulMax = 4] = "MatMulMax"; -})(Oo || (Oo = {})); -var hse = P().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"); -var gse = (r, e) => { - let t10 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch; - if (n.every((a) => a <= t10)) +})(Mo || (Mo = {})); +var Jse = A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"); +var eae = (r, e) => { + let t8 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch; + if (n.every((a) => a <= t8)) return n; - y.assert(n[0] > t10 && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension."); + y.assert(n[0] > t8 && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension."); let s = Math.ceil(Math.sqrt(n[0])); - return s > t10 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t10, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1]; + return s > t8 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t8, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1]; }; -var Cu = class extends ro { +var vu = class extends so { nextDataId() { - return Cu.nextDataId++; + return vu.nextDataId++; } - constructor(e, t10) { - if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchNumberInEncoder = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, !pm()) + constructor(e, t8) { + if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchCountInPass = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.queryResolveBuffer = null, this.querySet = null, this.querySetCount = 2, this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, this.hasReadSyncWarned = false, this.hasTimestampQueryWarned = false, !cm()) throw new Error("WebGPU is not supported on this device"); - this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = e.features.has("timestamp-query-inside-passes"), this.adapterInfo = new Wg(t10), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Ug(this.device), this.textureManager = new Gg(this.device), this.tensorMap = new Lo(this, ur()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), P().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas)); + this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.commandEncoder = null, this.computePassEncoder = null, this.adapterInfo = new Ug(t8), this.supportTimestampQuery = this.device.features.has("timestamp-query"), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Gg(this.device), this.textureManager = new Hg(this.device), this.tensorMap = new Bo(this, ur()), A().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas)); } floatPrecision() { return 32; } - defaultGpuBufferUsage() { - return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST; - } - disposeData(e, t10 = false) { - if (this.tensorDataPendingDisposal.indexOf(e) >= 0) - return false; + disposeData(e, t8 = false) { if (!this.tensorMap.has(e)) return true; let o = this.tensorMap.get(e); - if (this.decRef(e), !t10 && o.refCount > 0) - return false; - if (this.commandQueueOwnedIds.has(e)) - return this.tensorDataPendingDisposal.push(e), false; - let { complexTensorInfos: n } = this.tensorMap.get(e); - return n != null && (this.disposeData(n.real.dataId, t10), this.disposeData(n.imag.dataId, t10)), this.releaseResource(e), this.tensorMap.delete(e), true; + return t8 ? o.refCount = 0 : o.refCount--, o.refCount > 0 ? false : (o.complexTensorInfos != null && (this.disposeData(o.complexTensorInfos.real.dataId), this.disposeData(o.complexTensorInfos.imag.dataId)), this.commandQueueOwnedIds.has(e) ? (this.tensorDataPendingDisposal.push(e), true) : (this.releaseResource(e), this.tensorMap.delete(e), true)); } memory() { return { numBytesInGPU: this.bufferManager.numBytesUsed, numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, unreliable: false }; } releaseResource(e) { - let t10 = this.tensorMap.get(e); - if (!(!t10 || !t10.resourceInfo)) { - if (t10.external) { - t10.resourceInfo = null; + let t8 = this.tensorMap.get(e); + if (!(!t8 || !t8.resource)) { + if (t8.external) { + t8.resource = null; return; } - if ("texture" in t10.resourceInfo) { - let o = t10.resourceInfo; - o.texture instanceof GPUTexture && this.textureManager.releaseTexture(o.texture, o.width, o.height, o.format, o.usage), o.texture = null; - } else { - let o = t10.resourceInfo; - this.bufferManager.releaseBuffer(o.buffer, o.size, o.usage), o.buffer = null; - } - t10.resourceInfo = null; + t8.resource instanceof GPUBuffer ? this.bufferManager.releaseBuffer(t8.resource) : t8.resource instanceof GPUTexture && this.textureManager.releaseTexture(t8.resource), t8.resource = null; } } refCount(e) { return this.tensorMap.has(e) ? this.tensorMap.get(e).refCount : 0; } incRef(e) { - let t10 = this.tensorMap.get(e); - t10.refCount++; + let t8 = this.tensorMap.get(e); + t8.refCount++; } decRef(e) { if (this.tensorMap.has(e)) { - let t10 = this.tensorMap.get(e); - t10.refCount--; + let t8 = this.tensorMap.get(e); + t8.refCount--; } } - write(e, t10, o) { + write(e, t8, o) { if (o === "complex64" && e != null) throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); let n = { id: this.nextDataId() }; - return this.tensorMap.set(n, { dtype: o, shape: t10, values: e, refCount: 1 }), n; + return this.tensorMap.set(n, { dtype: o, shape: t8, values: e, refCount: 1 }), n; } - move(e, t10, o, n, s) { + move(e, t8, o, n, s) { if (n === "complex64") throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); - this.tensorMap.set(e, { dtype: n, shape: o, values: t10, refCount: s }); + this.tensorMap.set(e, { dtype: n, shape: o, values: t8, refCount: s }); } submitQueue() { - this.ensureComputePassEnded(), this.queue.submit([this.currentCommandEncoder.finish()]), this.currentCommandEncoder = null, this.dispatchNumberInEncoder = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDataPendingDisposal.forEach((e) => { + this.queue.submit([this.commandEncoder.finish()]), this.commandEncoder = null, this.dispatchCountInPass = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDataPendingDisposal.forEach((e) => { this.releaseResource(e), this.tensorMap.delete(e); - }), this.uniformPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e.buffer, e.size, e.usage)), this.stagingPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e.buffer, e.size, e.usage, false)), this.tensorDataPendingDisposal = [], this.uniformPendingDisposal = [], this.stagingPendingDisposal = []; + }), this.uniformPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e)), this.stagingPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e, false)), this.tensorDataPendingDisposal = [], this.uniformPendingDisposal = [], this.stagingPendingDisposal = []; } ensureCommandEncoderReady() { - this.currentCommandEncoder || (this.currentCommandEncoder = this.device.createCommandEncoder()); + this.commandEncoder || (this.commandEncoder = this.device.createCommandEncoder()); } - ensureComputePassEnded() { - this.currentComputePass && (this.currentComputePass.end(), this.currentComputePass = null); + endComputePassEncoder() { + this.computePassEncoder && (this.computePassEncoder.end(), this.computePassEncoder = null); } - getComputePass() { - return this.currentComputePass || (this.currentComputePass = this.currentCommandEncoder.beginComputePass()), this.currentComputePass; + async checkCompileCompletionAsync() { + let e; + try { + e = await Promise.all(Object.values(this.pipelineCache)); + } catch (t8) { + throw new Error(t8.message); + } + Object.keys(this.pipelineCache).map((t8, o) => { + this.pipelineCache[t8] = e[o]; + }); } - async getBufferData(e, t10) { - let o = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ); - this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, o, 0, t10), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); + async getBufferData(e) { + if (A().getBool("WEBGPU_ENGINE_COMPILE_ONLY")) + return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"), null; + let t8 = e.size, o = this.bufferManager.acquireBuffer(t8, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ); + this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(e, 0, o, 0, t8), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); let n = o.getMappedRange().slice(0); - return o.unmap(), o != null && this.bufferManager.releaseBuffer(o, t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), P().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n; + return o.unmap(), o != null && this.bufferManager.releaseBuffer(o), A().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n; } - convertAndCacheOnCPU(e, t10) { + convertAndCacheOnCPU(e, t8) { let o = this.tensorMap.get(e); - return this.releaseResource(e), o.values = t10, o.values; + return o.values = t8, o.values; } readSync(e) { - let t10 = this.tensorMap.get(e), { values: o, complexTensorInfos: n } = t10; - if (o != null || t10.dtype === "string") + let t8 = this.tensorMap.get(e), { values: o, complexTensorInfos: n } = t8; + if (o != null || t8.dtype === "string") return o; - if (t10.dtype === "complex64") { - let h = this.readSync(n.real.dataId), g = this.readSync(n.imag.dataId), x = y.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(h, g).buffer, "float32"); + if (t8.dtype === "complex64") { + let h = this.readSync(n.real.dataId), g = this.readSync(n.imag.dataId), x = y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h, g).buffer, "float32"); return this.convertAndCacheOnCPU(e, x), x; } - let s = ["opaque", "premultiplied"], a = t10.resourceInfo, i = a.size; + this.hasReadSyncWarned || (this.hasReadSyncWarned = true, console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.")); + let s = ["opaque", "premultiplied"], a = t8.resource, i = a.size; y.assert(i % 4 === 0, () => "Because there is 4 bytes for one pixel, buffer size must be multiple of 4."); let p = i / 4, u = new ArrayBuffer(i), c = 256, l = 256, m = s.map((h) => new OffscreenCanvas(c, l)), d = new OffscreenCanvas(c, l); - this.ensureComputePassEnded(), m.map((h, g) => { + this.endComputePassEncoder(), m.map((h, g) => { let x = h.getContext("webgpu"); return x.configure({ device: this.device, format: "bgra8unorm", usage: GPUTextureUsage.COPY_DST, alphaMode: s[g] }), x.getCurrentTexture(); }).map((h, g) => { - let x = c * 4, b = (R, D, F) => { - this.ensureCommandEncoderReady(), this.currentCommandEncoder.copyBufferToTexture({ buffer: a.buffer, bytesPerRow: x, offset: F }, { texture: h }, { width: R, height: D }), this.submitQueue(); + let x = c * 4, b = (R, D, P) => { + this.ensureCommandEncoderReady(), this.commandEncoder.copyBufferToTexture({ buffer: a, bytesPerRow: x, offset: P }, { texture: h }, { width: R, height: D }), this.submitQueue(); let O = d.getContext("2d", { willReadFrequently: true }); O.clearRect(0, 0, R, D), O.drawImage(m[g], 0, 0); - let M = O.getImageData(0, 0, R, D).data, L = s[g], B = new Uint8ClampedArray(u, F, R * D * 4); + let M = O.getImageData(0, 0, R, D).data, L = s[g], B = new Uint8ClampedArray(u, P, R * D * 4); for (let z = 0; z < B.length; z += 4) if (L === "premultiplied") B[z + 3] = M[z + 3]; @@ -27182,239 +27313,234 @@ var Cu = class extends ro { let U = M[z]; B[z] = M[z + 2], B[z + 1] = M[z + 1], B[z + 2] = U; } - }, w = Math.floor(p / (c * l)), S = c, k = l, _ = 0; - for (let R = 0; R < w; R++) + }, C = Math.floor(p / (c * l)), S = c, k = l, _ = 0; + for (let R = 0; R < C; R++) b(S, k, _), _ += c * l * 4; let E = p % (c * l); k = Math.floor(E / c), k > 0 && (b(S, k, _), _ += k * (c * 4)), S = E % c, S > 0 && b(S, 1, _); }); - let f = y.convertBackendValuesAndArrayBuffer(u, t10.dtype); + let f = y.convertBackendValuesAndArrayBuffer(u, t8.dtype); return this.convertAndCacheOnCPU(e, f), f; } async read(e) { if (!this.tensorMap.has(e)) throw new Error(`Tensor ${e} was not registered!`); - let t10 = this.tensorMap.get(e), { values: o } = t10; + let t8 = this.tensorMap.get(e), { values: o } = t8; if (o != null) return o; let n; - if (t10.dtype === "complex64") { - let s = await Promise.all([this.read(t10.complexTensorInfos.real.dataId), this.read(t10.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1]; - n = C.mergeRealAndImagArrays(a, i); + if (t8.dtype === "complex64") { + let s = await Promise.all([this.read(t8.complexTensorInfos.real.dataId), this.read(t8.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1]; + n = w.mergeRealAndImagArrays(a, i); } else { - let s = t10.resourceInfo, a = await this.getBufferData(s.buffer, s.size); - n = y.convertBackendValuesAndArrayBuffer(a, t10.dtype); + let s = await this.getBufferData(t8.resource); + n = y.convertBackendValuesAndArrayBuffer(s, t8.dtype); } return this.convertAndCacheOnCPU(e, n), n; } - copyBuffer(e, t10, o) { - let n = this.bufferManager.acquireBuffer(t10, o); - return this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, n, 0, t10), this.submitQueue(), n; + copyBuffer(e) { + let t8 = e.size, o = e.usage, n = this.bufferManager.acquireBuffer(t8, o); + return this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(e, 0, n, 0, t8), this.submitQueue(), n; } - createTensorFromGPUData(e, t10, o) { + createTensorFromGPUData(e, t8, o) { let n = e.buffer; if (o === "complex64") throw new Error("Cannot write to a complex64 dtype. "); let s = { id: this.nextDataId() }; - this.tensorMap.set(s, { dtype: o, shape: t10, values: null, refCount: 1, external: e.zeroCopy }); - let a = this.tensorMap.get(s), i = Hg(a.dtype) * y.sizeFromShape(a.shape); + this.tensorMap.set(s, { dtype: o, shape: t8, values: null, refCount: 1, external: e.zeroCopy }); + let a = this.tensorMap.get(s), i = Kg(a.dtype) * y.sizeFromShape(a.shape); if (e.buffer.size < i) throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`); if ((e.buffer.usage & (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) !== (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!"); - return e.zeroCopy !== true && (n = this.copyBuffer(n, i, n.usage)), a.resourceInfo = { size: n.size, usage: n.usage, buffer: n }, ur().makeTensorFromDataId(s, t10, o, this); + return e.zeroCopy !== true && (n = this.copyBuffer(n)), a.resource = n, ur().makeTensorFromDataId(s, t8, o, this); } readToGPU(e) { - let t10 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resourceInfo: a } = t10; + let t8 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resource: a } = t8; if (n === "complex64") throw new Error("Does not support reading buffer for complex64 dtype."); if (a == null) throw o != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU."); - let i = a.size, p = this.bufferManager.acquireBuffer(i, a.usage); - this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, p, 0, i), this.submitQueue(); - let u = this.makeTensorInfo(s, n), c = ur().makeTensorFromTensorInfo(u), l = this.tensorMap.get(u.dataId); - return l.resourceInfo = { size: i, usage: this.defaultGpuBufferUsage(), buffer: p }, { tensorRef: c, buffer: p }; + let i = a, p = i.size, u = i.usage, c = this.bufferManager.acquireBuffer(p, u); + this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(a, 0, c, 0, p), this.submitQueue(); + let l = this.makeTensorInfo(s, n), m = ur().makeTensorFromTensorInfo(l), d = this.tensorMap.get(l.dataId); + return d.resource = c, { tensorRef: m, buffer: c }; } bufferSync(e) { - let t10 = this.readSync(e.dataId); + let t8 = this.readSync(e.dataId); if (e.dtype === "string") try { - let o = t10.map((n) => y.decodeString(n)); + let o = t8.map((n) => y.decodeString(n)); return me(e.shape, e.dtype, o); } catch (o) { throw new Error("Failed to decode encoded string bytes into utf-8"); } - return me(e.shape, e.dtype, t10); + return me(e.shape, e.dtype, t8); } async time(e) { - this.supportTimeQuery || console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU."); - let t10 = this.activeTimers, o = [], n = false; + !this.supportTimestampQuery && !this.hasTimestampQueryWarned && (console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."), this.hasTimestampQueryWarned = true); + let t8 = this.activeTimers, o = [], n = false; this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e(); let s = y.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = y.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null); - this.activeTimers = t10, n && (this.programTimersStack = null); + this.activeTimers = t8, n && (this.programTimersStack = null); let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, p = await Promise.all(s); return i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i; } - makeTensorInfo(e, t10, o) { - return t10 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t10), shape: e, dtype: t10 }; + makeTensorInfo(e, t8, o) { + return t8 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t8), shape: e, dtype: t8 }; } tensorToBinding(e) { if (!e) return null; - let t10 = this.tensorMap.get(e.dataId); - if ("texture" in t10.resourceInfo) { - let n = t10.resourceInfo; - return n.texture instanceof GPUExternalTexture ? n.texture : n.texture.createView(); - } - let o = t10.resourceInfo; - return { offset: 0, size: o.size, buffer: o.buffer }; - } - async getQueryTime(e) { - return this.supportTimeQuery ? this.getTimeFromQuerySet(e) : 0; + let o = this.tensorMap.get(e.dataId).resource; + return o instanceof GPUBuffer ? { buffer: o } : o instanceof GPUTexture ? o.createView() : o; } uploadToGPU(e) { - let t10 = this.tensorMap.get(e); - if (t10.resourceInfo) + let t8 = this.tensorMap.get(e); + if (t8.resource != null) return; - let o = Hg(t10.dtype) * y.sizeFromShape(t10.shape), n; - if (t10.values) { - if (n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage(), true), n.mapState === "unmapped") { - let s = this.bufferManager.acquireBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, true, false), a = s.getMappedRange(); - t10.dtype === "int32" || t10.dtype === "bool" ? new Int32Array(a).set(t10.values) : new Float32Array(a).set(t10.values), s.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(s, 0, n, 0, o), this.stagingPendingDisposal.push({ size: o, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, buffer: s }); + let o = Kg(t8.dtype) * y.sizeFromShape(t8.shape), n, s = GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST; + if (t8.values) { + if (n = this.bufferManager.acquireBuffer(o, s, true), n.mapState === "unmapped") { + let a = this.bufferManager.acquireBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, true, false), i = a.getMappedRange(); + t8.dtype === "int32" || t8.dtype === "bool" ? new Int32Array(i).set(t8.values) : new Float32Array(i).set(t8.values), a.unmap(), this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(a, 0, n, 0, o), this.stagingPendingDisposal.push(a); } else { - let s = n.getMappedRange(); - t10.dtype === "int32" || t10.dtype === "bool" ? new Int32Array(s).set(t10.values) : new Float32Array(s).set(t10.values), n.unmap(); + let a = n.getMappedRange(); + t8.dtype === "int32" || t8.dtype === "bool" ? new Int32Array(a).set(t8.values) : new Float32Array(a).set(t8.values), n.unmap(); } - t10.values = null; + t8.values = null; } else - n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage()); - t10.resourceInfo = { size: o, usage: this.defaultGpuBufferUsage(), buffer: n }; + n = this.bufferManager.acquireBuffer(o, s); + t8.resource = n; } makeUniforms(e) { - let t10 = 0, o = 0, n = [], s = 1; - e.forEach((u) => { - u.data.length === 0 && (u.data = [1]); - let c; - switch (u.data.length) { + let t8 = 0, o = 0, n = [], s = 1; + e.forEach((p) => { + p.data.length === 0 && (p.data = [1]); + let u; + switch (p.data.length) { case 1: - c = 4; + u = 4; break; case 2: - c = 8; + u = 8; break; case 3: - c = 16; + u = 16; break; case 4: - c = 16; + u = 16; break; case 5: - c = 16; + u = 16; break; case 6: - c = 16; + u = 16; break; default: - y.assert(false, () => `Unsupported ${u.data.length}D shape`); + y.assert(false, () => `Unsupported ${p.data.length}D shape`); } - (o === 5 || o === 6) && (c = 16), c > s && (s = c), t10 = Math.ceil(t10 / c) * c, o = u.data.length, n.push(t10), t10 += u.data.length * 4; - }), t10 = Math.ceil(t10 / s) * s; - let a = new ArrayBuffer(t10); - e.forEach((u, c) => { - let l = n[c]; - u.type === "int32" ? new Int32Array(a, l, u.data.length).set(u.data) : u.type === "uint32" ? new Uint32Array(a, l, u.data.length).set(u.data) : new Float32Array(a, l, u.data.length).set(u.data); + (o === 5 || o === 6) && (u = 16), u > s && (s = u), t8 = Math.ceil(t8 / u) * u, o = p.data.length, n.push(t8), t8 += p.data.length * 4; + }), t8 = Math.ceil(t8 / s) * s; + let a = new ArrayBuffer(t8); + e.forEach((p, u) => { + let c = n[u]; + p.type === "int32" ? new Int32Array(a, c, p.data.length).set(p.data) : p.type === "uint32" ? new Uint32Array(a, c, p.data.length).set(p.data) : new Float32Array(a, c, p.data.length).set(p.data); }); - let i = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM); - this.queue.writeBuffer(i, 0, a, 0, t10); - let p = { size: t10, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: i }; - return this.uniformPendingDisposal.push(p), { offset: 0, size: t10, buffer: i }; + let i = this.bufferManager.acquireBuffer(t8, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM); + return this.queue.writeBuffer(i, 0, a, 0, t8), this.uniformPendingDisposal.push(i), { offset: 0, size: t8, buffer: i }; } - runWebGPUProgram(e, t10, o, n, s) { + runWebGPUProgram(e, t8, o, n, s) { if (s || (s = this.makeTensorInfo(e.outputShape, o)), y.sizeFromShape(s.shape) === 0) return this.tensorMap.get(s.dataId).values = y.getTypedArrayFromDType(s.dtype, 0), s; - this.uploadToGPU(s.dataId), e.dispatch = gse(this.device, e); - let a = [], i = []; + this.uploadToGPU(s.dataId), e.dispatch = eae(this.device, e); + let a = t8.map((p, u) => { + if (p.dtype === "complex64") + throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts."); + return this.uploadToGPU(p.dataId), { dtype: this.tensorMap.get(p.dataId).dtype, shape: p.shape, name: e.variableNames[u] }; + }); + e.shaderKey = UB(e, a, s); + let i = A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"); + return e.shaderKey in this.pipelineCache || (this.pipelineCache[e.shaderKey] = WB(this.device, e, a, s, i)), e.pipeline = this.pipelineCache[e.shaderKey], i || this.recordAndSubmit(e, s, t8, n), s; + } + recordAndSubmit(e, t8, o, n) { + if (e.pipeline instanceof Promise) + throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!"); + let s = [], a = []; if (!e.isFromPixels) { - a.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), i = t10.concat(s).map((g) => g.shape); - let h = "int32"; - if (i.map((g) => { - a.push({ type: h, data: g }); - let x = y.computeStrides(g); - a.push({ type: h, data: x }); + s.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), a = o.concat(t8).map((m) => m.shape); + let l = "int32"; + if (a.map((m) => { + s.push({ type: l, data: m }); + let d = y.computeStrides(m); + s.push({ type: l, data: d }); }), e.size) { - let g = y.sizeFromShape(e.outputShape); - a.push({ type: h, data: [e.outputComponent ? g / e.outputComponent : g] }); + let m = y.sizeFromShape(e.outputShape); + s.push({ type: l, data: [e.outputComponent ? m / e.outputComponent : m] }); } } - let p = t10.map((h, g) => { - if (h.dtype === "complex64") - throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts."); - return this.uploadToGPU(h.dataId), { dtype: this.tensorMap.get(h.dataId).dtype, shape: h.shape, name: e.variableNames[g] }; - }), u = yB(e, i, p, s), c; - u in this.pipelineCache ? c = this.pipelineCache[u] : (c = xB(this.device, e, p, s, u), this.pipelineCache[u] = c), n && (a = [...a, ...n]); - let l = [this.tensorToBinding(s), ...t10.map((h) => this.tensorToBinding(h)), this.makeUniforms(a)], m = this.device.createBindGroup({ layout: c.getBindGroupLayout(0), entries: l.map((h, g) => ({ binding: g, resource: h })) }); + n && (s = [...s, ...n]); + let i = [this.tensorToBinding(t8), ...o.map((l) => this.tensorToBinding(l)), this.makeUniforms(s)]; + o.forEach((l) => { + this.commandQueueOwnedIds.add(l.dataId); + }), this.commandQueueOwnedIds.add(t8.dataId); + let p = this.device.createBindGroup({ layout: e.pipeline.getBindGroupLayout(0), entries: i.map((l, m) => ({ binding: m, resource: l })) }), u = this.activeTimers != null; this.ensureCommandEncoderReady(); - let d = this.getComputePass(), f = this.activeTimers != null; - return f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 0), d.setPipeline(c), d.setBindGroup(0, m), d.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t10.forEach((h) => { - this.commandQueueOwnedIds.add(h.dataId); - }), this.commandQueueOwnedIds.add(s.dataId), P().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), f && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), s; + let c = {}; + u && this.supportTimestampQuery ? (this.endComputePassEncoder(), this.querySet == null && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: this.querySetCount })), c.timestampWrites = [{ querySet: this.querySet, queryIndex: 0, location: "beginning" }, { querySet: this.querySet, queryIndex: 1, location: "end" }], this.computePassEncoder = this.commandEncoder.beginComputePass(c)) : this.computePassEncoder || (this.computePassEncoder = this.commandEncoder.beginComputePass(c)), this.computePassEncoder.setPipeline(e.pipeline), this.computePassEncoder.setBindGroup(0, p), this.computePassEncoder.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), this.dispatchCountInPass++, (u || A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchCountInPass) && (this.endComputePassEncoder(), u ? this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime() }) : this.submitQueue()); } - async getTimeFromQuerySet(e) { - let t10 = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), o = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST); - this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t10, 0), this.currentCommandEncoder.copyBufferToBuffer(t10, 0, o, 0, 16), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); - let n = new BigUint64Array(o.getMappedRange()), s = Number(n[1] - n[0]); - return o.unmap(), this.bufferManager.releaseBuffer(o, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t10, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), s / 1e6; + async getQueryTime() { + if (!this.supportTimestampQuery) + return 0; + this.queryResolveBuffer == null && (this.queryResolveBuffer = this.bufferManager.acquireBuffer(this.querySetCount * 8, GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST | GPUBufferUsage.QUERY_RESOLVE)), this.commandEncoder.resolveQuerySet(this.querySet, 0, this.querySetCount, this.queryResolveBuffer, 0); + let e = this.bufferManager.acquireBuffer(this.querySetCount * 8, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST); + this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer, 0, e, 0, this.querySetCount * 8), this.submitQueue(), await e.mapAsync(GPUMapMode.READ); + let t8 = new BigUint64Array(e.getMappedRange()), o = Number(t8[1] - t8[0]) / 1e6; + return e.unmap(), this.bufferManager.releaseBuffer(e), o; } - shouldExecuteOnCPU(e, t10 = hse) { - return P().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resourceInfo == null && y.sizeFromShape(o.shape) < t10); + shouldExecuteOnCPU(e, t8 = Jse) { + return A().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resource == null && y.sizeFromShape(o.shape) < t8); } numDataIds() { return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length; } dispose() { - this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true); + this.disposed || (this.querySet != null && this.querySet.destroy(), this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true); } }; -Cu.nextDataId = 0; -pm() && eu("webgpu", async () => { - let r = { powerPreference: P().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t10 = {}; - e.features.has("timestamp-query-inside-passes") && (t10.requiredFeatures = ["timestamp-query-inside-passes"]); +vu.nextDataId = 0; +cm() && nu("webgpu", async () => { + let r = { powerPreference: A().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t8 = {}; + e.features.has("timestamp-query") && (t8.requiredFeatures = ["timestamp-query"]); let o = e.limits; - t10.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize, maxBufferSize: o.maxBufferSize, maxComputeWorkgroupSizeX: o.maxComputeWorkgroupSizeX, maxComputeInvocationsPerWorkgroup: o.maxComputeInvocationsPerWorkgroup }; - let n = await e.requestDevice(t10), s = await e.requestAdapterInfo(); - return new Cu(n, s); + t8.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize, maxBufferSize: o.maxBufferSize, maxComputeWorkgroupSizeX: o.maxComputeWorkgroupSizeX, maxComputeInvocationsPerWorkgroup: o.maxComputeInvocationsPerWorkgroup }; + let n = await e.requestDevice(t8), s = await e.requestAdapterInfo(); + return new vu(n, s); }, 3); var fe; (function(r) { r[r.ADD = 0] = "ADD", r[r.ATAN2 = 1] = "ATAN2", r[r.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r[r.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r[r.DIV = 4] = "DIV", r[r.ELU_DER = 5] = "ELU_DER", r[r.EQUAL = 6] = "EQUAL", r[r.GREATER = 7] = "GREATER", r[r.GREATER_EQUAL = 8] = "GREATER_EQUAL", r[r.INT_DIV = 9] = "INT_DIV", r[r.LESS = 10] = "LESS", r[r.LESS_EQUAL = 11] = "LESS_EQUAL", r[r.LOGICAL_AND = 12] = "LOGICAL_AND", r[r.LOGICAL_OR = 13] = "LOGICAL_OR", r[r.MAX = 14] = "MAX", r[r.MIN = 15] = "MIN", r[r.MOD = 16] = "MOD", r[r.MUL = 17] = "MUL", r[r.NOT_EQUAL = 18] = "NOT_EQUAL", r[r.POW = 19] = "POW", r[r.PRELU = 20] = "PRELU", r[r.SQUARED_DIFFERENCE = 21] = "SQUARED_DIFFERENCE", r[r.SUB = 22] = "SUB"; })(fe || (fe = {})); -var xse = ` - resultTemp = select(resultTemp, valueForNaN, isNaN | isnan(a) | isnan(b));`; -var yse = ` - resultTemp = select( - resultTemp, vec4(valueForNaN), - vec4(isNaN) | isnanVec4(a) | isnanVec4(b)); - `; -var bse = "return a + b;"; -var Cse = "var resultTemp = atan2(a, b);"; -var wse = "return areal * breal - aimag * bimag;"; -var Sse = "return areal * bimag + aimag * breal;"; -var Ise = "return a / b;"; -var vse = "return select(a * (b + 1.0), a, b >= 0.);"; -var kse = "return select(a * (b + vec4(1.0)), a, b >= vec4(0.));"; -var Nse = "return f32(a == b);"; -var Tse = "return vec4(a == b);"; -var _se = "return f32(a > b);"; -var $se = "return vec4(a > b);"; -var Ese = "return f32(a >= b);"; -var Rse = "return vec4(a >= b);"; -var Dse = ` +var tae = "return a + b;"; +var rae = "var resultTemp = atan2(a, b);"; +var oae = "return areal * breal - aimag * bimag;"; +var nae = "return areal * bimag + aimag * breal;"; +var sae = "return a / b;"; +var aae = "return select(a * (b + 1.0), a, b >= 0.);"; +var iae = "return select(a * (b + vec4(1.0)), a, b >= vec4(0.));"; +var uae = "return f32(a == b);"; +var pae = "return vec4(a == b);"; +var cae = "return f32(a > b);"; +var lae = "return vec4(a > b);"; +var mae = "return f32(a >= b);"; +var dae = "return vec4(a >= b);"; +var fae = ` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); `; -var Ase = ` +var hae = ` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); @@ -27436,25 +27562,25 @@ var Ase = ` } return vec4(resultTemp); `; -var Fse = "return f32(a < b);"; -var Pse = "return vec4(a < b);"; -var Ose = "return f32(a <= b);"; -var Mse = "return vec4(a <= b);"; -var Lse = "return f32(a >= 1.0 && b >= 1.0);"; -var Bse = `return (vec4(a >= vec4(1.0)) * +var gae = "return f32(a < b);"; +var xae = "return vec4(a < b);"; +var yae = "return f32(a <= b);"; +var bae = "return vec4(a <= b);"; +var Cae = "return f32(a >= 1.0 && b >= 1.0);"; +var wae = `return (vec4(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`; -var zse = "return f32(a >= 1.0 || b >= 1.0);"; -var Vse = `return min(vec4(a >= vec4(1.0)) + +var Sae = "return f32(a >= 1.0 || b >= 1.0);"; +var Iae = `return min(vec4(a >= vec4(1.0)) + vec4(b >= vec4(1.0)), vec4(1.0));`; -var Wse = "var resultTemp = max(a, b);"; -var Use = "var resultTemp = min(a, b);"; -var Gse = ` +var vae = "var resultTemp = max(a, b);"; +var kae = "var resultTemp = min(a, b);"; +var Nae = ` let isNaN = b == 0.; var resultTemp = a % b; resultTemp = select((resultTemp + b) % b, resultTemp, (a < 0. && b < 0.) || (a >= 0. && b > 0.)); `; -var Hse = ` +var Tae = ` let isNaN = !vec4(b); var resultTemp = vec4(a % b); if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { @@ -27470,16 +27596,16 @@ var Hse = ` resultTemp[3] = (resultTemp[3] + b[3]) % b[3]; } `; -var Kse = "return a * b;"; -var qse = ` +var _ae = "return a * b;"; +var $ae = ` var resultTemp = f32(a != b); let valueForNaN = 1.0; `; -var jse = ` +var Eae = ` var resultTemp = vec4(a != b); let valueForNaN = 1.0; `; -var Xse = ` +var Rae = ` let isNaN = a < 0.0 && floor(b) < b; if (b == 0.0) { return 1.0; @@ -27487,7 +27613,7 @@ var Xse = ` var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b), round(abs(b) % 2.0) != 1.0); `; -var Yse = ` +var Dae = ` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -27509,118 +27635,129 @@ var Yse = ` } let isNaN = (a < vec4(0.0)) & (floor(b) < b); `; -var Qse = "if (a < 0.0) { return b * a; } return a;"; -var Zse = ` +var Aae = "if (a < 0.0) { return b * a; } return a;"; +var Fae = ` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -var Jse = "return (a - b) * (a - b);"; -var eae = "return a - b;"; -function Kc(r, e) { +var Pae = "return (a - b) * (a - b);"; +var Oae = "return a - b;"; +function qc(r, e) { + let t8; do { - let t10; switch (r) { case fe.ATAN2: - t10 = Cse; + t8 = rae; break; case fe.MAX: - t10 = Wse; + t8 = vae; break; case fe.MIN: - t10 = Use; + t8 = kae; break; case fe.MOD: - t10 = e ? Hse : Gse; + t8 = e ? Tae : Nae; break; case fe.NOT_EQUAL: - t10 = e ? jse : qse; + t8 = e ? Eae : $ae; break; case fe.POW: - t10 = e ? Yse : Xse; + t8 = e ? Dae : Rae; break; default: continue; } - return ` + let o, n, s; + return e ? (o = "isnanVec4", n = "vec4", s = "vec4") : (o = "isnan", n = "f32", s = "bool"), ` + let aIsNaN = ${o}(a); + let aPostLegalization = select(a, ${n}(42), aIsNaN); + let bIsNaN = ${o}(b); + let bPostLegalization = select(b, ${n}(42), bIsNaN); let isNaN = false; let valueForNaN = uniforms.NAN; { - ${t10} - ${e ? yse : xse} - return resultTemp; + let a = aPostLegalization; + let b = bPostLegalization; + ${t8} + return select( + resultTemp, ${n}(valueForNaN), + ${s}(isNaN) | aIsNaN | bIsNaN); } `; } while (false); switch (r) { case fe.ADD: - return bse; + return tae; case fe.COMPLEX_MULTIPLY_IMAG: - return Sse; + return nae; case fe.COMPLEX_MULTIPLY_REAL: - return wse; + return oae; case fe.DIV: - return Ise; + return sae; case fe.ELU_DER: - return e ? kse : vse; + return e ? iae : aae; case fe.EQUAL: - return e ? Tse : Nse; + return e ? pae : uae; case fe.GREATER: - return e ? $se : _se; + return e ? lae : cae; case fe.GREATER_EQUAL: - return e ? Rse : Ese; + return e ? dae : mae; case fe.INT_DIV: - return e ? Ase : Dse; + return e ? hae : fae; case fe.LESS: - return e ? Pse : Fse; + return e ? xae : gae; case fe.LESS_EQUAL: - return e ? Mse : Ose; + return e ? bae : yae; case fe.LOGICAL_AND: - return e ? Bse : Lse; + return e ? wae : Cae; case fe.LOGICAL_OR: - return e ? Vse : zse; + return e ? Iae : Sae; case fe.MUL: - return Kse; + return _ae; case fe.PRELU: - return e ? Zse : Qse; + return e ? Fae : Aae; case fe.SQUARED_DIFFERENCE: - return Jse; + return Pae; case fe.SUB: - return eae; + return Oae; default: - throw new Error(`BinaryType ${r} is not implemented!`); } + return ` + ${t8} + return resultTemp; + `; } -var Q; +var Z; (function(r) { r[r.ABS = 0] = "ABS", r[r.ACOS = 1] = "ACOS", r[r.ACOSH = 2] = "ACOSH", r[r.ASIN = 3] = "ASIN", r[r.ASINH = 4] = "ASINH", r[r.ATAN = 5] = "ATAN", r[r.ATANH = 6] = "ATANH", r[r.CEIL = 7] = "CEIL", r[r.COS = 8] = "COS", r[r.COSH = 9] = "COSH", r[r.ELU = 10] = "ELU", r[r.ERF = 11] = "ERF", r[r.EXP = 12] = "EXP", r[r.EXPM1 = 13] = "EXPM1", r[r.FLOOR = 14] = "FLOOR", r[r.IS_FINITE = 15] = "IS_FINITE", r[r.IS_INF = 16] = "IS_INF", r[r.IS_NAN = 17] = "IS_NAN", r[r.LINEAR = 18] = "LINEAR", r[r.LOG = 19] = "LOG", r[r.LOG1P = 20] = "LOG1P", r[r.LOGICAL_NOT = 21] = "LOGICAL_NOT", r[r.NEG = 22] = "NEG", r[r.RELU = 23] = "RELU", r[r.RELU6 = 24] = "RELU6", r[r.LEAKYRELU = 25] = "LEAKYRELU", r[r.RECIPROCAL = 26] = "RECIPROCAL", r[r.ROUND = 27] = "ROUND", r[r.RSQRT = 28] = "RSQRT", r[r.SELU = 29] = "SELU", r[r.SIGMOID = 30] = "SIGMOID", r[r.SIGN = 31] = "SIGN", r[r.SIN = 32] = "SIN", r[r.SINH = 33] = "SINH", r[r.SOFTPLUS = 34] = "SOFTPLUS", r[r.SQRT = 35] = "SQRT", r[r.SQUARE = 36] = "SQUARE", r[r.STEP = 37] = "STEP", r[r.TAN = 38] = "TAN", r[r.TANH = 39] = "TANH", r[r.TO_INT = 40] = "TO_INT"; -})(Q || (Q = {})); -var tae = "return abs(a);"; -var rae = ` +})(Z || (Z = {})); +var Mae = "return abs(a);"; +var Lae = ` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); `; -var oae = ` +var Bae = ` if (a < 1.) { return uniforms.NAN; } return acosh(a); `; -var nae = ` +var zae = ` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); `; -var sae = "return asinh(a);"; -var aae = ` +var Vae = "return asinh(a);"; +var Wae = ` if (isnan(a)) { return uniforms.NAN; } return atan(a); `; -var iae = ` +var Uae = ` if (abs(a) > 1.) { return uniforms.NAN; } @@ -27632,15 +27769,15 @@ var iae = ` } return atanh(a); `; -var uae = "return ceil(a);"; -var pae = "return cos(a);"; -var cae = ` +var Gae = "return ceil(a);"; +var Hae = "return cos(a);"; +var Kae = ` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `; -var lae = "return exp(a) - 1.0;"; -var mae = "if (a >= 0.0) { return a; } return (exp(a) - 1.0);"; -var dae = ` +var qae = "return exp(a) - 1.0;"; +var jae = "if (a >= 0.0) { return a; } return (exp(a) - 1.0);"; +var Xae = ` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -27656,65 +27793,65 @@ var dae = ` } return resFloat; `; -var fae = ` +var Yae = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - let p = ${C.ERF_P}; - let a1 = ${C.ERF_A1}; - let a2 = ${C.ERF_A2}; - let a3 = ${C.ERF_A3}; - let a4 = ${C.ERF_A4}; - let a5 = ${C.ERF_A5}; + let p = ${w.ERF_P}; + let a1 = ${w.ERF_A1}; + let a2 = ${w.ERF_A2}; + let a3 = ${w.ERF_A3}; + let a4 = ${w.ERF_A4}; + let a5 = ${w.ERF_A5}; let sign = sign(a); let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); `; -var hae = "return exp(a);"; -var gae = "return floor(a);"; -var xae = "return f32(!isnan(a) && !isinf(a));"; -var yae = "return f32(isinf(a));"; -var bae = "return f32(isnan(a));"; -var Cae = "return a;"; -var wae = `if (a < 0.0) { return uniforms.NAN; } +var Qae = "return exp(a);"; +var Zae = "return floor(a);"; +var Jae = "return f32(!isnan(a) && !isinf(a));"; +var eie = "return f32(isinf(a));"; +var tie = "return f32(isnan(a));"; +var rie = "return a;"; +var oie = `if (a < 0.0) { return uniforms.NAN; } return log(a);`; -var Sae = ` +var nie = ` if (isnan(a)) { return a; } return log(1.0 + a); `; -var Iae = "return f32(!(a >= 1.0));"; -var vae = "return -a;"; -var kae = "if (a < 0.0) { return uniforms.alpha * a; } return a;"; -var Nae = ` +var sie = "return f32(!(a >= 1.0));"; +var aie = "return -a;"; +var iie = "if (a < 0.0) { return uniforms.alpha * a; } return a;"; +var uie = ` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -var Tae = "return 1.0 / a;"; -var _ae = "return select(a, 0.0, a < 0.0);"; -var $ae = "return clamp(a, 0.0, 6.0);"; -var Eae = "return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));"; -var Rae = ` +var pie = "return 1.0 / a;"; +var cie = "return select(a, 0.0, a < 0.0);"; +var lie = "return clamp(a, 0.0, 6.0);"; +var mie = "return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));"; +var die = ` return select(a, vec4(0.0), a < vec4(0.0)); `; -var Dae = "return round(a);"; -var Aae = "return inverseSqrt(a);"; -var Fae = ` +var fie = "return round(a);"; +var hie = "return inverseSqrt(a);"; +var gie = ` if (a >= 0.0) { - return ${C.SELU_SCALE} * a; + return ${w.SELU_SCALE} * a; } else { - return ${C.SELU_SCALEALPHA} * (exp(a) - 1.0); + return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0); } `; -var Pae = "return 1.0 / (1.0 + exp(-1.0 * a));"; -var Oae = "return sign(a);"; -var Mae = "return sin(a);"; -var Lae = ` +var xie = "return 1.0 / (1.0 + exp(-1.0 * a));"; +var yie = "return sign(a);"; +var bie = "return sin(a);"; +var Cie = ` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `; -var Bae = ` +var wie = ` let epsilon = 1.1920928955078125e-7; let threshold = log(epsilon) + 2.0; @@ -27730,130 +27867,130 @@ var Bae = ` return log(exp_a + 1.0); } `; -var zae = "return sqrt(a);"; -var Vae = "return a * a;"; -var Wae = ` +var Sie = "return sqrt(a);"; +var Iie = "return a * a;"; +var vie = ` if (isnan(a)) { return a; } return select(uniforms.stepAlpha, 1.0, a > 0.0); `; -var Uae = "return tan(a);"; -var Gae = ` +var kie = "return tan(a);"; +var Nie = ` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `; -var Hae = "return f32(i32((a)));"; -function yi(r, e) { +var Tie = "return f32(i32((a)));"; +function wi(r, e) { switch (r) { - case Q.ABS: - return tae; - case Q.ACOS: - return rae; - case Q.ACOSH: - return oae; - case Q.ASIN: - return nae; - case Q.ASINH: - return sae; - case Q.ATAN: - return aae; - case Q.ATANH: - return iae; - case Q.COS: - return pae; - case Q.COSH: - return cae; - case Q.CEIL: - return uae; - case Q.ELU: - return e ? dae : mae; - case Q.ERF: - return fae; - case Q.EXP: - return hae; - case Q.EXPM1: - return lae; - case Q.FLOOR: - return gae; - case Q.IS_FINITE: - return xae; - case Q.IS_INF: - return yae; - case Q.IS_NAN: - return bae; - case Q.LINEAR: - return Cae; - case Q.LOG: - return wae; - case Q.LOG1P: - return Sae; - case Q.LOGICAL_NOT: - return Iae; - case Q.NEG: - return vae; - case Q.LEAKYRELU: - return e ? Nae : kae; - case Q.RECIPROCAL: - return Tae; - case Q.RELU: - return e ? Rae : _ae; - case Q.RELU6: - return e ? Eae : $ae; - case Q.ROUND: - return Dae; - case Q.RSQRT: - return Aae; - case Q.SELU: - return Fae; - case Q.SIGMOID: - return Pae; - case Q.SIGN: - return Oae; - case Q.SIN: + case Z.ABS: return Mae; - case Q.SINH: + case Z.ACOS: return Lae; - case Q.SOFTPLUS: + case Z.ACOSH: return Bae; - case Q.SQRT: + case Z.ASIN: return zae; - case Q.SQUARE: + case Z.ASINH: return Vae; - case Q.STEP: + case Z.ATAN: return Wae; - case Q.TAN: + case Z.ATANH: return Uae; - case Q.TANH: - return Gae; - case Q.TO_INT: + case Z.COS: return Hae; + case Z.COSH: + return Kae; + case Z.CEIL: + return Gae; + case Z.ELU: + return e ? Xae : jae; + case Z.ERF: + return Yae; + case Z.EXP: + return Qae; + case Z.EXPM1: + return qae; + case Z.FLOOR: + return Zae; + case Z.IS_FINITE: + return Jae; + case Z.IS_INF: + return eie; + case Z.IS_NAN: + return tie; + case Z.LINEAR: + return rie; + case Z.LOG: + return oie; + case Z.LOG1P: + return nie; + case Z.LOGICAL_NOT: + return sie; + case Z.NEG: + return aie; + case Z.LEAKYRELU: + return e ? uie : iie; + case Z.RECIPROCAL: + return pie; + case Z.RELU: + return e ? die : cie; + case Z.RELU6: + return e ? mie : lie; + case Z.ROUND: + return fie; + case Z.RSQRT: + return hie; + case Z.SELU: + return gie; + case Z.SIGMOID: + return xie; + case Z.SIGN: + return yie; + case Z.SIN: + return bie; + case Z.SINH: + return Cie; + case Z.SOFTPLUS: + return wie; + case Z.SQRT: + return Sie; + case Z.SQUARE: + return Iie; + case Z.STEP: + return vie; + case Z.TAN: + return kie; + case Z.TANH: + return Nie; + case Z.TO_INT: + return Tie; default: throw new Error(`BinaryType ${r} is not implemented!`); } } -function dr(r, e = false, t10 = false, o = 3) { +function dr(r, e = false, t8 = false, o = 3) { if (r === null) return ""; let n = ""; if (r === "linear") - n = yi(Q.LINEAR); + n = wi(Z.LINEAR); else if (r === "relu") - n = yi(Q.RELU, t10); + n = wi(Z.RELU, t8); else if (r === "elu") - n = yi(Q.ELU, t10); + n = wi(Z.ELU, t8); else if (r === "relu6") - n = yi(Q.RELU6, t10); + n = wi(Z.RELU6, t8); else if (r === "prelu") - n = Kc(fe.PRELU, t10); + n = qc(fe.PRELU, t8); else if (r === "sigmoid") - n = yi(Q.SIGMOID, t10); + n = wi(Z.SIGMOID, t8); else if (r === "leakyrelu") - n = yi(Q.LEAKYRELU, t10); + n = wi(Z.LEAKYRELU, t8); else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`); - let a = Ae(t10 ? 4 : 1), i = ""; + let a = Ae(t8 ? 4 : 1), i = ""; return e ? i = ` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); @@ -27863,13 +28000,13 @@ function dr(r, e = false, t10 = false, o = 3) { ${n} }`, i; } -function jr(r, e) { +function Qr(r, e) { return ` ${r ? "value = value + getBiasByOutputCoords(coords);" : ""} ${e ? "value = activation(value, coords);" : ""} `; } -function Pv(r, e, t10 = false, o = false, n = false, s = 1) { +function Uv(r, e, t8 = false, o = false, n = false, s = 1) { y.assert(r && s === 1 || !r, () => `transposeA ${r} is not compatible with component size ${s}`); let a = ` ${r ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"} @@ -27879,7 +28016,7 @@ function Pv(r, e, t10 = false, o = false, n = false, s = 1) { fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} { var value = ${Ae(s)}(0.0); let col = colIn * ${s}; - ${t10 && n ? a : ` + ${t8 && n ? a : ` ${r ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { ${a} @@ -27896,22 +28033,22 @@ function Pv(r, e, t10 = false, o = false, n = false, s = 1) { } `; } -function lm(r, e, t10, o, n = false, s = false, a = false, i = 1) { +function mm(r, e, t8, o, n = false, s = false, a = false, i = 1) { return ` - ${Pv(t10, o, n, s, a, i)} + ${Uv(t8, o, n, s, a, i)} fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ae(i)}) { let col = colIn * ${i}; ${n && s ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${jr(r, e)} + ${Qr(r, e)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `; } -var Kae = (r, e) => r ? ` +var _ie = (r, e) => r ? ` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart / ${e} + inputCol); @@ -27920,33 +28057,33 @@ var Kae = (r, e) => r ? ` globalRow + innerRow, kStart / ${e} + inputCol); `; -var qae = (r, e, t10) => r ? ` +var $ie = (r, e, t8) => r ? ` let ACached0 = mm_Asub[k * ${e}][localRow]; let ACached1 = mm_Asub[k * ${e} + 1][localRow]; let ACached2 = mm_Asub[k * ${e} + 2][localRow]; ${e === 3 ? "" : `let ACached3 = mm_Asub[k * ${e} + 3][localRow];`} - for (var i = 0; i < ${t10}; i++) { + for (var i = 0; i < ${t8}; i++) { acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]); acc[i] = fma(BCached1, vec4(ACached1[i]), acc[i]); acc[i] = fma(BCached2, vec4(ACached2[i]), acc[i]); ${e === 3 ? "" : "acc[i] = fma(BCached3, vec4(ACached3[i]), acc[i]);"} }` : ` - for (var i = 0; i < ${t10}; i++) { + for (var i = 0; i < ${t8}; i++) { let ACached = mm_Asub[tileRow + i][k]; acc[i] = fma(BCached0, vec4(ACached.x), acc[i]); acc[i] = fma(BCached1, vec4(ACached.y), acc[i]); acc[i] = fma(BCached2, vec4(ACached.z), acc[i]); ${e === 3 ? "" : "acc[i] = fma(BCached3, vec4(ACached.w), acc[i]);"} }`; -function Tp(r, e, t10 = false, o = 32, n = false, s = 32, a = false) { - let i = e[1] * r[1], p = e[0] * r[0], u = t10 ? i : o, c = t10 ? o : i, l = u / e[0], m = o / e[1], d = r[1]; - return y.assert((t10 && l === 4 && r[1] === 4 || !t10 && (l === 3 || l === 4)) && u % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t10} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. +function Rp(r, e, t8 = false, o = 32, n = false, s = 32, a = false) { + let i = e[1] * r[1], p = e[0] * r[0], u = t8 ? i : o, c = t8 ? o : i, l = u / e[0], m = o / e[1], d = r[1]; + return y.assert((t8 && l === 4 && r[1] === 4 || !t8 && (l === 3 || l === 4)) && u % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t8} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. Otherwise, innerElementSize ${l} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`), ` var mm_Asub : array, ${u / l}>, ${c}>; var mm_Bsub : array, ${p / r[0]}>, ${o}>; - ${K()} { + ${H()} { let localRow = i32(localId.y); let tileRow = localRow * ${d}; let tileCol = i32(localId.x); @@ -27970,7 +28107,7 @@ function Tp(r, e, t10 = false, o = 32, n = false, s = 32, a = false) { for (var innerRow = 0; innerRow < ${d}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${Kae(t10, l)} + ${_ie(t8, l)} } // Load one tile of B into local memory. @@ -27989,7 +28126,7 @@ function Tp(r, e, t10 = false, o = 32, n = false, s = 32, a = false) { let BCached2 = mm_Bsub[k * ${l} + 2][tileCol]; ${l === 3 ? "" : `let BCached3 = mm_Bsub[k * ${l} + 3][tileCol];`} - ${qae(t10, l, d)} + ${$ie(t8, l, d)} } workgroupBarrier(); @@ -28000,7 +28137,7 @@ function Tp(r, e, t10 = false, o = 32, n = false, s = 32, a = false) { } }`; } -var CB = (r) => r ? ` +var HB = (r) => r ? ` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -28009,9 +28146,9 @@ var CB = (r) => r ? ` globalRowStart + inputRow, kStart + inputCol); `; -var jae = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];"; -function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) { - let p = r[1] * e[1], u = r[0] * e[0], c = t10 ? p : o, l = t10 ? o : p; +var Eie = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];"; +function Dp(r, e, t8 = false, o = 32, n = false, s = 32, a = false, i = false) { + let p = r[1] * e[1], u = r[0] * e[0], c = t8 ? p : o, l = t8 ? o : p; y.assert(l % e[1] === 0 && c % e[0] === 0 && o % e[1] === 0, () => `tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`); let m = l / e[1], d = c / e[0], f = o / e[1], h = r[1], g = r[0], x = a ? ` let localRow = i32(localId.y); @@ -28024,7 +28161,7 @@ function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) { for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) { - ${CB(t10)} + ${HB(t8)} } } // Load one tile of B into local memory. @@ -28045,7 +28182,7 @@ function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}]; } for (var innerRow = 0; innerRow < ${h}; innerRow++) { - let ACached = ${t10 ? `mm_Asub[k][localRow + innerRow * ${e[1]}];` : `mm_Asub[localRow + innerRow * ${e[1]}][k];`} + let ACached = ${t8 ? `mm_Asub[k][localRow + innerRow * ${e[1]}];` : `mm_Asub[localRow + innerRow * ${e[1]}][k];`} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); @@ -28079,7 +28216,7 @@ function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${CB(t10)} + ${HB(t8)} } } @@ -28104,7 +28241,7 @@ function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) } for (var innerRow = 0; innerRow < ${h}; innerRow++) { - ${jae(t10)} + ${Eie(t8)} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); @@ -28126,7 +28263,7 @@ function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) var mm_Asub : array, ${l}>; var mm_Bsub : array, ${o}>; - ${K()} { + ${H()} { let batch = ${n ? "0" : "i32(globalId.z)"}; let batchA = ${n || !i ? "batch" : "batch % uniforms.aShape[0]"}; let batchB = ${n || !i ? "batch" : "batch % uniforms.bShape[0]"}; @@ -28145,7 +28282,7 @@ function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) } `; } -var Xae = (r) => r ? ` +var Rie = (r) => r ? ` mm_readA(batchA, colA, globalRow), mm_readA(batchA, colA + 1, globalRow), mm_readA(batchA, colA + 2, globalRow), @@ -28156,18 +28293,18 @@ var Xae = (r) => r ? ` mm_readA(batchA, globalRow, colA + 2), mm_readA(batchA, globalRow, colA + 3) `; -function Yae(r, e = false) { +function Die(r, e = false) { y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`); - let t10 = r[0] * 4; + let t8 = r[0] * 4; return ` var mm_Asub : array, ${r[0]}>; - ${K()} { + ${H()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); - let numTiles = (uniforms.dimInner - 1) / ${t10} + 1; + let numTiles = (uniforms.dimInner - 1) / ${t8} + 1; let batch = i32(globalId.z); let batchA = batch % uniforms.aShape[0]; let batchB = batch % uniforms.bShape[0]; @@ -28177,13 +28314,13 @@ function Yae(r, e = false) { // Loop over shared dimension. for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - let colA = t * ${t10} + tileCol * 4; - mm_Asub[tileCol] = vec4(${Xae(e)}); + let colA = t * ${t8} + tileCol * 4; + mm_Asub[tileCol] = vec4(${Rie(e)}); workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < ${t10 / 4}; k++) { - let rowB = t * ${t10} + k * 4; + for (var k = 0; k < ${t8 / 4}; k++) { + let rowB = t * ${t8} + k * 4; let BCached = vec4(mm_readB(batchB, rowB, globalCol), mm_readB(batchB, rowB + 1, globalCol), mm_readB(batchB, rowB + 2, globalCol), @@ -28200,38 +28337,38 @@ function Yae(r, e = false) { } `; } -var Kg = class { - constructor(e, t10, o = false, n = false, s = null, a = null, i = null, p = false) { - this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t10, this.dispatchLayout = { x: [2], y: [1], z: [0] }; +var qg = class { + constructor(e, t8, o = false, n = false, s = null, a = null, i = null, p = false) { + this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t8, this.dispatchLayout = { x: [2], y: [1], z: [0] }; let u = o ? e[1] : e[2]; - if (this.isVec4 = (u % 4 === 0 && !o || t10[1] % 4 === 0 && o) && t10[2] % 4 === 0 && !n, this.outputComponent = this.isVec4 ? 4 : 1, this.isVectorA = t10[1] === 1 && !o, !this.isVec4 && this.isVectorA) + if (this.isVec4 = (u % 4 === 0 && !o || t8[1] % 4 === 0 && o) && t8[2] % 4 === 0 && !n, this.outputComponent = this.isVec4 ? 4 : 1, this.isVectorA = t8[1] === 1 && !o, !this.isVec4 && this.isVectorA) this.elementsPerThread = [1, 1, 1], this.workgroupSize = [32, 1, 1]; else { - let m = Av(t10[1], u, t10[2], o); + let m = Vv(t8[1], u, t8[2], o); this.workgroupSize = m.workgroupSize, this.elementsPerThread = m.elementsPerThread; } - this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread); + this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread); let c = s != null, l = i != null; - c && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = p, this.transposeA = o, this.transposeB = n, this.addBias = c, this.activation = a, this.hasPreluActivationWeights = l, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t10[1], t10[2], u), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`; + c && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = p, this.transposeA = o, this.transposeB = n, this.addBias = c, this.activation = a, this.hasPreluActivationWeights = l, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t8[1], t8[2], u), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`; } - getShapeFit(e, t10, o) { + getShapeFit(e, t8, o) { let n = this.workgroupSize[1] * this.elementsPerThread[1], s = this.workgroupSize[0] * this.elementsPerThread[0]; !this.isVec4 && this.isVectorA ? this.tileInner = this.workgroupSize[0] * 4 : this.tileInner = s; - let a = e % n === 0, i = t10 % s === 0, p = o % this.tileInner === 0; + let a = e % n === 0, i = t8 % s === 0, p = o % this.tileInner === 0; return [a, i, p]; } getUserCode() { return ` ${dr(this.activation, this.hasPreluActivationWeights, this.isVec4)} - ${lm(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)} - ${this.isVec4 ? Tp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, true) : this.isVectorA ? Yae(this.workgroupSize, this.transposeA) : _p(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)} + ${mm(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)} + ${this.isVec4 ? Rp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, true) : this.isVectorA ? Die(this.workgroupSize, this.transposeA) : Dp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)} `; } }; -function Qae(r) { +function Aie(r) { return ` var sumValues : array; - ${K()} { + ${H()} { let coords = getOutputCoords(); let batch = coords[0]; let batchA = batch % uniforms.aShape[0]; @@ -28264,25 +28401,25 @@ function Qae(r) { } `; } -var qg = class { - constructor(e, t10 = false, o = false, n = null, s = null, a = null) { - this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize); +var jg = class { + constructor(e, t8 = false, o = false, n = null, s = null, a = null) { + this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize); let i = n != null, p = a != null; - i && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), this.transposeA = t10, this.transposeB = o, this.addBias = i, this.activation = s, this.hasPreluActivationWeights = p, this.shaderKey = `matMulReduce_${this.activation}_${t10}_${o}`; + i && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), this.transposeA = t8, this.transposeB = o, this.addBias = i, this.activation = s, this.hasPreluActivationWeights = p, this.shaderKey = `matMulReduce_${this.activation}_${t8}_${o}`; } getUserCode() { return ` ${dr(this.activation, this.hasPreluActivationWeights)} - ${lm(this.addBias, this.activation, this.transposeA, this.transposeB)} - ${Qae(this.workgroupSize[0])} + ${mm(this.addBias, this.activation, this.transposeA, this.transposeB)} + ${Aie(this.workgroupSize[0])} `; } }; -function Zae(r) { - let e = r[1], t10 = r[0], o = e > t10 ? e : t10; +function Fie(r) { + let e = r[1], t8 = r[0], o = e > t8 ? e : t8; return ` var mm_Asub : array, ${e}>; - var mm_Bsub : array, ${o}>; + var mm_Bsub : array, ${o}>; // If the output size is small for matrix multiplication, avoid to use vec4 // and handle some elements per thread to optimally utilize the ALU. @@ -28290,7 +28427,7 @@ function Zae(r) { // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. - ${K()} { + ${H()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); @@ -28334,8 +28471,8 @@ function Zae(r) { } `; } -var jg = class { - constructor(e, t10, o, n = false, s = false, a = null, i = null, p = null) { +var Xg = class { + constructor(e, t8, o, n = false, s = false, a = null, i = null, p = null) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [16, 8, 1], this.outputShape = o, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(o[2] / this.workgroupSize[0]), Math.ceil(o[1] / this.workgroupSize[1]), o[0]]; let u = a != null; u && this.variableNames.push("bias"); @@ -28345,21 +28482,21 @@ var jg = class { getUserCode() { return ` ${dr(this.activation, this.hasPreluActivationWeights)} - ${lm(this.addBias, this.activation, this.transposeA, this.transposeB)} - ${Zae(this.workgroupSize)} + ${mm(this.addBias, this.activation, this.transposeA, this.transposeB)} + ${Fie(this.workgroupSize)} `; } }; -var Xg = class { - constructor(e, t10, o = false, n = false) { +var Yg = class { + constructor(e, t8, o = false, n = false) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.splitedDimInner = 128, y.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] }; - let s = (o && this.outputShape[1] % 4 === 0 || !o && t10 % 4 === 0) && this.outputShape[2] % 4 === 0; - this.elementsPerThread = [4, 4, this.splitedDimInner], this.outputComponent = s ? 4 : 1, s || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = q(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t10], this.workgroupSize, this.elementsPerThread), this.transposeA = o, this.transposeB = n, this.shaderKey = `matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`; + let s = (o && this.outputShape[1] % 4 === 0 || !o && t8 % 4 === 0) && this.outputShape[2] % 4 === 0; + this.elementsPerThread = [4, 4, this.splitedDimInner], this.outputComponent = s ? 4 : 1, s || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = K(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t8], this.workgroupSize, this.elementsPerThread), this.transposeA = o, this.transposeB = n, this.shaderKey = `matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`; } getUserCode() { let e = this.outputComponent; return ` - ${Pv(false, this.transposeB, false, false, false, e)} + ${Uv(false, this.transposeB, false, false, false, e)} fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Ae(e)}) { let col = colIn * ${e}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { @@ -28368,39 +28505,39 @@ var Xg = class { // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${e}; i = i + 1) { - ${Bs("&result[flatIndex + i]", `${e > 1 ? "value[i]" : "value"}`, "float32")} + ${Us("&result[flatIndex + i]", `${e > 1 ? "value[i]" : "value"}`, "float32")} } } } - ${e === 4 ? Tp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : _p(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)} + ${e === 4 ? Rp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : Dp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)} `; } }; -var Yg = class { - constructor(e, t10 = null, o = null, n = null) { - this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`; +var Qg = class { + constructor(e, t8 = null, o = null, n = null) { + this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t8 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`; } getUserCode() { return ` ${dr(this.activation, this.hasPreluActivationWeights)} - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${jr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtIndex(index, value); } } `; } }; -var Qg = class { +var Zg = class { constructor(e) { - this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill"; + this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } @@ -28409,39 +28546,39 @@ var Qg = class { } }; function Vt(r) { - let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10; + let { backend: e, attrs: t8 } = r, { shape: o, value: n } = t8, { dtype: s } = t8; if (s = s || y.inferDtype(n), s === "string") { let a = y.getArrayFromDType(s, y.sizeFromShape(o)); return a.fill(n), e.makeTensorInfo(o, s, a); } else { - let a = new Qg(o), i = [{ type: "float32", data: [n] }]; + let a = new Zg(o), i = [{ type: "float32", data: [n] }]; return e.runWebGPUProgram(a, [], s, i); } } -var wB = { kernelName: ea, backendName: "webgpu", kernelFunc: Vt }; +var KB = { kernelName: na, backendName: "webgpu", kernelFunc: Vt }; function pe(r) { - let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a); + let { inputs: e, attrs: t8 } = r, { x: o } = e, { shape: n } = t8, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a); return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; } -var SB = { kernelName: ia, backendName: "webgpu", kernelFunc: pe }; -function $p({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { - let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Sr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); - y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`); - let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = pe({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = pe({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), D = [E, R], F = Math.max(x, b), O = [E, R], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, B, z = [F, d, f], U = P().get("WEBGPU_MATMUL_PROGRAM_TYPE"); +var qB = { kernelName: ma, backendName: "webgpu", kernelFunc: pe }; +function Ap({ a: r, b: e, transposeA: t8, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { + let u = r.shape.length, c = e.shape.length, l = t8 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t8 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Ir.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); + y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t8} and transposeB=${o} must match.`); + let k = t8 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = pe({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = pe({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), D = [E, R], P = Math.max(x, b), O = [E, R], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, B, z = [P, d, f], U = A().get("WEBGPU_MATMUL_PROGRAM_TYPE"); if (U < 0) { - let H = P().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), X = H > 0 ? H : n.thresholdToIncreaseWorkgroups, J = F * Math.ceil(d / 32) * Math.ceil(f / 32); - J <= X || d <= 8 && J <= X * 2 ? F * d * f <= 128 ? U = Oo.MatMulReduceProgram : F === 1 && m >= 2e3 ? U = Oo.MatMulSplitKProgram : U = Oo.MatMulSmallOutputSizeProgram : U = Oo.MatMulPackedProgram; + let q = A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), Y = q > 0 ? q : n.thresholdToIncreaseWorkgroups, J = P * Math.ceil(d / 32) * Math.ceil(f / 32); + J <= Y || d <= 8 && J <= Y * 2 ? P * d * f <= 128 ? U = Mo.MatMulReduceProgram : P === 1 && m >= 2e3 ? U = Mo.MatMulSplitKProgram : U = Mo.MatMulSmallOutputSizeProgram : U = Mo.MatMulPackedProgram; } switch (U) { - case Oo.MatMulReduceProgram: - L = new qg(z, t10, o, s, p, a); + case Mo.MatMulReduceProgram: + L = new jg(z, t8, o, s, p, a); break; - case Oo.MatMulSplitKProgram: { - if (B = Vt({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Xg(z, m, t10, o), s || p) { + case Mo.MatMulSplitKProgram: { + if (B = Vt({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Yg(z, m, t8, o), s || p) { B = n.runWebGPUProgram(L, O, r.dtype, M, B); - let X = new Yg(B.shape, s, p, a), J = null, re = [B]; - s && re.push(s), a && re.push(a), p === "leakyrelu" && (J = [{ type: "float32", data: [i] }], X.uniforms += " alpha : f32,"); - let ne = n.runWebGPUProgram(X, re, B.dtype, J); + let Y = new Qg(B.shape, s, p, a), J = null, re = [B]; + s && re.push(s), a && re.push(a), p === "leakyrelu" && (J = [{ type: "float32", data: [i] }], Y.uniforms += " alpha : f32,"); + let ne = n.runWebGPUProgram(Y, re, B.dtype, J); D.push(B); let ee = pe({ inputs: { x: ne }, backend: n, attrs: { shape: S } }); D.push(ne); @@ -28451,12 +28588,12 @@ function $p({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = } break; } - case Oo.MatMulSmallOutputSizeProgram: - L = new jg(k, _, z, t10, o, s, p, a); + case Mo.MatMulSmallOutputSizeProgram: + L = new Xg(k, _, z, t8, o, s, p, a); break; - case Oo.MatMulPackedProgram: - let H = n.adapterInfo.isIntel(); - L = new Kg(k, z, t10, o, s, p, a, H); + case Mo.MatMulPackedProgram: + let q = n.adapterInfo.isIntel(); + L = new qg(k, z, t8, o, s, p, a, q); break; default: throw new Error(`Unsupported MatMulProgramType ${U}.`); @@ -28464,27 +28601,27 @@ function $p({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = s && O.push(s), a && O.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), B = n.runWebGPUProgram(L, O, r.dtype, M, B); let j = pe({ inputs: { x: B }, backend: n, attrs: { shape: S } }); D.push(B); - for (let H of D) - n.disposeData(H.dataId); + for (let q of D) + n.disposeData(q.dataId); return j; } -function Jae(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; - return $p({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); +function Pie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; + return Ap({ a: n, b: s, transposeA: p, transposeB: u, backend: t8, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); } -var IB = { kernelName: bo, backendName: "webgpu", kernelFunc: Jae }; -var mm = class { - constructor(e, t10, o) { - this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e; +var jB = { kernelName: So, backendName: "webgpu", kernelFunc: Pie }; +var dm = class { + constructor(e, t8, o) { + this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e; } getUserCode() { return ` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${Kc(this.op, false)} + ${qc(this.op, false)} } - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); @@ -28496,20 +28633,20 @@ var mm = class { `; } }; -var bi = class { - constructor(e, t10, o) { - if (this.size = true, this.variableNames = ["A", "B"], this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = Z(this.outputShape), this.op = e, this.useSharedMemoryWithA = t10.length <= 1 && o.length > 1 && t10[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t10.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB) - this.outputComponent = 1, this.variableComponents = [1, 1], this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t10[0], this.shaderKey = `binary_${e}_${this.lastDimensionSize}`, this.type = "shared", this.workgroupSize = [256, 1, 1]; +var Si = class { + constructor(e, t8, o) { + if (this.size = true, this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.dispatchLayout = X(this.outputShape), this.op = e, this.useSharedMemoryWithA = t8.length <= 1 && o.length > 1 && t8[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t8.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB) + this.outputComponent = 1, this.variableComponents = [1, 1], this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t8[0], this.shaderKey = `binary_${e}_${this.lastDimensionSize}`, this.type = "shared", this.workgroupSize = [256, 1, 1]; else { - let n = t10.length > 0 && t10[t10.length - 1] % 4 === 0, s = o.length > 0 && o[o.length - 1] % 4 === 0; - n && s ? (this.outputComponent = 4, this.variableComponents = [4, 4]) : n && (y.isScalarShape(o) || o[o.length - 1] === 1) || s && (y.isScalarShape(t10) || t10[t10.length - 1] === 1) ? (this.outputComponent = 4, this.variableComponents = n ? [4, 1] : [1, 4]) : (this.outputComponent = 1, this.variableComponents = [1, 1]), this.type = "nonshared", this.shaderKey = `binary_${e}_${this.variableComponents}`, this.workgroupSize = [128, 1, 1]; + let n = t8.length > 0 && t8[t8.length - 1] % 4 === 0, s = o.length > 0 && o[o.length - 1] % 4 === 0; + n && s ? (this.outputComponent = 4, this.variableComponents = [4, 4]) : n && (y.isScalarShape(o) || o[o.length - 1] === 1) || s && (y.isScalarShape(t8) || t8[t8.length - 1] === 1) ? (this.outputComponent = 4, this.variableComponents = n ? [4, 1] : [1, 4]) : (this.outputComponent = 1, this.variableComponents = [1, 1]), this.type = "nonshared", this.shaderKey = `binary_${e}_${this.variableComponents}`, this.workgroupSize = [128, 1, 1]; } - this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.outputComponent, 1, 1]); + this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.outputComponent, 1, 1]); } getUserCode() { - let e, t10 = this.outputComponent === 4 ? "vec4" : "f32", o = ` - fn binaryOperation(a : ${t10}, b : ${t10}) -> ${t10} { - ${Kc(this.op, this.outputComponent === 4)} + let e, t8 = this.outputComponent === 4 ? "vec4" : "f32", o = ` + fn binaryOperation(a : ${t8}, b : ${t8}) -> ${t8} { + ${qc(this.op, this.outputComponent === 4)} }; `; if (this.type === "shared") { @@ -28519,7 +28656,7 @@ var bi = class { e = ` ${o} var sharedBuf : array; - ${K("index")} { + ${H("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { @@ -28537,11 +28674,11 @@ var bi = class { } else e = ` ${o} - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index * ${this.outputComponent}); - let a = ${t10}(getAByOutputCoords(coords)); - let b = ${t10}(getBByOutputCoords(coords)); + let a = ${t8}(getAByOutputCoords(coords)); + let b = ${t8}(getBByOutputCoords(coords)); setOutputAtIndex(index, binaryOperation(a, b)); } } @@ -28550,27 +28687,27 @@ var bi = class { } }; function At(r) { - let { inputs: e } = r, { x: t10 } = e; - return r.backend.incRef(t10.dataId), { dataId: t10.dataId, shape: t10.shape, dtype: t10.dtype }; + let { inputs: e } = r, { x: t8 } = e; + return r.backend.incRef(t8.dataId), { dataId: t8.dataId, shape: t8.shape, dtype: t8.dtype }; } -var vB = { kernelName: xo, backendName: "webgpu", kernelFunc: At }; -function fo(r) { - let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.tensorMap.get(s.dataId), i = At({ inputs: { x: o }, backend: t10 }), p = At({ inputs: { x: n }, backend: t10 }); +var XB = { kernelName: Co, backendName: "webgpu", kernelFunc: At }; +function xo(r) { + let { inputs: e, backend: t8 } = r, { real: o, imag: n } = e, s = t8.makeTensorInfo(o.shape, "complex64"), a = t8.tensorMap.get(s.dataId), i = At({ inputs: { x: o }, backend: t8 }), p = At({ inputs: { x: n }, backend: t8 }); return a.complexTensorInfos = { real: i, imag: p }, s; } -var kB = { kernelName: Ti, backendName: "webgpu", kernelFunc: fo }; -var Xr = class { - constructor(e, t10, o = "") { +var YB = { kernelName: Ri, backendName: "webgpu", kernelFunc: xo }; +var Zr = class { + constructor(e, t8, o = "") { this.variableNames = ["A"], this.size = true; let n = 128; - this.workgroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t10, o !== "" && (this.uniforms = o), this.shaderKey = `unary_${t10}`; + this.workgroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t8, o !== "" && (this.uniforms = o), this.shaderKey = `unary_${t8}`; } getUserCode() { return ` fn unaryOperation(a : f32) -> f32 { - ${yi(this.op, false)} + ${wi(this.op, false)} } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); @@ -28579,99 +28716,99 @@ var Xr = class { `; } }; -function xe({ opType: r, cpuKernelImpl: e, dtype: t10 }) { +function ye({ opType: r, cpuKernelImpl: e, dtype: t8 }) { return ({ inputs: o, backend: n }) => { - let { x: s } = o, a = n, i = t10 || s.dtype; + let { x: s } = o, a = n, i = t8 || s.dtype; if (a.shouldExecuteOnCPU([s]) && e != null) { let u = a.tensorMap.get(s.dataId), c = e(u.values, i); return a.makeTensorInfo(s.shape, i, c); } - let p = new Xr(s.shape, r); + let p = new Zr(s.shape, r); return a.runWebGPUProgram(p, [s], i); }; } -function et({ opType: r, cpuKernelImpl: e, supportsComplex: t10 = false, dtype: o }) { +function et({ opType: r, cpuKernelImpl: e, supportsComplex: t8 = false, dtype: o }) { return ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; - if (t10 && a.dtype === "complex64") { + if (t8 && a.dtype === "complex64") { let l = p.tensorMap.get(a.dataId), m = p.tensorMap.get(i.dataId), d, f; if (r !== fe.MUL) [d, f] = [[l.complexTensorInfos.real, m.complexTensorInfos.real], [l.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((g) => { - let [x, b] = g, w = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, S = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new bi(r, a.shape, i.shape); - return p.runWebGPUProgram(k, [w, S], dt(x.dtype, b.dtype)); + let [x, b] = g, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, S = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Si(r, a.shape, i.shape); + return p.runWebGPUProgram(k, [C, S], dt(x.dtype, b.dtype)); }); else { - let g = new mm(fe.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new mm(fe.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }]; + let g = new dm(fe.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new dm(fe.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }]; d = p.runWebGPUProgram(g, b, "float32"), f = p.runWebGPUProgram(x, b, "float32"); } - let h = fo({ inputs: { real: d, imag: f }, backend: p }); + let h = xo({ inputs: { real: d, imag: f }, backend: p }); return p.disposeData(d.dataId), p.disposeData(f.dataId), h; } let u = o || dt(a.dtype, i.dtype); if ((a.dtype === "string" || i.dtype === "string" || p.shouldExecuteOnCPU([a, i])) && e != null) { - let l = p.tensorMap.get(a.dataId).values, m = p.tensorMap.get(i.dataId).values, d = a.dtype === "string" ? C.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? C.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, d, f, u); + let l = p.tensorMap.get(a.dataId).values, m = p.tensorMap.get(i.dataId).values, d = a.dtype === "string" ? w.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? w.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, d, f, u); return p.makeTensorInfo(g, u, h); } - let c = new bi(r, a.shape, i.shape); + let c = new Si(r, a.shape, i.shape); return p.runWebGPUProgram(c, [a, i], u); }; } -var { addImpl: NB, castImpl: TB, ceilImpl: _B, concatImpl: $B, equalImpl: EB, expImpl: RB, expm1Impl: DB, floorImpl: AB, floorDivImpl: FB, gatherNdImpl: PB, gatherV2Impl: OB, greaterEqualImpl: MB, greaterImpl: LB, lessEqualImpl: BB, lessImpl: zB, logImpl: VB, maxImpl: WB, maximumImpl: UB, minimumImpl: GB, multiplyImpl: HB, negImpl: KB, notEqualImpl: qB, prodImpl: jB, rangeImpl: XB, rsqrtImpl: YB, scatterImpl: QB, simpleAbsImpl: ZB, sliceImpl: JB, stridedSliceImpl: ez, stringNGramsImpl: tz, subImpl: rz, tileImpl: oz, topKImpl: nz, transposeImpl: sz, uniqueImpl: o3t } = Sc; -var eie = xe({ opType: Q.ABS, cpuKernelImpl: ZB }); -var az = { kernelName: Gs, backendName: "webgpu", kernelFunc: eie }; -var tie = xe({ opType: Q.ACOS }); -var iz = { kernelName: zo, backendName: "webgpu", kernelFunc: tie }; -var rie = xe({ opType: Q.ACOSH }); -var uz = { kernelName: Vo, backendName: "webgpu", kernelFunc: rie }; -var oie = et({ opType: fe.ADD, cpuKernelImpl: NB, supportsComplex: true }); -var pz = { kernelName: no, backendName: "webgpu", kernelFunc: oie }; -var Zg = class { +var { addImpl: QB, castImpl: ZB, ceilImpl: JB, concatImpl: ez, equalImpl: tz, expImpl: rz, expm1Impl: oz, floorImpl: nz, floorDivImpl: sz, gatherNdImpl: az, gatherV2Impl: iz, greaterEqualImpl: uz, greaterImpl: pz, lessEqualImpl: cz, lessImpl: lz, logImpl: mz, maxImpl: dz, maximumImpl: fz, minimumImpl: hz, multiplyImpl: gz, negImpl: xz, notEqualImpl: yz, prodImpl: bz, rangeImpl: Cz, rsqrtImpl: wz, scatterImpl: Sz, simpleAbsImpl: Iz, sliceImpl: vz, stridedSliceImpl: kz, stringNGramsImpl: Nz, subImpl: Tz, tileImpl: _z, topKImpl: $z, transposeImpl: Ez, uniqueImpl: NPt } = Ic; +var Oie = ye({ opType: Z.ABS, cpuKernelImpl: Iz }); +var Rz = { kernelName: js, backendName: "webgpu", kernelFunc: Oie }; +var Mie = ye({ opType: Z.ACOS }); +var Dz = { kernelName: Vo, backendName: "webgpu", kernelFunc: Mie }; +var Lie = ye({ opType: Z.ACOSH }); +var Az = { kernelName: Wo, backendName: "webgpu", kernelFunc: Lie }; +var Bie = et({ opType: fe.ADD, cpuKernelImpl: QB, supportsComplex: true }); +var Fz = { kernelName: io, backendName: "webgpu", kernelFunc: Bie }; +var Jg = class { constructor(e) { - this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN"; + this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t8, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN"; } getUserCode() { let e = []; this.variableNames.forEach((n) => { e.push(`let v${n} = get${n}ByOutputCoords(coords);`); }); - let t10 = this.variableNames.map((n) => `v${n}`).join(" + "); + let t8 = this.variableNames.map((n) => `v${n}`).join(" + "); return ` - ${K("index")} { + ${H("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); ${e.join(` `)} - setOutputAtIndex(flatIndex, ${t10}); + setOutputAtIndex(flatIndex, ${t8}); } } } `; } }; -function nie(r) { - let { inputs: e, backend: t10 } = r, o = e; +function zie(r) { + let { inputs: e, backend: t8 } = r, o = e; if (o.length === 1) - return At({ inputs: { x: o[0] }, backend: t10 }); - let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Zg(s); - return t10.runWebGPUProgram(a, o, n); + return At({ inputs: { x: o[0] }, backend: t8 }); + let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Jg(s); + return t8.runWebGPUProgram(a, o, n); } -var cz = { kernelName: Wo, backendName: "webgpu", kernelFunc: nie }; -var Jg = class { - constructor(e, t10) { +var Pz = { kernelName: Uo, backendName: "webgpu", kernelFunc: zie }; +var ex = class { + constructor(e, t8) { this.variableNames = ["A"], this.workgroupSize = [16, 16, 1]; let o = new Array(e.length); for (let n = 0; n < o.length; n++) - o[n] = e[t10[n]]; - this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]), this.shaderKey = "transposeShared"; + o[n] = e[t8[n]]; + this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]), this.shaderKey = "transposeShared"; } getUserCode() { y.assert(this.workgroupSize[0] === this.workgroupSize[1], () => `Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`); let e = this.workgroupSize[0]; return ` var tile : array, ${this.workgroupSize[0]}>; - ${K()} { + ${H()} { var x = i32(workgroupId.x) * ${e} + i32(localId.x); var y = i32(workgroupId.y) * ${e} + i32(localId.y); let width = uniforms.outShape[0]; @@ -28691,68 +28828,68 @@ var Jg = class { `; } }; -var ex = class { - constructor(e, t10) { +var tx = class { + constructor(e, t8) { this.variableNames = ["A"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true; let o = new Array(e.length); for (let n = 0; n < o.length; n++) - o[n] = e[t10[n]]; - this.outputShape = o, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t10, this.shaderKey = `transpose_${t10}`; + o[n] = e[t8[n]]; + this.outputShape = o, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t8, this.shaderKey = `transpose_${t8}`; } getUserCode() { - let e = Nt(this.outputShape.length), t10 = sie(this.newDim); + let e = ht(this.outputShape.length), t8 = Gv(this.newDim); return ` - ${K("index")} { + ${H("index")} { for(var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if(flatIndex < uniforms.size) { - let resRC = getCoordsFromIndex(flatIndex); + let coords = getCoordsFromIndex(flatIndex); setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D( - ${e}(${t10}), uniforms.aShape)]); + ${e}(${t8}), uniforms.aShape)]); } } } `; } }; -function sie(r) { +function Gv(r) { let e = r.length; if (e > 6) throw Error(`Transpose for rank ${e} is not yet supported`); - let t10 = new Array(e); + let t8 = new Array(e); for (let o = 0; o < r.length; o++) - t10[r[o]] = `resRC.${Po(o)}`; - return t10.join(); + t8[r[o]] = `coords.${Oo(o)}`; + return t8.join(); } -function rr(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i); +function yr(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t8, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; - if (t10.shouldExecuteOnCPU([n])) { - let l = a.tensorMap.get(n.dataId).values, m = sz(l, n.shape, n.dtype, s, p); - return t10.makeTensorInfo(p, n.dtype, m); + if (t8.shouldExecuteOnCPU([n])) { + let l = a.tensorMap.get(n.dataId).values, m = Ez(l, n.shape, n.dtype, s, p); + return t8.makeTensorInfo(p, n.dtype, m); } if (n.shape.length === 2 && y.arraysEqual(s, [1, 0])) { - let c = new Jg(n.shape, s); + let c = new ex(n.shape, s); return a.runWebGPUProgram(c, [n], n.dtype); } - let u = new ex(n.shape, s); + let u = new tx(n.shape, s); return a.runWebGPUProgram(u, [n], n.dtype); } -var lz = { kernelName: ao, backendName: "webgpu", kernelFunc: rr }; -var tx = class { - constructor(e, t10, o) { +var Oz = { kernelName: po, backendName: "webgpu", kernelFunc: yr }; +var rx = class { + constructor(e, t8, o) { this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize]; - let [n] = C.computeOutAndReduceShapes(this.inputShape, [1]); - this.outputShape = n.length === 0 ? [1] : n, e.inSize >= 32768 && o >= 512 ? this.workgroupSize = [512, 1, 1] : e.inSize >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t10, this.shaderKey = `reduce_${t10}`; + let [n] = w.computeOutAndReduceShapes(this.inputShape, [1]); + this.outputShape = n.length === 0 ? [1] : n, e.inSize >= 32768 && o >= 512 ? this.workgroupSize = [512, 1, 1] : e.inSize >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t8, this.shaderKey = `reduce_${t8}`; } getUserCode() { - let e = "", t10 = "0.0", o = this.workgroupSize[0]; + let e = "", t8 = "0.0", o = this.workgroupSize[0]; this.reduceType === "min" || this.reduceType === "max" ? (e = ` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue) - { bestValue = candidate; }`, t10 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t10 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t10 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t10 = "0.0"); + { bestValue = candidate; }`, t8 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t8 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t8 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t8 = "0.0"); let n = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);"; return ` fn DIV_CEIL(a : u32, b : u32) -> u32 { @@ -28767,10 +28904,10 @@ var tx = class { let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize; return offset; } - ${K("index")} { + ${H("index")} { let outputIndex = index / ${o}; let offset = getOffset(outputIndex); - var bestValue = ${t10}; + var bestValue = ${t8}; let Length = uniforms.reduceSize; let WorkPerThread = DIV_CEIL(u32(Length), ${o}u); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; @@ -28801,58 +28938,58 @@ var tx = class { `; } }; -function Yr(r, e, t10, o, n) { - let s = r.shape.length, a = [], i = y.parseAxisParam(e, r.shape), p = i, u = C.getAxesPermutation(p, s), c = r; - u != null && (c = rr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = C.getInnerMostAxes(p.length, s), a.push(c)), C.assertAxesAreInnerMostDims(o, p, s); - let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = l; - t10 && (d = C.expandShapeToKeepDim(l, i)); +function Jr(r, e, t8, o, n) { + let s = r.shape.length, a = [], i = y.parseAxisParam(e, r.shape), p = i, u = w.getAxesPermutation(p, s), c = r; + u != null && (c = yr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = w.getInnerMostAxes(p.length, s), a.push(c)), w.assertAxesAreInnerMostDims(o, p, s); + let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = l; + t8 && (d = w.expandShapeToKeepDim(l, i)); let f; if ((o === "max" || o === "prod") && n.shouldExecuteOnCPU([c])) { let h = n.tensorMap.get(c.dataId).values; switch (o) { case "max": - let g = WB(h, y.sizeFromShape(m), d, r.dtype); + let g = dz(h, y.sizeFromShape(m), d, r.dtype); f = n.makeTensorInfo(d, r.dtype, g); break; case "prod": - let { outVals: x, outShape: b, outDtype: w } = jB(c.shape, c.dtype, h, p); - f = n.makeTensorInfo(b, w, x); + let { outVals: x, outShape: b, outDtype: C } = bz(c.shape, c.dtype, h, p); + f = n.makeTensorInfo(b, C, x); break; default: throw new Error(`${o} CPU implementation is not yet supported.`); } } else { - let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, w = o === "mean" ? "float32" : Za(r.dtype), S = [{ type: "int32", data: [h] }], k = new tx(b, o, n.device.limits.maxComputeWorkgroupSizeX), _ = n.runWebGPUProgram(k, [c], w, S); + let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, C = o === "mean" ? "float32" : ti(r.dtype), S = [{ type: "int32", data: [h] }], k = new rx(b, o, n.device.limits.maxComputeWorkgroupSizeX), _ = n.runWebGPUProgram(k, [c], C, S); a.push(_), f = pe({ inputs: { x: _ }, attrs: { shape: d }, backend: n }); } return a.forEach((h) => n.disposeData(h.dataId)), f; } -function aie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; - return Yr(n, a, s, "all", t10); +function Vie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; + return Jr(n, a, s, "all", t8); } -var mz = { kernelName: Uo, backendName: "webgpu", kernelFunc: aie }; -function iie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; - return Yr(n, a, s, "any", t10); +var Mz = { kernelName: Go, backendName: "webgpu", kernelFunc: Vie }; +function Wie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; + return Jr(n, a, s, "any", t8); } -var dz = { kernelName: Go, backendName: "webgpu", kernelFunc: iie }; -var qc = class { - constructor(e, t10, o) { +var Lz = { kernelName: Ho, backendName: "webgpu", kernelFunc: Wie }; +var jc = class { + constructor(e, t8, o) { this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true; - let n = [t10]; + let n = [t8]; this.op = o === "min" ? "<" : ">"; - let [s, a] = C.computeOutAndReduceShapes(e, n); - this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = Z(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = q(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`; + let [s, a] = w.computeOutAndReduceShapes(e, n); + this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = X(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = K(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`; } getUserCode() { - let e = this.workgroupSize[0], t10 = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Po(this.inputShape.length - 1)}`, o = () => { + let e = this.workgroupSize[0], t8 = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Oo(this.inputShape.length - 1)}`, o = () => { let n = ""; if (this.outputShape.length === 1) this.inputShape.length !== 1 && (n += "outputCoords,"); else for (let s = 0; s < this.outputShape.length; s++) - n += `outputCoords.${Po(s)},`; + n += `outputCoords.${Oo(s)},`; return n; }; return this.type === "shared" ? ` @@ -28865,9 +29002,9 @@ var qc = class { var xBestValues : array; `} - ${K("index")} { + ${H("index")} { let outputIndex = index / ${e}; - let reduceLength = ${t10()}; + let reduceLength = ${t8()}; var bestIndex = i32(localId.x); var bestValue = uniforms.infinityValue; @@ -28905,12 +29042,12 @@ var qc = class { } } ` : ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; var bestValue = getX(${o()} 0); - let reduceLength = ${t10()}; + let reduceLength = ${t8()}; for (var i = 1; i < reduceLength; i++) { let candidate = getX(${o()} i); if (candidate ${this.op} bestValue) { @@ -28924,37 +29061,37 @@ var qc = class { `; } }; -function uie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = rr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); - let c = new qc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l); - return u.forEach((d) => t10.disposeData(d.dataId)), m; +function Uie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = yr({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); + let c = new jc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t8.runWebGPUProgram(c, [p], "int32", l); + return u.forEach((d) => t8.disposeData(d.dataId)), m; } -var fz = { kernelName: Hs, backendName: "webgpu", kernelFunc: uie }; -function pie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = rr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); - let c = new qc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l); - return u.forEach((d) => t10.disposeData(d.dataId)), m; +var Bz = { kernelName: Xs, backendName: "webgpu", kernelFunc: Uie }; +function Gie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = yr({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); + let c = new jc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t8.runWebGPUProgram(c, [p], "int32", l); + return u.forEach((d) => t8.disposeData(d.dataId)), m; } -var hz = { kernelName: Ks, backendName: "webgpu", kernelFunc: pie }; -var cie = xe({ opType: Q.ASIN }); -var gz = { kernelName: Ho, backendName: "webgpu", kernelFunc: cie }; -var lie = xe({ opType: Q.ASINH }); -var xz = { kernelName: Ko, backendName: "webgpu", kernelFunc: lie }; -var mie = xe({ opType: Q.ATAN }); -var yz = { kernelName: qo, backendName: "webgpu", kernelFunc: mie }; -var die = et({ opType: fe.ATAN2 }); -var bz = { kernelName: Xo, backendName: "webgpu", kernelFunc: die }; -var fie = xe({ opType: Q.ATANH }); -var Cz = { kernelName: jo, backendName: "webgpu", kernelFunc: fie }; -var rx = class { +var zz = { kernelName: Ys, backendName: "webgpu", kernelFunc: Gie }; +var Hie = ye({ opType: Z.ASIN }); +var Vz = { kernelName: Ko, backendName: "webgpu", kernelFunc: Hie }; +var Kie = ye({ opType: Z.ASINH }); +var Wz = { kernelName: qo, backendName: "webgpu", kernelFunc: Kie }; +var qie = ye({ opType: Z.ATAN }); +var Uz = { kernelName: jo, backendName: "webgpu", kernelFunc: qie }; +var jie = et({ opType: fe.ATAN2 }); +var Gz = { kernelName: Yo, backendName: "webgpu", kernelFunc: jie }; +var Xie = ye({ opType: Z.ATANH }); +var Hz = { kernelName: Xo, backendName: "webgpu", kernelFunc: Xie }; +var ox = class { constructor(e) { - this.variableNames = ["x"], this.uniforms = "strides : vec2,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne"; + this.variableNames = ["x"], this.uniforms = "strides : vec2,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -28971,11 +29108,11 @@ var rx = class { `; } }; -var Da = class { - constructor(e, t10, o = false, n = false, s = false) { - if (this.variableNames = ["x"], this.uniforms = "strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,", this.workgroupSize = [128, 1, 1], this.size = true, t10 === "avg" && o) +var Ma = class { + constructor(e, t8, o = false, n = false, s = false) { + if (this.variableNames = ["x"], this.uniforms = "strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,", this.workgroupSize = [128, 1, 1], this.size = true, t8 === "avg" && o) throw new Error("Cannot compute positions for average pool."); - this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t10, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool2D_${t10}_${o}_${n}_${s}`; + this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t8, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool2D_${t8}_${o}_${n}_${s}`; } getUserCode() { let e; @@ -28985,9 +29122,9 @@ var Da = class { maxValueFound = 1.0; maxPosition = ${this.flattenPositions ? this.includeBatchIndex ? "((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d" : "(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d" : "wR * uniforms.filterDims.y + wC"}; }` : e = "resultValue = max(value, resultValue);"; - let t10 = "resultValue"; - return this.poolType === "avg" && (t10 = "resultValue / max(count, 1.0)"), ` - ${K("index")} { + let t8 = "resultValue"; + return this.poolType === "avg" && (t8 = "resultValue / max(count, 1.0)"), ` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -29019,17 +29156,17 @@ var Da = class { } } - ${this.computePositions ? "setOutputAtIndexI32(index, maxPosition);" : `setOutputAtIndex(index, ${t10});`} + ${this.computePositions ? "setOutputAtIndexI32(index, maxPosition);" : `setOutputAtIndex(index, ${t8});`} } } `; } }; -var wu = class { - constructor(e, t10, o = false, n = false, s = false) { - if (this.variableNames = ["x"], this.uniforms = "strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,", this.workgroupSize = [128, 1, 1], this.size = true, t10 === "avg" && o) +var ku = class { + constructor(e, t8, o = false, n = false, s = false) { + if (this.variableNames = ["x"], this.uniforms = "strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,", this.workgroupSize = [128, 1, 1], this.size = true, t8 === "avg" && o) throw new Error("Cannot compute positions for average pool."); - this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t10, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool3D_${t10}_${o}_${n}_${s}`; + this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t8, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool3D_${t8}_${o}_${n}_${s}`; } getUserCode() { let e; @@ -29039,9 +29176,9 @@ var wu = class { maxValueFound = 1.0; maxPosition = ${this.flattenPositions ? this.includeBatchIndex ? "(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch" : "((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch" : "wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC"}; }` : e = "resultValue = max(value, resultValue);"; - let t10 = "resultValue"; - return this.poolType === "avg" && (t10 = "resultValue / max(count, 1.0)"), ` - ${K("index")} { + let t8 = "resultValue"; + return this.poolType === "avg" && (t8 = "resultValue / max(count, 1.0)"), ` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -29081,52 +29218,52 @@ var wu = class { } } - ${this.computePositions ? "setOutputAtIndexI32(index, maxPosition);" : `setOutputAtIndex(index, ${t10});`} + ${this.computePositions ? "setOutputAtIndexI32(index, maxPosition);" : `setOutputAtIndex(index, ${t8});`} } } `; } }; -function Ov(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o; - return Yr(n, s, a, "max", t10); +function Hv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o; + return Jr(n, s, a, "max", t8); } -var wz = { kernelName: Ln, backendName: "webgpu", kernelFunc: Ov }; -function Mv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; - return Yr(n, a, s, "mean", t10); +var Kz = { kernelName: zn, backendName: "webgpu", kernelFunc: Hv }; +function Kv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; + return Jr(n, a, s, "mean", t8); } -var Sz = { kernelName: Vn, backendName: "webgpu", kernelFunc: Mv }; -function ox(r, e, t10, o) { +var qz = { kernelName: Un, backendName: "webgpu", kernelFunc: Kv }; +function nx(r, e, t8, o) { if (e.filterWidth === 1 && e.filterHeight === 1 && y.arraysEqual(e.inShape, e.outShape)) return At({ inputs: { x: r }, backend: o }); if (e.filterWidth === e.inWidth && e.filterHeight === e.inHeight && e.batchSize === 1 && e.padInfo.type === "VALID") { let a = r.shape.length, i = pe({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p; - t10 === "avg" ? p = Mv({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), p = Ov({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } })); + t8 === "avg" ? p = Kv({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t8 === "max", () => `Invalid pool type ${t8}`), p = Hv({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } })); let u = pe({ inputs: { x: p }, backend: o, attrs: { shape: e.outShape } }); return o.disposeData(i.dataId), o.disposeData(p.dataId), u; } let n, s = [{ type: "int32", data: [e.strideHeight, e.strideWidth] }]; - return e.filterHeight === 1 && e.filterWidth === 1 ? n = new rx(e) : (t10 === "avg" ? n = new Da(e, "avg") : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), n = new Da(e, "max")), s.push({ type: "int32", data: [e.padInfo.top, e.padInfo.left] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }, { type: "int32", data: [e.inHeight, e.inWidth] }, { type: "int32", data: [e.effectiveFilterHeight, e.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s); + return e.filterHeight === 1 && e.filterWidth === 1 ? n = new ox(e) : (t8 === "avg" ? n = new Ma(e, "avg") : (y.assert(t8 === "max", () => `Invalid pool type ${t8}`), n = new Ma(e, "max")), s.push({ type: "int32", data: [e.padInfo.top, e.padInfo.left] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }, { type: "int32", data: [e.inHeight, e.inWidth] }, { type: "int32", data: [e.effectiveFilterHeight, e.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s); } -function hie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = C.computePool2DInfo(n.shape, s, a, u, i, p); - return ox(n, c, "avg", t10); +function Yie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p); + return nx(n, c, "avg", t8); } -var Iz = { kernelName: Yo, backendName: "webgpu", kernelFunc: hie }; -function gie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "avg"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; - return t10.runWebGPUProgram(m, [n], n.dtype, d); +var jz = { kernelName: Qo, backendName: "webgpu", kernelFunc: Yie }; +function Qie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new ku(l, "avg"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; + return t8.runWebGPUProgram(m, [n], n.dtype, d); } -var vz = { kernelName: qs, backendName: "webgpu", kernelFunc: gie }; -var nx = class { +var Xz = { kernelName: Qs, backendName: "webgpu", kernelFunc: Qie }; +var sx = class { constructor(e) { this.variableNames = ["dy"], this.uniforms = `strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool2DBackprop"; + outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool2DBackprop"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -29166,14 +29303,14 @@ var nx = class { `; } }; -var sx = class { +var ax = class { constructor(e) { this.variableNames = ["dy"], this.uniforms = `strides : vec3, pads : vec3, filterDims : vec3, - outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool3DBackprop"; + outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool3DBackprop"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -29223,87 +29360,87 @@ var sx = class { `; } }; -function xie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = C.computePool3DInfo(a.shape, i, p, 1, u, c), m = new sx(l), d = 1 / (l.filterDepth * l.filterHeight * l.filterWidth), f = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.effectiveFilterDepth - 1 - l.padInfo.front, l.effectiveFilterHeight - 1 - l.padInfo.top, l.effectiveFilterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }, { type: "int32", data: [l.outDepth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "float32", data: [d] }]; - return t10.runWebGPUProgram(m, [n], a.dtype, f); +function Zie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = w.computePool3DInfo(a.shape, i, p, 1, u, c), m = new ax(l), d = 1 / (l.filterDepth * l.filterHeight * l.filterWidth), f = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.effectiveFilterDepth - 1 - l.padInfo.front, l.effectiveFilterHeight - 1 - l.padInfo.top, l.effectiveFilterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }, { type: "int32", data: [l.outDepth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "float32", data: [d] }]; + return t8.runWebGPUProgram(m, [n], a.dtype, f); } -var kz = { kernelName: Ni, backendName: "webgpu", kernelFunc: xie }; -function yie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s; - cm([n, s], "avgPoolGrad"); - let { filterSize: i, strides: p, pad: u } = o, c = C.computePool2DInfo(a.shape, i, p, 1, u), l = new nx(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }]; - return t10.runWebGPUProgram(l, [n], a.dtype, d); +var Yz = { kernelName: Ei, backendName: "webgpu", kernelFunc: Zie }; +function Jie(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s; + lm([n, s], "avgPoolGrad"); + let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new sx(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }]; + return t8.runWebGPUProgram(l, [n], a.dtype, d); } -var Nz = { kernelName: Gp, backendName: "webgpu", kernelFunc: yie }; -function bie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; - return $p({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 }); +var Qz = { kernelName: $i, backendName: "webgpu", kernelFunc: Jie }; +function eue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; + return Ap({ a: n, b: s, transposeA: a, transposeB: i, backend: t8 }); } -var Tz = { kernelName: Qo, backendName: "webgpu", kernelFunc: bie }; -var ax = class { - constructor(e, t10) { - this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.rank = t10.length, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Nt(e.length)}, `, this.shaderKey = "slice"; +var Zz = { kernelName: Zo, backendName: "webgpu", kernelFunc: eue }; +var ix = class { + constructor(e, t8) { + this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.rank = t8.length, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${ht(e.length)}, `, this.shaderKey = "slice"; } getUserCode() { - let e = Nt(this.rank), t10 = Cie(this.rank), o; - return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${Lv[a]} = uniforms.start.${Po(a)} + coords.${Lv[a]};`), ` - ${K("index")} { + let e = ht(this.rank), t8 = tue(this.rank), o; + return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${qv[a]} = uniforms.start.${Oo(a)} + coords.${qv[a]};`), ` + ${H("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); ${o.join(` `)} - setOutputAtIndex(index, getSource(${t10})); + setOutputAtIndex(index, getSource(${t8})); } } `; } }; -var Lv = ["x", "y", "z", "w", "u", "v"]; -function Cie(r) { +var qv = ["x", "y", "z", "w", "u", "v"]; +function tue(r) { if (r === 1) return "sourceLoc"; if (r <= 6) - return Lv.slice(0, r).map((e) => `sourceLoc.${e}`).join(","); + return qv.slice(0, r).map((e) => `sourceLoc.${e}`).join(","); throw Error(`Slicing for rank ${r} is not yet supported`); } -function zs(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a); - if (ct.assertParamsValid(n, i, p), t10.shouldExecuteOnCPU([n]) || n.dtype === "string") { - let l = t10.tensorMap.get(n.dataId), m = JB(l.values, i, p, n.shape, n.dtype); - return t10.makeTensorInfo(p, n.dtype, m); +function Gs(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a); + if (ct.assertParamsValid(n, i, p), t8.shouldExecuteOnCPU([n]) || n.dtype === "string") { + let l = t8.tensorMap.get(n.dataId), m = vz(l.values, i, p, n.shape, n.dtype); + return t8.makeTensorInfo(p, n.dtype, m); } if (y.sizeFromShape(p) === 0) - return t10.makeTensorInfo(p, n.dtype, []); - let u = new ax(i, p), c = [{ type: "int32", data: i }]; - return t10.runWebGPUProgram(u, [n], n.dtype, c); + return t8.makeTensorInfo(p, n.dtype, []); + let u = new ix(i, p), c = [{ type: "int32", data: i }]; + return t8.runWebGPUProgram(u, [n], n.dtype, c); } -var _z = { kernelName: pa, backendName: "webgpu", kernelFunc: zs }; -var wie = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; +var Jz = { kernelName: fa, backendName: "webgpu", kernelFunc: Gs }; +var rue = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet"); - let i = s.reduce((b, w) => b * w), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = [], f = pe({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = rr({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = pe({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = zs({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } }); - return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeData(b.dataId)), x; + let i = s.reduce((b, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = pe({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), h = yr({ inputs: { x: f }, backend: t8, attrs: { perm: u } }), g = pe({ inputs: { x: h }, backend: t8, attrs: { shape: c } }), x = Gs({ inputs: { x: g }, backend: t8, attrs: { begin: l, size: m } }); + return d.push(f), d.push(h), d.push(g), d.forEach((b) => t8.disposeData(b.dataId)), x; }; -var $z = { kernelName: js, backendName: "webgpu", kernelFunc: wie }; -var Sie = ` +var eV = { kernelName: Zs, backendName: "webgpu", kernelFunc: rue }; +var oue = ` fn bincount_write(index: i32, value: f32) { - ${Bs("&result[index]", "value", "float32")} + ${Us("&result[index]", "value", "float32")} } `; -var Iie = ` +var nue = ` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } `; -var jc = class { - constructor(e, t10, o = false) { - this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t10, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`; +var Xc = class { + constructor(e, t8, o = false) { + this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t8, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`; } getUserCode() { return ` - ${this.binaryOutput ? Iie : Sie} - ${K("index")} { + ${this.binaryOutput ? nue : oue} + ${H("index")} { ${this.rank === 1 ? `if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { @@ -29322,18 +29459,18 @@ var jc = class { `; } }; -function vie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = Vt({ backend: t10, attrs: { shape: c, value: 0, dtype: l } }), d = new jc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n]; - return t10.runWebGPUProgram(d, h, l, f, m); +function sue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = Vt({ backend: t8, attrs: { shape: c, value: 0, dtype: l } }), d = new Xc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n]; + return t8.runWebGPUProgram(d, h, l, f, m); } -var Ez = { kernelName: Zo, backendName: "webgpu", kernelFunc: vie }; -var ix = class { +var tV = { kernelName: Jo, backendName: "webgpu", kernelFunc: sue }; +var ux = class { constructor(e) { - this.outputShape = [], this.variableNames = ["s0", "s1"], this.uniforms = "s0Size : i32, s1Size : i32, ", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "broadcastArgs"; + this.outputShape = [], this.variableNames = ["s0", "s1"], this.uniforms = "s0Size : i32, s1Size : i32, ", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "broadcastArgs"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { var s0 = 1.0; var s1 = 1.0; @@ -29360,65 +29497,65 @@ var ix = class { `; } }; -function kie(r) { - let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e; - if (t10.shouldExecuteOnCPU([o, n])) { - let c = t10.tensorMap.get(o.dataId), l = t10.tensorMap.get(n.dataId), m = c.values, d = l.values, f = C.assertAndGetBroadcastShape(Array.from(m), Array.from(d)); - return t10.makeTensorInfo([f.length], "int32", Int32Array.from(f)); +function aue(r) { + let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e; + if (t8.shouldExecuteOnCPU([o, n])) { + let c = t8.tensorMap.get(o.dataId), l = t8.tensorMap.get(n.dataId), m = c.values, d = l.values, f = w.assertAndGetBroadcastShape(Array.from(m), Array.from(d)); + return t8.makeTensorInfo([f.length], "int32", Int32Array.from(f)); } - let s = y.sizeFromShape(o.shape), a = y.sizeFromShape(n.shape), i = Math.max(s, a), p = new ix(i), u = [{ type: "int32", data: [s] }, { type: "int32", data: [a] }]; - return t10.runWebGPUProgram(p, [o, n], "int32", u); + let s = y.sizeFromShape(o.shape), a = y.sizeFromShape(n.shape), i = Math.max(s, a), p = new ux(i), u = [{ type: "int32", data: [s] }, { type: "int32", data: [a] }]; + return t8.runWebGPUProgram(p, [o, n], "int32", u); } -var Rz = { kernelName: Xs, backendName: "webgpu", kernelFunc: kie }; -var Bv = et({ opType: fe.NOT_EQUAL, dtype: "bool", cpuKernelImpl: qB }); -var Dz = { kernelName: qn, backendName: "webgpu", kernelFunc: Bv }; -function Ci(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId); - return At({ inputs: { x: n.complexTensorInfos.real }, backend: t10 }); +var rV = { kernelName: Js, backendName: "webgpu", kernelFunc: aue }; +var jv = et({ opType: fe.NOT_EQUAL, dtype: "bool", cpuKernelImpl: yz }); +var oV = { kernelName: Yn, backendName: "webgpu", kernelFunc: jv }; +function Ii(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.tensorMap.get(o.dataId); + return At({ inputs: { x: n.complexTensorInfos.real }, backend: t8 }); } -var Az = { kernelName: zi, backendName: "webgpu", kernelFunc: Ci }; -function Fz(r, e) { - let t10 = new Xr(r.shape, Q.TO_INT), o = e.runWebGPUProgram(t10, [r], "int32"); +var nV = { kernelName: Gi, backendName: "webgpu", kernelFunc: Ii }; +function sV(r, e) { + let t8 = new Zr(r.shape, Z.TO_INT), o = e.runWebGPUProgram(t8, [r], "int32"); return { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -function zv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o; +function Xv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return At({ inputs: { x: n }, backend: t10 }); - let a = Wr(n.shape), i = zv({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = fo({ inputs: { real: i, imag: a }, backend: t10 }); - return a.dispose(), t10.disposeData(i.dataId), p; + return At({ inputs: { x: n }, backend: t8 }); + let a = Gr(n.shape), i = Xv({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }), p = xo({ inputs: { real: i, imag: a }, backend: t8 }); + return a.dispose(), t8.disposeData(i.dataId), p; } if (n.dtype === "complex64") { - let a = Ci({ inputs: { input: n }, backend: t10 }), i = zv({ inputs: { x: a }, backend: t10, attrs: { dtype: s } }); - return t10.disposeData(a.dataId), i; + let a = Ii({ inputs: { input: n }, backend: t8 }), i = Xv({ inputs: { x: a }, backend: t8, attrs: { dtype: s } }); + return t8.disposeData(a.dataId), i; } if (!y.hasEncodingLoss(n.dtype, s)) { - let a = At({ inputs: { x: n }, backend: t10 }); + let a = At({ inputs: { x: n }, backend: t8 }); return { dataId: a.dataId, shape: a.shape, dtype: s }; } - if (t10.shouldExecuteOnCPU([n])) { - let a = t10.tensorMap.get(n.dataId).values, [i, p, u] = TB(a, n.shape, n.dtype, s); - return t10.makeTensorInfo(i, p, u); + if (t8.shouldExecuteOnCPU([n])) { + let a = t8.tensorMap.get(n.dataId).values, [i, p, u] = ZB(a, n.shape, n.dtype, s); + return t8.makeTensorInfo(i, p, u); } if (s === "int32") - return Fz(n, t10); + return sV(n, t8); if (s === "bool") { - let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = Bv({ inputs: { a: n, b: a }, backend: t10 }); - return t10.disposeData(a.dataId), p; + let a = t8.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = jv({ inputs: { a: n, b: a }, backend: t8 }); + return t8.disposeData(a.dataId), p; } throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`); } -var Pz = { kernelName: ho, backendName: "webgpu", kernelFunc: zv }; -var Nie = xe({ opType: Q.CEIL, cpuKernelImpl: _B }); -var Oz = { kernelName: Jo, backendName: "webgpu", kernelFunc: Nie }; -var ux = class { +var aV = { kernelName: yo, backendName: "webgpu", kernelFunc: Xv }; +var iue = ye({ opType: Z.CEIL, cpuKernelImpl: JB }); +var iV = { kernelName: en, backendName: "webgpu", kernelFunc: iue }; +var px = class { constructor(e) { - this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.outputComponent = 4, this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4"; + this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.outputComponent = 4, this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); var clampedValue = clamp( @@ -29430,13 +29567,13 @@ var ux = class { `; } }; -var px = class { +var cx = class { constructor(e) { - this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip"; + this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { @@ -29449,18 +29586,18 @@ var px = class { `; } }; -function Tie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }]; - return y.sizeFromShape(n.shape) % 4 === 0 ? i = new ux(n.shape) : i = new px(n.shape), t10.runWebGPUProgram(i, [n], n.dtype, p); +function uue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }]; + return y.sizeFromShape(n.shape) % 4 === 0 ? i = new px(n.shape) : i = new cx(n.shape), t8.runWebGPUProgram(i, [n], n.dtype, p); } -var Mz = { kernelName: go, backendName: "webgpu", kernelFunc: Tie }; -var cx = class { +var uV = { kernelName: bo, backendName: "webgpu", kernelFunc: uue }; +var lx = class { constructor(e) { - this.outputShape = [], this.variableNames = ["real", "imag"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "complexAbs"; + this.outputShape = [], this.variableNames = ["real", "imag"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "complexAbs"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); let im = abs(getImagByOutputIndex(index)); @@ -29474,19 +29611,19 @@ var cx = class { `; } }; -function Lz(r, e) { +function pV(r, e) { return { dataId: e.dataId, dtype: e.dtype, shape: r.shape }; } -function _ie(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.tensorMap.get(o.dataId), s = new cx(o.shape), a = [Lz(o, n.complexTensorInfos.real), Lz(o, n.complexTensorInfos.imag)]; - return t10.runWebGPUProgram(s, a, a[0].dtype); +function pue(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e, n = t8.tensorMap.get(o.dataId), s = new lx(o.shape), a = [pV(o, n.complexTensorInfos.real), pV(o, n.complexTensorInfos.imag)]; + return t8.runWebGPUProgram(s, a, a[0].dtype); } -var Bz = { kernelName: _i, backendName: "webgpu", kernelFunc: _ie }; -var lx = class { +var cV = { kernelName: Di, backendName: "webgpu", kernelFunc: pue }; +var mx = class { constructor(e) { - this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1; - for (let t10 = 0; t10 < this.offsetLength; t10++) - this.uniforms += `offset${t10} : i32,`; + this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = w.computeOutShape(e, 1), this.variableNames = e.map((t8, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1; + for (let t8 = 0; t8 < this.offsetLength; t8++) + this.uniforms += `offset${t8} : i32,`; this.shaderKey = "concat"; } getUserCode() { @@ -29500,7 +29637,7 @@ var lx = class { } else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));"); return ` - ${K("index")} { + ${H("index")} { for(var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if(flatIndex < uniforms.size) { @@ -29516,63 +29653,63 @@ var lx = class { `; } }; -function Ep(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId); - return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 }); +function Fp(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.tensorMap.get(o.dataId); + return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t8 }); } -var zz = { kernelName: Mi, backendName: "webgpu", kernelFunc: Ep }; -function Xc(r, e, t10) { +var lV = { kernelName: Vi, backendName: "webgpu", kernelFunc: Fp }; +function Yc(r, e, t8) { let o = r[0].dtype; if (o === "complex64") { - let f = r.map((w) => Ci({ inputs: { input: w }, backend: t10 })), h = r.map((w) => Ep({ inputs: { input: w }, backend: t10 })), g = Xc(f, e, t10), x = Xc(h, e, t10), b = fo({ inputs: { real: g, imag: x }, backend: t10 }); - return f.forEach((w) => t10.disposeData(w.dataId)), h.forEach((w) => t10.disposeData(w.dataId)), t10.disposeData(g.dataId), t10.disposeData(x.dataId), b; + let f = r.map((C) => Ii({ inputs: { input: C }, backend: t8 })), h = r.map((C) => Fp({ inputs: { input: C }, backend: t8 })), g = Yc(f, e, t8), x = Yc(h, e, t8), b = xo({ inputs: { real: g, imag: x }, backend: t8 }); + return f.forEach((C) => t8.disposeData(C.dataId)), h.forEach((C) => t8.disposeData(C.dataId)), t8.disposeData(g.dataId), t8.disposeData(x.dataId), b; } - let n = t10.shouldExecuteOnCPU(r); + let n = t8.shouldExecuteOnCPU(r); if (o === "string" && (n = true), n) { let f = r.map((k) => { let E = [-1, y.sizeFromShape(k.shape.slice(e))]; - return pe({ inputs: { x: k }, backend: t10, attrs: { shape: E } }); - }), h = f.map((k) => ({ vals: t10.readSync(k.dataId), shape: k.shape })), g = C.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = $B(h, g, o, x), w = C.computeOutShape(r.map((k) => k.shape), e), S = t10.makeTensorInfo(w, o, b); - return f.forEach((k) => t10.disposeData(k.dataId)), S; + return pe({ inputs: { x: k }, backend: t8, attrs: { shape: E } }); + }), h = f.map((k) => ({ vals: t8.readSync(k.dataId), shape: k.shape })), g = w.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = ez(h, g, o, x), C = w.computeOutShape(r.map((k) => k.shape), e), S = t8.makeTensorInfo(C, o, b); + return f.forEach((k) => t8.disposeData(k.dataId)), S; } - let s = t10.device.limits.maxStorageBuffersPerShaderStage - 1; + let s = t8.device.limits.maxStorageBuffersPerShaderStage - 1; if (r.length > s) { let f = []; for (let g = 0; g < r.length; g += s) { let x = r.slice(g, g + s); - f.push(Xc(x, e, t10)); + f.push(Yc(x, e, t8)); } - let h = Xc(f, e, t10); + let h = Yc(f, e, t8); for (let g of f) - t10.disposeData(g.dataId); + t8.disposeData(g.dataId); return h; } - let { tensors2D: a, outShape: i } = $ie(r, e, t10), p = a.map((f) => f.shape), u = new lx(p), c = [], l = new Array(p.length - 1); + let { tensors2D: a, outShape: i } = cue(r, e, t8), p = a.map((f) => f.shape), u = new mx(p), c = [], l = new Array(p.length - 1); if (l.length > 0) { l[0] = p[0][1], c.push({ type: "int32", data: [l[0]] }); for (let f = 1; f < l.length; f++) l[f] = l[f - 1] + p[f][1], c.push({ type: "int32", data: [l[f]] }); } - let m = t10.runWebGPUProgram(u, a, a[0].dtype, c); - a.forEach((f) => t10.disposeData(f.dataId)); - let d = pe({ inputs: { x: m }, backend: t10, attrs: { shape: i } }); - return t10.disposeData(m.dataId), d; + let m = t8.runWebGPUProgram(u, a, a[0].dtype, c); + a.forEach((f) => t8.disposeData(f.dataId)); + let d = pe({ inputs: { x: m }, backend: t8, attrs: { shape: i } }); + return t8.disposeData(m.dataId), d; } -function $ie(r, e, t10) { - let o = C.computeOutShape(r.map((s) => s.shape), e); - return { tensors2D: r.map((s) => pe({ inputs: { x: s }, backend: t10, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o }; +function cue(r, e, t8) { + let o = w.computeOutShape(r.map((s) => s.shape), e); + return { tensors2D: r.map((s) => pe({ inputs: { x: s }, backend: t8, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o }; } -function Vv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); - C.assertParamsConsistent(a, s); - let i = C.computeOutShape(e.map((u) => u.shape), s); +function Yv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); + w.assertParamsConsistent(a, s); + let i = w.computeOutShape(e.map((u) => u.shape), s); if (y.sizeFromShape(i) === 0) - return t10.makeTensorInfo(i, e[0].dtype, []); + return t8.makeTensorInfo(i, e[0].dtype, []); let p = e.filter((u) => y.sizeFromShape(u.shape) > 0); - return p.length === 1 ? At({ inputs: { x: p[0] }, backend: t10 }) : Xc(p, s, t10); + return p.length === 1 ? At({ inputs: { x: p[0] }, backend: t8 }) : Yc(p, s, t8); } -var Vz = { kernelName: Ys, backendName: "webgpu", kernelFunc: Vv }; -function Eie(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { +var mV = { kernelName: ea, backendName: "webgpu", kernelFunc: Yv }; +function lue(r, e, t8, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { let c = (D) => { switch (D) { case 1: @@ -29628,14 +29765,14 @@ function Eie(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${c(i)} } - return resData;`, w = r ? e && o ? ` + return resData;`, C = r ? e && o ? ` let col = colIn * ${i}; ${b}` : ` let col = colIn * ${i}; if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${b} } - return ${Ae(i)}(0.0);` : o && t10 ? ` + return ${Ae(i)}(0.0);` : o && t8 ? ` let col = colIn * ${i}; ${b}` : ` let col = colIn * ${i}; @@ -29646,11 +29783,11 @@ function Eie(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) return ` ${dr(s, a, u === 4, 4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} { - ${r ? w : S} + ${r ? C : S} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} { - ${r ? S : w} + ${r ? S : C} } fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) { @@ -29660,26 +29797,26 @@ function Eie(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) var value = valueIn; let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"}; ${d} - ${jr(n, s)} + ${Qr(n, s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`; } -var mx = class { - constructor(e, t10, o, n, s = false, a = null, i = false, p = false) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = ((e.inChannels % 4 === 0 || e.inChannels % 3 === 0) && this.isChannelsLast || e.outWidth % 4 === 0 && !this.isChannelsLast) && e.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = im(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = um(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.outputComponent = 4, this.isChannelsLast && e.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableComponents = [1, 4]) : (this.innerElementSize = 4, this.variableComponents = [4, 4]), s && (this.variableNames.push("bias"), this.variableComponents.push(4)), i && (this.variableNames.push("preluActivationWeights"), this.variableComponents.push(4))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = t10 % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`; +var dx = class { + constructor(e, t8, o, n, s = false, a = null, i = false, p = false) { + this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = ((e.inChannels % 4 === 0 || e.inChannels % 3 === 0) && this.isChannelsLast || e.outWidth % 4 === 0 && !this.isChannelsLast) && e.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = um(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = pm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.outputComponent = 4, this.isChannelsLast && e.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableComponents = [1, 4]) : (this.innerElementSize = 4, this.variableComponents = [4, 4]), s && (this.variableNames.push("bias"), this.variableComponents.push(4)), i && (this.variableNames.push("preluActivationWeights"), this.variableComponents.push(4))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = t8 % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`; } getUserCode() { - let e = this.isVec4 ? Tp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : _p(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t10 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1]; + let e = this.isVec4 ? Rp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : Dp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t8 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1]; return ` - ${Eie(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t10[0], t10[1], t10[2])} + ${lue(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t8[0], t8[1], t8[2])} ${e} `; } }; -var dx = class { - constructor(e, t10 = false, o = null, n = false) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10, this.activation = o, this.hasPreluActivationWeights = n, t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`; +var fx = class { + constructor(e, t8 = false, o = null, n = false) { + this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t8, this.activation = o, this.hasPreluActivationWeights = n, t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`; } getUserCode() { return ` @@ -29704,11 +29841,11 @@ var dx = class { let coords = ${this.isChannelsLast ? "vec4(batch, row, col, chan);" : "vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${jr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } - ${K("index")} { + ${H("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast ? "coords[3];" : "coords[1];"} @@ -29731,15 +29868,15 @@ var dx = class { `; } }; -var fx = class { - constructor(e, t10) { +var hx = class { + constructor(e, t8) { this.variableNames = ["x"], this.uniforms = `pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, - inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t10, this.shaderKey = `im2col_${this.isChannelsLast}`; + inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t8, this.shaderKey = `im2col_${this.isChannelsLast}`; } getUserCode() { - let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? "coords[1]" : "coords[2]", n = this.isChannelsLast ? "coords[2]" : "coords[1]", s = this.isChannelsLast ? "getX(batch, xRow, xCol, ch)" : "getX(batch, ch, xRow, xCol)"; + let e = this.isChannelsLast ? 1 : 2, t8 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? "coords[1]" : "coords[2]", n = this.isChannelsLast ? "coords[2]" : "coords[1]", s = this.isChannelsLast ? "getX(batch, xRow, xCol, ch)" : "getX(batch, ch, xRow, xCol)"; return ` - ${K("index")} { + ${H("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; @@ -29754,7 +29891,7 @@ var fx = class { let xCol = offsetX + uniforms.dilations[1] * ((col % uniforms.itemsPerBlockRow) / uniforms.inChannels); let ch = col % uniforms.inChannels; - if(xCol < uniforms.xShape[${t10}] && xCol >= 0) { + if(xCol < uniforms.xShape[${t8}] && xCol >= 0) { value = ${s}; } } @@ -29764,84 +29901,84 @@ var fx = class { `; } }; -function hx(r, e) { - let t10 = r.length; - return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null; +function gx(r, e) { + let t8 = r.length; + return t8 >= 3 ? e ? [...r.slice(0, -3), r[t8 - 3] * r[t8 - 2], r[t8 - 1]] : [...r.slice(0, -3), r[t8 - 3], r[t8 - 2] * r[t8 - 1]] : !e && t8 === 1 && r[0] > 1 ? [r[0], 1] : null; } -function Rie({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = t10.dataFormat === "channelsLast", u = !p, c = false, l = p && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = [], d, f; +function mue({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = t8.dataFormat === "channelsLast", u = !p, c = false, l = p && t8.filterHeight === t8.inHeight && t8.filterWidth === t8.inWidth && t8.padInfo.type === "VALID", m = [], d, f; if (l) { - let x = t10.inHeight * t10.inWidth * t10.inChannels; - d = pe({ inputs: { x: r }, backend: o, attrs: { shape: [1, t10.batchSize, x] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t10.outChannels] } }); + let x = t8.inHeight * t8.inWidth * t8.inChannels; + d = pe({ inputs: { x: r }, backend: o, attrs: { shape: [1, t8.batchSize, x] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t8.outChannels] } }); } else - d = pe({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t10.batchSize, t10.inHeight * t10.inWidth, t10.inChannels] : [t10.batchSize, t10.inChannels, t10.inHeight * t10.inWidth] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }); + d = pe({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t8.batchSize, t8.inHeight * t8.inWidth, t8.inChannels] : [t8.batchSize, t8.inChannels, t8.inHeight * t8.inWidth] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, t8.inChannels, t8.outChannels] } }); if (m.push(d), m.push(f), s != null) { - let x = hx(s.shape, p); + let x = gx(s.shape, p); x != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s)); } if (n != null) { - let x = hx(n.shape, p); + let x = gx(n.shape, p); x != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n)); } - let h = $p({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = pe({ inputs: { x: h }, backend: o, attrs: { shape: t10.outShape } }); + let h = Ap({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = pe({ inputs: { x: h }, backend: o, attrs: { shape: t8.outShape } }); m.push(h); for (let x of m) o.disposeData(x.dataId); return g; } -function Die({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = t10, w = b === "channelsLast", S = p * u * c, k = h * f, _ = w ? [t10.batchSize, k, S] : [t10.batchSize, S, k], E = new fx(_, w), R = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], D = o.runWebGPUProgram(E, [r], r.dtype, R), F = []; - F.push(D); +function due({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = t8, C = b === "channelsLast", S = p * u * c, k = h * f, _ = C ? [t8.batchSize, k, S] : [t8.batchSize, S, k], E = new hx(_, C), R = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], D = o.runWebGPUProgram(E, [r], r.dtype, R), P = []; + P.push(D); let O = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, S, -1] } }); - if (F.push(O), s != null) { - let U = hx(s.shape, w); - U != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: U } }), F.push(s)); + if (P.push(O), s != null) { + let U = gx(s.shape, C); + U != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: U } }), P.push(s)); } if (n != null) { - let U = hx(n.shape, w); - U != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: U } }), F.push(n)); + let U = gx(n.shape, C); + U != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: U } }), P.push(n)); } - let B = $p({ a: w ? D : O, b: w ? O : D, transposeA: !w, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = pe({ inputs: { x: B }, backend: o, attrs: { shape: t10.outShape } }); - F.push(B); - for (let U of F) + let B = Ap({ a: C ? D : O, b: C ? O : D, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = pe({ inputs: { x: B }, backend: o, attrs: { shape: t8.outShape } }); + P.push(B); + for (let U of P) o.disposeData(U.dataId); return z; } -function gx({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = n != null, u = s != null, c = t10.dataFormat === "channelsLast", l = c && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = P().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG"); - if (!m && (l || t10.filterHeight === 1 && t10.filterWidth === 1 && t10.dilationHeight === 1 && t10.dilationWidth === 1 && t10.strideHeight === 1 && t10.strideWidth === 1 && (t10.padInfo.type === "SAME" || t10.padInfo.type === "VALID"))) - return Rie({ x: r, filter: e, convInfo: t10, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); - let d = P().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t10.batchSize * Math.ceil(t10.outHeight * t10.outWidth / 32) * Math.ceil(t10.outChannels / 32); - if (P().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f) - return Die({ x: r, filter: e, convInfo: t10, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i }); - let g, x = [t10.padInfo.top, t10.padInfo.left], b = [{ type: "int32", data: [t10.filterHeight, t10.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t10.strideHeight, t10.strideWidth] }, { type: "int32", data: [t10.dilationHeight, t10.dilationWidth] }]; +function xx({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = n != null, u = s != null, c = t8.dataFormat === "channelsLast", l = c && t8.filterHeight === t8.inHeight && t8.filterWidth === t8.inWidth && t8.padInfo.type === "VALID", m = A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG"); + if (!m && (l || t8.filterHeight === 1 && t8.filterWidth === 1 && t8.dilationHeight === 1 && t8.dilationWidth === 1 && t8.strideHeight === 1 && t8.strideWidth === 1 && (t8.padInfo.type === "SAME" || t8.padInfo.type === "VALID"))) + return mue({ x: r, filter: e, convInfo: t8, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); + let d = A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t8.batchSize * Math.ceil(t8.outHeight * t8.outWidth / 32) * Math.ceil(t8.outChannels / 32); + if (A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f) + return due({ x: r, filter: e, convInfo: t8, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i }); + let g, x = [t8.padInfo.top, t8.padInfo.left], b = [{ type: "int32", data: [t8.filterHeight, t8.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t8.strideHeight, t8.strideWidth] }, { type: "int32", data: [t8.dilationHeight, t8.dilationWidth] }]; if (m) - g = new dx(t10, p, i, u); + g = new fx(t8, p, i, u); else { - let _ = c ? t10.outHeight * t10.outWidth : t10.outChannels, E = c ? t10.outChannels : t10.outHeight * t10.outWidth, R = t10.filterHeight * t10.filterWidth * t10.inChannels; + let _ = c ? t8.outHeight * t8.outWidth : t8.outChannels, E = c ? t8.outChannels : t8.outHeight * t8.outWidth, R = t8.filterHeight * t8.filterWidth * t8.inChannels; b.push({ type: "int32", data: [_] }, { type: "int32", data: [E] }, { type: "int32", data: [R] }); let D = o.adapterInfo.isIntel(); - g = new mx(t10, _, E, R, p, i, u, D); + g = new dx(t8, _, E, R, p, i, u, D); } - let w = [], S = [r, e]; - p && (!c && n.shape.length === 1 && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), w.push(n)), S.push(n)), u && (!c && s.shape.length === 1 && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), w.push(s)), S.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,"); + let C = [], S = [r, e]; + p && (!c && n.shape.length === 1 && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), C.push(n)), S.push(n)), u && (!c && s.shape.length === 1 && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), C.push(s)), S.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,"); let k = o.runWebGPUProgram(g, S, r.dtype, b); - for (let _ of w) + for (let _ of C) o.disposeData(_.dataId); return k; } -function Aie(r) { - let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t10, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l); - return gx({ x: n, filter: s, convInfo: m, backend: o }); +function fue(r) { + let { inputs: e, attrs: t8, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t8, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l); + return xx({ x: n, filter: s, convInfo: m, backend: o }); } -var Wz = { kernelName: en, backendName: "webgpu", kernelFunc: Aie }; -var xx = class { +var dV = { kernelName: tn, backendName: "webgpu", kernelFunc: fue }; +var yx = class { constructor(e) { - this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,", this.workgroupSize = [64, 1, 1], this.size = false, this.isVec4 = false, this.workPerThread = 1, this.outputShape = e.inShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = this.isChannelsLast && e.outChannels % 4 === 0 && e.inChannels % 4 === 0, this.isVec4 ? (this.workPerThread = 2, this.outputComponent = 4, this.workgroupSize = [4, 4, 4], this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1])) : (this.size = true, this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize)), this.shaderKey = `conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`; + this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,", this.workgroupSize = [64, 1, 1], this.size = false, this.isVec4 = false, this.workPerThread = 1, this.outputShape = e.inShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = this.isChannelsLast && e.outChannels % 4 === 0 && e.inChannels % 4 === 0, this.isVec4 ? (this.workPerThread = 2, this.outputComponent = 4, this.workgroupSize = [4, 4, 4], this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1])) : (this.size = true, this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize)), this.shaderKey = `conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`; } getUserCode() { - let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1, n = ` - ${K()} { + let e = this.isChannelsLast ? 1 : 2, t8 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1, n = ` + ${H()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; @@ -29943,13 +30080,13 @@ var xx = class { return this.isVec4 ? ` ${n} ` : ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d1 = coords[${o}]; - let dyCorner = vec2(coords[${e}], coords[${t10}]) - uniforms.pads; + let dyCorner = vec2(coords[${e}], coords[${t8}]) - uniforms.pads; let dyRCorner = dyCorner.x; let dyCCorner = dyCorner.y; @@ -29987,13 +30124,13 @@ var xx = class { `; } }; -var yx = class { +var bx = class { constructor(e) { - this.variableNames = ["x", "dy"], this.uniforms = "pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`; + this.variableNames = ["x", "dy"], this.uniforms = "pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; @@ -30036,14 +30173,14 @@ var yx = class { `; } }; -var bx = class { +var Cx = class { constructor(e) { this.variableNames = ["x", "dy"], this.uniforms = `pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, - outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerFilter"; + outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerFilter"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wF = coords.x; @@ -30085,14 +30222,14 @@ var bx = class { `; } }; -var Cx = class { +var wx = class { constructor(e) { this.variableNames = ["dy", "W"], this.uniforms = `filterDims : vec3, pads : vec3, strides : vec3, - outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerInput"; + outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerInput"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -30147,12 +30284,12 @@ var Cx = class { `; } }; -function Fie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new yx(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }]; - return t10.runWebGPUProgram(d, [n, s], n.dtype, f); +function hue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new bx(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }]; + return t8.runWebGPUProgram(d, [n, s], n.dtype, f); } -var Uz = { kernelName: $i, backendName: "webgpu", kernelFunc: Fie }; -function Pie(r = 4) { +var fV = { kernelName: Ai, backendName: "webgpu", kernelFunc: hue }; +function gue(r = 4) { let e = (s) => { switch (s) { case 1: @@ -30228,37 +30365,37 @@ function Pie(r = 4) { } }`; } -var wx = class { +var Sx = class { constructor(e) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = e.inChannels % 4 === 0 && e.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = im(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = um(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.outputComponent = 4, this.variableComponents = [4, 1]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`; + this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = e.inChannels % 4 === 0 && e.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = um(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = pm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.outputComponent = 4, this.variableComponents = [4, 1]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`; } getUserCode() { - let e = this.isVec4 ? Tp(this.elementsPerThread, this.workgroupSize) : _p(this.elementsPerThread, this.workgroupSize); + let e = this.isVec4 ? Rp(this.elementsPerThread, this.workgroupSize) : Dp(this.elementsPerThread, this.workgroupSize); return ` - ${Pie(this.isVec4 ? 4 : 1)} + ${gue(this.isVec4 ? 4 : 1)} ${e} `; } }; -function Oie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(u), m = C.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f; - if (P().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.dataFormat !== "channelsLast") - f = new xx(m); +function xue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f; + if (A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.dataFormat !== "channelsLast") + f = new yx(m); else { - f = new wx(m); + f = new Sx(m); let h = m.inHeight * m.inWidth, g = m.inChannels, x = m.filterHeight * m.filterWidth * m.outChannels; d.push({ type: "uint32", data: [h] }, { type: "uint32", data: [g] }, { type: "uint32", data: [x] }); } - return t10.runWebGPUProgram(f, [n, s], "float32", d); + return t8.runWebGPUProgram(f, [n, s], "float32", d); } -var Gz = { kernelName: tn, backendName: "webgpu", kernelFunc: Oie }; -var Sx = class { +var hV = { kernelName: rn, backendName: "webgpu", kernelFunc: xue }; +var Ix = class { constructor(e) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3dnaive"; + this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3dnaive"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords.x; @@ -30342,35 +30479,35 @@ var Sx = class { }`; } }; -function Mie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), c = [u.padInfo.front, u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationDepth, u.dilationHeight, u.dilationWidth] }], m = new Sx(u), d = dt(n.dtype, s.dtype); - return t10.runWebGPUProgram(m, [n, s], d, l); +function yue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = [u.padInfo.front, u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationDepth, u.dilationHeight, u.dilationWidth] }], m = new Ix(u), d = dt(n.dtype, s.dtype); + return t8.runWebGPUProgram(m, [n, s], d, l); } -var Hz = { kernelName: rn, backendName: "webgpu", kernelFunc: Mie }; -function Lie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = C.computeConv3DInfo(n.shape, p, a, 1, i), c = new bx(u), l = [{ type: "int32", data: [u.padInfo.front, u.padInfo.top, u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.batchSize] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.inDepth] }, { type: "int32", data: [u.inHeight] }, { type: "int32", data: [u.inWidth] }]; - return t10.runWebGPUProgram(c, [n, s], s.dtype, l); +var gV = { kernelName: on, backendName: "webgpu", kernelFunc: yue }; +function bue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Cx(u), l = [{ type: "int32", data: [u.padInfo.front, u.padInfo.top, u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.batchSize] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.inDepth] }, { type: "int32", data: [u.inHeight] }, { type: "int32", data: [u.inWidth] }]; + return t8.runWebGPUProgram(c, [n, s], s.dtype, l); } -var Kz = { kernelName: za, backendName: "webgpu", kernelFunc: Lie }; -function Bie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, pad: i, inputShape: p } = o, u = C.computeConv3DInfo(p, s.shape, a, 1, i), c = new Cx(u), l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [u.filterDepth - 1 - u.padInfo.front, u.filterHeight - 1 - u.padInfo.top, u.filterWidth - 1 - u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.outChannels] }]; - return t10.runWebGPUProgram(c, [n, s], n.dtype, l); +var xV = { kernelName: Ka, backendName: "webgpu", kernelFunc: bue }; +function Cue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, pad: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, a, 1, i), c = new wx(u), l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [u.filterDepth - 1 - u.padInfo.front, u.filterHeight - 1 - u.padInfo.top, u.filterWidth - 1 - u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.outChannels] }]; + return t8.runWebGPUProgram(c, [n, s], n.dtype, l); } -var qz = { kernelName: on, backendName: "webgpu", kernelFunc: Bie }; -var zie = xe({ opType: Q.COS }); -var jz = { kernelName: nn, backendName: "webgpu", kernelFunc: zie }; -var Vie = xe({ opType: Q.COSH }); -var Xz = { kernelName: sn, backendName: "webgpu", kernelFunc: Vie }; -var Ix = class { - constructor(e, t10, o, n) { +var yV = { kernelName: nn, backendName: "webgpu", kernelFunc: Cue }; +var wue = ye({ opType: Z.COS }); +var bV = { kernelName: sn, backendName: "webgpu", kernelFunc: wue }; +var Sue = ye({ opType: Z.COSH }); +var CV = { kernelName: an, backendName: "webgpu", kernelFunc: Sue }; +var vx = class { + constructor(e, t8, o, n) { this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true; - let [s] = t10; - this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.methodId = n === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`; + let [s] = t8; + this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.methodId = n === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`; } getUserCode() { - let [e, t10] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [o, n, s] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t10} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t10} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t10}`]; + let [e, t8] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [o, n, s] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t8} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t8} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t8}`]; return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${o}); @@ -30397,7 +30534,7 @@ var Ix = class { return; } let in_x = ${p}; - if( in_x < 0.0 || in_x > ${t10} ) { + if( in_x < 0.0 || in_x > ${t8} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } @@ -30428,33 +30565,33 @@ var Ix = class { `; } }; -var Wie = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Ix(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }]; - return t10.runWebGPUProgram(c, [n, s, a], "float32", l); +var Iue = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new vx(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }]; + return t8.runWebGPUProgram(c, [n, s, a], "float32", l); }; -var Yz = { kernelName: pn, backendName: "webgpu", kernelFunc: Wie }; -var Rp; +var wV = { kernelName: cn, backendName: "webgpu", kernelFunc: Iue }; +var Pp; (function(r) { r.Prod = "*", r.Sum = "+"; -})(Rp || (Rp = {})); -var dm = class { - constructor(e, t10, o, n) { - this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`; +})(Pp || (Pp = {})); +var fm = class { + constructor(e, t8, o, n) { + this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`; } getUserCode() { - let e = this.outputShape.length, t10 = this.op === Rp.Prod ? "1.0" : "0.0", o = this.exclusive ? t10 : `getX(${Qz(e, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = ""; + let e = this.outputShape.length, t8 = this.op === Pp.Prod ? "1.0" : "0.0", o = this.exclusive ? t8 : `getX(${SV(e, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = ""; return this.exclusive ? (s = this.reverse ? `end != ${n - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (s = this.reverse ? `end + pow2 < ${n}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${Zz(e, "coords", this.op)}; + let end = ${IV(e, "coords", this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; - ${Zz(e, "coords", this.op)} = idx; - val ${this.op}= getX(${Qz(e, "coords", this.op)}); + ${IV(e, "coords", this.op)} = idx; + val ${this.op}= getX(${SV(e, "coords", this.op)}); } setOutputAtIndex(index, val); } @@ -30462,7 +30599,7 @@ var dm = class { `; } }; -function Qz(r, e, t10) { +function SV(r, e, t8) { if (r === 1) return `${e}`; if (r === 2) @@ -30471,9 +30608,9 @@ function Qz(r, e, t10) { return `${e}.x, ${e}.y, ${e}.z`; if (r === 4) return `${e}.x, ${e}.y, ${e}.z, ${e}.w`; - throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`); + throw Error(`Cumulative ${t8} for rank ${r} is not yet supported`); } -function Zz(r, e, t10) { +function IV(r, e, t8) { if (r === 1) return `${e}`; if (r === 2) @@ -30482,51 +30619,51 @@ function Zz(r, e, t10) { return `${e}.z`; if (r === 4) return `${e}.w`; - throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`); + throw Error(`Cumulative ${t8} for rank ${r} is not yet supported`); } -function vx(r, e, t10, o, n, s) { - let a = e.shape.length, i = C.getAxesPermutation([o], a), p = e; - i != null && (p = rr({ inputs: { x: e }, backend: t10, attrs: { perm: i } })); - let u = C.getInnerMostAxes(1, a)[0]; +function kx(r, e, t8, o, n, s) { + let a = e.shape.length, i = w.getAxesPermutation([o], a), p = e; + i != null && (p = yr({ inputs: { x: e }, backend: t8, attrs: { perm: i } })); + let u = w.getInnerMostAxes(1, a)[0]; if (u !== a - 1) throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`); - let c = p.shape[u], l = At({ inputs: { x: p }, backend: t10 }); + let c = p.shape[u], l = At({ inputs: { x: p }, backend: t8 }); for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) { - let d = new dm(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }]; - l = t10.runWebGPUProgram(d, [l], l.dtype, h), t10.disposeData(f.dataId); + let d = new fm(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }]; + l = t8.runWebGPUProgram(d, [l], l.dtype, h), t8.disposeData(f.dataId); } if (n) { - let m = new dm(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }]; - l = t10.runWebGPUProgram(m, [l], l.dtype, f), t10.disposeData(d.dataId); + let m = new fm(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }]; + l = t8.runWebGPUProgram(m, [l], l.dtype, f), t8.disposeData(d.dataId); } if (i != null) { - let m = C.getUndoAxesPermutation(i), d = rr({ inputs: { x: l }, backend: t10, attrs: { perm: m } }); - return t10.disposeData(l.dataId), t10.disposeData(p.dataId), d; + let m = w.getUndoAxesPermutation(i), d = yr({ inputs: { x: l }, backend: t8, attrs: { perm: m } }); + return t8.disposeData(l.dataId), t8.disposeData(p.dataId), d; } return l; } -function Uie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return vx(Rp.Prod, n, t10, s, a, i); +function vue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return kx(Pp.Prod, n, t8, s, a, i); } -var Jz = { kernelName: an, backendName: "webgpu", kernelFunc: Uie }; -function Gie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return vx(Rp.Sum, n, t10, s, a, i); +var vV = { kernelName: un, backendName: "webgpu", kernelFunc: vue }; +function kue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return kx(Pp.Sum, n, t8, s, a, i); } -var eV = { kernelName: un, backendName: "webgpu", kernelFunc: Gie }; -function Hie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = Vt({ backend: t10, attrs: { shape: d, value: 0, dtype: l } }), h = new jc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n]; - return t10.runWebGPUProgram(h, x, l, g, f); +var kV = { kernelName: pn, backendName: "webgpu", kernelFunc: kue }; +function Nue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = Vt({ backend: t8, attrs: { shape: d, value: 0, dtype: l } }), h = new Xc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n]; + return t8.runWebGPUProgram(h, x, l, g, f); } -var tV = { kernelName: Qs, backendName: "webgpu", kernelFunc: Hie }; -var kx = class { - constructor(e, t10) { - this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t10}`, this.dataFormat = t10; +var NV = { kernelName: ta, backendName: "webgpu", kernelFunc: Nue }; +var Nx = class { + constructor(e, t8) { + this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t8}`, this.dataFormat = t8; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -30563,17 +30700,17 @@ var kx = class { return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)"; } }; -function Kie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new kx(f, a); - return t10.runWebGPUProgram(g, [n], n.dtype, h); +function Tue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new Nx(f, a); + return t8.runWebGPUProgram(g, [n], n.dtype, h); } -var rV = { kernelName: cn, backendName: "webgpu", kernelFunc: Kie }; -var Nx = class { - constructor(e, t10, o, n = false, s = null, a = false) { - this.variableNames = ["x", "W"], this.uniforms = "pads : vec2, inDims : vec2,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t10, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`; +var TV = { kernelName: ln, backendName: "webgpu", kernelFunc: Tue }; +var Tx = class { + constructor(e, t8, o, n = false, s = null, a = false) { + this.variableNames = ["x", "W"], this.uniforms = "pads : vec2, inDims : vec2,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t8, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`; } getUserCode() { - let e = this.filterWidth * this.filterHeight, t10 = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1; + let e = this.filterWidth * this.filterHeight, t8 = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1; return ` ${dr(this.activation, this.hasPreluActivation, false, 4)} @@ -30588,7 +30725,7 @@ var Nx = class { return value; } - ${K()} { + ${H()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pads; @@ -30613,7 +30750,7 @@ var Nx = class { // Load one tile of W into local memory. var wIndex = i32(localIndex); - ${e < t10 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t10})`} + ${e < t8 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t8})`} { let wRow = wIndex / ${this.filterWidth}; @@ -30631,7 +30768,7 @@ var Nx = class { value = fma(xVal, wVal, value); } } - ${jr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } @@ -30639,12 +30776,14 @@ var Nx = class { `; } }; -var Yc = class { - constructor(e, t10 = false, o = null, n = false) { - this.variableNames = ["x", "W"], this.uniforms = "pads : vec2, inDims : vec2,", this.workgroupSize = [4, 4, 4], this.workPerThread = 4, this.outputComponent = 4, this.outputShape = e.outShape, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`; +var Qc = class { + constructor(e, t8 = false, o = null, n = false) { + this.variableNames = ["x", "W"], this.uniforms = "pads : vec2, inDims : vec2, virtualWidth : i32,", this.workgroupSize = [64, 1, 1], this.workPerThread = 4, this.outputComponent = 4, this.outputShape = e.outShape, this.virtualWidth = Math.ceil(this.outputShape[2] / this.workPerThread) * this.workPerThread; + let s = [this.outputShape[0], this.outputShape[1], this.virtualWidth, this.outputShape[3]]; + this.dispatchLayout = X(s), this.dispatch = K(this.dispatchLayout, s, this.workgroupSize, [this.outputComponent * this.workPerThread, 1, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t8, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`; } getUserCode() { - let e = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth, t10 = this.convInfo.strideHeight, o = this.convInfo.strideWidth; + let e = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth, t8 = this.convInfo.strideHeight, o = this.convInfo.strideWidth; return ` ${dr(this.activation, this.hasPreluActivation, true, 4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { @@ -30655,12 +30794,17 @@ var Yc = class { return value; } - ${K()} { - let batch = i32(globalId.z) / uniforms.outShape[1]; - let r = i32(globalId.z) % uniforms.outShape[1]; - let c = i32(globalId.y) * ${this.workPerThread}; - let d1 = i32(globalId.x) * 4; - let xRCCorner = vec2(r, c) * vec2(${t10}, ${o}) - uniforms.pads; + ${H("index")} { + let width0 = uniforms.outShape[3] / ${this.outputComponent}; + let d1 = (index % width0) * ${this.outputComponent}; + var index1 = index / width0; + let width1 = uniforms.virtualWidth / ${this.workPerThread}; + let c = (index1 % width1) * ${this.workPerThread}; + index1 = index1 / width1; + let r = index1 % uniforms.outShape[1]; + let batch = index1 / uniforms.outShape[1]; + + let xRCCorner = vec2(r, c) * vec2(${t8}, ${o}) - uniforms.pads; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; @@ -30690,7 +30834,7 @@ var Yc = class { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${jr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } @@ -30698,17 +30842,17 @@ var Yc = class { `; } }; -var Qc = class { - constructor(e, t10 = false, o = null, n = false) { +var Zc = class { + constructor(e, t8 = false, o = null, n = false) { this.variableNames = ["x", "W"], this.uniforms = `pads : vec2, inDims : vec2, filterHeight : i32, - filterWidth : i32, strides : vec2, dilations : vec2,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`; + filterWidth : i32, strides : vec2, dilations : vec2,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t8, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`; } getUserCode() { let e = this.isChannelsLast ? "getX(batch, xR, xC, d1);" : "getX(batch, d1, xR, xC);"; return ` ${dr(this.activation, this.hasPreluActivation, false, 4)} - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -30767,28 +30911,28 @@ var Qc = class { } } } - ${jr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `; } }; -function qie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(p), m = u; +function _ue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = u; m == null && (m = [1, 1]); - let d = C.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), f = [{ type: "int32", data: [d.padInfo.top, d.padInfo.left] }, { type: "int32", data: [d.inHeight, d.inWidth] }], h = d.dataFormat === "channelsLast", g; - return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new Nx(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? g = new Yc(d) : (g = new Qc(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), t10.runWebGPUProgram(g, [n, s], n.dtype, f); + let d = w.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), f = [{ type: "int32", data: [d.padInfo.top, d.padInfo.left] }, { type: "int32", data: [d.inHeight, d.inWidth] }], h = d.dataFormat === "channelsLast", g; + return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new Tx(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? (g = new Qc(d), f.push({ type: "int32", data: [g.virtualWidth] })) : (g = new Zc(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), t8.runWebGPUProgram(g, [n, s], n.dtype, f); } -var oV = { kernelName: ln, backendName: "webgpu", kernelFunc: qie }; -var Tx = class { +var _V = { kernelName: mn, backendName: "webgpu", kernelFunc: _ue }; +var _x = class { constructor(e) { this.variableNames = ["x", "dy"], this.uniforms = `strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, - outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_filter"; + outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_filter"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; @@ -30825,14 +30969,14 @@ var Tx = class { `; } }; -var _x = class { +var $x = class { constructor(e) { this.variableNames = ["dy", "W"], this.uniforms = `strides : vec2, pads : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_input"; + outHeight : i32, outWidth : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_input"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -30876,23 +31020,23 @@ var _x = class { `; } }; -function jie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = C.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Tx(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.inHeight] }, { type: "int32", data: [l.inWidth] }, { type: "int32", data: [l.batchSize] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; - return t10.runWebGPUProgram(m, [n, s], "float32", d); +function $ue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new _x(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.inHeight] }, { type: "int32", data: [l.inWidth] }, { type: "int32", data: [l.batchSize] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; + return t8.runWebGPUProgram(m, [n, s], "float32", d); } -var nV = { kernelName: Ei, backendName: "webgpu", kernelFunc: jie }; -function Xie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = C.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new _x(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.filterHeight - 1 - l.padInfo.top, l.filterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; - return t10.runWebGPUProgram(m, [n, s], n.dtype, d); +var $V = { kernelName: Fi, backendName: "webgpu", kernelFunc: $ue }; +function Eue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new $x(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.filterHeight - 1 - l.padInfo.top, l.filterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; + return t8.runWebGPUProgram(m, [n, s], n.dtype, d); } -var sV = { kernelName: Ri, backendName: "webgpu", kernelFunc: Xie }; -var $x = class { +var EV = { kernelName: Pi, backendName: "webgpu", kernelFunc: Eue }; +var Ex = class { constructor(e) { - this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag"; + this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, e], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); @@ -30902,18 +31046,18 @@ var $x = class { `; } }; -function Yie(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = pe({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new $x(s), p = t10.runWebGPUProgram(i, [a], a.dtype), u = pe({ inputs: { x: p }, backend: t10, attrs: { shape: n } }); - return t10.disposeData(a.dataId), t10.disposeData(p.dataId), u; +function Rue(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = pe({ inputs: { x: o }, backend: t8, attrs: { shape: [s] } }), i = new Ex(s), p = t8.runWebGPUProgram(i, [a], a.dtype), u = pe({ inputs: { x: p }, backend: t8, attrs: { shape: n } }); + return t8.disposeData(a.dataId), t8.disposeData(p.dataId), u; } -var aV = { kernelName: Zs, backendName: "webgpu", kernelFunc: Yie }; -var Ex = class { +var RV = { kernelName: ra, backendName: "webgpu", kernelFunc: Rue }; +var Rx = class { constructor(e) { - this.variableNames = ["x", "w"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d"; + this.variableNames = ["x", "w"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; let coords = getOutputCoords(); @@ -30947,21 +31091,21 @@ var Ex = class { `; } }; -function Qie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new Ex(u); - return t10.runWebGPUProgram(m, [n, s], n.dtype, l); +function Due(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new Rx(u); + return t8.runWebGPUProgram(m, [n, s], n.dtype, l); } -var iV = { kernelName: mn, backendName: "webgpu", kernelFunc: Qie }; -var Rx = class { - constructor(e, t10) { - if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e.inShape, this.dispatchLayout = Z(e.outShape), this.dispatch = q(this.dispatchLayout, e.outShape, this.workgroupSize), t10 !== "float32" && t10 !== "int32") +var DV = { kernelName: dn, backendName: "webgpu", kernelFunc: Due }; +var Dx = class { + constructor(e, t8) { + if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e.inShape, this.dispatchLayout = X(e.outShape), this.dispatch = K(this.dispatchLayout, e.outShape, this.workgroupSize), t8 !== "float32" && t8 !== "int32") throw new Error(`Dilation2DBackpropInput only supports float32 and int32 - types, does not support ${t10} type.`); - this.type = t10, this.shaderKey = "dilation2DBackpropInput"; + types, does not support ${t8} type.`); + this.type = t8, this.shaderKey = "dilation2DBackpropInput"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; @@ -31000,22 +31144,22 @@ var Rx = class { let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${Bs("&result[flatIndexIn]", "value", this.type)} + ${Us("&result[flatIndexIn]", "value", this.type)} } } `; } }; -var Dx = class { - constructor(e, t10, o) { - if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(e.outShape), this.dispatch = q(this.dispatchLayout, e.outShape, this.workgroupSize), o !== "float32" && o !== "int32") +var Ax = class { + constructor(e, t8, o) { + if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e.filterShape, this.dispatchLayout = X(e.outShape), this.dispatch = K(this.dispatchLayout, e.outShape, this.workgroupSize), o !== "float32" && o !== "int32") throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 types, does not support ${o} type.`); this.type = o, this.shaderKey = "dilation2DBackpropFilter"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; @@ -31053,72 +31197,72 @@ var Dx = class { let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${Bs("&result[flatIndexIn]", "value", this.type)} + ${Us("&result[flatIndexIn]", "value", this.type)} } } `; } }; -function Zie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = s.dtype, m = new Dx(c, s.shape, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = Vt({ backend: t10, attrs: { shape: s.shape, value: 0, dtype: l } }); - return t10.runWebGPUProgram(m, [n, s, a], l, d, f); +function Aue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = s.dtype, m = new Ax(c, s.shape, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = Vt({ backend: t8, attrs: { shape: s.shape, value: 0, dtype: l } }); + return t8.runWebGPUProgram(m, [n, s, a], l, d, f); } -var uV = { kernelName: Ai, backendName: "webgpu", kernelFunc: Zie }; -function Jie(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = n.dtype, m = new Rx(c, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = Vt({ backend: t10, attrs: { shape: c.inShape, value: 0, dtype: l } }); - return t10.runWebGPUProgram(m, [n, s, a], l, d, f); +var AV = { kernelName: Mi, backendName: "webgpu", kernelFunc: Aue }; +function Fue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = n.dtype, m = new Dx(c, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = Vt({ backend: t8, attrs: { shape: c.inShape, value: 0, dtype: l } }); + return t8.runWebGPUProgram(m, [n, s, a], l, d, f); } -var pV = { kernelName: Di, backendName: "webgpu", kernelFunc: Jie }; -var Wv = et({ opType: fe.MUL, cpuKernelImpl: HB, supportsComplex: true }); -var cV = { kernelName: Kn, backendName: "webgpu", kernelFunc: Wv }; -function Uv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return Yr(n, s, a, "sum", t10); +var FV = { kernelName: Oi, backendName: "webgpu", kernelFunc: Fue }; +var Qv = et({ opType: fe.MUL, cpuKernelImpl: gz, supportsComplex: true }); +var PV = { kernelName: Xn, backendName: "webgpu", kernelFunc: Qv }; +function Zv(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return Jr(n, s, a, "sum", t8); } -var lV = { kernelName: ys, backendName: "webgpu", kernelFunc: Uv }; -function eue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = C.decodeEinsumEquation(n, s.length); - C.checkEinsumDimSizes(a.length, p, s); - let { path: u, steps: c } = C.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; +var OV = { kernelName: Ss, backendName: "webgpu", kernelFunc: Zv }; +function Pue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); + w.checkEinsumDimSizes(a.length, p, s); + let { path: u, steps: c } = w.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { - let { permutationIndices: x, expandDims: b } = C.getEinsumPermutation(d, p[g]), w; - C.isIdentityPermutation(x) ? w = s[g] : (w = rr({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(w)); - let S = w.shape.slice(); + let { permutationIndices: x, expandDims: b } = w.getEinsumPermutation(d, p[g]), C; + w.isIdentityPermutation(x) ? C = s[g] : (C = yr({ inputs: { x: s[g] }, backend: t8, attrs: { perm: x } }), f.push(C)); + let S = C.shape.slice(); for (let k = 0; k < b.length; ++k) S.splice(b[k], 0, 1); - y.arraysEqual(w.shape, S) || (w = pe({ inputs: { x: w }, backend: t10, attrs: { shape: S } }), f.push(w)), m === null ? m = w : (m = Wv({ inputs: { a: w, b: m }, backend: t10 }), f.push(m)); + y.arraysEqual(C.shape, S) || (C = pe({ inputs: { x: C }, backend: t8, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = Qv({ inputs: { a: C, b: m }, backend: t8 }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = Uv({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = Zv({ inputs: { x: m }, backend: t8, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) - h !== m && t10.disposeData(h.dataId); + h !== m && t8.disposeData(h.dataId); return m; } -var mV = { kernelName: Fi, backendName: "webgpu", kernelFunc: eue }; -var tue = xe({ opType: Q.ELU }); -var dV = { kernelName: fn, backendName: "webgpu", kernelFunc: tue }; -var rue = (r) => { - let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = new bi(fe.ELU_DER, o.shape, n.shape); - return t10.runWebGPUProgram(s, [o, n], o.dtype); +var MV = { kernelName: Li, backendName: "webgpu", kernelFunc: Pue }; +var Oue = ye({ opType: Z.ELU }); +var LV = { kernelName: hn, backendName: "webgpu", kernelFunc: Oue }; +var Mue = (r) => { + let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e, s = new Si(fe.ELU_DER, o.shape, n.shape); + return t8.runWebGPUProgram(s, [o, n], o.dtype); }; -var fV = { kernelName: Va, backendName: "webgpu", kernelFunc: rue }; -var oue = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: EB }); -var hV = { kernelName: hn, backendName: "webgpu", kernelFunc: oue }; -var nue = xe({ opType: Q.ERF }); -var gV = { kernelName: Wa, backendName: "webgpu", kernelFunc: nue }; -var sue = xe({ opType: Q.EXP, cpuKernelImpl: RB, dtype: "float32" }); -var xV = { kernelName: gn, backendName: "webgpu", kernelFunc: sue }; -function Ax(r) { - let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; +var BV = { kernelName: qa, backendName: "webgpu", kernelFunc: Mue }; +var Lue = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: tz }); +var zV = { kernelName: xn, backendName: "webgpu", kernelFunc: Lue }; +var Bue = ye({ opType: Z.ERF }); +var VV = { kernelName: gn, backendName: "webgpu", kernelFunc: Bue }; +var zue = ye({ opType: Z.EXP, cpuKernelImpl: rz, dtype: "float32" }); +var WV = { kernelName: yn, backendName: "webgpu", kernelFunc: zue }; +function Fx(r) { + let { inputs: e, attrs: t8, backend: o } = r, { dim: n } = t8, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), pe({ inputs: { x: s }, backend: o, attrs: { shape: i } }); } -var yV = { kernelName: Js, backendName: "webgpu", kernelFunc: Ax }; -var aue = xe({ opType: Q.EXPM1, cpuKernelImpl: DB }); -var bV = { kernelName: xn, backendName: "webgpu", kernelFunc: aue }; -var fm = class { - constructor(e, t10) { - this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${e}`; +var UV = { kernelName: oa, backendName: "webgpu", kernelFunc: Fx }; +var Vue = ye({ opType: Z.EXPM1, cpuKernelImpl: oz }); +var GV = { kernelName: bn, backendName: "webgpu", kernelFunc: Vue }; +var hm = class { + constructor(e, t8) { + this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${e}`; } getUserCode() { return ` @@ -31148,7 +31292,7 @@ var fm = class { return result; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); @@ -31157,30 +31301,30 @@ var fm = class { `; } }; -function Fx(r, e, t10) { - let o = t10.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = pe({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }); +function Px(r, e, t8) { + let o = t8.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = pe({ inputs: { x: r }, backend: t8, attrs: { shape: [a, s] } }); i.push(p); - let u = p.shape, c = new fm("real", u), l = new fm("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = e ? 2 * Math.PI : -2 * Math.PI, f = e ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = t10.runWebGPUProgram(c, m, "float32", h); + let u = p.shape, c = new hm("real", u), l = new hm("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = e ? 2 * Math.PI : -2 * Math.PI, f = e ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = t8.runWebGPUProgram(c, m, "float32", h); i.push(g); - let x = t10.runWebGPUProgram(l, m, "float32", h); + let x = t8.runWebGPUProgram(l, m, "float32", h); i.push(x); - let b = fo({ inputs: { real: g, imag: x }, backend: t10 }); + let b = xo({ inputs: { real: g, imag: x }, backend: t8 }); i.push(b); - let w = pe({ inputs: { x: b }, backend: t10, attrs: { shape: r.shape } }); - return i.forEach((S) => t10.disposeData(S.dataId)), w; + let C = pe({ inputs: { x: b }, backend: t8, attrs: { shape: r.shape } }); + return i.forEach((S) => t8.disposeData(S.dataId)), C; } -function iue(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e; - return Fx(o, false, t10); +function Wue(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e; + return Px(o, false, t8); } -var CV = { kernelName: Pi, backendName: "webgpu", kernelFunc: iue }; -var Px = class { +var HV = { kernelName: Bi, backendName: "webgpu", kernelFunc: Wue }; +var Ox = class { constructor(e) { - this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight"; + this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; @@ -31191,23 +31335,23 @@ var Px = class { `; } }; -var wV = { kernelName: yn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { - let { image: t10 } = r, o = e, n = new Px(t10.shape); - return o.runWebGPUProgram(n, [t10], t10.dtype); +var KV = { kernelName: Cn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { + let { image: t8 } = r, o = e, n = new Ox(t8.shape); + return o.runWebGPUProgram(n, [t8], t8.dtype); } }; -var uue = xe({ opType: Q.FLOOR, cpuKernelImpl: AB }); -var SV = { kernelName: bn, backendName: "webgpu", kernelFunc: uue }; -var pue = et({ opType: fe.INT_DIV, cpuKernelImpl: FB, dtype: "int32" }); -var IV = { kernelName: Cn, backendName: "webgpu", kernelFunc: pue }; -var Ox = class { - constructor(e, t10, o = false) { - this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [t10, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`; +var Uue = ye({ opType: Z.FLOOR, cpuKernelImpl: nz }); +var qV = { kernelName: wn, backendName: "webgpu", kernelFunc: Uue }; +var Gue = et({ opType: fe.INT_DIV, cpuKernelImpl: sz, dtype: "int32" }); +var jV = { kernelName: Sn, backendName: "webgpu", kernelFunc: Gue }; +var Mx = class { + constructor(e, t8, o = false) { + this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [t8, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`; } getUserCode() { let e = this.importVideo ? "textureLoad(src, vec2(coords.yx));" : "textureLoad(src, vec2(coords.yx), 0)"; return ` @binding(1) @group(0) var src: ${this.importVideo ? "texture_external" : "texture_2d"}; - ${K("index")} { + ${H("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); @@ -31220,58 +31364,58 @@ var Ox = class { `; } }; -var vV = { kernelName: $u, backendName: "webgpu", kernelFunc: cue }; -var Zc; -var Gv = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); -function cue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o; +var XV = { kernelName: Au, backendName: "webgpu", kernelFunc: Hue }; +var Jc; +var Jv = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); +function Hue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o; if (n == null) throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); let a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, p = typeof HTMLCanvasElement != "undefined" && n instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && n instanceof OffscreenCanvas, u = typeof ImageBitmap != "undefined" && n instanceof ImageBitmap, [c, l] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], m = [l, c, s], d = false, f = a || i; if (u || p || f) { let b; if (d) - b = { width: c, height: l, format: null, usage: null, texture: t10.device.importExternalTexture({ source: n }) }; + b = t8.device.importExternalTexture({ source: n }); else { if (f) { - let L = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); - (Zc == null || L !== Gv) && (Gv = L, Zc = document.createElement("canvas").getContext("2d", { willReadFrequently: Gv })), Zc.canvas.width = c, Zc.canvas.height = l, Zc.drawImage(n, 0, 0, c, l), n = Zc.canvas; + let L = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); + (Jc == null || L !== Jv) && (Jv = L, Jc = document.createElement("canvas").getContext("2d", { willReadFrequently: Jv })), Jc.canvas.width = c, Jc.canvas.height = l, Jc.drawImage(n, 0, 0, c, l), n = Jc.canvas; } - let F = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, O = "rgba8unorm", M = t10.textureManager.acquireTexture(m[1], m[0], O, F); - t10.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = { width: c, height: l, format: O, usage: F, texture: M }; + let P = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, O = "rgba8unorm", M = t8.textureManager.acquireTexture(m[1], m[0], O, P); + t8.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = M; } - let w = y.sizeFromShape(m), S = y.computeStrides(m), k = new Ox(m, s, d), _ = [{ type: "uint32", data: [w] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...S] }], E = t10.makeTensorInfo([l, c], "int32"), R = t10.tensorMap.get(E.dataId); - R.resourceInfo = b; - let D = t10.runWebGPUProgram(k, [E], "int32", _); - return t10.disposeData(E.dataId), D; + let C = y.sizeFromShape(m), S = y.computeStrides(m), k = new Mx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...S] }], E = t8.makeTensorInfo([l, c], "int32"), R = t8.tensorMap.get(E.dataId); + R.resource = b; + let D = t8.runWebGPUProgram(k, [E], "int32", _); + return t8.disposeData(E.dataId), D; } let h = n.data, g = h; if (s != null && s !== 4) { g = new Uint8Array(n.width * n.height * s); - let b = h.length, w = 0; + let b = h.length, C = 0; for (let S = 0; S < b; S++) - S % 4 < s && (g[w++] = h[S]); + S % 4 < s && (g[C++] = h[S]); } - let x = t10.makeTensorInfo(m, "int32", new Int32Array(g)); - return t10.uploadToGPU(x.dataId), x; + let x = t8.makeTensorInfo(m, "int32", new Int32Array(g)); + return t8.uploadToGPU(x.dataId), x; } -var Mx = class { - constructor(e, t10, o, n, s) { - this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t10), C.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (C.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm"; +var Lx = class { + constructor(e, t8, o, n, s) { + this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(e, t8), w.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (w.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (w.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm"; } getUserCode() { let e = "0.0"; this.offsetShape != null && (e = "getOffsetByOutputIndex(index)"); - let t10 = "1.0"; - return this.scaleShape != null && (t10 = "getScaleByOutputIndex(index)"), ` - ${K("index")} { + let t8 = "1.0"; + return this.scaleShape != null && (t8 = "getScaleByOutputIndex(index)"), ` + ${H("index")} { if (index < uniforms.size) { let xValue = getXByOutputIndex(index); let meanValue = getMeanByOutputIndex(index); let varianValue = getVarianceByOutputIndex(index); let offsetValue = ${e}; - let scaleValue = ${t10}; + let scaleValue = ${t8}; let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon)); setOutputAtIndex(index,dot(vec3(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } @@ -31279,36 +31423,36 @@ var Mx = class { `; } }; -var kV = { kernelName: wn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t10, c = [o, a, i], l = null; +var YV = { kernelName: In, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t8, c = [o, a, i], l = null; s != null && (l = s.shape, c.push(s)); let m = null; n != null && (m = n.shape, c.push(n)); - let d = new Mx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }]; + let d = new Lx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }]; return u.runWebGPUProgram(d, c, o.dtype, f); } }; -function lue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h); - return gx({ x: n, filter: s, convInfo: g, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d }); +function Kue(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h); + return xx({ x: n, filter: s, convInfo: g, backend: t8, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d }); } -var NV = { kernelName: Co, backendName: "webgpu", kernelFunc: lue }; -function mue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c; - f == null && (f = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`); - let h = C.computeConv2DInfo(n.shape, s.shape, p, f, u, l, true), g = [n, s], x = a != null, b = i != null; +var QV = { kernelName: Io, backendName: "webgpu", kernelFunc: Kue }; +function que(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c; + f == null && (f = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`); + let h = w.computeConv2DInfo(n.shape, s.shape, p, f, u, l, true), g = [n, s], x = a != null, b = i != null; x && g.push(a), b && g.push(i); - let w = [{ type: "int32", data: [h.padInfo.top, h.padInfo.left] }, { type: "int32", data: [h.inHeight, h.inWidth] }], S; - return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? S = new Yc(h, x, m, b) : (S = new Qc(h, x, m, b), w.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (w.push({ type: "float32", data: [d] }), S.uniforms += " alpha : f32,"), t10.runWebGPUProgram(S, g, "float32", w); + let C = [{ type: "int32", data: [h.padInfo.top, h.padInfo.left] }, { type: "int32", data: [h.inHeight, h.inWidth] }], S; + return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? (S = new Qc(h, x, m, b), C.push({ type: "int32", data: [S.virtualWidth] })) : (S = new Zc(h, x, m, b), C.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (C.push({ type: "float32", data: [d] }), S.uniforms += " alpha : f32,"), t8.runWebGPUProgram(S, g, "float32", C); } -var TV = { kernelName: wo, backendName: "webgpu", kernelFunc: mue }; -var Lx = class { - constructor(e, t10) { - this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Nt(e)},`; +var ZV = { kernelName: vo, backendName: "webgpu", kernelFunc: que }; +var Bx = class { + constructor(e, t8) { + this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${ht(e)},`; } getUserCode() { let e; return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; @@ -31324,24 +31468,24 @@ var Lx = class { `; } }; -function due(r) { - let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = C.prepareAndValidate(o, n), m = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = pe({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); - if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { - let b = t10.readSync(n.dataId), w = t10.bufferSync(o), S = PB(b, w, o.dtype, u, a, c, l, o.shape, i); - return t10.makeTensorInfo(p, o.dtype, S.values); +function jue(r) { + let { inputs: e, backend: t8 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u, a] } }), d = pe({ inputs: { x: o }, backend: t8, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); + if (t8.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { + let b = t8.readSync(n.dataId), C = t8.bufferSync(o), S = az(b, C, o.dtype, u, a, c, l, o.shape, i); + return t8.makeTensorInfo(p, o.dtype, S.values); } - let f = new Lx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t10.runWebGPUProgram(f, [d, m], d.dtype, h), x = pe({ inputs: { x: g }, backend: t10, attrs: { shape: p } }); - return t10.disposeData(m.dataId), t10.disposeData(d.dataId), t10.disposeData(g.dataId), x; + let f = new Bx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t8.runWebGPUProgram(f, [d, m], d.dtype, h), x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: p } }); + return t8.disposeData(m.dataId), t8.disposeData(d.dataId), t8.disposeData(g.dataId), x; } -var _V = { kernelName: Sn, backendName: "webgpu", kernelFunc: due }; -var Bx = class { - constructor(e, t10) { - this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather"; +var JV = { kernelName: vn, backendName: "webgpu", kernelFunc: jue }; +var zx = class { + constructor(e, t8) { + this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather"; } getUserCode() { - let e = fue(this.aShape); + let e = Xue(this.aShape); return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); @@ -31352,57 +31496,57 @@ var Bx = class { `; } }; -function fue(r) { - let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t10 = []; +function Xue(r) { + let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t8 = []; for (let o = 0; o < r.length; o++) - o === 2 ? t10.push("indexZ") : t10.push(`${e[o]}`); - return t10.join(); + o === 2 ? t8.push("indexZ") : t8.push(`${e[o]}`); + return t8.join(); } -function Hv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = C.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = pe({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } }); +function e0(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = pe({ inputs: { x: s }, backend: t8, attrs: { shape: [u.batchSize, c / u.batchSize] } }); l.push(m), l.push(d); let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize]; - if (t10.shouldExecuteOnCPU([n, s])) { - let w = t10.tensorMap.get(d.dataId).values, S = me(d.shape, d.dtype, w), _ = t10.tensorMap.get(m.dataId).values, E = me(m.shape, m.dtype, _), R = OB(E, S, f); - return l.forEach((D) => t10.disposeData(D.dataId)), t10.makeTensorInfo(u.outputShape, R.dtype, R.values); + if (t8.shouldExecuteOnCPU([n, s])) { + let C = t8.tensorMap.get(d.dataId).values, S = me(d.shape, d.dtype, C), _ = t8.tensorMap.get(m.dataId).values, E = me(m.shape, m.dtype, _), R = iz(E, S, f); + return l.forEach((D) => t8.disposeData(D.dataId)), t8.makeTensorInfo(u.outputShape, R.dtype, R.values); } - let h = new Bx(m.shape, f), g = t10.runWebGPUProgram(h, [m, d], m.dtype); + let h = new zx(m.shape, f), g = t8.runWebGPUProgram(h, [m, d], m.dtype); l.push(g); - let x = pe({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } }); - return l.forEach((b) => t10.disposeData(b.dataId)), x; + let x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: u.outputShape } }); + return l.forEach((b) => t8.disposeData(b.dataId)), x; } -var $V = { kernelName: ta, backendName: "webgpu", kernelFunc: Hv }; -var hue = et({ opType: fe.GREATER, cpuKernelImpl: LB, dtype: "bool" }); -var EV = { kernelName: In, backendName: "webgpu", kernelFunc: hue }; -var gue = et({ opType: fe.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: MB }); -var RV = { kernelName: vn, backendName: "webgpu", kernelFunc: gue }; -function xue(r) { - let { inputs: e, backend: t10 } = r, { input: o } = e; - return Fx(o, true, t10); +var eW = { kernelName: sa, backendName: "webgpu", kernelFunc: e0 }; +var Yue = et({ opType: fe.GREATER, cpuKernelImpl: pz, dtype: "bool" }); +var tW = { kernelName: kn, backendName: "webgpu", kernelFunc: Yue }; +var Que = et({ opType: fe.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: uz }); +var rW = { kernelName: Nn, backendName: "webgpu", kernelFunc: Que }; +function Zue(r) { + let { inputs: e, backend: t8 } = r, { input: o } = e; + return Px(o, true, t8); } -var DV = { kernelName: Oi, backendName: "webgpu", kernelFunc: xue }; -var yue = xe({ opType: Q.IS_FINITE, dtype: "bool" }); -var AV = { kernelName: kn, backendName: "webgpu", kernelFunc: yue }; -var bue = xe({ opType: Q.IS_INF, dtype: "bool" }); -var FV = { kernelName: Nn, backendName: "webgpu", kernelFunc: bue }; -var Cue = xe({ opType: Q.IS_NAN, dtype: "bool" }); -var PV = { kernelName: Tn, backendName: "webgpu", kernelFunc: Cue }; -function wue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Xr(n.shape, Q.LEAKYRELU, "alpha : f32,"); - return t10.runWebGPUProgram(i, [n], "float32", a); +var oW = { kernelName: zi, backendName: "webgpu", kernelFunc: Zue }; +var Jue = ye({ opType: Z.IS_FINITE, dtype: "bool" }); +var nW = { kernelName: Tn, backendName: "webgpu", kernelFunc: Jue }; +var epe = ye({ opType: Z.IS_INF, dtype: "bool" }); +var sW = { kernelName: _n, backendName: "webgpu", kernelFunc: epe }; +var tpe = ye({ opType: Z.IS_NAN, dtype: "bool" }); +var aW = { kernelName: $n, backendName: "webgpu", kernelFunc: tpe }; +function rpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Zr(n.shape, Z.LEAKYRELU, "alpha : f32,"); + return t8.runWebGPUProgram(i, [n], "float32", a); } -var OV = { kernelName: _n, backendName: "webgpu", kernelFunc: wue }; -var Sue = et({ opType: fe.LESS, dtype: "bool", cpuKernelImpl: zB }); -var MV = { kernelName: $n, backendName: "webgpu", kernelFunc: Sue }; -var Iue = et({ opType: fe.LESS_EQUAL, dtype: "bool", cpuKernelImpl: BB }); -var LV = { kernelName: En, backendName: "webgpu", kernelFunc: Iue }; -var zx = class { +var iW = { kernelName: En, backendName: "webgpu", kernelFunc: rpe }; +var ope = et({ opType: fe.LESS, dtype: "bool", cpuKernelImpl: lz }); +var uW = { kernelName: Rn, backendName: "webgpu", kernelFunc: ope }; +var npe = et({ opType: fe.LESS_EQUAL, dtype: "bool", cpuKernelImpl: cz }); +var pW = { kernelName: Dn, backendName: "webgpu", kernelFunc: npe }; +var Vx = class { constructor(e) { - this.variableNames = [], this.outputShape = [], this.uniforms = "start : f32, step : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace"; + this.variableNames = [], this.outputShape = [], this.uniforms = "start : f32, step : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } @@ -31410,22 +31554,22 @@ var zx = class { `; } }; -function vue(r) { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = (n - o) / (s - 1), i = new zx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }]; +function spe(r) { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, num: s } = t8, a = (n - o) / (s - 1), i = new Vx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }]; return e.runWebGPUProgram(i, [], "float32", p); } -var BV = { kernelName: Rn, backendName: "webgpu", kernelFunc: vue }; -var kue = xe({ opType: Q.LOG, cpuKernelImpl: VB }); -var zV = { kernelName: Dn, backendName: "webgpu", kernelFunc: kue }; -var Nue = xe({ opType: Q.LOG1P }); -var VV = { kernelName: An, backendName: "webgpu", kernelFunc: Nue }; -var Tue = et({ opType: fe.LOGICAL_AND, dtype: "bool" }); -var WV = { kernelName: Fn, backendName: "webgpu", kernelFunc: Tue }; -var _ue = xe({ opType: Q.LOGICAL_NOT }); -var UV = { kernelName: Pn, backendName: "webgpu", kernelFunc: _ue }; -var $ue = et({ opType: fe.LOGICAL_OR }); -var GV = { kernelName: On, backendName: "webgpu", kernelFunc: $ue }; -var HV = ` +var cW = { kernelName: An, backendName: "webgpu", kernelFunc: spe }; +var ape = ye({ opType: Z.LOG, cpuKernelImpl: mz }); +var lW = { kernelName: Fn, backendName: "webgpu", kernelFunc: ape }; +var ipe = ye({ opType: Z.LOG1P }); +var mW = { kernelName: Pn, backendName: "webgpu", kernelFunc: ipe }; +var upe = et({ opType: fe.LOGICAL_AND, dtype: "bool" }); +var dW = { kernelName: On, backendName: "webgpu", kernelFunc: upe }; +var ppe = ye({ opType: Z.LOGICAL_NOT }); +var fW = { kernelName: Mn, backendName: "webgpu", kernelFunc: ppe }; +var cpe = et({ opType: fe.LOGICAL_OR }); +var hW = { kernelName: Ln, backendName: "webgpu", kernelFunc: cpe }; +var gW = ` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -31436,13 +31580,13 @@ var HV = ` powValue = exp(log(basis) * (-uniforms.beta)); } `; -var Vx = class { +var Wx = class { constructor(e) { - this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn"; + this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -31459,7 +31603,7 @@ var Vx = class { sum = sum + z * z; } } - ${HV} + ${gW} setOutputAtIndex(index, x * powValue); } @@ -31467,9 +31611,9 @@ var Vx = class { `; } }; -var Wx = class { - constructor(e, t10) { - this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [256, 1, 1], this.maxAllowRadius = 16, y.assert(t10 <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t10}`), this.outputShape = e, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, [this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]]), this.shaderKey = "lrn_shared"; +var Ux = class { + constructor(e, t8) { + this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [256, 1, 1], this.maxAllowRadius = 16, y.assert(t8 <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t8}`), this.outputShape = e, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, [this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]]), this.shaderKey = "lrn_shared"; } getUserCode() { return ` @@ -31477,7 +31621,7 @@ var Wx = class { const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; const maxAllowRadius = ${this.maxAllowRadius}; - ${K()} { + ${H()} { let localDepth = i32(localId.x); let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; let xDepth = workgroupDepth + localDepth - maxAllowRadius; @@ -31500,27 +31644,27 @@ var Wx = class { let z = lrnSub[index + i]; sum = sum + z * z; } - ${HV} + ${gW} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } } `; } }; -function Eue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u; - s > 16 ? u = new Vx(n.shape) : u = new Wx(n.shape, s); +function lpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u; + s > 16 ? u = new Wx(n.shape) : u = new Ux(n.shape, s); let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }]; - return t10.runWebGPUProgram(u, [n], n.dtype, c); + return t8.runWebGPUProgram(u, [n], n.dtype, c); } -var KV = { kernelName: Mn, backendName: "webgpu", kernelFunc: Eue }; -var Ux = class { +var xW = { kernelName: Bn, backendName: "webgpu", kernelFunc: lpe }; +var Gx = class { constructor(e) { - this.outputShape = [], this.variableNames = ["inputImage", "outputImage", "dy"], this.uniforms = "depthRadius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn_grad"; + this.outputShape = [], this.variableNames = ["inputImage", "outputImage", "dy"], this.uniforms = "depthRadius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn_grad"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -31572,31 +31716,31 @@ var Ux = class { `; } }; -function Rue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Ux(n.shape), m = [{ type: "int32", data: [i] }, { type: "float32", data: [p] }, { type: "float32", data: [u] }, { type: "float32", data: [c] }]; - return t10.runWebGPUProgram(l, [n, s, a], n.dtype, m); +function mpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Gx(n.shape), m = [{ type: "int32", data: [i] }, { type: "float32", data: [p] }, { type: "float32", data: [u] }, { type: "float32", data: [c] }]; + return t8.runWebGPUProgram(l, [n, s, a], n.dtype, m); } -var qV = { kernelName: Ua, backendName: "webgpu", kernelFunc: Rue }; -var Due = et({ opType: fe.MAX, cpuKernelImpl: UB }); -var jV = { kernelName: Bn, backendName: "webgpu", kernelFunc: Due }; -function Aue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = C.computePool2DInfo(n.shape, s, a, u, i, p); - return ox(n, c, "max", t10); +var yW = { kernelName: ja, backendName: "webgpu", kernelFunc: mpe }; +var dpe = et({ opType: fe.MAX, cpuKernelImpl: fz }); +var bW = { kernelName: Vn, backendName: "webgpu", kernelFunc: dpe }; +function fpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p); + return nx(n, c, "max", t8); } -var XV = { kernelName: zn, backendName: "webgpu", kernelFunc: Aue }; -function Fue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "max"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; - return t10.runWebGPUProgram(m, [n], n.dtype, d); +var CW = { kernelName: Wn, backendName: "webgpu", kernelFunc: fpe }; +function hpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new ku(l, "max"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; + return t8.runWebGPUProgram(m, [n], n.dtype, d); } -var YV = { kernelName: ra, backendName: "webgpu", kernelFunc: Fue }; -var Gx = class { +var wW = { kernelName: aa, backendName: "webgpu", kernelFunc: hpe }; +var Hx = class { constructor(e) { this.variableNames = ["dy", "maxPos"], this.uniforms = `strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool2DBackprop"; + outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool2DBackprop"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -31642,14 +31786,14 @@ var Gx = class { `; } }; -var Hx = class { +var Kx = class { constructor(e) { this.variableNames = ["dy", "maxPos"], this.uniforms = `strides : vec3, pads : vec3, filterDims : vec3, - outDepth : i32, outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool3DBackprop"; + outDepth : i32, outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool3DBackprop"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -31708,52 +31852,52 @@ var Hx = class { `; } }; -function Pue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = C.computePool3DInfo(a.shape, i, p, l, u, c), d = new wu(m, "max", true), f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.front, m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.inDepth, m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = t10.runWebGPUProgram(d, [a], "int32", f), g = new Hx(m); +function gpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new ku(m, "max", true), f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.front, m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.inDepth, m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = t8.runWebGPUProgram(d, [a], "int32", f), g = new Kx(m); f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.effectiveFilterDepth - 1 - m.padInfo.front, m.effectiveFilterHeight - 1 - m.padInfo.top, m.effectiveFilterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }, { type: "int32", data: [m.outDepth] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }]; - let x = t10.runWebGPUProgram(g, [n, h], a.dtype, f); - return t10.disposeData(h.dataId), x; + let x = t8.runWebGPUProgram(g, [n, h], a.dtype, f); + return t8.disposeData(h.dataId), x; } -var QV = { kernelName: Li, backendName: "webgpu", kernelFunc: Pue }; -function Oue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; - cm([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = C.computePool2DInfo(i.shape, p, u, 1, c, l), d = new Da(m, "max", true), f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = t10.runWebGPUProgram(d, [i], "int32", f), g = new Gx(m); +var SW = { kernelName: Ui, backendName: "webgpu", kernelFunc: gpe }; +function xpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; + lm([s, a], "maxPoolGrad"); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = new Ma(m, "max", true), f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = t8.runWebGPUProgram(d, [i], "int32", f), g = new Hx(m); f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.effectiveFilterHeight - 1 - m.padInfo.top, m.effectiveFilterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }]; - let x = t10.runWebGPUProgram(g, [n, h], i.dtype, f); - return t10.disposeData(h.dataId), x; + let x = t8.runWebGPUProgram(g, [n, h], i.dtype, f); + return t8.disposeData(h.dataId), x; } -var ZV = { kernelName: Hp, backendName: "webgpu", kernelFunc: Oue }; -function Mue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = o, { x: p } = e; +var IW = { kernelName: Wi, backendName: "webgpu", kernelFunc: xpe }; +function ype(r) { + let { inputs: e, backend: t8, attrs: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = o, { x: p } = e; y.assert(p.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`); let u = [1, 1]; - y.assert(C.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); - let c = C.computePool2DInfo(p.shape, n, s, u, a), l = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }], m = new Da(c, "max", false), d = t10.runWebGPUProgram(m, [p], p.dtype, l); - m = new Da(c, "max", true, true, i); - let f = t10.runWebGPUProgram(m, [p], "int32", l); + y.assert(w.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); + let c = w.computePool2DInfo(p.shape, n, s, u, a), l = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }], m = new Ma(c, "max", false), d = t8.runWebGPUProgram(m, [p], p.dtype, l); + m = new Ma(c, "max", true, true, i); + let f = t8.runWebGPUProgram(m, [p], "int32", l); return [d, f]; } -var JV = { kernelName: Bi, backendName: "webgpu", kernelFunc: Mue }; -function Lue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return Yr(n, s, a, "min", t10); +var vW = { kernelName: ia, backendName: "webgpu", kernelFunc: ype }; +function bpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return Jr(n, s, a, "min", t8); } -var eW = { kernelName: Wn, backendName: "webgpu", kernelFunc: Lue }; -var Bue = et({ opType: fe.MIN, cpuKernelImpl: GB }); -var tW = { kernelName: Un, backendName: "webgpu", kernelFunc: Bue }; -var Kx = class { - constructor(e, t10, o) { - this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t10.map((n, s) => { +var kW = { kernelName: Gn, backendName: "webgpu", kernelFunc: bpe }; +var Cpe = et({ opType: fe.MIN, cpuKernelImpl: hz }); +var NW = { kernelName: Hn, backendName: "webgpu", kernelFunc: Cpe }; +var qx = class { + constructor(e, t8, o) { + this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t8.map((n, s) => { this.uniforms += ` pad${s} : vec2,`; }), this.offset = o === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${o}`; } getUserCode() { - let e = this.xShape.length, t10 = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${e > 1 ? `[${c}]` : ""}`).join(","), n = e === 1 ? "start" : "start[i]", s = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Nt(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; + let e = this.xShape.length, t8 = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${e > 1 ? `[${c}]` : ""}`).join(","), n = e === 1 ? "start" : "start[i]", s = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = ht(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { - let start = ${i}(${t10}); + let start = ${i}(${t8}); let end = ${i}(${o}); var outC = getCoordsFromIndex(index); for (var i = 0; i < ${e}; i = i + 1) { @@ -31770,15 +31914,15 @@ var Kx = class { `; } }; -var rW = { kernelName: Gn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { x: o } = r, { paddings: n, mode: s } = e, a = t10, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new Kx(o.shape, n, s); +var TW = { kernelName: Kn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { x: o } = r, { paddings: n, mode: s } = e, a = t8, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new qx(o.shape, n, s); return a.runWebGPUProgram(p, [o], o.dtype, i); } }; -var zue = et({ opType: fe.MOD }); -var oW = { kernelName: Ga, backendName: "webgpu", kernelFunc: zue }; -var qx = class { - constructor(e, t10) { - this.variableNames = ["probs"], this.outputShape = [], this.uniforms = "seed : f32, numOutcomes: i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "multinomial"; +var wpe = et({ opType: fe.MOD }); +var _W = { kernelName: qn, backendName: "webgpu", kernelFunc: wpe }; +var jx = class { + constructor(e, t8) { + this.variableNames = ["probs"], this.outputShape = [], this.uniforms = "seed : f32, numOutcomes: i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t8], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "multinomial"; } getUserCode() { return ` @@ -31792,7 +31936,7 @@ var qx = class { return fract((p3.x + p3.y) * p3.z); } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -31817,9 +31961,9 @@ var qx = class { `; } }; -var jx = class { +var Xx = class { constructor(e) { - this.variableNames = ["logits"], this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = [this.outputShape[0], 1, 1], this.outputShape[1] >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.shaderKey = "softmax"; + this.variableNames = ["logits"], this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = [this.outputShape[0], 1, 1], this.outputShape[1] >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.shaderKey = "softmax"; } getUserCode() { return ` @@ -31827,7 +31971,7 @@ var jx = class { var rowMaxShared : f32; var rowSumShared : f32; const blockSize = ${this.workgroupSize[0]}; - ${K("index")} { + ${H("index")} { let row = index / blockSize; let tid = i32(localId.x); let cols = uniforms.outShape[1]; @@ -31884,45 +32028,45 @@ var jx = class { `; } }; -function Kv(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [y.sizeFromShape(n.shape) / n.shape[s], n.shape[s]] } }), i = new jx(a.shape), p = t10.runWebGPUProgram(i, [a], n.dtype), u = pe({ inputs: { x: p }, backend: t10, attrs: { shape: n.shape } }); - return t10.disposeData(a.dataId), t10.disposeData(p.dataId), u; +function t0(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [y.sizeFromShape(n.shape) / n.shape[s], n.shape[s]] } }), i = new Xx(a.shape), p = t8.runWebGPUProgram(i, [a], n.dtype), u = pe({ inputs: { x: p }, backend: t8, attrs: { shape: n.shape } }); + return t8.disposeData(a.dataId), t8.disposeData(p.dataId), u; } -var nW = { kernelName: bs, backendName: "webgpu", kernelFunc: Kv }; -function Vue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : Kv({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new qx(u, s), m = [{ type: "float32", data: [a] }, { type: "int32", data: [c] }], d = t10.runWebGPUProgram(l, [p], "int32", m); - return i || t10.disposeData(p.dataId), d; +var $W = { kernelName: Is, backendName: "webgpu", kernelFunc: t0 }; +function Spe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : t0({ inputs: { logits: n }, backend: t8, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new jx(u, s), m = [{ type: "float32", data: [a] }, { type: "int32", data: [c] }], d = t8.runWebGPUProgram(l, [p], "int32", m); + return i || t8.disposeData(p.dataId), d; } -var sW = { kernelName: Hn, backendName: "webgpu", kernelFunc: Vue }; -function Wue(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; - if (t10.shouldExecuteOnCPU([o])) { - let s = t10.tensorMap.get(o.dataId), [a, i] = KB(s.values, o.shape, o.dtype); - return t10.makeTensorInfo(i, o.dtype, a); +var EW = { kernelName: jn, backendName: "webgpu", kernelFunc: Spe }; +function Ipe(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; + if (t8.shouldExecuteOnCPU([o])) { + let s = t8.tensorMap.get(o.dataId), [a, i] = xz(s.values, o.shape, o.dtype); + return t8.makeTensorInfo(i, o.dtype, a); } - let n = new Xr(o.shape, Q.NEG); - return t10.runWebGPUProgram(n, [o], o.dtype); + let n = new Zr(o.shape, Z.NEG); + return t8.runWebGPUProgram(n, [o], o.dtype); } -var aW = { kernelName: oa, backendName: "webgpu", kernelFunc: Wue }; -function Uue(r) { +var RW = { kernelName: ua, backendName: "webgpu", kernelFunc: Ipe }; +function vpe(r) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = Wt.nonMaxSuppressionV3Impl(u, c, a, i, p); - return t10.makeTensorInfo([l.length], "int32", new Int32Array(l)); + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t8.readSync(n.dataId), c = t8.readSync(s.dataId), { selectedIndices: l } = Wt.nonMaxSuppressionV3Impl(u, c, a, i, p); + return t8.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var iW = { kernelName: jn, backendName: "webgpu", kernelFunc: Uue }; -function Gue(r) { +var DW = { kernelName: Qn, backendName: "webgpu", kernelFunc: vpe }; +function kpe(r) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Wt.nonMaxSuppressionV5Impl(c, l, m, d, f, h); - return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))]; + let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t8.readSync(n.dataId), l = t8.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Wt.nonMaxSuppressionV5Impl(c, l, m, d, f, h); + return [t8.makeTensorInfo([g.length], "int32", new Int32Array(g)), t8.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var uW = { kernelName: Xn, backendName: "webgpu", kernelFunc: Gue }; -var Xx = class { - constructor(e, t10) { - this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot"; +var AW = { kernelName: Zn, backendName: "webgpu", kernelFunc: kpe }; +var Yx = class { + constructor(e, t8) { + this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t8], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, @@ -31932,120 +32076,124 @@ var Xx = class { `; } }; -function Hue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Xx(u, a), l = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t10.runWebGPUProgram(c, [l], s, m); - t10.disposeData(l.dataId); - let f = [...n.shape, a], h = pe({ inputs: { x: d }, backend: t10, attrs: { shape: f } }); - return t10.disposeData(d.dataId), h; +function Npe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Yx(u, a), l = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t8.runWebGPUProgram(c, [l], s, m); + t8.disposeData(l.dataId); + let f = [...n.shape, a], h = pe({ inputs: { x: d }, backend: t8, attrs: { shape: f } }); + return t8.disposeData(d.dataId), h; } -var pW = { kernelName: Yn, backendName: "webgpu", kernelFunc: Hue }; -function hm(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; +var FW = { kernelName: Jn, backendName: "webgpu", kernelFunc: Npe }; +function gm(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; if (o.dtype === "complex64") { - let n = Ci({ inputs: { input: o }, backend: t10 }), s = hm({ inputs: { x: n }, backend: t10 }), a = Ep({ inputs: { input: o }, backend: t10 }), i = hm({ inputs: { x: a }, backend: t10 }), p = fo({ inputs: { real: s, imag: i }, backend: t10 }); - return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p; + let n = Ii({ inputs: { input: o }, backend: t8 }), s = gm({ inputs: { x: n }, backend: t8 }), a = Fp({ inputs: { input: o }, backend: t8 }), i = gm({ inputs: { x: a }, backend: t8 }), p = xo({ inputs: { real: s, imag: i }, backend: t8 }); + return t8.disposeData(n.dataId), t8.disposeData(s.dataId), t8.disposeData(a.dataId), t8.disposeData(i.dataId), p; } else - return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 }); + return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t8 }); } -var cW = { kernelName: fa, backendName: "webgpu", kernelFunc: hm }; -function lW(r) { - let { inputs: e, backend: t10 } = r, { x: o } = e; +var PW = { kernelName: ba, backendName: "webgpu", kernelFunc: gm }; +function OW(r) { + let { inputs: e, backend: t8 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("onesLike is not supported under string dtype"); if (o.dtype === "complex64") { - let n = Ci({ inputs: { input: o }, backend: t10 }), s = lW({ inputs: { x: n }, backend: t10 }), a = Ep({ inputs: { input: o }, backend: t10 }), i = hm({ inputs: { x: a }, backend: t10 }), p = fo({ inputs: { real: s, imag: i }, backend: t10 }); - return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p; + let n = Ii({ inputs: { input: o }, backend: t8 }), s = OW({ inputs: { x: n }, backend: t8 }), a = Fp({ inputs: { input: o }, backend: t8 }), i = gm({ inputs: { x: a }, backend: t8 }), p = xo({ inputs: { real: s, imag: i }, backend: t8 }); + return t8.disposeData(n.dataId), t8.disposeData(s.dataId), t8.disposeData(a.dataId), t8.disposeData(i.dataId), p; } else - return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 }); + return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t8 }); } -var mW = { kernelName: na, backendName: "webgpu", kernelFunc: lW }; -function Kue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; +var MW = { kernelName: pa, backendName: "webgpu", kernelFunc: OW }; +function Tpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return Ax({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); + return Fx({ inputs: { input: e[0] }, backend: t8, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = Ax({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); + let l = Fx({ inputs: { input: c }, backend: t8, attrs: { dim: n } }); return i.push(l), l; - }), u = Vv({ inputs: p, backend: t10, attrs: { axis: n } }); - return i.forEach((c) => t10.disposeData(c.dataId)), u; + }), u = Yv({ inputs: p, backend: t8, attrs: { axis: n } }); + return i.forEach((c) => t8.disposeData(c.dataId)), u; } -var dW = { kernelName: sa, backendName: "webgpu", kernelFunc: Kue }; -var Yx = class { - constructor(e, t10) { - this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), t10.map((o, n) => { +var LW = { kernelName: ca, backendName: "webgpu", kernelFunc: Tpe }; +function r0(r, e = false) { + let t8 = r.length, o = ht(t8), n = r.map((l, m) => `uniforms.pad${m}[0]`).join(","), s = r.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${t8 > 1 ? `[${m}]` : ""}`).join(","), a = t8 > 1 ? `${o}(${n})` : `${n}`, i = t8 > 1 ? `${o}(${s})` : `${s}`, p = t8 > 1 ? "any(paddedCoords < start)" : "paddedCoords < start", u = t8 > 1 ? "any(paddedCoords >= end)" : "paddedCoords >= end", c = t8 > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, t8) : "coords"; + return ` + let start = ${a}; + let end = ${i}; + if (${p} || ${u}) { + setOutputAtIndex(index, ${e ? 0 : "uniforms.constantValue"}); + } else { + let coords = paddedCoords - start; + setOutputAtIndex(index, getX(${c})); + } + `; +} +var Qx = class { + constructor(e, t8) { + this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), t8.map((o, n) => { this.uniforms += ` pad${n} : vec2,`; }), this.xShape = e, this.shaderKey = "pad"; } getUserCode() { - let e = this.xShape.length, t10 = Nt(e), o = this.xShape.map((l, m) => `uniforms.pad${m}[0]`).join(","), n = this.xShape.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), s = e > 1 ? `${t10}(${o})` : `${o}`, a = e > 1 ? `${t10}(${n})` : `${n}`, i = e > 1 ? "any(outC < start)" : "outC < start", p = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { - let start = ${s}; - let end = ${a}; - let outC = getCoordsFromIndex(index); - - if (${i} || ${p}) { - setOutputAtIndex(index, uniforms.constantValue); - } else { - let coords = outC - start; - setOutputAtIndex(index, getX(${u})); - } + let paddedCoords = getCoordsFromIndex(index); + ${r0(this.xShape)} } } `; } }; -var qv = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; +var _pe = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; if (s.every((u) => y.arraysEqual(u, [0, 0]))) - return At({ inputs: { x: n }, backend: t10 }); + return At({ inputs: { x: n }, backend: t8 }); if (y.sizeFromShape(n.shape) === 0) { let u = s.map((c, l) => c[0] + n.shape[l] + c[1]); - return Vt({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } }); + return Vt({ backend: t8, attrs: { shape: u, value: a, dtype: n.dtype } }); } let i = [{ type: "float32", data: [a] }]; s.map((u) => i.push({ type: "int32", data: [u[0], u[1]] })); - let p = new Yx(n.shape, s); - return t10.runWebGPUProgram(p, [n], n.dtype, i); + let p = new Qx(n.shape, s); + return t8.runWebGPUProgram(p, [n], n.dtype, i); }; -var fW = { kernelName: Qn, backendName: "webgpu", kernelFunc: qv }; -var que = et({ opType: fe.POW }); -var hW = { kernelName: Zn, backendName: "webgpu", kernelFunc: que }; -function jue(r) { - let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = new bi(fe.PRELU, o.shape, n.shape); - return t10.runWebGPUProgram(s, [o, n], "float32"); +var BW = { kernelName: es, backendName: "webgpu", kernelFunc: _pe }; +var $pe = et({ opType: fe.POW }); +var zW = { kernelName: ts, backendName: "webgpu", kernelFunc: $pe }; +function Epe(r) { + let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e, s = new Si(fe.PRELU, o.shape, n.shape); + return t8.runWebGPUProgram(s, [o, n], "float32"); } -var gW = { kernelName: Jn, backendName: "webgpu", kernelFunc: jue }; -function Xue(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return Yr(n, s, a, "prod", t10); +var VW = { kernelName: rs, backendName: "webgpu", kernelFunc: Epe }; +function Rpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return Jr(n, s, a, "prod", t8); } -var xW = { kernelName: es, backendName: "webgpu", kernelFunc: Xue }; -var Yue = (r) => { - let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = XB(o, n, s, a); +var WW = { kernelName: os, backendName: "webgpu", kernelFunc: Rpe }; +var Dpe = (r) => { + let { backend: e, attrs: t8 } = r, { start: o, stop: n, step: s, dtype: a } = t8, i = Cz(o, n, s, a); return e.makeTensorInfo([i.length], a, i); }; -var yW = { kernelName: aa, backendName: "webgpu", kernelFunc: Yue }; -var Que = et({ opType: fe.DIV }); -var bW = { kernelName: dn, backendName: "webgpu", kernelFunc: Que }; -var Zue = xe({ opType: Q.RECIPROCAL }); -var CW = { kernelName: ts, backendName: "webgpu", kernelFunc: Zue }; -var Jue = xe({ opType: Q.RELU }); -var wW = { kernelName: rs, backendName: "webgpu", kernelFunc: Jue }; -var epe = xe({ opType: Q.RELU6 }); -var SW = { kernelName: ss, backendName: "webgpu", kernelFunc: epe }; -var Qx = class { - constructor(e, t10, o) { - this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear"; +var UW = { kernelName: la, backendName: "webgpu", kernelFunc: Dpe }; +var Ape = et({ opType: fe.DIV }); +var GW = { kernelName: fn, backendName: "webgpu", kernelFunc: Ape }; +var Fpe = ye({ opType: Z.RECIPROCAL }); +var HW = { kernelName: ns, backendName: "webgpu", kernelFunc: Fpe }; +var Ppe = ye({ opType: Z.RELU }); +var KW = { kernelName: ss, backendName: "webgpu", kernelFunc: Ppe }; +var Ope = ye({ opType: Z.RELU6 }); +var qW = { kernelName: us, backendName: "webgpu", kernelFunc: Ope }; +var Zx = class { + constructor(e, t8, o) { + this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t8, o, e[3]], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -32090,19 +32238,19 @@ var Qx = class { `; } }; -function tpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new Qx(n.shape, p, u); - return t10.runWebGPUProgram(f, [n], "float32", d); +function Mpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new Zx(n.shape, p, u); + return t8.runWebGPUProgram(f, [n], "float32", d); } -var IW = { kernelName: ns, backendName: "webgpu", kernelFunc: tpe }; -var Zx = class { - constructor(e, t10) { +var jW = { kernelName: is, backendName: "webgpu", kernelFunc: Mpe }; +var Jx = class { + constructor(e, t8) { this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, - invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t10, this.shaderKey = `resizeBilinearBackprop_${t10}`; + invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t8, this.shaderKey = `resizeBilinearBackprop_${t8}`; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -32178,19 +32326,19 @@ var Zx = class { `; } }; -function rpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, w = new Zx(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [d] }, { type: "float32", data: [f] }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; - return t10.runWebGPUProgram(w, [s], s.dtype, S); +function Lpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, C = new Jx(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [d] }, { type: "float32", data: [f] }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; + return t8.runWebGPUProgram(C, [s], s.dtype, S); } -var vW = { kernelName: qa, backendName: "webgpu", kernelFunc: rpe }; -var Jx = class { - constructor(e, t10, o, n) { - this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`; +var XW = { kernelName: Qa, backendName: "webgpu", kernelFunc: Lpe }; +var ey = class { + constructor(e, t8, o, n) { + this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t8, o, e[3]], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`; } getUserCode() { let e; return this.halfPixelCenters ? e = "max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : e = "vec2(rc) * effectiveInputOverOutputRatioRC", ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -32223,19 +32371,19 @@ var Jx = class { `; } }; -function ope(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new Jx(n.shape, p, u, a); - return t10.runWebGPUProgram(f, [n], n.dtype, d); +function Bpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new ey(n.shape, p, u, a); + return t8.runWebGPUProgram(f, [n], n.dtype, d); } -var kW = { kernelName: os, backendName: "webgpu", kernelFunc: ope }; -var ey = class { - constructor(e, t10) { +var YW = { kernelName: as, backendName: "webgpu", kernelFunc: Bpe }; +var ty = class { + constructor(e, t8) { this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, - winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t10, this.shaderKey = `resizeNearestNeigborBackprop_${t10}`; + winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t8, this.shaderKey = `resizeNearestNeigborBackprop_${t8}`; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -32296,14 +32444,14 @@ var ey = class { `; } }; -function npe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, w = new ey(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; - return t10.runWebGPUProgram(w, [s], s.dtype, S); +function zpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, C = new ty(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; + return t8.runWebGPUProgram(C, [s], s.dtype, S); } -var NW = { kernelName: Ka, backendName: "webgpu", kernelFunc: npe }; -var ty = class { +var QW = { kernelName: Ya, backendName: "webgpu", kernelFunc: zpe }; +var ry = class { constructor(e) { - this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4,", this.shaderKey = "reverse"; + this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4,", this.shaderKey = "reverse"; } getUserCode() { return ` @@ -32328,7 +32476,7 @@ var ty = class { return reverseCoords; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); @@ -32339,10 +32487,10 @@ var ty = class { `; } }; -function spe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length; +function Vpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length; if (a === 0) - return At({ inputs: { x: n }, backend: t10 }); + return At({ inputs: { x: n }, backend: t8 }); let i = n.shape, p = [1, 1, 1, 1]; i.forEach((g, x) => { let b = x + 4 - a; @@ -32353,20 +32501,20 @@ function spe(r) { let x = g + 4 - a; c[x] = 1; }); - let l = [{ type: "int32", data: c }], m = pe({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), d = new ty(p), f = t10.runWebGPUProgram(d, [m], m.dtype, l); - t10.disposeData(m.dataId); - let h = pe({ inputs: { x: f }, backend: t10, attrs: { shape: i } }); - return t10.disposeData(f.dataId), h; + let l = [{ type: "int32", data: c }], m = pe({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), d = new ry(p), f = t8.runWebGPUProgram(d, [m], m.dtype, l); + t8.disposeData(m.dataId); + let h = pe({ inputs: { x: f }, backend: t8, attrs: { shape: i } }); + return t8.disposeData(f.dataId), h; } -var TW = { kernelName: as, backendName: "webgpu", kernelFunc: spe }; -var ry = class { - constructor(e, t10) { - this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32, - cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t10 == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3"); +var ZW = { kernelName: ps, backendName: "webgpu", kernelFunc: Vpe }; +var oy = class { + constructor(e, t8) { + this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32, + cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t8 == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3"); } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * @@ -32388,24 +32536,24 @@ var ry = class { `; } }; -var _W = { kernelName: _s, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new ry(o.shape, s), [u, c] = C.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }]; +var JW = { kernelName: Ds, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => { + let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t8, p = new oy(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }]; return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l); } }; -var ape = xe({ opType: Q.ROUND }); -var $W = { kernelName: is, backendName: "webgpu", kernelFunc: ape }; -var ipe = xe({ opType: Q.RSQRT, cpuKernelImpl: YB }); -var EW = { kernelName: us, backendName: "webgpu", kernelFunc: ipe }; -var Aa = class { - constructor(e, t10, o, n, s, a, i, p = true) { - this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = Z(e), this.dispatch = q(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t10 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`; - let u = Nt(s.length); +var Wpe = ye({ opType: Z.ROUND }); +var eU = { kernelName: cs, backendName: "webgpu", kernelFunc: Wpe }; +var Upe = ye({ opType: Z.RSQRT, cpuKernelImpl: wz }); +var tU = { kernelName: ls, backendName: "webgpu", kernelFunc: Upe }; +var La = class { + constructor(e, t8, o, n, s, a, i, p = true) { + this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = X(e), this.dispatch = K(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t8 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`; + let u = ht(s.length); this.uniforms = `sliceDim : i32, strides: ${u}, updatesSize: i32,`, this.updatesRank = n, this.indicesRank = o; } getUserCode() { let e = ""; this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j"); - let t10 = `getIndices(${e})`, o = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", n = "", s = ""; + let t8 = `getIndices(${e})`, o = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", n = "", s = ""; this.dispatchLayout.x.length === 1 ? (n = "flattenedIndex", s = ` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; @@ -32425,34 +32573,34 @@ var Aa = class { let i = `getUpdates(${Array.from({ length: this.updatesRank }, (u, c) => `coords[${c}]`).join(", ")})`; return ` ${s} - ${K("index")} { + ${H("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { - let indexInside = i32(round(${t10})); + let indexInside = i32(round(${t8})); flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = - ${kp(this.type)}(${i}); + ${$p(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices ? Bs("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices ? Us("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast(updateValue));"} } }`; } }; -function upe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(s, n, a), m = [l / u, u]; +function Gpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u]; if (l === 0) - return t10.makeTensorInfo(a, n.dtype); - let d = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = pe({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = f.dtype, g = Vt({ backend: t10, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], w = new Aa(f.shape, i, d.shape.length, f.shape.length, c, m, h), S = t10.runWebGPUProgram(w, [f, d], h, b, g), k = pe({ inputs: { x: S }, backend: t10, attrs: { shape: a } }); - return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(S.dataId), k; + return t8.makeTensorInfo(a, n.dtype); + let d = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [p, i] } }), f = pe({ inputs: { x: s }, backend: t8, attrs: { shape: [p, u] } }), h = f.dtype, g = Vt({ backend: t8, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new La(f.shape, i, d.shape.length, f.shape.length, c, m, h), S = t8.runWebGPUProgram(C, [f, d], h, b, g), k = pe({ inputs: { x: S }, backend: t8, attrs: { shape: a } }); + return t8.disposeData(d.dataId), t8.disposeData(f.dataId), t8.disposeData(S.dataId), k; } -var RW = { kernelName: ps, backendName: "webgpu", kernelFunc: upe }; -var oy = class { - constructor(e, t10) { - this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t10, this.shaderKey = `search_sorted_${t10}`; +var rU = { kernelName: ms, backendName: "webgpu", kernelFunc: Gpe }; +var ny = class { + constructor(e, t8) { + this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t8, this.shaderKey = `search_sorted_${t8}`; } getUserCode() { return ` @@ -32470,7 +32618,7 @@ var oy = class { return right; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); @@ -32480,82 +32628,109 @@ var oy = class { `; } }; -function ppe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new oy([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }]; - return t10.runWebGPUProgram(i, [n, s], "int32", p); +function Hpe(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new ny([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }]; + return t8.runWebGPUProgram(i, [n, s], "int32", p); } -var DW = { kernelName: ls, backendName: "webgpu", kernelFunc: ppe }; -var ny = class { - constructor(e, t10, o) { - this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select"; +var oU = { kernelName: fs, backendName: "webgpu", kernelFunc: Hpe }; +var sy = class { + constructor(e, t8, o) { + this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select"; } getUserCode() { - let e, t10; + let e, t8; if (this.rank > 4) throw Error(`Where for rank ${this.rank} is not yet supported`); if (this.rank === 1) - t10 = "resRC", e = "resRC"; + t8 = "resRC", e = "resRC"; else { let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [], a = []; for (let i = 0; i < this.outputShape.length; i++) a.push(`${n[i]}`), i < this.cRank && s.push(`${n[i]}`); - e = s.join(), t10 = a.join(); + e = s.join(), t8 = a.join(); } return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let cVal = getC(${e}); if (cVal >= 1.0) { - setOutputAtIndex(index, getA(${t10})); + setOutputAtIndex(index, getA(${t8})); } else { - setOutputAtIndex(index, getB(${t10})); + setOutputAtIndex(index, getB(${t8})); } } } `; } }; -function cpe(r) { - let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new ny(o.shape.length, n.shape, n.shape.length); - return t10.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype)); +function Kpe(r) { + let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e, a = new sy(o.shape.length, n.shape, n.shape.length); + return t8.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype)); } -var AW = { kernelName: ua, backendName: "webgpu", kernelFunc: cpe }; -var lpe = xe({ opType: Q.SELU }); -var FW = { kernelName: ms, backendName: "webgpu", kernelFunc: lpe }; -var mpe = xe({ opType: Q.SIGMOID }); -var PW = { kernelName: hs, backendName: "webgpu", kernelFunc: mpe }; -var dpe = xe({ opType: Q.SIGN }); -var OW = { kernelName: fs, backendName: "webgpu", kernelFunc: dpe }; -var fpe = xe({ opType: Q.SIN }); -var MW = { kernelName: ds, backendName: "webgpu", kernelFunc: fpe }; -var hpe = xe({ opType: Q.SINH }); -var LW = { kernelName: ja, backendName: "webgpu", kernelFunc: hpe }; -var gpe = xe({ opType: Q.SOFTPLUS }); -var BW = { kernelName: gs, backendName: "webgpu", kernelFunc: gpe }; -var xpe = (r) => { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; - y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet"); - let i = s.reduce((x, b) => x * b), p = [[0, 0]]; - p.push(...a); - for (let x = 1 + s.length; x < n.shape.length; ++x) - p.push([0, 0]); - let u = [], c = qv({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = C.getReshaped(c.shape, s, i, false), m = C.getPermuted(l.length, s.length, false), d = C.getReshapedPermuted(c.shape, s, i, false), f = pe({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = rr({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = pe({ inputs: { x: h }, backend: t10, attrs: { shape: d } }); - return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeData(x.dataId)), g; +var nU = { kernelName: da, backendName: "webgpu", kernelFunc: Kpe }; +var qpe = ye({ opType: Z.SELU }); +var sU = { kernelName: hs, backendName: "webgpu", kernelFunc: qpe }; +var jpe = ye({ opType: Z.SIGMOID }); +var aU = { kernelName: bs, backendName: "webgpu", kernelFunc: jpe }; +var Xpe = ye({ opType: Z.SIGN }); +var iU = { kernelName: ys, backendName: "webgpu", kernelFunc: Xpe }; +var Ype = ye({ opType: Z.SIN }); +var uU = { kernelName: gs, backendName: "webgpu", kernelFunc: Ype }; +var Qpe = ye({ opType: Z.SINH }); +var pU = { kernelName: xs, backendName: "webgpu", kernelFunc: Qpe }; +var Zpe = ye({ opType: Z.SOFTPLUS }); +var cU = { kernelName: Cs, backendName: "webgpu", kernelFunc: Zpe }; +var ay = class { + constructor(e, t8, o, n, s, a) { + this.variableNames = ["x"], this.outputShape = [], this.uniforms = "", this.workgroupSize = [64, 1, 1], this.size = true; + let i = new Array(n.length); + for (let p = 0; p < i.length; p++) + i[p] = n[s[p]]; + this.outputShape = i, this.newDim = s, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, this.paddedXShape = t8, this.uniforms += `reshapedPaddedXShape : ${ht(n.length)}, paddedXShapeStrides : ${ht(a)}, `, o.map((p, u) => { + this.uniforms += ` pad${u} : vec2,`; + }), this.shaderKey = `spaceToBatchND_${s}`; + } + getUserCode() { + let e = ht(this.outputShape.length), t8 = Gv(this.newDim); + return ` + ${im(this.paddedXShape, "PaddedX")} + ${H("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t8}), uniforms.reshapedPaddedXShape); + let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); + ${r0(this.xShape, true)} + } + } + `; + } }; -var zW = { kernelName: ca, backendName: "webgpu", kernelFunc: xpe }; -var sy = class { - constructor(e, t10) { +var Jpe = (r) => { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; + y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet"); + let i = s.reduce((b, C) => b * C), p = [[0, 0]]; + p.push(...a); + for (let b = 1 + s.length; b < n.shape.length; ++b) + p.push([0, 0]); + let u = p.map((b, C) => b[0] + n.shape[C] + b[1]), c = w.getReshaped(u, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u, s, i, false), d = y.computeStrides(u), f = new ay(n.shape, u, p, c, l, d.length), h = [{ type: "int32", data: c }, { type: "int32", data: d }]; + p.map((b) => h.push({ type: "int32", data: [b[0], b[1]] })); + let g = t8.runWebGPUProgram(f, [n], n.dtype, h), x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: m } }); + return t8.disposeData(g.dataId), x; +}; +var lU = { kernelName: ha, backendName: "webgpu", kernelFunc: Jpe }; +var iy = class { + constructor(e, t8) { this.variableNames = ["A"], this.workgroupSize = [64, 1, 1], this.size = true; let o = new Array(e.length); for (let n = 0; n < o.length; n++) - o[n] = e[n] * t10[n]; - this.outputShape = o, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile"; + o[n] = e[n] * t8[n]; + this.outputShape = o, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile"; } getUserCode() { - let e = ype(this.rank, "uniforms."); + let e = ece(this.rank, "uniforms."); return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); setOutputAtIndex(index, getA(${e})); @@ -32564,156 +32739,156 @@ var sy = class { `; } }; -function ype(r, e = "") { +function ece(r, e = "") { if (r >= 5) throw Error(`Tile for rank ${r} is not yet supported`); if (r === 1) return `(resRC % ${e}aShape)`; - let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; + let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; for (let n = 0; n < r; n++) - o.push(`(${t10[n]} % ${e}aShape[${n}])`); + o.push(`(${t8[n]} % ${e}aShape[${n}])`); return o.join(); } -function gm(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o; - if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) { - let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = oz(c, s); - return t10.makeTensorInfo(l.shape, l.dtype, l.values); +function xm(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reps: s } = o; + if (t8.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) { + let p = t8.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = _z(c, s); + return t8.makeTensorInfo(l.shape, l.dtype, l.values); } - let a = new sy(n.shape, s); - return t10.runWebGPUProgram(a, [n], n.dtype); + let a = new iy(n.shape, s); + return t8.runWebGPUProgram(a, [n], n.dtype); } -var VW = { kernelName: so, backendName: "webgpu", kernelFunc: gm }; -function bpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = C.calculateShapes(s, n, i), d = false; +var mU = { kernelName: uo, backendName: "webgpu", kernelFunc: xm }; +function tce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false; if (s.dtype === "string") { - let R = t10.bufferSync(n), D = t10.bufferSync(s), F = y.decodeString(t10.readSync(a.dataId)[0]), O = QB(R, D, i, m, c, u, p, l, F, d); - return t10.makeTensorInfo(i, O.dtype, O.values); + let R = t8.bufferSync(n), D = t8.bufferSync(s), P = y.decodeString(t8.readSync(a.dataId)[0]), O = Sz(R, D, i, m, c, u, p, l, P, d); + return t8.makeTensorInfo(i, O.dtype, O.values); } - let f = [m / c, c], h = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u, p] } }), g = s.shape.length ? pe({ inputs: { x: s }, backend: t10, attrs: { shape: [u, c] } }) : At({ inputs: { x: s }, backend: t10 }), x = g.dtype, b = t10.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), w = pe({ inputs: { x: a }, backend: t10, attrs: { shape: Array(f.length).fill(1) } }), S = gm({ inputs: { x: w }, backend: t10, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }]; + let f = [m / c, c], h = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u, p] } }), g = s.shape.length ? pe({ inputs: { x: s }, backend: t8, attrs: { shape: [u, c] } }) : At({ inputs: { x: s }, backend: t8 }), x = g.dtype, b = t8.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = pe({ inputs: { x: a }, backend: t8, attrs: { shape: Array(f.length).fill(1) } }), S = xm({ inputs: { x: C }, backend: t8, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }]; switch (u) { case 0: break; case 1: { - let R = new Aa([u, c], p, h.shape.length, g.shape.length, l, f, x, d); - t10.runWebGPUProgram(R, [g, h], x, _, S); + let R = new La([u, c], p, h.shape.length, g.shape.length, l, f, x, d); + t8.runWebGPUProgram(R, [g, h], x, _, S); } break; default: { - let R = new Aa([u, c], p, h.shape.length, b.shape.length, l, f, x, d); - t10.runWebGPUProgram(R, [b, h], x, _, S); + let R = new La([u, c], p, h.shape.length, b.shape.length, l, f, x, d); + t8.runWebGPUProgram(R, [b, h], x, _, S); } { - let R = new Aa([u, c], p, h.shape.length, g.shape.length, l, f, x); - t10.runWebGPUProgram(R, [g, h], x, _, S); + let R = new La([u, c], p, h.shape.length, g.shape.length, l, f, x); + t8.runWebGPUProgram(R, [g, h], x, _, S); } } - let E = pe({ inputs: { x: S }, backend: t10, attrs: { shape: i } }); - return t10.disposeData(h.dataId), t10.disposeData(g.dataId), t10.disposeData(w.dataId), t10.disposeData(b.dataId), t10.disposeData(S.dataId), E; + let E = pe({ inputs: { x: S }, backend: t8, attrs: { shape: i } }); + return t8.disposeData(h.dataId), t8.disposeData(g.dataId), t8.disposeData(C.dataId), t8.disposeData(b.dataId), t8.disposeData(S.dataId), E; } -var WW = { kernelName: Cs, backendName: "webgpu", kernelFunc: bpe }; -function Cpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); +var dU = { kernelName: vs, backendName: "webgpu", kernelFunc: tce }; +function rce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); return p.map((m) => { let d = [...l]; d[i] = m; - let f = zs({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } }); + let f = Gs({ inputs: { x: n }, backend: t8, attrs: { begin: c, size: d } }); return c[i] += m, f; }); } -var UW = { kernelName: la, backendName: "webgpu", kernelFunc: Cpe }; -var wpe = xe({ opType: Q.SQRT }); -var GW = { kernelName: xs, backendName: "webgpu", kernelFunc: wpe }; -var HW = { kernelName: Gi, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { - let { x: t10 } = r, o = e, n = new Xr(t10.shape, Q.SQUARE); - return o.runWebGPUProgram(n, [t10], t10.dtype); +var fU = { kernelName: ga, backendName: "webgpu", kernelFunc: rce }; +var oce = ye({ opType: Z.SQRT }); +var hU = { kernelName: ws, backendName: "webgpu", kernelFunc: oce }; +var gU = { kernelName: ji, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { + let { x: t8 } = r, o = e, n = new Zr(t8.shape, Z.SQUARE); + return o.runWebGPUProgram(n, [t8], t8.dtype); } }; -var Spe = et({ opType: fe.SQUARED_DIFFERENCE }); -var KW = { kernelName: ws, backendName: "webgpu", kernelFunc: Spe }; -function Ipe({ inputs: r, attrs: e, backend: t10 }) { - let { x: o } = r, n = new Xr(o.shape, Q.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [e.alpha] }]; - return t10.runWebGPUProgram(n, [o], o.dtype, s); +var nce = et({ opType: fe.SQUARED_DIFFERENCE }); +var xU = { kernelName: ks, backendName: "webgpu", kernelFunc: nce }; +function sce({ inputs: r, attrs: e, backend: t8 }) { + let { x: o } = r, n = new Zr(o.shape, Z.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [e.alpha] }]; + return t8.runWebGPUProgram(n, [o], o.dtype, s); } -var qW = { kernelName: yo, backendName: "webgpu", kernelFunc: Ipe }; -var ay = class { +var yU = { kernelName: wo, backendName: "webgpu", kernelFunc: sce }; +var uy = class { constructor(e) { - this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); - let t10 = Nt(this.outputShape.length); - this.uniforms = `begin : ${t10}, strides : ${t10}, `, this.shaderKey = "stridedSlice"; + this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); + let t8 = ht(this.outputShape.length); + this.uniforms = `begin : ${t8}, strides : ${t8}, `, this.shaderKey = "stridedSlice"; } getUserCode() { - let e = this.outputShape.length, t10 = ""; + let e = this.outputShape.length, t8 = ""; if (e === 1) - t10 = "coords * uniforms.strides + uniforms.begin"; + t8 = "coords * uniforms.strides + uniforms.begin"; else { let n = 0; - t10 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(","); + t8 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(","); } return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); - setOutputAtIndex(index, getX(${t10})); + setOutputAtIndex(index, getX(${t8})); } } `; } }; -function vpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function ace(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = pe({ inputs: { x: n }, backend: t10, attrs: { shape: f } }); + k = pe({ inputs: { x: n }, backend: t8, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ct.computeOutShape(b, w, S), E = zs({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } }); - k = pe({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeData(E.dataId); - } else if (t10.shouldExecuteOnCPU([n])) { - let E = t10.readSync(n.dataId), R = me(n.shape, n.dtype, E), D = ez(d, R, S, b); - k = t10.makeTensorInfo(f, n.dtype, D.values); + let _ = ct.computeOutShape(b, C, S), E = Gs({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: _ } }); + k = pe({ inputs: { x: E }, backend: t8, attrs: { shape: f } }), t8.disposeData(E.dataId); + } else if (t8.shouldExecuteOnCPU([n])) { + let E = t8.readSync(n.dataId), R = me(n.shape, n.dtype, E), D = kz(d, R, S, b); + k = t8.makeTensorInfo(f, n.dtype, D.values); } else { - let E = new ay(d), R = [{ type: "int32", data: b }, { type: "int32", data: S }], D = t10.runWebGPUProgram(E, [n], n.dtype, R); - k = pe({ inputs: { x: D }, backend: t10, attrs: { shape: f } }), t10.disposeData(D.dataId); + let E = new uy(d), R = [{ type: "int32", data: b }, { type: "int32", data: S }], D = t8.runWebGPUProgram(E, [n], n.dtype, R); + k = pe({ inputs: { x: D }, backend: t8, attrs: { shape: f } }), t8.disposeData(D.dataId); } return k; } -var jW = { kernelName: Ss, backendName: "webgpu", kernelFunc: vpe }; -function kpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = tz(m, d, n, s, a, i, p, u); - return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)]; +var bU = { kernelName: Ns, backendName: "webgpu", kernelFunc: ace }; +function ice(r) { + let { inputs: e, backend: t8, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t8.readSync(c.dataId), d = t8.readSync(l.dataId), [f, h] = Nz(m, d, n, s, a, i, p, u); + return [t8.makeTensorInfo([f.length], "string", f), t8.makeTensorInfo(l.shape, "int32", h)]; } -var XW = { kernelName: ma, backendName: "webgpu", kernelFunc: kpe }; -var Npe = et({ opType: fe.SUB, cpuKernelImpl: rz, supportsComplex: true }); -var YW = { kernelName: Is, backendName: "webgpu", kernelFunc: Npe }; -var Tpe = xe({ opType: Q.TAN }); -var QW = { kernelName: vs, backendName: "webgpu", kernelFunc: Tpe }; -var _pe = xe({ opType: Q.TANH }); -var ZW = { kernelName: ks, backendName: "webgpu", kernelFunc: _pe }; -function $pe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(a, s, n.shape), m = [l / u, u]; +var CU = { kernelName: xa, backendName: "webgpu", kernelFunc: ice }; +var uce = et({ opType: fe.SUB, cpuKernelImpl: Tz, supportsComplex: true }); +var wU = { kernelName: Ts, backendName: "webgpu", kernelFunc: uce }; +var pce = ye({ opType: Z.TAN }); +var SU = { kernelName: _s, backendName: "webgpu", kernelFunc: pce }; +var cce = ye({ opType: Z.TANH }); +var IU = { kernelName: $s, backendName: "webgpu", kernelFunc: cce }; +function lce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u]; if (l === 0) - return t10.makeTensorInfo(n.shape, s.dtype); - let d = [], f = pe({ inputs: { x: s }, backend: t10, attrs: { shape: [p, i] } }); + return t8.makeTensorInfo(n.shape, s.dtype); + let d = [], f = pe({ inputs: { x: s }, backend: t8, attrs: { shape: [p, i] } }); d.push(f); - let h = pe({ inputs: { x: a }, backend: t10, attrs: { shape: [p, u] } }); + let h = pe({ inputs: { x: a }, backend: t8, attrs: { shape: [p, u] } }); d.push(h); - let g = pe({ inputs: { x: n }, backend: t10, attrs: { shape: m } }); + let g = pe({ inputs: { x: n }, backend: t8, attrs: { shape: m } }); d.push(g); - let x = gm({ inputs: { x: g }, backend: t10, attrs: { reps: Array(m.length).fill(1) } }), b = new Aa([p, u], i, f.shape.length, h.shape.length, c, m, n.dtype, false), w = y.sizeFromShape([p, u]), S = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [w] }], k = t10.runWebGPUProgram(b, [h, f], g.dtype, S, x); + let x = xm({ inputs: { x: g }, backend: t8, attrs: { reps: Array(m.length).fill(1) } }), b = new La([p, u], i, f.shape.length, h.shape.length, c, m, n.dtype, false), C = y.sizeFromShape([p, u]), S = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [C] }], k = t8.runWebGPUProgram(b, [h, f], g.dtype, S, x); d.push(k); - let _ = pe({ inputs: { x: k }, backend: t10, attrs: { shape: n.shape } }); - return d.forEach((E) => t10.disposeData(E.dataId)), _; + let _ = pe({ inputs: { x: k }, backend: t8, attrs: { shape: n.shape } }); + return d.forEach((E) => t8.disposeData(E.dataId)), _; } -var JW = { kernelName: cs, backendName: "webgpu", kernelFunc: $pe }; -var iy = class { +var vU = { kernelName: ds, backendName: "webgpu", kernelFunc: lce }; +var py = class { constructor(e) { - this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32, + this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`, this.shaderKey = "swap"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -32782,13 +32957,13 @@ var iy = class { `; } }; -var uy = class { +var cy = class { constructor(e) { - this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge"; + this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -32849,28 +33024,28 @@ var uy = class { `; } }; -function Jc(r, e) { +function el(r, e) { e !== null && r.disposeData(e.dataId); } -function eU(r) { +function kU(r) { let e = 1; for (; e < r; ) e *= 2; return e; } -function Epe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1]; - if (t10.shouldExecuteOnCPU([n])) { - let k = t10.readSync(n.dataId), [_, E] = nz(k, i, n.dtype, s, a); - return [t10.makeTensorInfo(_.shape, _.dtype, _.values), t10.makeTensorInfo(E.shape, E.dtype, E.values)]; +function mce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1]; + if (t8.shouldExecuteOnCPU([n])) { + let k = t8.readSync(n.dataId), [_, E] = $z(k, i, n.dtype, s, a); + return [t8.makeTensorInfo(_.shape, _.dtype, _.values), t8.makeTensorInfo(E.shape, E.dtype, E.values)]; } if (s === 0) - return i[i.length - 1] = 0, [t10.makeTensorInfo(i, n.dtype, []), t10.makeTensorInfo(i, "int32", [])]; + return i[i.length - 1] = 0, [t8.makeTensorInfo(i, n.dtype, []), t8.makeTensorInfo(i, "int32", [])]; if (p === 1) - return [n, Vt({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t10 })]; - let c = y.sizeFromShape(i) / p, l = pe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t10 }), m = eU(s), d = eU(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => { - let R = h(), D = new iy(E), O = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f; - f = t10.runWebGPUProgram(D, R, "int32", O), Jc(t10, M); + return [n, Vt({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t8 })]; + let c = y.sizeFromShape(i) / p, l = pe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t8 }), m = kU(s), d = kU(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => { + let R = h(), D = new py(E), O = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f; + f = t8.runWebGPUProgram(D, R, "int32", O), el(t8, M); }; for (let k = 1; k < m; k *= 2) { let _ = k * 2; @@ -32878,25 +33053,25 @@ function Epe(r) { g(_, E, [c, d]); } for (let k = d; k > m; k /= 2) { - let _ = h(), E = new uy([c, k / 2]), D = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], F = f; - f = t10.runWebGPUProgram(E, _, "int32", D), Jc(t10, F); + let _ = h(), E = new cy([c, k / 2]), D = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], P = f; + f = t8.runWebGPUProgram(E, _, "int32", D), el(t8, P); let O = m / 2, M = O * 2; for (let L = O; L >= 1; L /= 2) g(M, L, f.shape); } let x = f; - f = zs({ inputs: { x: f }, backend: t10, attrs: { begin: 0, size: [c, s] } }), Jc(t10, x); - let b = Hv({ inputs: { x: l, indices: f }, backend: t10, attrs: { axis: 1, batchDims: 1 } }); - Jc(t10, l); - let w = i.slice(0, -1); - w.push(s), x = f, f = pe({ inputs: { x: f }, attrs: { shape: w }, backend: t10 }), Jc(t10, x); + f = Gs({ inputs: { x: f }, backend: t8, attrs: { begin: 0, size: [c, s] } }), el(t8, x); + let b = e0({ inputs: { x: l, indices: f }, backend: t8, attrs: { axis: 1, batchDims: 1 } }); + el(t8, l); + let C = i.slice(0, -1); + C.push(s), x = f, f = pe({ inputs: { x: f }, attrs: { shape: C }, backend: t8 }), el(t8, x); let S = b; - return b = pe({ inputs: { x: b }, attrs: { shape: w }, backend: t10 }), Jc(t10, S), [b, f]; + return b = pe({ inputs: { x: b }, attrs: { shape: C }, backend: t8 }), el(t8, S), [b, f]; } -var tU = { kernelName: Ns, backendName: "webgpu", kernelFunc: Epe }; -var py = class { +var NU = { kernelName: Es, backendName: "webgpu", kernelFunc: mce }; +var ly = class { constructor(e) { - this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform"; + this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform"; } getUserCode() { return ` @@ -32963,7 +33138,7 @@ var py = class { return outputValue; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; @@ -33018,31 +33193,31 @@ var py = class { `; } }; -function Rpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new py(g), b = a === "nearest" ? 1 : 2, w; +function dce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new ly(g), b = a === "nearest" ? 1 : 2, C; switch (i) { case "constant": - w = 1; + C = 1; break; case "reflect": - w = 2; + C = 2; break; case "wrap": - w = 3; + C = 3; break; case "nearest": - w = 4; + C = 4; break; default: - w = 1; + C = 1; break; } - let S = [{ type: "int32", data: [b] }, { type: "int32", data: [w] }, { type: "float32", data: [p] }]; - return t10.runWebGPUProgram(x, [n, s], "float32", S); + let S = [{ type: "int32", data: [b] }, { type: "int32", data: [C] }, { type: "float32", data: [p] }]; + return t8.runWebGPUProgram(x, [n, s], "float32", S); } -var rU = { kernelName: Ts, backendName: "webgpu", kernelFunc: Rpe }; -function Dpe(r) { - let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; +var TU = { kernelName: Rs, backendName: "webgpu", kernelFunc: dce }; +function fce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0; for (let h = 0; h < i; h++) @@ -33052,22 +33227,22 @@ function Dpe(r) { let f = new Array(p); for (let h = 0; h < f.length; h++) { m[s] = h; - let g = zs({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = pe({ inputs: { x: g }, backend: t10, attrs: { shape: u } }); + let g = Gs({ inputs: { x: a }, backend: t8, attrs: { begin: m, size: d } }), x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: u } }); f[h] = x, l.push(g); } - return l.forEach((h) => t10.disposeData(h.dataId)), f; + return l.forEach((h) => t8.disposeData(h.dataId)), f; } -var oU = { kernelName: da, backendName: "webgpu", kernelFunc: Dpe }; -var cy = class { - constructor(e, t10, o) { - if (this.outputShape = [], this.variableNames = ["x", "segmentIds"], this.uniforms = "numSegments : i32, xSize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t10, this.dispatchLayout = Z(e), this.dispatch = q(this.dispatchLayout, e, this.workgroupSize), o !== "float32" && o !== "int32") +var _U = { kernelName: ya, backendName: "webgpu", kernelFunc: fce }; +var my = class { + constructor(e, t8, o) { + if (this.outputShape = [], this.variableNames = ["x", "segmentIds"], this.uniforms = "numSegments : i32, xSize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t8, this.dispatchLayout = X(e), this.dispatch = K(this.dispatchLayout, e, this.workgroupSize), o !== "float32" && o !== "int32") throw new Error(`UnsortedSegmentSum only supports float32 and int32 types, does not support ${o} type.`); this.type = o, this.shaderKey = "unsortedSegmentSum"; } getUserCode() { return ` - ${K("index")} { + ${H("index")} { if (index < uniforms.xSize) { let coords = getXCoordsFromIndex(index); let b = coords[0]; @@ -33078,39 +33253,39 @@ var cy = class { let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments; let value = getX(b, inCol); - ${Bs("&result[flatIndex]", "value", this.type)} + ${Us("&result[flatIndex]", "value", this.type)} } } } `; } }; -function Ape(r) { - let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = C.getAxesPermutation([u], i), l = n; - c != null && (l = rr({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = C.getInnerMostAxes(1, i)[0]); - let m = C.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = pe({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } }); +function hce(r) { + let { inputs: e, backend: t8, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n; + c != null && (l = yr({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]); + let m = w.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = pe({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, d] } }); p.push(f); - let h = n.dtype, g = [f.shape[0], a], x = Vt({ backend: t10, attrs: { shape: g, value: 0, dtype: h } }), b = new cy(f.shape, g, h), w = [{ type: "int32", data: [a] }, { type: "int32", data: [y.sizeFromShape(f.shape)] }], S = t10.runWebGPUProgram(b, [f, s], h, w, x), k = pe({ inputs: { x: S }, backend: t10, attrs: { shape: m } }); + let h = n.dtype, g = [f.shape[0], a], x = Vt({ backend: t8, attrs: { shape: g, value: 0, dtype: h } }), b = new my(f.shape, g, h), C = [{ type: "int32", data: [a] }, { type: "int32", data: [y.sizeFromShape(f.shape)] }], S = t8.runWebGPUProgram(b, [f, s], h, C, x), k = pe({ inputs: { x: S }, backend: t8, attrs: { shape: m } }); p.push(S); let _ = k; if (c != null) { p.push(k); - let E = C.getUndoAxesPermutation(c); - _ = rr({ inputs: { x: _ }, backend: t10, attrs: { perm: E } }); + let E = w.getUndoAxesPermutation(c); + _ = yr({ inputs: { x: _ }, backend: t8, attrs: { perm: E } }); } - return p.forEach((E) => t10.disposeData(E.dataId)), _; + return p.forEach((E) => t8.disposeData(E.dataId)), _; } -var nU = { kernelName: ji, backendName: "webgpu", kernelFunc: Ape }; -var Fpe = [IB, az, iz, uz, pz, cz, mz, dz, fz, hz, gz, xz, yz, bz, Cz, Iz, vz, kz, Nz, Tz, $z, Ez, Rz, Pz, Oz, Mz, kB, Bz, Vz, Wz, Uz, Gz, Hz, Kz, qz, jz, Xz, Yz, Jz, eV, tV, rV, nV, sV, oV, aV, iV, uV, pV, mV, dV, fV, hV, gV, xV, yV, bV, CV, wB, wV, vV, SV, IV, kV, NV, TV, _V, $V, EV, RV, vB, DV, zz, AV, FV, PV, OV, MV, LV, BV, VV, zV, WV, UV, GV, KV, qV, wz, jV, XV, ZV, YV, QV, JV, Sz, eW, tW, rW, oW, sW, cV, aW, iW, uW, Dz, pW, mW, dW, fW, hW, gW, xW, yW, Az, bW, CW, wW, SW, SB, IW, vW, kW, NW, TW, _W, $W, EW, RW, DW, AW, FW, PW, OW, MW, LW, _z, qW, jW, XW, nW, BW, zW, WW, UW, GW, HW, KW, YW, lV, QW, ZW, JW, VW, tU, rU, lz, oU, nU, cW]; -for (let r of Fpe) - Ya(r); -var sU = "4.5.0"; -var Ppe = "4.5.0"; -var Ope = "4.5.0"; -var Mpe = "4.5.0"; -var Lpe = "4.5.0"; -var Bpe = "0.0.1-alpha.20"; -var zpe = { tfjs: sU, "tfjs-core": sU, "tfjs-converter": Ppe, "tfjs-backend-cpu": Ope, "tfjs-backend-webgl": Mpe, "tfjs-backend-wasm": Lpe, "tfjs-backend-webgpu": Bpe }; +var $U = { kernelName: Zi, backendName: "webgpu", kernelFunc: hce }; +var gce = [jB, Rz, Dz, Az, Fz, Pz, Mz, Lz, Bz, zz, Vz, Wz, Uz, Gz, Hz, jz, Xz, Yz, Qz, Zz, eV, tV, rV, aV, iV, uV, YB, cV, mV, dV, fV, hV, gV, xV, yV, bV, CV, wV, vV, kV, NV, TV, $V, EV, _V, RV, DV, AV, FV, MV, LV, BV, zV, VV, WV, UV, GV, HV, KB, KV, XV, qV, jV, YV, QV, ZV, JV, eW, tW, rW, XB, oW, lV, nW, sW, aW, iW, uW, pW, cW, mW, lW, dW, fW, hW, xW, yW, Kz, bW, CW, IW, wW, SW, vW, qz, kW, NW, TW, _W, EW, PV, RW, DW, AW, oV, FW, MW, LW, BW, zW, VW, WW, UW, nV, GW, HW, KW, qW, qB, jW, XW, YW, QW, ZW, JW, eU, tU, rU, oU, nU, sU, aU, iU, uU, pU, Jz, yU, bU, CU, $W, cU, lU, dU, fU, hU, gU, xU, wU, OV, SU, IU, vU, mU, NU, TU, Oz, _U, $U, PW]; +for (let r of gce) + Ja(r); +var EU = "4.7.0"; +var xce = "4.7.0"; +var yce = "4.7.0"; +var bce = "4.7.0"; +var Cce = "4.7.0"; +var wce = "4.7.0"; +var Sce = { tfjs: EU, "tfjs-core": EU, "tfjs-converter": xce, "tfjs-backend-cpu": yce, "tfjs-backend-webgl": bce, "tfjs-backend-wasm": Cce, "tfjs-backend-webgpu": wce }; // src/util/util.ts function log(...msg) { @@ -33595,20 +33770,20 @@ function GLImageFilter() { }, saturation: (amount) => { const x = (amount || 0) * 2 / 3 + 1; - const y5 = (x - 1) * -0.5; + const y10 = (x - 1) * -0.5; filter.colorMatrix([ x, - y5, - y5, + y10, + y10, 0, 0, - y5, + y10, x, - y5, + y10, 0, 0, - y5, - y5, + y10, + y10, x, 0, 0, @@ -33623,22 +33798,22 @@ function GLImageFilter() { filter.saturation(-1); }, contrast: (amount) => { - const v5 = (amount || 0) + 1; - const o = -128 * (v5 - 1); + const v10 = (amount || 0) + 1; + const o = -128 * (v10 - 1); filter.colorMatrix([ - v5, + v10, 0, 0, 0, o, 0, - v5, + v10, 0, 0, o, 0, 0, - v5, + v10, 0, o, 0, @@ -34009,10 +34184,10 @@ function GLImageFilter() { // src/image/enhance.ts async function histogramEqualization(inputImage) { - const squeeze = inputImage.shape.length === 4 ? lc(inputImage) : inputImage; - const rgb3 = ai(squeeze, 3, 2); + const squeeze = inputImage.shape.length === 4 ? mc(inputImage) : inputImage; + const rgb3 = pi(squeeze, 3, 2); const min = [vl(rgb3[0]), vl(rgb3[1]), vl(rgb3[2])]; - const max = [Ia(rgb3[0]), Ia(rgb3[1]), Ia(rgb3[2])]; + const max = [_a(rgb3[0]), _a(rgb3[1]), _a(rgb3[2])]; const absMax = await Promise.all(max.map((channel) => channel.data())); const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); const maxRange = maxValue > 1 ? 255 : 1; @@ -34022,11 +34197,11 @@ async function histogramEqualization(inputImage) { const sub = [Te(rgb3[0], min[0]), Te(rgb3[1], min[1]), Te(rgb3[2], min[2])]; const range = [Te(max[0], min[0]), Te(max[1], min[1]), Te(max[2], min[2])]; const enh = [se(sub[0], factor), se(sub[1], factor), se(sub[2], factor)]; - const stack = vr([enh[0], enh[1], enh[2]], 2); + const stack = kr([enh[0], enh[1], enh[2]], 2); final = W(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]); Ot([...sub, ...range, ...enh, stack]); } else { - final = oi(squeeze, 0); + final = ai(squeeze, 0); } Ot([...rgb3, ...min, ...max, rgb3, squeeze, inputImage]); return final; @@ -34094,10 +34269,10 @@ async function process2(input, config3, getTensor = true) { log("input error: input is missing"); return { tensor: null, canvas: null }; } - if (!(input instanceof pt) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + if (!(input instanceof ut) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { throw new Error("input error: type not recognized"); } - if (input instanceof pt) { + if (input instanceof ut) { let tensor2 = null; if (input["isDisposedInternal"]) throw new Error("input error: attempted to use tensor but it is disposed"); @@ -34105,23 +34280,23 @@ async function process2(input, config3, getTensor = true) { throw new Error("input error: attempted to use tensor without a shape"); if (input.shape.length === 3) { if (input.shape[2] === 3) { - tensor2 = oi(input, 0); + tensor2 = ai(input, 0); } else if (input.shape[2] === 4) { - const rgb3 = g1(input, [0, 0, 0], [-1, -1, 3]); - tensor2 = oi(rgb3, 0); + const rgb3 = N1(input, [0, 0, 0], [-1, -1, 3]); + tensor2 = ai(rgb3, 0); Ot(rgb3); } } else if (input.shape.length === 4) { if (input.shape[3] === 3) { - tensor2 = Vr(input); + tensor2 = Ur(input); } else if (input.shape[3] === 4) { - tensor2 = x1(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + tensor2 = T1(input, [0, 0, 0, 0], [-1, -1, -1, 3]); } } if (tensor2 == null || tensor2.shape.length !== 4 || tensor2.shape[0] !== 1 || tensor2.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); if (tensor2.dtype === "int32") { - const cast = Ye(tensor2, "float32"); + const cast = qe(tensor2, "float32"); Ot(tensor2); tensor2 = cast; } @@ -34234,8 +34409,8 @@ async function process2(input, config3, getTensor = true) { let pixels; let depth = 3; if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && MN) { - pixels = MN ? MN.fromPixels(input) : null; + if (env.browser && XN) { + pixels = XN ? XN.fromPixels(input) : null; } else { depth = input.data.length / input.height / input.width; const arr = new Uint8Array(input.data.buffer); @@ -34244,12 +34419,12 @@ async function process2(input, config3, getTensor = true) { } else { if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (MN && env.browser) { + if (XN && env.browser) { if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = MN.fromPixels(outCanvas); + pixels = XN.fromPixels(outCanvas); } else { tmpCanvas = copy(outCanvas); - pixels = MN.fromPixels(tmpCanvas); + pixels = XN.fromPixels(tmpCanvas); } } else { const tempCanvas = copy(outCanvas); @@ -34261,17 +34436,17 @@ async function process2(input, config3, getTensor = true) { } } if (depth === 4) { - const rgb3 = g1(pixels, [0, 0, 0], [-1, -1, 3]); + const rgb3 = N1(pixels, [0, 0, 0], [-1, -1, 3]); Ot(pixels); pixels = rgb3; } if (!pixels) throw new Error("input error: cannot create tensor"); - const casted = Ye(pixels, "float32"); - const tensor = config3.filter.equalization ? await histogramEqualization(casted) : oi(casted, 0); + const casted = qe(pixels, "float32"); + const tensor = config3.filter.equalization ? await histogramEqualization(casted) : ai(casted, 0); Ot([pixels, casted]); if (config3.filter.autoBrightness) { - const max = Ia(tensor); + const max = _a(tensor); const maxVal = await max.data(); config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; Ot(max); @@ -34283,25 +34458,25 @@ async function skip(config3, input) { if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; if (!last.inputTensor) { - last.inputTensor = Vr(input); + last.inputTensor = Ur(input); } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { Ot(last.inputTensor); - last.inputTensor = Vr(input); + last.inputTensor = Ur(input); } else { - const t10 = {}; - t10.diff = Te(input, last.inputTensor); - t10.squared = se(t10.diff, t10.diff); - t10.sum = ot(t10.squared); - const diffSum = await t10.sum.data(); + const t8 = {}; + t8.diff = Te(input, last.inputTensor); + t8.squared = se(t8.diff, t8.diff); + t8.sum = ot(t8.squared); + const diffSum = await t8.sum.data(); const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - Ot([last.inputTensor, t10.diff, t10.squared, t10.sum]); - last.inputTensor = Vr(input); + Ot([last.inputTensor, t8.diff, t8.squared, t8.sum]); + last.inputTensor = Ur(input); skipFrame = diffRelative <= (config3.cacheSensitivity || 0); } return skipFrame; } async function compare(config3, input1, input2) { - const t10 = {}; + const t8 = {}; if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { if (!config3.debug) log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); @@ -34312,14 +34487,14 @@ async function compare(config3, input1, input2) { log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); return 0; } - t10.input1 = Vr(input1); - t10.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? uj.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Vr(input2); - t10.diff = Te(t10.input1, t10.input2); - t10.squared = se(t10.diff, t10.diff); - t10.sum = ot(t10.squared); - const diffSum = await t10.sum.data(); + t8.input1 = Ur(input1); + t8.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? Fj.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Ur(input2); + t8.diff = Te(t8.input1, t8.input2); + t8.squared = se(t8.diff, t8.diff); + t8.sum = ot(t8.squared); + const diffSum = await t8.sum.data(); const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - Ot([t10.input1, t10.input2, t10.diff, t10.squared, t10.sum]); + Ot([t8.input1, t8.input2, t8.diff, t8.squared, t8.sum]); return diffRelative; } @@ -34389,7 +34564,7 @@ var Env = class { __privateAdd(this, _imageData, void 0); this.browser = typeof navigator !== "undefined"; this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: zpe["tfjs-core"] }; + this.tfjs = { version: Sce["tfjs-core"] }; this.offscreen = typeof OffscreenCanvas !== "undefined"; this.initial = true; this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; @@ -34437,16 +34612,16 @@ var Env = class { this.backends = Object.keys(ur().registryFactory); try { this.tensorflow = { - version: Tme()["binding"] ? Tme()["binding"].TF_Version : void 0, - gpu: Tme()["binding"] ? Tme()["binding"].isUsingGpuDevice() : void 0 + version: dde()["binding"] ? dde()["binding"].TF_Version : void 0, + gpu: dde()["binding"] ? dde()["binding"].isUsingGpuDevice() : void 0 }; } catch (e) { } this.wasm.supported = typeof WebAssembly !== "undefined"; this.wasm.backend = this.backends.includes("wasm"); if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await P().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + this.wasm.simd = await A().getAsync("WASM_HAS_SIMD_SUPPORT"); + this.wasm.multithread = await A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); } const c = canvas(100, 100); const gl2 = c ? c.getContext("webgl2") : void 0; @@ -34469,7 +34644,7 @@ var Env = class { this.webgpu.supported = false; } try { - this.kernels = Km(Ime()).map((kernel) => kernel.kernelName.toLowerCase()); + this.kernels = jm(pde()).map((kernel) => kernel.kernelName.toLowerCase()); } catch (e) { } } @@ -34527,20 +34702,20 @@ var WebCam = class { this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; if (webcamConfig == null ? void 0 : webcamConfig.element) { if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; + const el2 = document.getElementById(webcamConfig.element); + if (el2 && el2 instanceof HTMLVideoElement) { + this.element = el2; } else { if (this.config.debug) log("webcam", "cannot get dom element", webcamConfig.element); - return; + return `webcam error: cannot get dom element: ${webcamConfig.element}`; } } else if (webcamConfig.element instanceof HTMLVideoElement) { this.element = webcamConfig.element; } else { if (this.config.debug) log("webcam", "unknown dom element", webcamConfig.element); - return; + return `webcam error: unknown dom element: ${webcamConfig.element}`; } } else { this.element = document.createElement("video"); @@ -34577,19 +34752,19 @@ var WebCam = class { }); if (!(navigator == null ? void 0 : navigator.mediaDevices)) { if (this.config.debug) - log("webcam", "no devices"); - return; + log("webcam error", "no devices"); + return "webcam error: no devices"; } try { this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); } catch (err) { log("webcam", err); - return; + return `webcam error: ${err}`; } if (!this.stream) { if (this.config.debug) - log("webcam", "no stream"); - return; + log("webcam error", "no stream"); + return "webcam error no stream"; } this.element.srcObject = this.stream; const ready = new Promise((resolve) => { @@ -34612,6 +34787,7 @@ var WebCam = class { capabilities: this.capabilities }); } + return `webcam: ${this.label}`; }); /** pause webcam video method */ __publicField(this, "pause", () => { @@ -34907,7 +35083,7 @@ async function loadModel(modelPath) { options.cacheSupported = typeof indexedDB !== "undefined"; let cachedModels = {}; try { - cachedModels = options.cacheSupported && options.cacheModels ? await pi.listModels() : {}; + cachedModels = options.cacheSupported && options.cacheModels ? await mi.listModels() : {}; } catch (e) { options.cacheSupported = false; } @@ -34929,7 +35105,7 @@ async function loadModel(modelPath) { if (artifacts) model23.loadSync(artifacts); else - model23 = await W5(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); + model23 = await h8(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); modelStats[shortModelName].sizeLoadedWeights = ((_d2 = (_c2 = model23.artifacts) == null ? void 0 : _c2.weightData) == null ? void 0 : _d2.byteLength) || 0; if (options.verbose) log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); @@ -34950,7 +35126,7 @@ async function loadModel(modelPath) { } // package.json -var version = "3.0.6"; +var version = "3.0.7"; // src/tfjs/humangl.ts var config2 = { @@ -34987,7 +35163,7 @@ function register(instance) { log("humangl error: backend invalid context"); instance.models.reset(); } - if (!kme(config2.name)) { + if (!lde(config2.name)) { try { config2.canvas = canvas(100, 100); } catch (err) { @@ -35025,37 +35201,37 @@ function register(instance) { return; } try { - iI(2, config2.gl); + gI(2, config2.gl); } catch (err) { log("humangl error: cannot set webgl context:", err); return; } try { - const ctx = new xp(config2.gl); - eu(config2.name, () => new hu(ctx), config2.priority); + const ctx = new wp(config2.gl); + nu(config2.name, () => new bu(ctx), config2.priority); } catch (err) { log("humangl error: cannot register webgl backend:", err); return; } try { - const kernels = Km("webgl"); + const kernels = jm("webgl"); kernels.forEach((kernelConfig) => { const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - Ya(newKernelConfig); + Ja(newKernelConfig); }); } catch (err) { log("humangl error: cannot update webgl backend registration:", err); return; } try { - if (P().flagRegistry.WEBGL_VERSION) - P().set("WEBGL_VERSION", 2); + if (A().flagRegistry.WEBGL_VERSION) + A().set("WEBGL_VERSION", 2); } catch (err) { log("humangl error: cannot set WebGL backend flags:", err); return; } extensions(); - const backend = Tme(); + const backend = dde(); const current = typeof backend["gpgpu"] !== "undefined" ? backend["getGPGPUContext"]().gl : null; if (current) { if (instance.config.debug) @@ -35103,36 +35279,36 @@ function registerCustomOps(config3) { if (!env.kernels.includes("mod")) { const kernelMod = { kernelName: "Mod", - backendName: Ime(), - kernelFunc: (op2) => De(() => Te(op2.inputs.a, se(Ke(op2.inputs.a, op2.inputs.b), op2.inputs.b))) + backendName: pde(), + kernelFunc: (op2) => De(() => Te(op2.inputs.a, se(je(op2.inputs.a, op2.inputs.b), op2.inputs.b))) }; - Ya(kernelMod); + Ja(kernelMod); env.kernels.push("mod"); newKernels.push("mod"); } if (!env.kernels.includes("floormod")) { const kernelFloorMod = { kernelName: "FloorMod", - backendName: Ime(), - kernelFunc: (op2) => De(() => be(se(pd(op2.inputs.a, op2.inputs.b), op2.inputs.b), x2(op2.inputs.a, op2.inputs.b))) + backendName: pde(), + kernelFunc: (op2) => De(() => Ce(se(ld(op2.inputs.a, op2.inputs.b), op2.inputs.b), T2(op2.inputs.a, op2.inputs.b))) }; - Ya(kernelFloorMod); + Ja(kernelFloorMod); env.kernels.push("floormod"); newKernels.push("floormod"); } if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { const kernelRotateWithOffset = { kernelName: "RotateWithOffset", - backendName: Ime(), + backendName: pde(), kernelFunc: (op2) => De(() => { - const backend = Ime(); - wme("cpu"); - const t10 = uj.rotateWithOffset(op2.inputs.image, op2.attrs.radians, op2.attrs.fillValue, op2.attrs.center); - wme(backend); - return t10; + const backend = pde(); + ide("cpu"); + const t8 = Fj.rotateWithOffset(op2.inputs.image, op2.attrs.radians, op2.attrs.fillValue, op2.attrs.center); + ide(backend); + return t8; }) }; - Ya(kernelRotateWithOffset); + Ja(kernelRotateWithOffset); env.kernels.push("rotatewithoffset"); newKernels.push("rotatewithoffset"); } @@ -35145,7 +35321,7 @@ async function check(instance, force = false) { instance.state = "backend"; if (((_a2 = instance.config.backend) == null ? void 0 : _a2.length) === 0) instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Ime() !== instance.config.backend) { + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && pde() !== instance.config.backend) { const timeStamp = now(); if (instance.config.backend && instance.config.backend.length > 0) { if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { @@ -35199,19 +35375,19 @@ async function check(instance, force = false) { if (instance.config.debug) log("setting backend:", [instance.config.backend]); if (instance.config.backend === "wasm") { - if (P().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) - P().set("CANVAS2D_WILL_READ_FREQUENTLY", true); + if (A().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) + A().set("CANVAS2D_WILL_READ_FREQUENTLY", true); if (instance.config.debug) log("wasm path:", instance.config.wasmPath); - if (typeof Zne !== "undefined") - Zne(instance.config.wasmPath, instance.config.wasmPlatformFetch); + if (typeof Ose !== "undefined") + Ose(instance.config.wasmPath, instance.config.wasmPlatformFetch); else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); let mt = false; let simd = false; try { - mt = await P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await P().getAsync("WASM_HAS_SIMD_SUPPORT"); + mt = await A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + simd = await A().getAsync("WASM_HAS_SIMD_SUPPORT"); if (instance.config.debug) log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); if (instance.config.debug && !simd) @@ -35221,29 +35397,29 @@ async function check(instance, force = false) { } } try { - await wme(instance.config.backend); - await Sme(); + await ide(instance.config.backend); + await ude(); } catch (err) { log("error: cannot set backend:", instance.config.backend, err); return false; } if (instance.config.debug) - defaultFlags = JSON.parse(JSON.stringify(P().flags)); + defaultFlags = JSON.parse(JSON.stringify(A().flags)); } - if (Ime() === "humangl" || Ime() === "webgl") { - if (P().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) - P().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (P().flagRegistry.WEBGL_EXP_CONV) - P().set("WEBGL_EXP_CONV", true); + if (pde() === "humangl" || pde() === "webgl") { + if (A().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) + A().set("WEBGL_USE_SHAPES_UNIFORMS", true); + if (A().flagRegistry.WEBGL_EXP_CONV) + A().set("WEBGL_EXP_CONV", true); if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - P().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + A().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); } } - if (Ime() === "webgpu") { + if (pde() === "webgpu") { } if (instance.config.debug) { - const newFlags = P().flags; + const newFlags = A().flags; const updatedFlags = {}; for (const key of Object.keys(newFlags)) { if (defaultFlags[key] === newFlags[key]) @@ -35251,19 +35427,19 @@ async function check(instance, force = false) { updatedFlags[key] = newFlags[key]; } if (instance.config.debug && Object.keys(updatedFlags).length > 0) - log("backend:", Ime(), "flags:", updatedFlags); + log("backend:", pde(), "flags:", updatedFlags); } if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { if (instance.config.debug) log("flags:", instance.config["flags"]); for (const [key, val] of Object.entries(instance.config.flags)) { - P().set(key, val); + A().set(key, val); } } - fme(); + ede(); init(); instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = Ime(); + instance.config.backend = pde(); await env.updateBackend(); registerCustomOps(instance.config); } @@ -35283,9 +35459,9 @@ function fakeOps(kernelNames, config3) { // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, }; - Ya(kernelConfig); + Ja(kernelConfig); } - env.kernels = Km(Ime()).map((kernel) => kernel.kernelName.toLowerCase()); + env.kernels = jm(pde()).map((kernel) => kernel.kernelName.toLowerCase()); } // src/draw/draw.ts @@ -35310,7 +35486,7 @@ var getCanvasContext = (input) => { else if (!input.getContext) log("draw error: canvas context not defined"); else { - const ctx = input.getContext("2d"); + const ctx = input.getContext("2d", { willReadFrequently: true }); if (!ctx) log("draw error: cannot get canvas context"); else @@ -35330,38 +35506,38 @@ function labels(ctx, str, startX, startY, localOptions2) { const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); const x = Math.max(0, startX); for (let i = line.length - 1; i >= 0; i--) { - const y5 = i * localOptions2.lineHeight + startY; + const y10 = i * localOptions2.lineHeight + startY; if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y5 + 16); + ctx.fillText(line[i], x + 5, y10 + 16); } ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y5 + 15); + ctx.fillText(line[i], x + 4, y10 + 15); } } -function point(ctx, x, y5, z, localOptions2) { +function point(ctx, x, y10, z, localOptions2) { ctx.fillStyle = colorDepth(z, localOptions2); ctx.beginPath(); - ctx.arc(x, y5, localOptions2.pointSize, 0, 2 * Math.PI); + ctx.arc(x, y10, localOptions2.pointSize, 0, 2 * Math.PI); ctx.fill(); } -function rect(ctx, x, y5, width, height, localOptions2) { +function rect(ctx, x, y10, width, height, localOptions2) { ctx.beginPath(); ctx.lineWidth = localOptions2.lineWidth; if (localOptions2.useCurves) { const cx2 = (x + x + width) / 2; - const cy2 = (y5 + y5 + height) / 2; + const cy2 = (y10 + y10 + height) / 2; ctx.ellipse(cx2, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI); } else { - ctx.moveTo(x + localOptions2.roundRect, y5); - ctx.lineTo(x + width - localOptions2.roundRect, y5); - ctx.quadraticCurveTo(x + width, y5, x + width, y5 + localOptions2.roundRect); - ctx.lineTo(x + width, y5 + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y5 + height, x + width - localOptions2.roundRect, y5 + height); - ctx.lineTo(x + localOptions2.roundRect, y5 + height); - ctx.quadraticCurveTo(x, y5 + height, x, y5 + height - localOptions2.roundRect); - ctx.lineTo(x, y5 + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y5, x + localOptions2.roundRect, y5); + ctx.moveTo(x + localOptions2.roundRect, y10); + ctx.lineTo(x + width - localOptions2.roundRect, y10); + ctx.quadraticCurveTo(x + width, y10, x + width, y10 + localOptions2.roundRect); + ctx.lineTo(x + width, y10 + height - localOptions2.roundRect); + ctx.quadraticCurveTo(x + width, y10 + height, x + width - localOptions2.roundRect, y10 + height); + ctx.lineTo(x + localOptions2.roundRect, y10 + height); + ctx.quadraticCurveTo(x, y10 + height, x, y10 + height - localOptions2.roundRect); + ctx.lineTo(x, y10 + localOptions2.roundRect); + ctx.quadraticCurveTo(x, y10, x + localOptions2.roundRect, y10); ctx.closePath(); } ctx.stroke(); @@ -35405,22 +35581,22 @@ function curves(ctx, points, localOptions2) { function arrow(ctx, from, to, radius = 5) { let angle; let x; - let y5; + let y10; ctx.beginPath(); ctx.moveTo(from[0], from[1]); ctx.lineTo(to[0], to[1]); angle = Math.atan2(to[1] - from[1], to[0] - from[0]); x = radius * Math.cos(angle) + to[0]; - y5 = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y5); + y10 = radius * Math.sin(angle) + to[1]; + ctx.moveTo(x, y10); angle += 1 / 3 * (2 * Math.PI); x = radius * Math.cos(angle) + to[0]; - y5 = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y5); + y10 = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y10); angle += 1 / 3 * (2 * Math.PI); x = radius * Math.cos(angle) + to[0]; - y5 = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y5); + y10 = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y10); ctx.closePath(); ctx.stroke(); ctx.fill(); @@ -39368,10 +39544,10 @@ function drawFacePoints(f, ctx) { } } } else { - for (const [k, v5] of Object.entries((f == null ? void 0 : f.annotations) || {})) { - if (!(v5 == null ? void 0 : v5[0])) + for (const [k, v10] of Object.entries((f == null ? void 0 : f.annotations) || {})) { + if (!(v10 == null ? void 0 : v10[0])) continue; - const pt2 = v5[0]; + const pt2 = v10[0]; point(ctx, pt2[0], pt2[1], 0, localOptions); if (localOptions.drawLabels) labels(ctx, k, pt2[0], pt2[1], localOptions); @@ -39795,10 +39971,10 @@ function createAnchors() { const stride = strides[layerId]; const featureMapHeight = Math.ceil(inputSize / stride); const featureMapWidth = Math.ceil(inputSize / stride); - for (let y5 = 0; y5 < featureMapHeight; ++y5) { + for (let y10 = 0; y10 < featureMapHeight; ++y10) { for (let x = 0; x < featureMapWidth; ++x) { for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y5 + 0.5) / featureMapHeight }); + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y10 + 0.5) / featureMapHeight }); } } } @@ -39821,33 +39997,33 @@ async function loadDetector(config3) { var cropFactor = [5, 5]; function decodeBoxes(boxesTensor, anchor) { return De(() => { - const split = ai(boxesTensor, 12, 1); - let xCenter = lc(split[0]); - let yCenter = lc(split[1]); - let width = lc(split[2]); - let height = lc(split[3]); - xCenter = be(Ke(xCenter, inputSize), anchor.x); - yCenter = be(Ke(yCenter, inputSize), anchor.y); - width = se(Ke(width, inputSize), cropFactor[0]); - height = se(Ke(height, inputSize), cropFactor[1]); - const xMin = Te(xCenter, Ke(width, 2)); - const yMin = Te(yCenter, Ke(height, 2)); - const xMax = be(xMin, width); - const yMax = be(yMin, height); - const boxes = vr([xMin, yMin, xMax, yMax], 1); + const split = pi(boxesTensor, 12, 1); + let xCenter = mc(split[0]); + let yCenter = mc(split[1]); + let width = mc(split[2]); + let height = mc(split[3]); + xCenter = Ce(je(xCenter, inputSize), anchor.x); + yCenter = Ce(je(yCenter, inputSize), anchor.y); + width = se(je(width, inputSize), cropFactor[0]); + height = se(je(height, inputSize), cropFactor[1]); + const xMin = Te(xCenter, je(width, 2)); + const yMin = Te(yCenter, je(height, 2)); + const xMax = Ce(xMin, width); + const yMax = Ce(yMin, height); + const boxes = kr([xMin, yMin, xMax, yMax], 1); return boxes; }); } async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { var _a2, _b; const detectedBoxes = []; - const t10 = {}; - t10.boxes = decodeBoxes(boxesTensor, anchorTensor); - t10.scores = wa(logitsTensor); - t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t10.nms.data(); - const scores = await t10.scores.data(); - const boxes = await t10.boxes.array(); + const t8 = {}; + t8.boxes = decodeBoxes(boxesTensor, anchorTensor); + t8.scores = Na(logitsTensor); + t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); + const nms = await t8.nms.data(); + const scores = await t8.scores.data(); + const boxes = await t8.boxes.array(); for (const i of Array.from(nms)) { const score = scores[i]; const boxRaw = boxes[i]; @@ -39855,18 +40031,18 @@ async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { const detectedBox = { score, boxRaw, box }; detectedBoxes.push(detectedBox); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return detectedBoxes; } async function detectBoxes(input, config3, outputSize2) { - const t10 = {}; - t10.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t10.logitsRaw = qe(t10.res, [0, 0, 0], [1, -1, 1]); - t10.boxesRaw = qe(t10.res, [0, 0, 1], [1, -1, -1]); - t10.logits = lc(t10.logitsRaw); - t10.boxes = lc(t10.boxesRaw); - const boxes = await decodeResults(t10.boxes, t10.logits, config3, outputSize2); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + const t8 = {}; + t8.res = model == null ? void 0 : model.execute(input, ["Identity"]); + t8.logitsRaw = Xe(t8.res, [0, 0, 0], [1, -1, 1]); + t8.boxesRaw = Xe(t8.res, [0, 0, 1], [1, -1, -1]); + t8.logits = mc(t8.logitsRaw); + t8.boxes = mc(t8.boxesRaw); + const boxes = await decodeResults(t8.boxes, t8.logits, config3, outputSize2); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return boxes; } @@ -39926,12 +40102,12 @@ async function loadPose(config3) { } function prepareImage(input, size2, cropBox) { var _a2, _b; - const t10 = {}; + const t8 = {}; if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; let final; if (cropBox) { - t10.cropped = uj.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + t8.cropped = Fj.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); } if (input.shape[1] !== input.shape[2]) { const height = [ @@ -39952,16 +40128,16 @@ function prepareImage(input, size2, cropBox) { [0, 0] // dont touch rbg ]; - t10.pad = ka(t10.cropped || input, padding); - t10.resize = uj.resizeBilinear(t10.pad, [size2, size2]); - final = Ke(t10.resize, constants.tf255); + t8.pad = Ea(t8.cropped || input, padding); + t8.resize = Fj.resizeBilinear(t8.pad, [size2, size2]); + final = je(t8.resize, constants.tf255); } else if (input.shape[1] !== size2) { - t10.resize = uj.resizeBilinear(t10.cropped || input, [size2, size2]); - final = Ke(t10.resize, constants.tf255); + t8.resize = Fj.resizeBilinear(t8.cropped || input, [size2, size2]); + final = je(t8.resize, constants.tf255); } else { - final = Ke(t10.cropped || input, constants.tf255); + final = je(t8.cropped || input, constants.tf255); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return final; } function rescaleKeypoints(keypoints, outputSize2, cropBox) { @@ -40006,19 +40182,19 @@ function fixKeypoints(keypoints) { async function detectLandmarks(input, config3, outputSize2) { if (!(model2 == null ? void 0 : model2["executor"])) return null; - const t10 = {}; + const t8 = {}; [ - t10.ld, - t10.segmentation, - t10.heatmap, - t10.world, - t10.poseflag + t8.ld, + t8.segmentation, + t8.heatmap, + t8.world, + t8.poseflag /* 1,1 */ ] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t10.poseflag.data())[0]; - const points = await t10.ld.data(); - const distances = await t10.world.data(); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + const poseScore = (await t8.poseflag.data())[0]; + const points = await t8.ld.data(); + const distances = await t8.world.data(); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); const keypointsRelative = []; const depth = 5; for (let i = 0; i < points.length / depth; i++) { @@ -40186,18 +40362,18 @@ async function load(config3) { async function process3(res, outputShape, config3) { if (!res) return []; - const t10 = {}; + const t8 = {}; const results = []; const detections = await res.array(); - t10.squeeze = lc(res); - const arr = ai(t10.squeeze, 6, 1); - t10.stack = vr([arr[1], arr[0], arr[3], arr[2]], 1); - t10.boxes = lc(t10.stack); - t10.scores = lc(arr[4]); - t10.classes = lc(arr[5]); + t8.squeeze = mc(res); + const arr = pi(t8.squeeze, 6, 1); + t8.stack = kr([arr[1], arr[0], arr[3], arr[2]], 1); + t8.boxes = mc(t8.stack); + t8.scores = mc(arr[4]); + t8.classes = mc(arr[5]); Ot([res, ...arr]); - t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t10.nms.data(); + t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t8.nms.data(); let i = 0; for (const id2 of Array.from(nms)) { const score = Math.trunc(100 * detections[0][id2][4]) / 100; @@ -40205,15 +40381,15 @@ async function process3(res, outputShape, config3) { if (Number.isNaN(classVal)) continue; const label = labels2[classVal].label; - const [x, y5] = [ + const [x, y10] = [ detections[0][id2][0] / inputSize3, detections[0][id2][1] / inputSize3 ]; const boxRaw = [ x, - y5, + y10, detections[0][id2][2] / inputSize3 - x, - detections[0][id2][3] / inputSize3 - y5 + detections[0][id2][3] / inputSize3 - y10 ]; const box = [ Math.trunc(boxRaw[0] * outputShape[0]), @@ -40223,7 +40399,7 @@ async function process3(res, outputShape, config3) { ]; results.push({ id: i++, score, class: classVal, label, box, boxRaw }); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return results; } async function predict2(input, config3) { @@ -40238,7 +40414,7 @@ async function predict2(input, config3) { skipped2 = 0; return new Promise(async (resolve) => { const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = uj.resizeBilinear(input, [inputSize3, inputSize3]); + const resize = Fj.resizeBilinear(input, [inputSize3, inputSize3]); const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; lastTime2 = now(); Ot(resize); @@ -40298,16 +40474,16 @@ async function load2(config3) { async function max2d(inputs, minScore) { const [width, height] = inputs.shape; const reshaped = W(inputs, [height * width]); - const max = Ia(reshaped, 0); + const max = _a(reshaped, 0); const newScore = (await max.data())[0]; if (newScore > minScore) { - const coordinates = mk(reshaped, 0); - const mod = x2(coordinates, width); + const coordinates = Sk(reshaped, 0); + const mod = T2(coordinates, width); const x = (await mod.data())[0]; - const div = Ke(coordinates, width); - const y5 = (await div.data())[0]; + const div = je(coordinates, width); + const y10 = (await div.data())[0]; Ot([reshaped, max, coordinates, mod, div]); - return [x, y5, newScore]; + return [x, y10, newScore]; } Ot([reshaped, max]); return [0, 0, newScore]; @@ -40325,7 +40501,7 @@ async function predict3(image, config3) { return new Promise(async (resolve) => { const tensor = De(() => { var _a2, _b; - const resize = uj.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); + const resize = Fj.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); const enhance2 = se(resize, constants.tf2); const norm = Te(enhance2, constants.tf1); return norm; @@ -40337,12 +40513,12 @@ async function predict3(image, config3) { Ot(tensor); if (resT) { cache2.keypoints.length = 0; - const squeeze = lc(resT); + const squeeze = mc(resT); Ot(resT); - const stack = po(squeeze, 2); + const stack = mo(squeeze, 2); Ot(squeeze); for (let id2 = 0; id2 < stack.length; id2++) { - const [x5, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence); + const [x5, y11, partScore] = await max2d(stack[id2], config3.body.minConfidence); if (partScore > (config3.body.minConfidence || 0)) { cache2.keypoints.push({ score: Math.round(100 * partScore) / 100, @@ -40351,13 +40527,13 @@ async function predict3(image, config3) { // normalized to 0..1 // @ts-ignore model is not undefined here x5 / model4.inputs[0].shape[2], - y10 / model4.inputs[0].shape[1] + y11 / model4.inputs[0].shape[1] ], position: [ // normalized to input image size // @ts-ignore model is not undefined here Math.round(image.shape[2] * x5 / model4.inputs[0].shape[2]), - Math.round(image.shape[1] * y10 / model4.inputs[0].shape[1]) + Math.round(image.shape[1] * y11 / model4.inputs[0].shape[1]) ] }); } @@ -40366,12 +40542,12 @@ async function predict3(image, config3) { } cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); const x = cache2.keypoints.map((a) => a.position[0]); - const y5 = cache2.keypoints.map((a) => a.position[1]); + const y10 = cache2.keypoints.map((a) => a.position[1]); cache2.box = [ Math.min(...x), - Math.min(...y5), + Math.min(...y10), Math.max(...x) - Math.min(...x), - Math.max(...y5) - Math.min(...y5) + Math.max(...y10) - Math.min(...y10) ]; const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); @@ -40418,10 +40594,10 @@ var scaleBoxCoordinates = (box, factor, anchor) => { }; var cutAndResize = (box, image, cropSize) => { const h = image.shape[1]; - const w = image.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = uj.cropAndResize(image, [cutBox], [0], cropSize); - const norm = Ke(crop, constants.tf255); + const w10 = image.shape[2]; + const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w10, box.endPoint[1] / h, box.endPoint[0] / w10]; + const crop = Fj.cropAndResize(image, [cutBox], [0], cropSize); + const norm = je(crop, constants.tf255); Ot(crop); return norm; }; @@ -40451,17 +40627,17 @@ var squarifyBox = (box) => { }; var calculateLandmarksBoundingBox = (landmarks) => { const x = landmarks.map((d) => d[0]); - const y5 = landmarks.map((d) => d[1]); + const y10 = landmarks.map((d) => d[1]); return { - startPoint: [Math.min(...x), Math.min(...y5)], - endPoint: [Math.max(...x), Math.max(...y5)], + startPoint: [Math.min(...x), Math.min(...y10)], + endPoint: [Math.max(...x), Math.max(...y10)], landmarks }; }; var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y5) => [[1, 0, x], [0, 1, y5], [0, 0, 1]]; +var buildTranslationMatrix = (x, y10) => [[1, 0, x], [0, 1, y10], [0, 0, 1]]; var dot = (v12, v22) => { let product = 0; for (let i = 0; i < v12.length; i++) @@ -40550,7 +40726,7 @@ function correctFaceRotation(rotate, box, input, inputSize10) { if (largeAngle) { const center = getBoxCenter(box); const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = uj.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); + const rotated = Fj.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); rotationMatrix = buildRotationMatrix(-angle, center); face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); Ot(rotated); @@ -40564,8 +40740,8 @@ function correctFaceRotation(rotate, box, input, inputSize10) { } var findFaceCenter = (mesh) => { const x = mesh.map((m) => m[0]); - const y5 = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y5) + (Math.max(...y5) - Math.min(...y5)) / 2]; + const y10 = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y10) + (Math.max(...y10) - Math.min(...y10)) / 2]; }; var calculateFaceBox = (mesh, previousBox) => { const center = findFaceCenter(mesh); @@ -40595,63 +40771,63 @@ async function load3(config3) { log("cached model:", model5["modelUrl"]); inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; inputSizeT = ke(inputSize4, "int32"); - anchors = uu(generateAnchors(inputSize4)); + anchors = mu(generateAnchors(inputSize4)); return model5; } function decodeBoxes2(boxOutputs) { if (!anchors || !inputSizeT) - return Wr([0, 0]); - const t10 = {}; - t10.boxStarts = qe(boxOutputs, [0, 1], [-1, 2]); - t10.centers = be(t10.boxStarts, anchors); - t10.boxSizes = qe(boxOutputs, [0, 3], [-1, 2]); - t10.boxSizesNormalized = Ke(t10.boxSizes, inputSizeT); - t10.centersNormalized = Ke(t10.centers, inputSizeT); - t10.halfBoxSize = Ke(t10.boxSizesNormalized, constants.tf2); - t10.starts = Te(t10.centersNormalized, t10.halfBoxSize); - t10.ends = be(t10.centersNormalized, t10.halfBoxSize); - t10.startNormalized = se(t10.starts, inputSizeT); - t10.endNormalized = se(t10.ends, inputSizeT); - const boxes = Dk([t10.startNormalized, t10.endNormalized], 1); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + return Gr([0, 0]); + const t8 = {}; + t8.boxStarts = Xe(boxOutputs, [0, 1], [-1, 2]); + t8.centers = Ce(t8.boxStarts, anchors); + t8.boxSizes = Xe(boxOutputs, [0, 3], [-1, 2]); + t8.boxSizesNormalized = je(t8.boxSizes, inputSizeT); + t8.centersNormalized = je(t8.centers, inputSizeT); + t8.halfBoxSize = je(t8.boxSizesNormalized, constants.tf2); + t8.starts = Te(t8.centersNormalized, t8.halfBoxSize); + t8.ends = Ce(t8.centersNormalized, t8.halfBoxSize); + t8.startNormalized = se(t8.starts, inputSizeT); + t8.endNormalized = se(t8.ends, inputSizeT); + const boxes = Wk([t8.startNormalized, t8.endNormalized], 1); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return boxes; } async function getBoxes(inputImage, config3) { var _a2, _b, _c2, _d2, _e, _f2; if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) return []; - const t10 = {}; - t10.resized = uj.resizeBilinear(inputImage, [inputSize4, inputSize4]); - t10.div = Ke(t10.resized, constants.tf127); - t10.normalized = Te(t10.div, constants.tf05); - const res = model5 == null ? void 0 : model5.execute(t10.normalized); + const t8 = {}; + t8.resized = Fj.resizeBilinear(inputImage, [inputSize4, inputSize4]); + t8.div = je(t8.resized, constants.tf127); + t8.normalized = Te(t8.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t8.normalized); if (Array.isArray(res) && res.length > 2) { const sorted = res.sort((a, b) => a.size - b.size); - t10.concat384 = yt([sorted[0], sorted[2]], 2); - t10.concat512 = yt([sorted[1], sorted[3]], 2); - t10.concat = yt([t10.concat512, t10.concat384], 1); - t10.batch = lc(t10.concat, [0]); + t8.concat384 = bt([sorted[0], sorted[2]], 2); + t8.concat512 = bt([sorted[1], sorted[3]], 2); + t8.concat = bt([t8.concat512, t8.concat384], 1); + t8.batch = mc(t8.concat, [0]); } else if (Array.isArray(res)) { - t10.batch = lc(res[0]); + t8.batch = mc(res[0]); } else { - t10.batch = lc(res); + t8.batch = mc(res); } Ot(res); - t10.boxes = decodeBoxes2(t10.batch); - t10.logits = qe(t10.batch, [0, 0], [-1, 1]); - t10.sigmoid = wa(t10.logits); - t10.scores = lc(t10.sigmoid); - t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0); - const nms = await t10.nms.array(); + t8.boxes = decodeBoxes2(t8.batch); + t8.logits = Xe(t8.batch, [0, 0], [-1, 1]); + t8.sigmoid = Na(t8.logits); + t8.scores = mc(t8.sigmoid); + t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0); + const nms = await t8.nms.array(); const boxes = []; - const scores = await t10.scores.data(); + const scores = await t8.scores.data(); for (let i = 0; i < nms.length; i++) { const confidence = scores[nms[i]]; if (confidence > (((_d2 = config3.face.detector) == null ? void 0 : _d2.minConfidence) || 0)) { const b = {}; - b.bbox = qe(t10.boxes, [nms[i], 0], [1, -1]); - b.slice = qe(t10.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = lc(b.slice); + b.bbox = Xe(t8.boxes, [nms[i], 0], [1, -1]); + b.slice = Xe(t8.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.squeeze = mc(b.slice); b.landmarks = W(b.squeeze, [keypointsCount, -1]); const points = await b.bbox.data(); const rawBox = { @@ -40660,7 +40836,7 @@ async function getBoxes(inputImage, config3) { landmarks: await b.landmarks.array(), confidence }; - b.anchor = qe(anchors, [nms[i], 0], [1, 2]); + b.anchor = Xe(anchors, [nms[i], 0], [1, 2]); const anchor = await b.anchor.data(); const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor); const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); @@ -40670,7 +40846,7 @@ async function getBoxes(inputImage, config3) { Object.keys(b).forEach((tensor) => Ot(b[tensor])); } } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return boxes; } @@ -40727,14 +40903,14 @@ var getLeftToRightEyeDepthDifference = (rawCoords) => { var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); const boxSize = getBoxSize(box); - let crop = uj.cropAndResize(face4, [[ + let crop = Fj.cropAndResize(face4, [[ box.startPoint[1] / meshSize, box.startPoint[0] / meshSize, box.endPoint[1] / meshSize, box.endPoint[0] / meshSize ]], [0], [inputSize5, inputSize5]); if (flip && env.kernels.includes("flipleftright")) { - const flipped = uj.flipLeftRight(crop); + const flipped = Fj.flipLeftRight(crop); Ot(crop); crop = flipped; } @@ -40744,11 +40920,11 @@ var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { const eyeRawCoords = []; for (let i = 0; i < irisLandmarks.numCoordinates; i++) { const x = eyeData[i * 3]; - const y5 = eyeData[i * 3 + 1]; + const y10 = eyeData[i * 3 + 1]; const z = eyeData[i * 3 + 2]; eyeRawCoords.push([ (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y5 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + y10 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], z ]); } @@ -40773,7 +40949,7 @@ async function augmentIris(rawCoords, face4, meshSize) { return rawCoords; const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = yt([leftEyeCrop, rightEyeCrop]); + const combined = bt([leftEyeCrop, rightEyeCrop]); Ot(leftEyeCrop); Ot(rightEyeCrop); const eyePredictions = model6.execute(combined); @@ -40802,7 +40978,7 @@ async function augmentIris(rawCoords, face4, meshSize) { // src/face/attention.ts async function augment(rawCoords, results) { var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2; - const t10 = { + const t8 = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords // mesh: results[0], // already have it in rawCoords // output_mesh_identity // flag: results[1], // already processed in parent // conv_faceflag @@ -40817,22 +40993,22 @@ async function augment(rawCoords, results) { eyeR: await ((_j2 = (_i2 = results.filter((r) => r.size === 142)) == null ? void 0 : _i2[1]) == null ? void 0 : _j2.data()) // 71 x 2d = 142// output_left_eye }; - for (const val of Object.values(t10)) { + for (const val of Object.values(t8)) { if (!val) return rawCoords; } const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t10.irisL.length / 2; i++) - rawCoords.push([t10.irisL[2 * i + 0], t10.irisL[2 * i + 1], irisLDepth]); + for (let i = 0; i < t8.irisL.length / 2; i++) + rawCoords.push([t8.irisL[2 * i + 0], t8.irisL[2 * i + 1], irisLDepth]); const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t10.irisR.length / 2; i++) - rawCoords.push([t10.irisR[2 * i + 0], t10.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t10.eyeL.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t10.eyeL[2 * i + 0], t10.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t10.eyeR.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t10.eyeR[2 * i + 0], t10.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t10.lips.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t10.lips[2 * i + 0], t10.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; + for (let i = 0; i < t8.irisR.length / 2; i++) + rawCoords.push([t8.irisR[2 * i + 0], t8.irisR[2 * i + 1], irisRDepth]); + for (let i = 0; i < t8.eyeL.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t8.eyeL[2 * i + 0], t8.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t8.eyeR.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t8.eyeR[2 * i + 0], t8.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t8.lips.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t8.lips[2 * i + 0], t8.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; return rawCoords; } @@ -40905,7 +41081,7 @@ async function predict4(input, config3) { return faces; } const results = model7.execute(face4.tensor); - const confidenceT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1); + const confidenceT = results.find((t8) => t8.shape[t8.shape.length - 1] === 1); const faceConfidence = await confidenceT.data(); face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) { @@ -40913,6 +41089,7 @@ async function predict4(input, config3) { if (config3.face.mesh["keepInvalid"]) { face4.box = clampBox(box, input); face4.boxRaw = getRawBox(box, input); + face4.size = box.size; face4.score = face4.boxScore; face4.mesh = box.landmarks; face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 1), pt2[1] / (input.shape[1] || 1), (pt2[2] || 0) / size2]); @@ -40921,7 +41098,7 @@ async function predict4(input, config3) { } } } else { - const meshT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1404); + const meshT = results.find((t8) => t8.shape[t8.shape.length - 1] === 1404); const coordsReshaped = W(meshT, [-1, 3]); let rawCoords = await coordsReshaped.array(); Ot(coordsReshaped); @@ -40943,6 +41120,7 @@ async function predict4(input, config3) { }; face4.box = clampBox(calculatedBox, input); face4.boxRaw = getRawBox(calculatedBox, input); + face4.size = calculatedBox.size; newCache.push(calculatedBox); } Ot(results); @@ -41016,34 +41194,34 @@ async function predict5(image, config3, idx, count2) { var _a3, _b2, _c2; const obj = []; if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) { - const t10 = {}; + const t8 = {}; const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; if (((_b2 = config3.face.emotion) == null ? void 0 : _b2["crop"]) > 0) { const crop = (_c2 = config3.face.emotion) == null ? void 0 : _c2["crop"]; const box = [[crop, crop, 1 - crop, 1 - crop]]; - t10.resize = uj.cropAndResize(image, box, [0], [inputSize10, inputSize10]); + t8.resize = Fj.cropAndResize(image, box, [0], [inputSize10, inputSize10]); } else { - t10.resize = uj.resizeBilinear(image, [inputSize10, inputSize10], false); + t8.resize = Fj.resizeBilinear(image, [inputSize10, inputSize10], false); } if (rgb) { - t10.mul = se(t10.resize, 255); - t10.normalize = Te(t10.mul, [103.939, 116.779, 123.68]); - t10.emotion = model8 == null ? void 0 : model8.execute(t10.normalize); + t8.mul = se(t8.resize, 255); + t8.normalize = Te(t8.mul, [103.939, 116.779, 123.68]); + t8.emotion = model8 == null ? void 0 : model8.execute(t8.normalize); } else { - t10.channels = se(t10.resize, constants.rgb); - t10.grayscale = ot(t10.channels, 3, true); - t10.grayscaleSub = Te(t10.grayscale, constants.tf05); - t10.grayscaleMul = se(t10.grayscaleSub, constants.tf2); - t10.emotion = model8 == null ? void 0 : model8.execute(t10.grayscaleMul); + t8.channels = se(t8.resize, constants.rgb); + t8.grayscale = ot(t8.channels, 3, true); + t8.grayscaleSub = Te(t8.grayscale, constants.tf05); + t8.grayscaleMul = se(t8.grayscaleSub, constants.tf2); + t8.emotion = model8 == null ? void 0 : model8.execute(t8.grayscaleMul); } lastTime4 = now(); - const data = await t10.emotion.data(); + const data = await t8.emotion.data(); for (let i = 0; i < data.length; i++) { if (data[i] > (config3.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); } obj.sort((a, b) => b.score - a.score); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } last3[idx] = obj; lastCount = count2; @@ -41076,9 +41254,9 @@ function enhance(input, config3) { if (((_a2 = config3.face.description) == null ? void 0 : _a2["crop"]) > 0) { const cropval = (_b = config3.face.description) == null ? void 0 : _b["crop"]; const box = [[cropval, cropval, 1 - cropval, 1 - cropval]]; - crop = uj.cropAndResize(tensor, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]); + crop = Fj.cropAndResize(tensor, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]); } else { - crop = uj.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + crop = Fj.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); } const norm = se(crop, constants.tf255); Ot(crop); @@ -41108,25 +41286,25 @@ async function predict6(image, config3, idx, count2) { const resT = model9 == null ? void 0 : model9.execute(enhanced); lastTime5 = now(); Ot(enhanced); - const genderT = resT.find((t10) => t10.shape[1] === 1); + const genderT = resT.find((t8) => t8.shape[1] === 1); const gender2 = await genderT.data(); const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; if (confidence > (config3.face.description.minConfidence || 0)) { obj.gender = gender2[0] <= 0.5 ? "female" : "male"; obj.genderScore = Math.min(0.99, confidence); } - const argmax = mk(resT.find((t10) => t10.shape[1] === 100), 1); + const argmax = Sk(resT.find((t8) => t8.shape[1] === 100), 1); const ageIdx = (await argmax.data())[0]; Ot(argmax); - const ageT = resT.find((t10) => t10.shape[1] === 100); + const ageT = resT.find((t8) => t8.shape[1] === 100); const all2 = await ageT.data(); obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t10) => t10.shape[1] === 1024); + const desc = resT.find((t8) => t8.shape[1] === 1024); const descriptor = desc ? await desc.data() : []; obj.descriptor = Array.from(descriptor); - resT.forEach((t10) => Ot(t10)); + resT.forEach((t8) => Ot(t8)); } last4[idx] = obj; lastCount2 = count2; @@ -41137,11 +41315,11 @@ async function predict6(image, config3, idx, count2) { // src/face/mask.ts var expandFact = 0.1; var alpha = 0.5; -function insidePoly(x, y5, polygon) { +function insidePoly(x, y10, polygon) { let inside = false; let j = polygon.length - 1; for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y5 !== polygon[j].y > y5 && x < (polygon[j].x - polygon[i].x) * (y5 - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + if (polygon[i].y > y10 !== polygon[j].y > y10 && x < (polygon[j].x - polygon[i].x) * (y10 - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) inside = !inside; } return inside; @@ -41160,12 +41338,12 @@ async function mask(face4) { if (expandFact && expandFact > 0) silhouette = silhouette.map((pt2) => ({ x: pt2.x > 0.5 ? pt2.x + expandFact : pt2.x - expandFact, y: pt2.y > 0.5 ? pt2.y + expandFact : pt2.y - expandFact })); for (let x = 0; x < width; x++) { - for (let y5 = 0; y5 < height; y5++) { - const inside = insidePoly(x / width, y5 / width, silhouette); + for (let y10 = 0; y10 < height; y10++) { + const inside = insidePoly(x / width, y10 / width, silhouette); if (!inside) { - buffer.set(alpha * buffer.get(0, y5, x, 0), 0, y5, x, 0); - buffer.set(alpha * buffer.get(0, y5, x, 1), 0, y5, x, 1); - buffer.set(alpha * buffer.get(0, y5, x, 2), 0, y5, x, 2); + buffer.set(alpha * buffer.get(0, y10, x, 0), 0, y10, x, 0); + buffer.set(alpha * buffer.get(0, y10, x, 1), 0, y10, x, 1); + buffer.set(alpha * buffer.get(0, y10, x, 2), 0, y10, x, 2); } } } @@ -41201,7 +41379,7 @@ async function predict7(image, config3, idx, count2) { } skipped6 = 0; return new Promise(async (resolve) => { - const resize = uj.resizeBilinear(image, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); + const resize = Fj.resizeBilinear(image, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); const res = model10 == null ? void 0 : model10.execute(resize); const num = (await res.data())[0]; cached[idx] = Math.round(100 * num) / 100; @@ -41240,7 +41418,7 @@ async function predict8(image, config3, idx, count2) { } skipped7 = 0; return new Promise(async (resolve) => { - const resize = uj.resizeBilinear(image, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); + const resize = Fj.resizeBilinear(image, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); const res = model11 == null ? void 0 : model11.execute(resize); const num = (await res.data())[0]; cached2[idx] = Math.round(100 * num) / 100; @@ -41284,32 +41462,32 @@ async function predict9(image, config3, idx, count2) { var _a3, _b2, _c2, _d2; if (!(model12 == null ? void 0 : model12.inputs[0].shape)) return; - const t10 = {}; + const t8 = {}; let box = [[0, 0.1, 0.9, 0.9]]; if (((_a3 = config3.face.gear) == null ? void 0 : _a3["crop"]) > 0) { const crop = (_b2 = config3.face.gear) == null ? void 0 : _b2["crop"]; box = [[crop, crop, 1 - crop, 1 - crop]]; } - t10.resize = uj.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); + t8.resize = Fj.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; if ((_c2 = config3.face.gear) == null ? void 0 : _c2.enabled) - [t10.age, t10.gender, t10.race] = model12.execute(t10.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t10.gender.data(); + [t8.age, t8.gender, t8.race] = model12.execute(t8.resize, ["age_output", "gender_output", "race_output"]); + const gender2 = await t8.gender.data(); obj.gender = gender2[0] > gender2[1] ? "male" : "female"; obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t10.race.data(); + const race = await t8.race.data(); for (let i = 0; i < race.length; i++) { if (race[i] > (((_d2 = config3.face.gear) == null ? void 0 : _d2.minConfidence) || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); } obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t10.age.data()); + const ageDistribution = Array.from(await t8.age.data()); const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); let age2 = ageSorted[0][0]; for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); obj.age = Math.round(10 * age2) / 10; - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); last5[idx] = obj; lastCount5 = count2; lastTime8 = now(); @@ -41347,23 +41525,23 @@ async function predict10(image, config3, idx, count2) { var _a3, _b2, _c3; if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) return; - const t10 = {}; + const t8 = {}; if (((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3["crop"]) > 0) { const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; const box = [[crop, crop, 1 - crop, 1 - crop]]; - t10.resize = uj.cropAndResize(image, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]); + t8.resize = Fj.cropAndResize(image, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]); } else { - t10.resize = uj.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); + t8.resize = Fj.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); } - t10.enhance = se(t10.resize, constants.tf255); + t8.enhance = se(t8.resize, constants.tf255); const obj = { age: 0 }; if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled) - t10.age = model13.execute(t10.enhance); - if (t10.age) { - const data = await t10.age.data(); + t8.age = model13.execute(t8.enhance); + if (t8.age) { + const data = await t8.age.data(); obj.age = Math.trunc(10 * data[0]) / 10; } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); last6[idx] = obj; lastCount6 = count2; lastTime9 = now(); @@ -41403,36 +41581,36 @@ async function predict11(image, config3, idx, count2) { var _a3, _b2, _c3; if (!(model14 == null ? void 0 : model14.inputs[0].shape)) return; - const t10 = {}; + const t8 = {}; if (((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3["crop"]) > 0) { const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; const box = [[crop, crop, 1 - crop, 1 - crop]]; - t10.resize = uj.cropAndResize(image, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]); + t8.resize = Fj.cropAndResize(image, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]); } else { - t10.resize = uj.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); + t8.resize = Fj.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); } - t10.enhance = De(() => { + t8.enhance = De(() => { var _a4, _b3; let normalize2; if (((_b3 = (_a4 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a4[0].shape) == null ? void 0 : _b3[3]) === 1) { - const [red, green, blue] = ai(t10.resize, 3, 3); + const [red, green, blue] = pi(t8.resize, 3, 3); const redNorm = se(red, rgb2[0]); const greenNorm = se(green, rgb2[1]); const blueNorm = se(blue, rgb2[2]); - const grayscale = pk([redNorm, greenNorm, blueNorm]); + const grayscale = bk([redNorm, greenNorm, blueNorm]); normalize2 = se(Te(grayscale, constants.tf05), 2); } else { - normalize2 = se(Te(t10.resize, constants.tf05), 2); + normalize2 = se(Te(t8.resize, constants.tf05), 2); } return normalize2; }); const obj = { gender: "unknown", genderScore: 0 }; if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled) - t10.gender = model14.execute(t10.enhance); - const data = await t10.gender.data(); + t8.gender = model14.execute(t8.enhance); + const data = await t8.gender.data(); obj.gender = data[0] > data[1] ? "female" : "male"; obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); last7[idx] = obj; lastCount7 = count2; lastTime10 = now(); @@ -41470,12 +41648,12 @@ async function predict12(input, config3, idx, count2) { var _a3; let data = []; if (((_a3 = config3.face["mobilefacenet"]) == null ? void 0 : _a3.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t10 = {}; - t10.crop = uj.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t10.data = model15.execute(t10.crop); - const output = await t10.data.data(); + const t8 = {}; + t8.crop = Fj.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); + t8.data = model15.execute(t8.crop); + const output = await t8.data.data(); data = Array.from(output); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } last8[idx] = data; lastCount8 = count2; @@ -41513,12 +41691,12 @@ async function predict13(input, config3, idx, count2) { var _a3; let data = []; if (((_a3 = config3.face["insightface"]) == null ? void 0 : _a3.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t10 = {}; - t10.crop = uj.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t10.data = model16.execute(t10.crop); - const output = await t10.data.data(); + const t8 = {}; + t8.crop = Fj.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); + t8.data = model16.execute(t8.crop); + const output = await t8.data.data(); data = Array.from(output); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } last9[idx] = data; lastCount9 = count2; @@ -41549,24 +41727,24 @@ var calculateGaze = (face4) => { return { bearing, strength }; }; var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v5) => { - const length = Math.sqrt(v5[0] * v5[0] + v5[1] * v5[1] + v5[2] * v5[2]); - v5[0] /= length; - v5[1] /= length; - v5[2] /= length; - return v5; + const normalize2 = (v10) => { + const length = Math.sqrt(v10[0] * v10[0] + v10[1] * v10[1] + v10[2] * v10[2]); + v10[0] /= length; + v10[1] /= length; + v10[2] /= length; + return v10; }; const subVectors = (a, b) => { const x = a[0] - b[0]; - const y5 = a[1] - b[1]; + const y10 = a[1] - b[1]; const z = a[2] - b[2]; - return [x, y5, z]; + return [x, y10, z]; }; const crossVectors = (a, b) => { const x = a[1] * b[2] - a[2] * b[1]; - const y5 = a[2] * b[0] - a[0] * b[2]; + const y10 = a[2] * b[0] - a[0] * b[2]; const z = a[0] * b[1] - a[1] * b[0]; - return [x, y5, z]; + return [x, y10, z]; }; const rotationMatrixToEulerAngle = (r) => { const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; @@ -41633,7 +41811,7 @@ function calculateCameraDistance(face4, width) { // src/face/face.ts var detectFace = async (instance, input) => { - var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w; + var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u, _v2, _w2; let timeStamp = now(); let ageRes; let gearRes; @@ -41771,11 +41949,11 @@ var detectFace = async (instance, input) => { if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { descRes.descriptor = mobilefacenetRes; } - if (((_u2 = instance.config.face["insightface"]) == null ? void 0 : _u2.enabled) && insightfaceRes) { + if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { descRes.descriptor = insightfaceRes; } const irisSize = ((_v2 = instance.config.face.iris) == null ? void 0 : _v2.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? lc(faces[i].tensor) : null; + const tensor = ((_w2 = instance.config.face.detector) == null ? void 0 : _w2.return) ? mc(faces[i].tensor) : null; Ot(faces[i].tensor); if (faces[i].tensor) delete faces[i].tensor; @@ -41894,7 +42072,7 @@ var FingerGesture = class { weight(finger, weight) { this.weights[finger] = weight; const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); + this.weightsRelative = this.weights.map((el2) => el2 * 5 / total); } matchAgainst(detectedCurls, detectedDirections) { let confidence = 0; @@ -42372,14 +42550,14 @@ function getBoxCenter2(box) { } function cutBoxFromImageAndResize(box, image, cropSize) { const h = image.shape[1]; - const w = image.shape[2]; + const w10 = image.shape[2]; const boxes = [[ box.startPoint[1] / h, - box.startPoint[0] / w, + box.startPoint[0] / w10, box.endPoint[1] / h, - box.endPoint[0] / w + box.endPoint[0] / w10 ]]; - return uj.cropAndResize(image, boxes, [0], cropSize); + return Fj.cropAndResize(image, boxes, [0], cropSize); } function scaleBoxCoordinates2(box, factor) { const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; @@ -42414,7 +42592,7 @@ function computeRotation2(point1, point2) { const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); return normalizeRadians2(radians); } -var buildTranslationMatrix2 = (x, y5) => [[1, 0, x], [0, 1, y5], [0, 0, 1]]; +var buildTranslationMatrix2 = (x, y10) => [[1, 0, x], [0, 1, y10], [0, 0, 1]]; function dot2(v12, v22) { let product = 0; for (let i = 0; i < v12.length; i++) { @@ -45429,56 +45607,56 @@ var HandDetector = class { var _a2, _b, _c2, _d2; this.model = model23; this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = uu(this.anchors); + this.anchorsTensor = mu(this.anchors); this.inputSize = ((_d2 = (_c2 = (_b = (_a2 = this == null ? void 0 : this.model) == null ? void 0 : _a2.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c2.shape) == null ? void 0 : _d2[2]) || 0; this.inputSizeTensor = xr([this.inputSize, this.inputSize]); this.doubleInputSizeTensor = xr([this.inputSize * 2, this.inputSize * 2]); } normalizeBoxes(boxes) { - const t10 = {}; - t10.boxOffsets = qe(boxes, [0, 0], [-1, 2]); - t10.boxSizes = qe(boxes, [0, 2], [-1, 2]); - t10.div = Ke(t10.boxOffsets, this.inputSizeTensor); - t10.boxCenterPoints = be(t10.div, this.anchorsTensor); - t10.halfBoxSizes = Ke(t10.boxSizes, this.doubleInputSizeTensor); - t10.sub = Te(t10.boxCenterPoints, t10.halfBoxSizes); - t10.startPoints = se(t10.sub, this.inputSizeTensor); - t10.add = be(t10.boxCenterPoints, t10.halfBoxSizes); - t10.endPoints = se(t10.add, this.inputSizeTensor); - const res = Dk([t10.startPoints, t10.endPoints], 1); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + const t8 = {}; + t8.boxOffsets = Xe(boxes, [0, 0], [-1, 2]); + t8.boxSizes = Xe(boxes, [0, 2], [-1, 2]); + t8.div = je(t8.boxOffsets, this.inputSizeTensor); + t8.boxCenterPoints = Ce(t8.div, this.anchorsTensor); + t8.halfBoxSizes = je(t8.boxSizes, this.doubleInputSizeTensor); + t8.sub = Te(t8.boxCenterPoints, t8.halfBoxSizes); + t8.startPoints = se(t8.sub, this.inputSizeTensor); + t8.add = Ce(t8.boxCenterPoints, t8.halfBoxSizes); + t8.endPoints = se(t8.add, this.inputSizeTensor); + const res = Wk([t8.startPoints, t8.endPoints], 1); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return res; } normalizeLandmarks(rawPalmLandmarks, index2) { - const t10 = {}; - t10.reshape = W(rawPalmLandmarks, [-1, 7, 2]); - t10.div = Ke(t10.reshape, this.inputSizeTensor); - t10.landmarks = be(t10.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = se(t10.landmarks, this.inputSizeTensor); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + const t8 = {}; + t8.reshape = W(rawPalmLandmarks, [-1, 7, 2]); + t8.div = je(t8.reshape, this.inputSizeTensor); + t8.landmarks = Ce(t8.div, this.anchors[index2] ? this.anchors[index2] : 0); + const res = se(t8.landmarks, this.inputSizeTensor); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return res; } async predict(input, config3) { var _a2; - const t10 = {}; - t10.resize = uj.resizeBilinear(input, [this.inputSize, this.inputSize]); - t10.div = Ke(t10.resize, constants.tf127); - t10.image = Te(t10.div, constants.tf1); - t10.batched = this.model.execute(t10.image); - t10.predictions = lc(t10.batched); - t10.slice = qe(t10.predictions, [0, 0], [-1, 1]); - t10.sigmoid = wa(t10.slice); - t10.scores = lc(t10.sigmoid); - const scores = await t10.scores.data(); - t10.boxes = qe(t10.predictions, [0, 1], [-1, 4]); - t10.norm = this.normalizeBoxes(t10.boxes); - t10.nms = await uj.nonMaxSuppressionAsync(t10.norm, t10.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t10.nms.array(); + const t8 = {}; + t8.resize = Fj.resizeBilinear(input, [this.inputSize, this.inputSize]); + t8.div = je(t8.resize, constants.tf127); + t8.image = Te(t8.div, constants.tf1); + t8.batched = this.model.execute(t8.image); + t8.predictions = mc(t8.batched); + t8.slice = Xe(t8.predictions, [0, 0], [-1, 1]); + t8.sigmoid = Na(t8.slice); + t8.scores = mc(t8.sigmoid); + const scores = await t8.scores.data(); + t8.boxes = Xe(t8.predictions, [0, 1], [-1, 4]); + t8.norm = this.normalizeBoxes(t8.boxes); + t8.nms = await Fj.nonMaxSuppressionAsync(t8.norm, t8.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t8.nms.array(); const hands = []; for (const index2 of nms) { const p = {}; - p.box = qe(t10.norm, [index2, 0], [1, -1]); - p.slice = qe(t10.predictions, [index2, 5], [1, 14]); + p.box = Xe(t8.norm, [index2, 0], [1, -1]); + p.slice = Xe(t8.predictions, [index2, 5], [1, 14]); p.norm = this.normalizeLandmarks(p.slice, index2); p.palmLandmarks = W(p.norm, [-1, 2]); const box = await p.box.data(); @@ -45490,7 +45668,7 @@ var HandDetector = class { hands.push(scaled); Object.keys(p).forEach((tensor) => Ot(p[tensor])); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return hands; } }; @@ -45570,11 +45748,11 @@ var HandPipeline = class { const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; const skipFrame = this.skipped < (config3.hand.skipFrames || 0); if (config3.skipAllowed && skipTime && skipFrame) { + this.skipped++; + } else { boxes = await this.handDetector.predict(image, config3); this.skipped = 0; } - if (config3.skipAllowed) - this.skipped++; if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { this.detectedHands = 0; this.storedBoxes = [...boxes]; @@ -45590,11 +45768,11 @@ var HandPipeline = class { const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; const palmCenter = getBoxCenter2(currentBox); const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? uj.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone(); + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? Fj.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone(); const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = Ke(croppedInput, constants.tf255); + const handImage = je(croppedInput, constants.tf255); Ot(croppedInput); Ot(rotatedImage); const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); @@ -45654,7 +45832,14 @@ var meshAnnotations2 = { var handDetectorModel; var handPoseModel; var handPipeline; +function initPipeline() { + const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; + if (handDetector && handPoseModel) + handPipeline = new HandPipeline(handDetector, handPoseModel); +} async function predict14(input, config3) { + if (!handPipeline) + initPipeline(); const predictions = await handPipeline.estimateHands(input, config3); if (!predictions) return []; @@ -45713,27 +45898,25 @@ async function predict14(input, config3) { } return hands; } -async function load15(config3) { - var _a2, _b; - if (env.initial) { +async function loadDetect2(config3) { + var _a2; + if (env.initial) handDetectorModel = null; + if (!handDetectorModel) + handDetectorModel = await loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath); + else if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + return handDetectorModel; +} +async function loadSkeleton(config3) { + var _a2; + if (env.initial) handPoseModel = null; - } - if (!handDetectorModel || !handPoseModel) { - [handDetectorModel, handPoseModel] = await Promise.all([ - config3.hand.enabled ? loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath) : null, - config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null - ]); - } else { - if (config3.debug) - log("cached model:", handDetectorModel["modelUrl"]); - if (config3.debug) - log("cached model:", handPoseModel["modelUrl"]); - } - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) - handPipeline = new HandPipeline(handDetector, handPoseModel); - return [handDetectorModel, handPoseModel]; + if (!handPoseModel) + handPoseModel = await loadModel((_a2 = config3.hand.skeleton) == null ? void 0 : _a2.modelPath); + else if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + return handPoseModel; } // src/hand/handtrack.ts @@ -45769,7 +45952,7 @@ var fingerMap = { base: [0], palm: [0, 17, 13, 9, 5, 1, 0] }; -async function loadDetect2(config3) { +async function loadDetect3(config3) { var _a2; if (env.initial) models2[0] = null; @@ -45783,7 +45966,7 @@ async function loadDetect2(config3) { log("cached model:", models2[0]["modelUrl"]); return models2[0]; } -async function loadSkeleton(config3) { +async function loadSkeleton2(config3) { var _a2; if (env.initial) models2[1] = null; @@ -45800,29 +45983,29 @@ async function detectHands(input, config3) { const hands = []; if (!input || !models2[0]) return hands; - const t10 = {}; + const t8 = {}; const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); const width = Math.round(height * ratio2 / 8) * 8; - t10.resize = uj.resizeBilinear(input, [height, width]); - t10.cast = Ye(t10.resize, "int32"); - [t10.rawScores, t10.rawBoxes] = await models2[0].executeAsync(t10.cast, modelOutputNodes); - t10.boxes = lc(t10.rawBoxes, [0, 2]); - t10.scores = lc(t10.rawScores, [0]); - const classScores = po(t10.scores, 1); + t8.resize = Fj.resizeBilinear(input, [height, width]); + t8.cast = qe(t8.resize, "int32"); + [t8.rawScores, t8.rawBoxes] = await models2[0].executeAsync(t8.cast, modelOutputNodes); + t8.boxes = mc(t8.rawBoxes, [0, 2]); + t8.scores = mc(t8.rawScores, [0]); + const classScores = mo(t8.scores, 1); Ot(classScores[faceIndex]); classScores.splice(faceIndex, 1); - t10.filtered = vr(classScores, 1); + t8.filtered = kr(classScores, 1); Ot(classScores); - t10.max = Ia(t10.filtered, 1); - t10.argmax = mk(t10.filtered, 1); + t8.max = _a(t8.filtered, 1); + t8.argmax = Sk(t8.filtered, 1); let id2 = 0; - t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t10.nms.data(); - const scores = await t10.max.data(); - const classNum = await t10.argmax.data(); + t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t8.nms.data(); + const scores = await t8.max.data(); + const classNum = await t8.argmax.data(); for (const nmsIndex of Array.from(nms)) { - const boxSlice = qe(t10.boxes, nmsIndex, 1); + const boxSlice = Xe(t8.boxes, nmsIndex, 1); const boxYX = await boxSlice.data(); Ot(boxSlice); const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; @@ -45833,7 +46016,7 @@ async function detectHands(input, config3) { const hand3 = { id: id2++, score, box: boxFull, boxRaw, label }; hands.push(hand3); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); hands.sort((a, b) => b.score - a.score); if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; @@ -45854,17 +46037,17 @@ async function detectFingers(input, h, config3) { annotations: {} }; if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t10 = {}; + const t8 = {}; const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t10.crop = uj.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t10.div = Ke(t10.crop, constants.tf255); - [t10.score, t10.keypoints] = models2[1].execute(t10.div, ["Identity_1", "Identity"]); - const rawScore = (await t10.score.data())[0]; + t8.crop = Fj.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t8.div = je(t8.crop, constants.tf255); + [t8.score, t8.keypoints] = models2[1].execute(t8.div, ["Identity_1", "Identity"]); + const rawScore = (await t8.score.data())[0]; const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; if (score >= (config3.hand.minConfidence || 0)) { hand3.fingerScore = score; - t10.reshaped = W(t10.keypoints, [-1, 3]); - const coordsData = await t10.reshaped.array(); + t8.reshaped = W(t8.keypoints, [-1, 3]); + const coordsData = await t8.reshaped.array(); const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); @@ -45873,7 +46056,7 @@ async function detectFingers(input, h, config3) { hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); } } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } return hand3; } @@ -45991,8 +46174,8 @@ var connected3 = { var bufferedResult = empty(); var interpolateTime = 0; function calc2(newResult, config3) { - var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w, _x2, _y, _z2; - const t0 = now(); + var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u, _v2, _w2, _x2, _y, _z2; + const t02 = now(); if (!newResult) return empty(); const elapsed = Date.now() - newResult.timestamp; @@ -46092,11 +46275,11 @@ function calc2(newResult, config3) { rotation.angle = { roll: ((bufferedFactor - 1) * (((_l2 = (_k2 = bufferedResult.face[i].rotation) == null ? void 0 : _k2.angle) == null ? void 0 : _l2.roll) || 0) + (((_n2 = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n2.roll) || 0)) / bufferedFactor, yaw: ((bufferedFactor - 1) * (((_p2 = (_o2 = bufferedResult.face[i].rotation) == null ? void 0 : _o2.angle) == null ? void 0 : _p2.yaw) || 0) + (((_r2 = (_q2 = newResult.face[i].rotation) == null ? void 0 : _q2.angle) == null ? void 0 : _r2.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_t = (_s2 = bufferedResult.face[i].rotation) == null ? void 0 : _s2.angle) == null ? void 0 : _t.pitch) || 0) + (((_v2 = (_u2 = newResult.face[i].rotation) == null ? void 0 : _u2.angle) == null ? void 0 : _v2.pitch) || 0)) / bufferedFactor + pitch: ((bufferedFactor - 1) * (((_t = (_s2 = bufferedResult.face[i].rotation) == null ? void 0 : _s2.angle) == null ? void 0 : _t.pitch) || 0) + (((_v2 = (_u = newResult.face[i].rotation) == null ? void 0 : _u.angle) == null ? void 0 : _v2.pitch) || 0)) / bufferedFactor }; rotation.gaze = { // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive - bearing: ((bufferedFactor - 1) * (((_w = bufferedResult.face[i].rotation) == null ? void 0 : _w.gaze.bearing) || 0) + (((_x2 = newResult.face[i].rotation) == null ? void 0 : _x2.gaze.bearing) || 0)) / bufferedFactor, + bearing: ((bufferedFactor - 1) * (((_w2 = bufferedResult.face[i].rotation) == null ? void 0 : _w2.gaze.bearing) || 0) + (((_x2 = newResult.face[i].rotation) == null ? void 0 : _x2.gaze.bearing) || 0)) / bufferedFactor, strength: ((bufferedFactor - 1) * (((_y = bufferedResult.face[i].rotation) == null ? void 0 : _y.gaze.strength) || 0) + (((_z2 = newResult.face[i].rotation) == null ? void 0 : _z2.gaze.strength) || 0)) / bufferedFactor }; bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations: annotations2 }; @@ -46129,7 +46312,7 @@ function calc2(newResult, config3) { bufferedResult.width = newResult.width; bufferedResult.height = newResult.height; const t12 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t0) : Math.round(t12 - t0); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t02) : Math.round(t12 - t02); if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; return bufferedResult; @@ -46137,7 +46320,7 @@ function calc2(newResult, config3) { // src/segmentation/meet.ts var model17; -async function load16(config3) { +async function load15(config3) { if (!model17 || env.initial) model17 = await loadModel(config3.segmentation.modelPath); else if (config3.debug) @@ -46147,33 +46330,33 @@ async function load16(config3) { async function predict16(input, config3) { var _a2; if (!model17) - model17 = await load16(config3); + model17 = await load15(config3); if (!(model17 == null ? void 0 : model17["executor"]) || !((_a2 = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a2[0].shape)) return null; - const t10 = {}; - t10.resize = uj.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t10.norm = Ke(t10.resize, constants.tf255); - t10.res = model17.execute(t10.norm); - t10.squeeze = lc(t10.res, [0]); - [t10.bgRaw, t10.fgRaw] = po(t10.squeeze, 2); - t10.fg = y1(t10.fgRaw); - t10.mul = se(t10.fg, constants.tf255); - t10.expand = oi(t10.mul, 2); - t10.output = uj.resizeBilinear(t10.expand, [input.shape[1] || 0, input.shape[2] || 0]); + const t8 = {}; + t8.resize = Fj.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + t8.norm = je(t8.resize, constants.tf255); + t8.res = model17.execute(t8.norm); + t8.squeeze = mc(t8.res, [0]); + [t8.bgRaw, t8.fgRaw] = mo(t8.squeeze, 2); + t8.fg = _1(t8.fgRaw); + t8.mul = se(t8.fg, constants.tf255); + t8.expand = ai(t8.mul, 2); + t8.output = Fj.resizeBilinear(t8.expand, [input.shape[1] || 0, input.shape[2] || 0]); let rgba; switch (config3.segmentation.mode || "default") { case "default": - t10.input = lc(input); - t10.concat = yt([t10.input, t10.output], -1); - rgba = Ye(t10.concat, "int32"); + t8.input = mc(input); + t8.concat = bt([t8.input, t8.output], -1); + rgba = qe(t8.concat, "int32"); break; case "alpha": - rgba = Ye(t10.output, "int32"); + rgba = qe(t8.output, "int32"); break; default: rgba = ir(0); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return rgba; } @@ -46298,7 +46481,7 @@ function jitter(keypoints) { } function padInput(input, inputSize10) { var _a2, _b; - const t10 = {}; + const t8 = {}; if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; cache5.padding = [ @@ -46311,10 +46494,10 @@ function padInput(input, inputSize10) { [0, 0] // dont touch rbg ]; - t10.pad = ka(input, cache5.padding); - t10.resize = uj.resizeBilinear(t10.pad, [inputSize10, inputSize10]); - const final = Ye(t10.resize, "int32"); - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + t8.pad = Ea(input, cache5.padding); + t8.resize = Fj.resizeBilinear(t8.pad, [inputSize10, inputSize10]); + const final = qe(t8.resize, "int32"); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return final; } function rescaleBody(body4, outputSize2) { @@ -46344,7 +46527,7 @@ var cache6 = { bodies: [], last: 0 }; -async function load17(config3) { +async function load16(config3) { var _a2; if (env.initial) model18 = null; @@ -46356,8 +46539,8 @@ async function load17(config3) { inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a2 = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a2[0].shape) ? model18.inputs[0].shape[2] : 0; if (inputSize8 < 64) inputSize8 = 256; - if (P().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) - P().set("WEBGL_USE_SHAPES_UNIFORMS", false); + if (A().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) + A().set("WEBGL_USE_SHAPES_UNIFORMS", false); return model18; } function parseSinglePose(res, config3, image) { @@ -46454,18 +46637,18 @@ async function predict17(input, config3) { return cache6.bodies; } return new Promise(async (resolve) => { - const t10 = {}; + const t8 = {}; skipped14 = 0; - t10.input = padInput(input, inputSize8); - t10.res = model18 == null ? void 0 : model18.execute(t10.input); + t8.input = padInput(input, inputSize8); + t8.res = model18 == null ? void 0 : model18.execute(t8.input); cache6.last = now(); - const res = await t10.res.array(); - cache6.bodies = t10.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); + const res = await t8.res.array(); + cache6.bodies = t8.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); for (const body4 of cache6.bodies) { rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); jitter(body4.keypoints); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); resolve(cache6.bodies); }); } @@ -46477,7 +46660,7 @@ var lastTime15 = 0; var skipped15 = Number.MAX_SAFE_INTEGER; var inputSize9 = 0; var scaleBox = 2.5; -async function load18(config3) { +async function load17(config3) { if (!model19 || env.initial) { model19 = await loadModel(config3.object.modelPath); const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; @@ -46493,11 +46676,11 @@ async function process4(res, outputShape, config3) { const size2 = inputSize9; for (const strideSize of [1, 2, 4]) { const baseSize = strideSize * 13; - const scoresT = lc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); + const scoresT = mc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); const scores = await scoresT.array(); - const featuresT = lc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); + const featuresT = mc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); const boxesMaxT = W(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]); - const boxIdxT = mk(boxesMaxT, 2); + const boxIdxT = Sk(boxesMaxT, 2); const boxIdx = await boxIdxT.array(); for (let i = 0; i < scoresT.shape[0]; i++) { for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { @@ -46506,15 +46689,15 @@ async function process4(res, outputShape, config3) { const cx2 = (0.5 + Math.trunc(i % baseSize)) / baseSize; const cy2 = (0.5 + Math.trunc(i / baseSize)) / baseSize; const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y5] = [ + const [x, y10] = [ cx2 - scaleBox / strideSize * boxOffset[0], cy2 - scaleBox / strideSize * boxOffset[1] ]; - const [w, h] = [ + const [w10, h] = [ cx2 + scaleBox / strideSize * boxOffset[2] - x, - cy2 + scaleBox / strideSize * boxOffset[3] - y5 + cy2 + scaleBox / strideSize * boxOffset[3] - y10 ]; - let boxRaw = [x, y5, w, h]; + let boxRaw = [x, y10, w10, h]; boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); const box = [ // results normalized to input image pixels @@ -46544,7 +46727,7 @@ async function process4(res, outputShape, config3) { const nmsScores = results.map((a) => a.score); let nmsIdx = []; if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await uj.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); + const nms = await Fj.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); nmsIdx = Array.from(await nms.data()); Ot(nms); } @@ -46565,9 +46748,9 @@ async function predict18(image, config3) { return last10; return new Promise(async (resolve) => { const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0]; - const resizeT = uj.resizeBilinear(image, [inputSize9, inputSize9], false); - const normT = Ke(resizeT, constants.tf255); - const transposeT = dc(normT, [0, 3, 1, 2]); + const resizeT = Fj.resizeBilinear(image, [inputSize9, inputSize9], false); + const normT = je(resizeT, constants.tf255); + const transposeT = fc(normT, [0, 3, 1, 2]); let objectT; if (config3.object.enabled) objectT = model19.execute(transposeT); @@ -46638,11 +46821,11 @@ var poseChain = [ ["rightKnee", "rightAnkle"] ]; function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y: y5 } }) => ({ + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y: y10 } }) => ({ maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y5), + maxY: Math.max(maxY, y10), minX: Math.min(minX, x), - minY: Math.min(minY, y5) + minY: Math.min(minY, y10) }), { maxX: Number.NEGATIVE_INFINITY, maxY: Number.NEGATIVE_INFINITY, @@ -46728,23 +46911,23 @@ var MaxHeap = class { return this.getValueAt(i) < this.getValueAt(j); } exchange(i, j) { - const t10 = this.priorityQueue[i]; + const t8 = this.priorityQueue[i]; this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t10; + this.priorityQueue[j] = t8; } }; -function getOffsetPoint(y5, x, keypoint, offsets) { +function getOffsetPoint(y10, x, keypoint, offsets) { return { - y: offsets.get(y5, x, keypoint), - x: offsets.get(y5, x, keypoint + count) + y: offsets.get(y10, x, keypoint), + x: offsets.get(y10, x, keypoint + count) }; } function getImageCoords(part, outputStride2, offsets) { const { heatmapY, heatmapX, id: keypoint } = part; - const { y: y5, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + const { y: y10, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); return { x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y5 + y: part.heatmapY * outputStride2 + y10 }; } function clamp(a, min, max) { @@ -46865,13 +47048,13 @@ function buildPartWithScoreQueue(minConfidence2, scores) { } return queue; } -function withinRadius(poses, { x, y: y5 }, keypointId) { +function withinRadius(poses, { x, y: y10 }, keypointId) { return poses.some(({ keypoints }) => { var _a2; const correspondingKeypoint = (_a2 = keypoints[keypointId]) == null ? void 0 : _a2.position; if (!correspondingKeypoint) return false; - return squaredDistance(y5, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + return squaredDistance(y10, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; }); } function getInstanceScore(existingPoses, keypoints) { @@ -46905,23 +47088,23 @@ async function predict19(input, config3) { const res = De(() => { if (!model20.inputs[0].shape) return []; - const resized = uj.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = Te(Ke(Ye(resized, "float32"), 127.5), 1); + const resized = Fj.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + const normalized = Te(je(qe(resized, "float32"), 127.5), 1); const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y5) => lc(y5, [0])); - results3d[1] = wa(results3d[1]); + const results3d = results.map((y10) => mc(y10, [0])); + results3d[1] = Na(results3d[1]); return results3d; }); const buffers = await Promise.all(res.map((tensor) => tensor.buffer())); - for (const t10 of res) - Ot(t10); + for (const t8 of res) + Ot(t8); const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); if (!model20.inputs[0].shape) return []; const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); return scaled; } -async function load19(config3) { +async function load18(config3) { if (!model20 || env.initial) model20 = await loadModel(config3.body.modelPath); else if (config3.debug) @@ -46943,7 +47126,7 @@ function init3(config3) { ratio = config3.segmentation.ratio || 0.5; t.downsample_ratio = ir(ratio); } -async function load20(config3) { +async function load19(config3) { if (!model21 || env.initial) model21 = await loadModel(config3.segmentation.modelPath); else if (config3.debug) @@ -46952,41 +47135,41 @@ async function load20(config3) { return model21; } var normalize = (r) => De(() => { - const squeeze = lc(r, [0]); + const squeeze = mc(r, [0]); const mul = se(squeeze, constants.tf255); - const cast = Ye(mul, "int32"); + const cast = qe(mul, "int32"); return cast; }); function getRGBA(fgr, pha) { - const rgb3 = fgr ? normalize(fgr) : Sa([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : Sa([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = yt([rgb3, a], -1); + const rgb3 = fgr ? normalize(fgr) : Ta([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); + const a = pha ? normalize(pha) : Ta([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); + const rgba = bt([rgb3, a], -1); Ot([rgb3, a]); return rgba; } function getState(state) { return De(() => { const r = {}; - r.unstack = po(state, -1); - r.concat = yt(r.unstack, 1); - r.split = ai(r.concat, 4, 1); - r.stack = yt(r.split, 2); - r.squeeze = lc(r.stack, [0]); - r.expand = oi(r.squeeze, -1); - r.add = be(r.expand, 1); + r.unstack = mo(state, -1); + r.concat = bt(r.unstack, 1); + r.split = pi(r.concat, 4, 1); + r.stack = bt(r.split, 2); + r.squeeze = mc(r.stack, [0]); + r.expand = ai(r.squeeze, -1); + r.add = Ce(r.expand, 1); r.mul = se(r.add, 127.5); - r.cast = Ye(r.mul, "int32"); - r.tile = nu(r.cast, [1, 1, 3]); - r.alpha = Sa([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return yt([r.tile, r.alpha], -1); + r.cast = qe(r.mul, "int32"); + r.tile = uu(r.cast, [1, 1, 3]); + r.alpha = Ta([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); + return bt([r.tile, r.alpha], -1); }); } async function predict20(input, config3) { if (!model21) - model21 = await load20(config3); + model21 = await load19(config3); if (!(model21 == null ? void 0 : model21["executor"])) return null; - t.src = Ke(input, 255); + t.src = je(input, 255); if (ratio !== config3.segmentation.ratio) init3(config3); const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); @@ -47014,7 +47197,7 @@ async function predict20(input, config3) { // src/segmentation/selfie.ts var model22; -async function load21(config3) { +async function load20(config3) { if (!model22 || env.initial) model22 = await loadModel(config3.segmentation.modelPath); else if (config3.debug) @@ -47024,30 +47207,30 @@ async function load21(config3) { async function predict21(input, config3) { var _a2; if (!model22) - model22 = await load21(config3); + model22 = await load20(config3); if (!(model22 == null ? void 0 : model22["executor"]) || !((_a2 = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a2[0].shape)) return null; - const t10 = {}; - t10.resize = uj.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t10.norm = Ke(t10.resize, constants.tf255); - t10.res = model22.execute(t10.norm); - t10.squeeze = lc(t10.res, [0]); - t10.alpha = uj.resizeBilinear(t10.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t10.mul = se(t10.alpha, constants.tf255); + const t8 = {}; + t8.resize = Fj.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); + t8.norm = je(t8.resize, constants.tf255); + t8.res = model22.execute(t8.norm); + t8.squeeze = mc(t8.res, [0]); + t8.alpha = Fj.resizeBilinear(t8.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); + t8.mul = se(t8.alpha, constants.tf255); let rgba; switch (config3.segmentation.mode || "default") { case "default": - t10.input = lc(input); - t10.concat = yt([t10.input, t10.mul], -1); - rgba = Ye(t10.concat, "int32"); + t8.input = mc(input); + t8.concat = bt([t8.input, t8.mul], -1); + rgba = qe(t8.concat, "int32"); break; case "alpha": - rgba = Ye(t10.mul, "int32"); + rgba = qe(t8.mul, "int32"); break; default: rgba = ir(0); } - Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return rgba; } @@ -47116,7 +47299,7 @@ var Models = class { this.models[model23] = null; } async load(instance) { - var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w, _x2, _y, _z2, _A2; + var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u, _v2, _w2, _x2, _y, _z2, _A2; if (env.initial) this.reset(); if (instance) @@ -47137,17 +47320,19 @@ var Models = class { m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n2 = this.instance.config.body.modelPath) == null ? void 0 : _n2.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o2 = this.instance.config.body.modelPath) == null ? void 0 : _o2.includes("movenet")) ? load17(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p2 = this.instance.config.body.modelPath) == null ? void 0 : _p2.includes("posenet")) ? load19(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r2 = (_q2 = this.instance.config.hand.detector) == null ? void 0 : _q2.modelPath) == null ? void 0 : _r2.includes("handtrack")) ? loadDetect2(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s2 = this.instance.config.hand.detector) == null ? void 0 : _s2.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; - if ((_v2 = (_u2 = this.instance.config.hand.detector) == null ? void 0 : _u2.modelPath) == null ? void 0 : _v2.includes("handdetect")) - [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x2 = this.instance.config.object.modelPath) == null ? void 0 : _x2.includes("nanodet")) ? load18(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _z2.includes("meet")) ? load16(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _A2.includes("rvm")) ? load20(this.instance.config) : null; + m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o2 = this.instance.config.body.modelPath) == null ? void 0 : _o2.includes("movenet")) ? load16(this.instance.config) : null; + m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p2 = this.instance.config.body.modelPath) == null ? void 0 : _p2.includes("posenet")) ? load18(this.instance.config) : null; + m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r2 = (_q2 = this.instance.config.hand.detector) == null ? void 0 : _q2.modelPath) == null ? void 0 : _r2.includes("handtrack")) ? loadDetect3(this.instance.config) : null; + m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s2 = this.instance.config.hand.detector) == null ? void 0 : _s2.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton2(this.instance.config) : null; + if (this.instance.config.hand.enabled && !this.models.handdetect && ((_v2 = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v2.includes("handdetect"))) { + m.handdetect = loadDetect2(this.instance.config); + m.handskeleton = loadSkeleton(this.instance.config); + } + m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w2 = this.instance.config.object.modelPath) == null ? void 0 : _w2.includes("centernet")) ? load(this.instance.config) : null; + m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x2 = this.instance.config.object.modelPath) == null ? void 0 : _x2.includes("nanodet")) ? load17(this.instance.config) : null; + m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load20(this.instance.config) : null; + m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _z2.includes("meet")) ? load15(this.instance.config) : null; + m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _A2.includes("rvm")) ? load19(this.instance.config) : null; for (const [model23, promise] of Object.entries(m)) { if (promise == null ? void 0 : promise["then"]) promise["then"]((val) => this.models[model23] = val); @@ -47224,11 +47409,11 @@ function join2(faces, bodies, hands, gestures, shape) { person2.gestures.push(gesture2); } const x = []; - const y5 = []; + const y10 = []; const extractXY = (box) => { if (box && box.length === 4) { x.push(box[0], box[0] + box[2]); - y5.push(box[1], box[1] + box[3]); + y10.push(box[1], box[1] + box[3]); } }; extractXY(person2.face.box); @@ -47236,8 +47421,8 @@ function join2(faces, bodies, hands, gestures, shape) { extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); extractXY((_f2 = person2.hands.right) == null ? void 0 : _f2.box); const minX = Math.min(...x); - const minY = Math.min(...y5); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y5) - minY]; + const minY = Math.min(...y10); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y10) - minY]; if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; persons.push(person2); @@ -48042,9 +48227,9 @@ async function warmupNode(instance) { else img = atob2(body3); let res; - if ("node" in tfjs_esm_exports && Ime() === "tensorflow") { + if ("node" in tfjs_esm_exports && pde() === "tensorflow") { const data = (void 0).decodeJpeg(img); - const expanded = oi(data, 0); + const expanded = ai(data, 0); instance.tf.dispose(data); res = await instance.detect(expanded, instance.config); instance.tf.dispose(expanded); @@ -48066,14 +48251,14 @@ async function runInference(instance) { } async function runCompile(instance) { var _a2, _b, _c2, _d2; - if (!P().flagRegistry.ENGINE_COMPILE_ONLY) + if (!A().flagRegistry.ENGINE_COMPILE_ONLY) return; - const backendType = Ime(); - const webGLBackend = Tme(); + const backendType = pde(); + const webGLBackend = dde(); if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { return; } - P().set("ENGINE_COMPILE_ONLY", true); + A().set("ENGINE_COMPILE_ONLY", true); const numTensorsStart = ur().state.numTensors; const compiledModels = []; for (const [modelName, model23] of Object.entries(instance.models.models)) { @@ -48085,12 +48270,12 @@ async function runCompile(instance) { if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; } - const tensor = Wr(shape, dtype); + const tensor = Gr(shape, dtype); try { const res = model23.execute(tensor); compiledModels.push(modelName); if (Array.isArray(res)) - res.forEach((t10) => Ot(t10)); + res.forEach((t8) => Ot(t8)); else Ot(res); } catch (e) { @@ -48103,14 +48288,14 @@ async function runCompile(instance) { webGLBackend["getUniformLocations"](); if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); - P().set("ENGINE_COMPILE_ONLY", false); + A().set("ENGINE_COMPILE_ONLY", false); const numTensorsEnd = ur().state.numTensors; if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); } async function warmup(instance, userConfig) { await check(instance, false); - const t0 = now(); + const t02 = now(); instance.state = "warmup"; if (userConfig) instance.config = mergeDeep(instance.config, userConfig); @@ -48123,7 +48308,7 @@ async function warmup(instance, userConfig) { const res = await runInference(instance); const t12 = now(); if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t12 - t0), "ms"); + log("warmup", instance.config.warmup, Math.round(t12 - t02), "ms"); instance.emit("warmup"); resolve(res); }); @@ -48216,7 +48401,7 @@ var Human = class { return null; if (!input) return "input is not defined"; - if (this.env.node && !(input instanceof pt)) + if (this.env.node && !(input instanceof ut)) return "input must be a tensor"; try { this.tf.getBackend(); @@ -48237,7 +48422,7 @@ var Human = class { }); /** internal structure that keeps track of processed videos @hidden */ __privateAdd(this, _loops, {}); - const tfVersion = (zpe.tfjs || Vj).replace(/-(.*)/, ""); + const tfVersion = (Sce.tfjs || fX).replace(/-(.*)/, ""); config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; config.modelBasePath = env.browser ? "../models/" : "file://models/"; this.version = version; @@ -48361,7 +48546,7 @@ var Human = class { if (this.env.initial) { if (!await check(this, false)) log("error: backend check failed"); - await Sme(); + await ude(); if (this.env.browser) { if (this.config.debug) log("configuration:", this.config); @@ -48398,10 +48583,10 @@ var Human = class { * @returns result - {@link Result} */ async warmup(userConfig) { - const t0 = now(); + const t02 = now(); const res = await warmup(this, userConfig); const t12 = now(); - this.performance.warmup = Math.trunc(t12 - t0); + this.performance.warmup = Math.trunc(t12 - t02); return res; } /** Run detect with tensorflow profiling @@ -48443,7 +48628,7 @@ var Human = class { async detect(input, userConfig) { this.state = "detect"; return new Promise(async (resolve) => { - var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2; + var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u; this.state = "config"; let timeStamp; this.config = mergeDeep(this.config, userConfig); @@ -48576,7 +48761,7 @@ var Human = class { delete this.performance.gesture; } this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u2 = this.process.tensor) == null ? void 0 : _u2.shape) || [0, 0, 0, 0]; + const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; this.result = { face: faceRes, body: bodyRes, diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index dbff3b3d..56f62eb7 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/util/util.ts", "../src/config.ts", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../models/models.json", "../src/tfjs/load.ts", "../package.json", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/result.ts", "../src/body/movenetcoords.ts", "../src/util/interpolate.ts", "../src/segmentation/meet.ts", "../src/face/match.ts", "../src/models.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/rvm.ts", "../src/segmentation/selfie.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"], - "sourcesContent": ["/*\n Human\n homepage: \n author: '\n*/\n\nvar BU=Object.create;var PC=Object.defineProperty;var zU=Object.getOwnPropertyDescriptor;var VU=Object.getOwnPropertyNames;var WU=Object.getPrototypeOf,UU=Object.prototype.hasOwnProperty;var 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g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=sw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=HC(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&Bo(e[0])&&(s=e.map(p=>Yi(p)));let a=n.write(s,t,o),i=new pt(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=VC(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new pt(n,s,o,this.nextTensorId());return 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Qa||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Vp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return 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s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));$(s instanceof pt,()=>\"The result y returned by f() must be a tensor.\");let a=_0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. 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s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=be(se(p,this.rho),se(Jt(i),1-this.rho)),l=se(Ke(Rr(be(u,this.epsilon)),Rr(be(p,this.epsilon))),i),m=be(se(u,this.rho),se(Jt(l),1-this.rho));p.assign(c),u.assign(m);let d=be(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var Qu=class extends kr{static get className(){return\"Adagrad\"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Sa(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=be(i,Jt(a));i.assign(p);let u=be(se(Ke(a,Rr(be(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var Zu=class extends kr{static get className(){return\"Adam\"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=be(se(c,this.beta1),se(u,1-this.beta1)),d=be(se(l,this.beta2),se(Jt(u),1-this.beta2)),f=Ke(m,o),h=Ke(d,n);c.assign(m),l.assign(d);let g=be(se(Ke(f,be(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ri(this.beta1,this.iterations_+1)),this.accBeta2.assign(ri(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var Ju=class extends kr{static get className(){return\"Adamax\"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Ke(-this.learningRate,be(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=be(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Zt(u),h=Ed(d,f);c.assign(m),l.assign(h);let g=be(se(Ke(n,o),Ke(m,be(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var ii=class extends kr{static get className(){return\"SGD\"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=be(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Er(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var ep=class extends ii{static get className(){return\"Momentum\"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=be(se(this.m,a),i);this.useNesterov?p=be(se(this.c,be(i,se(u,this.m))),s):p=be(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var tp=class extends kr{static get className(){return\"RMSProp\"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=be(se(p,this.decay),se(Jt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=be(se(l,this.decay),se(i,1-this.decay)),d=Ke(se(i,this.learningRate),Rr(Te(c,be(Jt(m),this.epsilon)))),f=be(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=be(se(p,this.decay),se(Jt(i),1-this.decay)),m=be(se(u,this.momentum),Ke(se(i,this.learningRate),Rr(be(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var dj=[Yu,Qu,Zu,Ju,ep,tp,ii];function kN(){for(let r of dj)Lw(r)}var pi={};He(pi,{browserFiles:()=>TN,browserHTTPRequest:()=>EN,concatenateArrayBuffers:()=>tc,copyModel:()=>ok,decodeWeights:()=>rd,encodeWeights:()=>V0,fromMemory:()=>RN,fromMemorySync:()=>Gw,getLoadHandlers:()=>q0,getModelArtifactsForJSON:()=>rc,getModelArtifactsForJSONSync:()=>mw,getModelArtifactsInfoForJSON:()=>ga,getSaveHandlers:()=>K0,getWeightSpecs:()=>nd,http:()=>tf,isHTTPScheme:()=>ef,listModels:()=>tk,loadWeights:()=>_N,moveModel:()=>nk,registerLoadRouter:()=>H0,registerSaveRouter:()=>G0,removeModel:()=>rk,weightsLoaderFactory:()=>Ww,withSaveHandler:()=>DN,withSaveHandlerSync:()=>AN});var fj=\"model\",hj=\".json\",gj=\".weights.bin\";function NN(r){return new Promise(e=>setTimeout(e)).then(r)}var ui=class{constructor(e){if(!P().getBool(\"IS_BROWSER\"))throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");e.startsWith(ui.URL_SCHEME)&&(e=e.slice(ui.URL_SCHEME.length)),(e==null||e.length===0)&&(e=fj),this.modelJsonFileName=e+hj,this.weightDataFileName=e+gj}async save(e){if(typeof document==\"undefined\")throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:\"application/octet-stream\"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");{let o=[{paths:[\"./\"+this.weightDataFileName],weights:e.weightSpecs}],n=od(e,o),s=window.URL.createObjectURL(new Blob([JSON.stringify(n)],{type:\"application/json\"})),a=this.modelJsonAnchor==null?document.createElement(\"a\"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=s,await NN(()=>a.dispatchEvent(new MouseEvent(\"click\"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement(\"a\"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await NN(()=>i.dispatchEvent(new MouseEvent(\"click\")))}return{modelArtifactsInfo:ga(e)}}}};ui.URL_SCHEME=\"downloads://\";var Bw=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let o=new FileReader;o.onload=n=>{let s=JSON.parse(n.target.result),a=s.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let p=rc(s,u=>this.loadWeights(u));e(p)},o.onerror=n=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. 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w)l[S]=w[S]}),m+=f}),l}}var Cj=\"application/octet-stream\",wj=\"application/json\",El=class{constructor(e,t){if(this.DEFAULT_METHOD=\"POST\",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?($(typeof t.fetchFunc==\"function\",()=>\"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\"),this.fetch=t.fetchFunc):this.fetch=P().platform.fetch,$(e!=null&&e.length>0,()=>\"URL path for http must not be null, undefined or empty.\"),Array.isArray(e)&&$(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserHTTPRequest.save() 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n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gT=(r,e,t,o=Ze)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var xT=(r,e,t,o=Ze)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: 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yT=(r,e,t,o=Ze)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var bT=(r,e,t,o=Ze)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var CT=(r,e,t,o=Ze)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let 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ST=(r,e,t,o=Ze)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var IT=(r,e,t,o=Ze)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type 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NT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=A5(f,p);return F5(h,p),h}function A5(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var hc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function F5(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new hc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new hc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new hc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new hc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function TT(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>cu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new op(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(e,t){let o=CS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=NT(this.graph,o),p=TT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Er(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=P().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,w]=Nr(x,m),S=[];S[w]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=bS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(cu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(cu(p))continue;let u=Qw(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return cu(p)||s.has(p.name)}if(!(cu(e)||a==null))for(let p of a){if(i(p))continue;let u=Qw(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=P().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Pl(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=CS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),E=[];E[_]=e[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let w=u.filter(S=>!cu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(w.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ds(l.node.name,o)),n[l.node.name]==null){let d=bS(l.node,n,o,this._resourceManager);m||([m]=Ds(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ds(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Sf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var z5=\"?tfjs-format=file\",V5=\"model.json\",Ol=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},o=pi){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new Sf}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new op(Fl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(e.modelInitializer);this.initializer=new op(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof pt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof pt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ot(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function W5(r,e={},t=pi){if(r==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");e==null&&(e={}),e.fromTFHub&&typeof r==\"string\"&&(r=G5(r));let o=new Ol(r,e,t);return await o.load(),o}function U5(r){if(r==null)throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model\");let e;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error(\"modelJSON must be the first element of the array\");if(!n||!(n instanceof ArrayBuffer))throw new Error(\"An ArrayBuffer of weights must be the second element of the array\");if(!(\"modelTopology\"in o))throw new Error(\"Model JSON is missing 'modelTopology'\");if(!(\"weightsManifest\"in o))throw new Error(\"Model JSON is missing 'weightsManifest'\");let s=pi.getWeightSpecs(o.weightsManifest),a=pi.getModelArtifactsForJSONSync(o,s,n);e=pi.fromMemorySync(a)}else if(\"load\"in r)e=r;else if(\"modelTopology\"in r&&\"weightSpecs\"in r&&\"weightData\"in r)e=pi.fromMemorySync(r);else throw new Error(\"Unknown model format\");let t=new Ol(e);return t.load(),t}function G5(r){return r.endsWith(\"/\")||(r=r+\"/\"),`${r}${V5}${z5}`}var H5=\"4.5.0\";function Y(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var K5=Wt.whereImpl,lu=class extends ro{nextDataId(){return lu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Lo(this,ur())}write(e,t,o){this.firstUse&&(this.firstUse=!1,P().get(\"IS_NODE\")&&C.warn(`\n============================\nHi, looks like you are running TensorFlow.js in Node.js. 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c=C.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,S=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,E=S-1-c.padInfo.front,R=_-1-c.padInfo.left,D=k-1-c.padInfo.top,F=me(s.shape,\"float32\"),O=1/(f*h*g),M=t.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=c.outHeight||Math.floor(ie)!==ie))for(let le=0;le<_;le+=w){let ye=(J+le)/d;if(ye<0||ye>=c.outWidth||Math.floor(ye)!==ye)continue;let _e=M.get(L,ee,ie,ye,B);re+=_e}}}F.set(re*O,L,z,U,j,B)}return t.makeTensorInfo(F.shape,F.dtype,F.values)}var O_={kernelName:Ni,backendName:\"cpu\",kernelFunc:V8};function W8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Y([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=C.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,\"float32\"),_=1/(d*f),E=t.data.get(n.dataId).values,R=me(n.shape,\"float32\",E);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let H=0;H=c.outWidth||Math.floor(X)!==X)continue;let J=R.get(D,j,X,F);z+=J}}k.set(z*_,D,O,M,F)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var M_={kernelName:Gp,backendName:\"cpu\",kernelFunc:W8};function U8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||i.shape.length===a.shape.length,()=>\"Batch normalization 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i=s.reduce((x,b)=>x*b),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=Ve({inputs:{x:n},backend:t,attrs:{shape:p}}),f=St({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Ve({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Eo({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var B_={kernelName:js,backendName:\"cpu\",kernelFunc:G8};function H8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=yc(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var z_={kernelName:Zo,backendName:\"cpu\",kernelFunc:H8};function K8(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var V_={kernelName:Xs,backendName:\"cpu\",kernelFunc:K8};var q8=Ie(go,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return lr({inputs:{x:p[0]},backend:t});if(p[0].dtype===\"complex64\"){let h=p.map(S=>To({inputs:{input:S},backend:t})),g=p.map(S=>$a({inputs:{input:S},backend:t})),x=mu({inputs:h,backend:t,attrs:{axis:s}}),b=mu({inputs:g,backend:t,attrs:{axis:s}}),w=Kt({inputs:{real:x,imag:b},backend:t});return h.forEach(S=>t.disposeIntermediateTensorInfo(S)),g.forEach(S=>t.disposeIntermediateTensorInfo(S)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),w}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Ve({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=C.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=np(c,i,e[0].dtype,l),d=C.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var H_={kernelName:Ys,backendName:\"cpu\",kernelFunc:mu};function tI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Y([n,s],\"conv2d\");let l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=w?k[1]:k[2],D=w?k[2]:1,F=w?1:k[1],O=S.strides[0],M=w?S.strides[1]:S.strides[2],L=w?S.strides[2]:1,B=w?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let H=0;H=m.inHeight)continue;let le=oe*_[0],ye=X+ie*R;for(let _e=0;_e=m.inWidth)continue;let lt=le+Pe*_[1],We=ye+st*D,mt=lt;for(let it=0;it=u.inDepth)continue;let H=U*D[0],X=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=H+ee*D[1],le=X+oe*R[2];for(let ye=0;ye=u.inWidth)continue;let st=ie+Fe*D[2],lt=le+Pe*u.inChannels,We=st;for(let mt=0;mtMath.cos(r)),Z_={kernelName:nn,backendName:\"cpu\",kernelFunc:eY};var tY=Ie(sn,r=>Math.cosh(r)),J_={kernelName:sn,backendName:\"cpu\",kernelFunc:tY};function rY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,w=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-F)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let H=0;H1?F*(m-1)+re*z:.5*(F+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?F*(m-1)+H*z:.5*(F+M)*(m-1);if(X<0||X>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=C.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,w=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,D=_.values;for(let F=0;F=d.inHeight)continue;let H=U*l[0],X=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=H+ee*l[1],le=X+oe*d.inChannels,ye=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:F}=C.computeDilation2DInfo(o.shape,n.shape,s,a,\"NHWC\",i),O=y.sizeFromShape(F),M=F.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,H,J],M,y.computeStrides(F));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),F,o.dtype),shape:F,dtype:o.dtype}}};var c$={kernelName:Ai,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=C.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Ai}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let F=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieX&&(X=le,J=ne,re=oe)}}}O[J][re][H]+=F[L][B][U][H]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var l$={kernelName:Di,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=C.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Di}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let F=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieX&&(X=le,J=ee,re=ie)}}}O[L][J][re][H]+=F[L][B][U][H]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function li(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Y(n,\"sum\");let i;n.dtype===\"bool\"?i=_o({inputs:{x:n},backend:t,attrs:{dtype:\"int32\"}}):i=lr({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=C.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=St({inputs:{x:i},backend:t,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,p)),C.assertAxesAreInnerMostDims(\"sum\",l,m.shape.length);let[d,f]=C.computeOutAndReduceShapes(m.shape,l),h=C.upcastType(m.dtype,\"int32\"),g=gc(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,w=t.data.get(m.dataId).values;for(let S=0;S=0&&(m=li({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var d$={kernelName:Fi,backendName:\"cpu\",kernelFunc:cY};function lY(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;Y([o,n],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=0?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,\"float32\",s)}var f$={kernelName:Va,backendName:\"cpu\",kernelFunc:lY};var mY=C.ERF_P,dY=C.ERF_A1,fY=C.ERF_A2,hY=C.ERF_A3,gY=C.ERF_A4,xY=C.ERF_A5,yY=Ie(Wa,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+mY*t);return e*(1-((((xY*o+gY)*o+hY)*o+fY)*o+dY)*o*Math.exp(-t*t))}),h$={kernelName:Wa,backendName:\"cpu\",kernelFunc:yY};function vc(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Ve({inputs:{x:n},backend:t,attrs:{shape:i}})}var g$={kernelName:Js,backendName:\"cpu\",kernelFunc:vc};var bY=ze((r,e)=>r/e),zl=je(dn,bY),Vl={kernelName:dn,backendName:\"cpu\",kernelFunc:zl};function Lf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=C.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Ve({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Ve({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),w=kf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,w.dtype,w.values)}var I$={kernelName:ta,backendName:\"cpu\",kernelFunc:_Y};function $Y(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Ve({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=Lf(i,!0,t),u=Ve({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var v$={kernelName:Oi,backendName:\"cpu\",kernelFunc:$Y};var EY=Ie(kn,r=>Number.isFinite(r)?1:0,\"bool\"),k$={kernelName:kn,backendName:\"cpu\",kernelFunc:EY};var RY=Ie(Nn,r=>Math.abs(r)===1/0?1:0,\"bool\"),N$={kernelName:Nn,backendName:\"cpu\",kernelFunc:RY};var DY=Ie(Tn,r=>Number.isNaN(r)?1:0,\"bool\"),T$={kernelName:Tn,backendName:\"cpu\",kernelFunc:DY};function AY(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=Nf(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var _$={kernelName:Rn,backendName:\"cpu\",kernelFunc:AY};var FY=Ie(An,r=>Math.log1p(r)),$$={kernelName:An,backendName:\"cpu\",kernelFunc:FY};var PY=ze((r,e)=>r&&e),OY=je(Fn,PY,null,\"bool\"),E$={kernelName:Fn,backendName:\"cpu\",kernelFunc:OY};var MY=Ie(Pn,r=>r?0:1,\"bool\"),R$={kernelName:Pn,backendName:\"cpu\",kernelFunc:MY};var LY=ze((r,e)=>r||e),BY=je(On,LY,null,\"bool\"),D$={kernelName:On,backendName:\"cpu\",kernelFunc:BY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;Y(n,\"LRN\");let u=n.shape[3],c=u-1,l=t.data.get(n.dataId).values,m=y.sizeFromShape(n.shape),d=new Float32Array(m);function f(h){let g=h%u,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let S=l[x];w+=S*S}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function UI(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function GI(r,e,t,o){let[n,s]=fp(e,t),a=4,i=new Uint8Array(XE(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function HI(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(YE(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function KI(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var xp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=P().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,iI(t,e)):this.gl=Gr(t),e=this.gl,P().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),P().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=kc(this.gl,s),Hr(this.gl,a))this.textureHalfFloatExtension=kc(this.gl,a);else if(P().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Hr(this.gl,n))this.colorBufferHalfFloatExtension=kc(this.gl,n);else if(P().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",Hr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Hr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=DI(this.gl),this.indexBuffer=AI(this.gl),this.framebuffer=bI(this.gl),this.textureConfig=Kl(this.gl,this.textureHalfFloatExtension)}get debug(){return P().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),FI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),PI(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),OI(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),VI(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),zI(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),LI(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),MI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Kf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>GI(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return HI(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return UI(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=WI(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(P().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>KI(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=RI(t));let o=dI(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),fI(t,o);let n=Object.assign(o,{vao:this.createVertexArray()});return this.debug&&ql(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),BI(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ql(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?CI(this.gl,e,t):wI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),SI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ea(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ql(this.gl,this.program),Nc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=kc(this.gl,P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=oZ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in P().platform&&(o=P().platform.setTimeoutCustom.bind(P().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),jl(this.gl,e,this.framebuffer),this.debug&&Nc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(jl(this.gl,this.outputTexture,this.framebuffer),this.debug&&Nc(this.gl)):Kf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;jl(n,e,this.framebuffer),this.debug&&Nc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function oZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:jI(r,e)}function eD(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var Oc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${nZ(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Ec():$c(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function nZ(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?tR([\"r\",\"c\",\"d\"],\"inputShape\"):Os([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var ch=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=rD(t,o),s=oD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=tD(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=rD(o,n),a=oD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=tD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=P().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function sZ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function tD(r,e,t,o,n){let s=aZ(e,o),a;if(n){let[p,u]=Ea(r[0],r[1]);a=p*u}else{let[p,u]=fp(r[0],r[1]);a=p*u}let i=sZ(t,s);return a*i}function aZ(r,e){switch(r){case er.PACKED_2X2_FLOAT32:return nh(e);case er.PACKED_2X2_FLOAT16:return sh(e);case er.UNPACKED_FLOAT32:return th(e);case er.UNPACKED_FLOAT16:return rh(e);case er.PACKED_4X1_UNSIGNED_BYTE:return oh(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function iZ(r){return P().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function rD(r,e){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return iZ(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function oD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var tr=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Ut=\"if (isnan(x)) return x;\",nD=\"return x;\",XI=\"return abs(x);\";var sD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",aD=Ut+`\n return (x < 0.0) ? 0.0 : x;\n`,iD=Ut+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ra=\"return x;\",uD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var cD=\"return x;\",lD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,mD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,dD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,fD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Ar=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var lh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.length,o=Rt(\"rc\",t),n=Re(t),s=eD(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var pZ=Wt.whereImpl,cZ=1e-7,lZ=1e-4,mh={};function mZ(r){return r in mh||(mh[r]={}),mh[r]}var dZ=P().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),fZ=600;function hZ(){return P().global.screen==null?1024:P().global.screen.height*P().global.screen.width*window.devicePixelRatio*fZ/1024/1024}var hu=class extends ro{nextDataId(){return hu.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!P().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof xp)t=e;else{let o=Gr(P().getNumber(\"WEBGL_VERSION\"),e);t=new xp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Gr(P().getNumber(\"WEBGL_VERSION\"));t=new xp(o),this.binaryCache=mZ(P().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new ch(this.gpgpu),this.numMBBeforeWarning=hZ(),this.texData=new Lo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=Tc(t),c=new Xl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((P().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||P().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(P().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Ar(i,Ra):m=new tr(i,Ra);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=C.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Ra):f=new tr(n,Ra);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(P().getBool(\"DEBUG\")&&!P().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&P().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&P().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Hl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=C.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Ar(s,Ra):d=new tr(s,Ra);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(P().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=dZ){return P().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new lh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ph(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[di(e.shape),...fi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[di(t),...fi(t)],a=new Oc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=Tc(s),p;n?p=new Qf(i):p=new Yf(i);let u=!0,c=[t!=null?t:Hl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===du.DENSE){let x=a!=null?a:Hl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=P().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!fu(b.shape,x.shape)){let w=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),w.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=pR(e,c,l),d=this.getAndSaveBinary(m,()=>iR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),P().get(\"ENGINE_COMPILE_ONLY\")||uR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=P().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!P().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(P().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!P().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=P().getBool(\"DEBUG\");P().set(\"DEBUG\",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(P().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?cZ:lZ}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=II(o,p),t.texShape=l),s!=null){let m=Tc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ea(l[0],l[1])),p?d=new eh(m,g):d=new Xl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),w=this.texData.get(b.dataId);g?w.usage=mr.PIXELS:w.usage=mr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,P().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=gZ(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Kw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Gf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=EI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};hu.nextDataId=0;function gZ(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew hu,2);var cst={forceHalfFloat:hD};var Mc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var lo=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=C.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Kr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var Ro=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var gD={kernelName:xo,backendName:\"webgl\",kernelFunc:Dt};function Fr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var xD={kernelName:Ti,backendName:\"webgl\",kernelFunc:Fr};var YI=\"return (a < 0.) ? b * a : a;\",QI=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function yZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=P().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Ro(QI,n.shape,a.shape):new lo(YI,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var yD={kernelName:_n,backendName:\"webgl\",kernelFunc:yZ};var ZI=\"return (a < 0.) ? b * a : a;\",JI=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function bZ(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=P().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Ro(JI,o.shape,n.shape):new lo(ZI,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var bD={kernelName:Jn,backendName:\"webgl\",kernelFunc:bZ};var Do=\"if (isnan(x)) return x;\";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=P().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[S,k]=w,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new lo(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Fr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?C.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?C.fromUint8ToStringArray(h):h,[b,w]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(w,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=P().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new Ro(e,p.shape,u.shape,t):d=new lo(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function hi(r,e=!1){if(r===\"linear\")return e?cD:nD;if(r===\"relu\")return e?mD:aD;if(r===\"elu\")return e?lD:sD;if(r===\"relu6\")return e?dD:iD;if(r===\"prelu\")return e?JI:ZI;if(r===\"leakyrelu\")return e?QI:YI;if(r===\"sigmoid\")return e?fD:uD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Lc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",S=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!fu(n.shape,p)&&!(c.texture!==null&&fu(c.shape,p))?SD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ID={kernelName:ia,backendName:\"webgl\",kernelFunc:te};var Jl=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var dh=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function wZ(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=C.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function qr(r,e,t,o){let n=wZ(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=jI(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],F=Math.max(x,b),O=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,B=p===\"leakyrelu\",z=p!=null?hi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>tv&&U===!1){let X=E,J=R;t&&(X=bt({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(X)),o&&(J=bt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=X;re&&(ee=te({inputs:{x:X},backend:n,attrs:{shape:[F,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[F,1,O]}}),D.push(ie));let le=Zl({inputs:{a:ee,b:ie},backend:n});j=bp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let X=dt(r.dtype,e.dtype),J=new Lc(k,_,[F,d,f],t,o,M,z,L,B),re=[E,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,X)}let H=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let X of D)n.disposeIntermediateTensorInfo(X);return H}function IZ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Cp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var TD={kernelName:bo,backendName:\"webgl\",kernelFunc:IZ};var _D=\"return abs(x);\";function vZ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=ih(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return P().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Ar(o.shape,_D):n=new tr(o.shape,_D),t.runWebGLProgram(n,[o],o.dtype)}var $D={kernelName:Gs,backendName:\"webgl\",kernelFunc:vZ};var kZ=Ut+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,NZ=ge({opSnippet:kZ}),ED={kernelName:zo,backendName:\"webgl\",kernelFunc:NZ};var TZ=Ut+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,backendName:\"webgl\",kernelFunc:_Z};var DD=\"return a + b;\",$Z=nt({opSnippet:DD,packedOpSnippet:DD,supportsComplex:!0,cpuKernelImpl:cR}),AD={kernelName:no,backendName:\"webgl\",kernelFunc:$Z};var gh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var xh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function yh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>P().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=yh({inputs:o.slice(0,p),backend:t}),c=yh({inputs:o.slice(p),backend:t});return yh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=P().getBool(\"WEBGL_PACK\")?new xh(o[0].shape,s):new gh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var FD={kernelName:Wo,backendName:\"webgl\",kernelFunc:yh};function EZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=C.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=C.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=qr(h,h.dtype,\"all\",t),x;if(a){let b=C.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var PD={kernelName:Uo,backendName:\"webgl\",kernelFunc:EZ};function RZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=C.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims(\"any\",u,i);let[m,d]=C.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=qr(h,h.dtype,\"any\",t),x;if(a){let b=C.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var OD={kernelName:Go,backendName:\"webgl\",kernelFunc:RZ};var bh=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Ch=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Rt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = ${R}(${c.join()}, 0);\n ++${c[p-1]};\n ${R} sourceLocG = ${R}(${c.join()}, 0);\n ++${c[p-2]};\n ${R} sourceLocA = ${R}(${c.join()}, 0);\n --${c[p-1]};\n ${R} sourceLocB = ${R}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(R=>\"int \"+R),g=Rt(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Rt(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Rt(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=Rt(\"sourceLocA\",m-1).concat(\"inIdx.a\"),S=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,E=n?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${E}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n ${l}\n ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n sourceLocB${f}, sourceLocA${f}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${k}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${S}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function MD(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=C.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new bh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=MD(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function LD(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=C.computeOptimalWindowSize(s),i=new Ch(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=LD(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function wh(r,e,t,o){let n=[t];if(C.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!P().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=C.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=MD(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return LD(r,e,o)}function DZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=wh(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var BD={kernelName:Hs,backendName:\"webgl\",kernelFunc:DZ};function AZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=wh(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var zD={kernelName:Ks,backendName:\"webgl\",kernelFunc:AZ};var FZ=Ut+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,PZ=ge({opSnippet:FZ}),VD={kernelName:Ho,backendName:\"webgl\",kernelFunc:PZ};var OZ=Ut+\"return log(x + sqrt(x * x + 1.0));\",MZ=ge({opSnippet:OZ}),WD={kernelName:Ko,backendName:\"webgl\",kernelFunc:MZ};var LZ=Ut+`\n return atan(x);\n`,BZ=ge({opSnippet:LZ}),UD={kernelName:qo,backendName:\"webgl\",kernelFunc:BZ};var zZ=Mc+`\n return atan(a, b);\n`,VZ=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Kr+`\n return result;\n`,WZ=nt({opSnippet:zZ,packedOpSnippet:VZ}),GD={kernelName:Xo,backendName:\"webgl\",kernelFunc:WZ};var UZ=Ut+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelName:jo,backendName:\"webgl\",kernelFunc:GZ};var Ms=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,E=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${E}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${E}\n }\n }\n setOutput(${S});\n }\n `}},xu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t===\"avg\",S=\"0.0\";if(w||(S=\"-1.0 / 1e-20\"),o){let F=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${F} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,R=a%4,D=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${E}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${E};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function HZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Ps(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Ms(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var KD={kernelName:Yo,backendName:\"webgl\",kernelFunc:HZ};function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new xu(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var qD={kernelName:qs,backendName:\"webgl\",kernelFunc:KZ};var Sh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},Ih=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function qZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,l,u,c),d=new Ih(m);return t.runWebGLProgram(d,[n],a.dtype)}var jD={kernelName:Ni,backendName:\"webgl\",kernelFunc:qZ};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Ps([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=C.computePool2DInfo(a.shape,i,p,1,u),l=new Sh(c);return t.runWebGLProgram(l,[n],a.dtype)}var XD={kernelName:Gp,backendName:\"webgl\",kernelFunc:jZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Cp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var YD={kernelName:Qo,backendName:\"webgl\",kernelFunc:XZ};var vh=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var kh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var YZ=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=P().getBool(\"WEBGL_PACK_NORMALIZATION\")?new kh(o.shape,n.shape,s.shape,c,l,p):new vh(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},QD={kernelName:wn,backendName:\"webgl\",kernelFunc:YZ};var Nh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=QZ(this.rank),n,s=e.map((a,i)=>`sourceLoc.${rv[i]} = start[${i}] + coords.${rv[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},rv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function QZ(r){if(r===1)return\"sourceLoc\";if(r<=6)return rv.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var Th=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function ZZ(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ct.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Ls(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=VR(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=P().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Th(p):new Nh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),ZZ(n,i,p,t)}var ZD={kernelName:pa,backendName:\"webgl\",kernelFunc:Ls};var JZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,w)=>b*w),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ls({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},JD={kernelName:js,backendName:\"webgl\",kernelFunc:JZ};function e9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ah(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var eA={kernelName:Zo,backendName:\"webgl\",kernelFunc:e9};function t9(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var tA={kernelName:Xs,backendName:\"webgl\",kernelFunc:t9};var r9=\"return float(a != b);\",ov=nt({opSnippet:r9,cpuKernelImpl:DR,dtype:\"bool\"}),rA={kernelName:qn,backendName:\"webgl\",kernelFunc:ov};function gi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var oA={kernelName:zi,backendName:\"webgl\",kernelFunc:gi};var o9=\"return float(int(x));\";function nA(r,e){let t=new tr(r.shape,o9),o=e.runWebGLProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function nv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Wr(n.shape),i=nv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=Fr({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=gi({inputs:{input:n},backend:t}),i=nv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=mR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return nA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=ov({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var sA={kernelName:ho,backendName:\"webgl\",kernelFunc:nv};var aA=\"return ceil(x);\",n9=ge({opSnippet:aA,packedOpSnippet:aA,cpuKernelImpl:dR}),iA={kernelName:Jo,backendName:\"webgl\",kernelFunc:n9};var _h=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var $h=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;P().getBool(\"WEBGL_PACK_CLIP\")?i=new $h(n.shape):i=new _h(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var uA={kernelName:go,backendName:\"webgl\",kernelFunc:s9};var Eh=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function pA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function a9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Eh(o.shape),a=[pA(o,n.complexTensorInfos.real),pA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var cA={kernelName:_i,backendName:\"webgl\",kernelFunc:a9};var Rh=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) {\n return getChannel(\n getT${h}(${Dh(i,u,g)}),\n vec2(${Dh(c,u,g)}));\n }`}let d=p.length,f=p[p.length-1];m+=`\n return getChannel(\n getT${d}(${Dh(i,u,f)}),\n vec2(${Dh(c,u,f)}));`,this.userCode=`\n float getValue(${i.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n ${a[n-1]} = ${a[n-1]} + 1;\n if (${a[n-1]} < ${o[n-1]}) {\n result.g = getValue(${a});\n }\n\n ${a[n-2]} = ${a[n-2]} + 1;\n if (${a[n-2]} < ${o[n-2]}) {\n result.a = getValue(${a});\n }\n\n ${a[n-1]} = ${a[n-1]} - 1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function Dh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function wp(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var lA={kernelName:Mi,backendName:\"webgl\",kernelFunc:wp};function Bc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>gi({inputs:{input:b},backend:t})),f=r.map(b=>wp({inputs:{input:b},backend:t})),h=Bc(d,e,t),g=Bc(f,e,t),x=Fr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=C.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=fR(f,h,o,g),b=C.computeOutShape(r.map(S=>S.shape),e),w=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),w}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=P().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,Ra):new Ar(r[0].shape,Ra);return t.runWebGLProgram(d,r,o)}let i=P().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=i9(s,e,t),c=new Rh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function i9(r,e,t){let o=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function sv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):Bc(p,s,t)}var mA={kernelName:Ys,backendName:\"webgl\",kernelFunc:sv};var zc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,S=\"\",k=\"\";o&&(n?S=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?S=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:S=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${S}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Fh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Vc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Oh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Mh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Oh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Oh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>tv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(fu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let R=Cp({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=Cp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Lh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,w=!1,S=[];if(s!=null){let H=Oh(s.shape,f);H!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:H}}),S.push(s))}if(n!=null){let H=Oh(n.shape,f);H!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:H}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Ph(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let F=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?hi(i,!0):null,B=new Lc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,w,F,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(H),S.push(H)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let H of S)o.disposeIntermediateTensorInfo(H);return j}function u9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Mh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&P().getBool(\"WEBGL_EXP_CONV\")){let h=new Vc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(P().getBool(\"WEBGL_CONV_IM2COL\"))d=Lh({x:n,filter:s,convInfo:m,backend:t});else{let h=new zc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var dA={kernelName:en,backendName:\"webgl\",kernelFunc:u9};var Bh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},zh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Vh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Wh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function p9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Bh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var fA={kernelName:$i,backendName:\"webgl\",kernelFunc:p9};var Uh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=e.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function c9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(P().getBool(\"WEBGL_PACK\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Uh(m);return t.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new zh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}}var hA={kernelName:tn,backendName:\"webgl\",kernelFunc:c9};function l9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Fh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var gA={kernelName:rn,backendName:\"webgl\",kernelFunc:l9};function m9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=C.computeConv3DInfo(n.shape,p,a,1,i),c=new Vh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var xA={kernelName:za,backendName:\"webgl\",kernelFunc:m9};function d9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=C.computeConv3DInfo(p,s.shape,i,1,a),c=new Wh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var yA={kernelName:on,backendName:\"webgl\",kernelFunc:d9};var f9=Do+`\n return cos(x);\n`,h9=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Kr}\n return result;\n`,g9=ge({opSnippet:f9,packedOpSnippet:h9}),bA={kernelName:nn,backendName:\"webgl\",kernelFunc:g9};var x9=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,y9=ge({opSnippet:x9}),CA={kernelName:sn,backendName:\"webgl\",kernelFunc:y9};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[w,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var b9=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Gh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},wA={kernelName:pn,backendName:\"webgl\",kernelFunc:b9};var Sp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Sp||(Sp={}));var em=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Sp.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${SA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${IA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${IA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${SA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function SA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function IA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Hh(r,e,t,o,n,s){let a=e.shape.length,i=C.getAxesPermutation([o],a),p=e;i!=null&&(p=bt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new em(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new em(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=C.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function C9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Hh(Sp.Prod,n,t,s,a,i)}var vA={kernelName:an,backendName:\"webgl\",kernelFunc:C9};function w9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Hh(Sp.Sum,n,t,s,a,i)}var kA={kernelName:un,backendName:\"webgl\",kernelFunc:w9};function S9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ah(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=lR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var NA={kernelName:Qs,backendName:\"webgl\",kernelFunc:S9};var Kh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function I9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Kh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var TA={kernelName:cn,backendName:\"webgl\",kernelFunc:I9};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=C.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;P().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Uc(l):m=new Wc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var _A={kernelName:ln,backendName:\"webgl\",kernelFunc:v9};var qh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function k9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=C.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new qh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var $A={kernelName:Ei,backendName:\"webgl\",kernelFunc:k9};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=C.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new jh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var EA={kernelName:Ri,backendName:\"webgl\",kernelFunc:N9};var Xh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function T9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Xh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var RA={kernelName:Zs,backendName:\"webgl\",kernelFunc:T9};var Yh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Yh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var DA={kernelName:mn,backendName:\"webgl\",kernelFunc:_9};function $9(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=C.decodeEinsumEquation(n,s.length);C.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=C.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=bp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var AA={kernelName:Fi,backendName:\"webgl\",kernelFunc:$9};var E9=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",R9=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,D9=ge({opSnippet:E9,packedOpSnippet:R9}),FA={kernelName:fn,backendName:\"webgl\",kernelFunc:D9};var A9=\"return (b >= 0.0) ? a : a * (b + 1.0);\",F9=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,P9=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=P().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Ro(F9,o.shape,n.shape):new lo(A9,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},PA={kernelName:Va,backendName:\"webgl\",kernelFunc:P9};var O9=`\n return vec4(equal(a, b));\n`,M9=\"return float(a == b);\",L9=nt({opSnippet:M9,packedOpSnippet:O9,dtype:\"bool\",cpuKernelImpl:hR}),OA={kernelName:hn,backendName:\"webgl\",kernelFunc:L9};var B9=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${C.ERF_P};\n float a1 = ${C.ERF_A1};\n float a2 = ${C.ERF_A2};\n float a3 = ${C.ERF_A3};\n float a4 = ${C.ERF_A4};\n float a5 = ${C.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,z9=ge({opSnippet:B9}),MA={kernelName:Wa,backendName:\"webgl\",kernelFunc:z9};var V9=Do+`\n return exp(x);\n`,W9=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,av=ge({opSnippet:V9,packedOpSnippet:W9,cpuKernelImpl:gR,dtype:\"float32\"}),LA={kernelName:gn,backendName:\"webgl\",kernelFunc:av};function Qh(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var BA={kernelName:Js,backendName:\"webgl\",kernelFunc:Qh};var zA=\"return exp(x) - 1.0;\",U9=ge({opSnippet:zA,packedOpSnippet:zA,cpuKernelImpl:xR}),VA={kernelName:xn,backendName:\"webgl\",kernelFunc:U9};var tm=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function Zh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new tm(\"real\",p,e),c=new tm(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Fr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function G9(r){let{inputs:e,backend:t}=r,{input:o}=e;return Zh(o,!1,t)}var WA={kernelName:Pi,backendName:\"webgl\",kernelFunc:G9};var Jh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function xi(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Jh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var UA={kernelName:ea,backendName:\"webgl\",kernelFunc:xi};var eg=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var GA={kernelName:yn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new eg(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var HA=\"return floor(x);\",H9=ge({opSnippet:HA,packedOpSnippet:HA,cpuKernelImpl:yR}),KA={kernelName:bn,backendName:\"webgl\",kernelFunc:H9};var K9=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,q9=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,j9=nt({opSnippet:K9,packedOpSnippet:q9,dtype:\"int32\"}),qA={kernelName:Cn,backendName:\"webgl\",kernelFunc:j9};var tg=class{constructor(e){this.variableNames=[\"A\"];let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var rg=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var jA={kernelName:$u,backendName:\"webgl\",kernelFunc:X9},Gc,iv=P().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function X9(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=P().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Gc==null||h!==iv)&&(iv=h,Gc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:iv})),Gc.canvas.width=p,Gc.canvas.height=u,Gc.drawImage(n,0,0,p,u),n=Gc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=mr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=P().getBool(\"WEBGL_PACK\")?new rg(l):new tg(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function Y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],w=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(F,O)=>{if(O===\"NCHW\"&&F.shape.length===1&&F.shape[0]!==1){let M=te({inputs:{x:F},backend:t,attrs:{shape:[F.shape[0],1,1]}});return b.push(M),M}return F};if(w&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let F=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(F),b.push(F)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Mh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&P().getBool(\"WEBGL_EXP_CONV\")){let R=d?hi(d,!0):null,D=new Vc(g,w,R,S,k),F=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=t.runWebGLProgram(D,O,\"float32\",F)}else if(P().getBool(\"WEBGL_CONV_IM2COL\"))x=Lh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?hi(d,!1):null,D=new zc(g,w,R,S,k),F=_();x=t.runWebGLProgram(D,F,\"float32\")}let E=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),E}var XA={kernelName:Co,backendName:\"webgl\",kernelFunc:Y9};function Q9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(C.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);let g=C.computeConv2DInfo(n.shape,s.shape,p,h,u,l,!0),x=P().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?hi(m,x):null,w=[n,s],S=a!=null,k=i!=null,_=m===\"leakyrelu\";if(S&&w.push(a),k&&w.push(i),_){let F=t.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));w.push(F),f.push(F)}let E;x?E=new Uc(g,S,b,k,_):E=new Wc(g,S,b,k,_);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],D=t.runWebGLProgram(E,w,\"float32\",R);return f.forEach(F=>t.disposeIntermediateTensorInfo(F)),D}var YA={kernelName:wo,backendName:\"webgl\",kernelFunc:Q9};var og=class{constructor(e,t,o,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=[\"x\",\"indices\"],this.outputShape=o;let s=Re(o.length),a=`\n int index;`;for(let i=0;i= ${this.paramsShape[i]};\n flattenIndex += index * ${this.strides[i]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Z9(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=C.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),w=bR(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new og(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var QA={kernelName:Sn,backendName:\"webgl\",kernelFunc:Z9};var ng=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=J9(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function J9(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${w-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),w=t.bufferSync(m),S=CR(w,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ng(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var ZA={kernelName:ta,backendName:\"webgl\",kernelFunc:uv};var eJ=\"return float(a > b);\",tJ=`\n return vec4(greaterThan(a, b));\n`,rJ=nt({opSnippet:eJ,packedOpSnippet:tJ,cpuKernelImpl:wR,dtype:\"bool\"}),JA={kernelName:In,backendName:\"webgl\",kernelFunc:rJ};var oJ=\"return float(a >= b);\",nJ=`\n return vec4(greaterThanEqual(a, b));\n`,sJ=nt({opSnippet:oJ,packedOpSnippet:nJ,dtype:\"bool\",cpuKernelImpl:SR}),eF={kernelName:vn,backendName:\"webgl\",kernelFunc:sJ};function aJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Zh(o,!0,t)}var tF={kernelName:Oi,backendName:\"webgl\",kernelFunc:aJ};var iJ=\"return float(!isnan(x) && !isinf(x));\",uJ=ge({opSnippet:iJ,dtype:\"bool\"}),rF={kernelName:kn,backendName:\"webgl\",kernelFunc:uJ};var pJ=\"return float(isinf(x));\",cJ=ge({opSnippet:pJ,dtype:\"bool\"}),oF={kernelName:Nn,backendName:\"webgl\",kernelFunc:cJ};var lJ=\"return float(isnan(x));\",mJ=ge({opSnippet:lJ,dtype:\"bool\"}),nF={kernelName:Tn,backendName:\"webgl\",kernelFunc:mJ};var dJ=\"return float(a < b);\",fJ=`\n return vec4(lessThan(a, b));\n`,hJ=nt({opSnippet:dJ,packedOpSnippet:fJ,cpuKernelImpl:IR,dtype:\"bool\"}),sF={kernelName:$n,backendName:\"webgl\",kernelFunc:hJ};var gJ=\"return float(a <= b);\",xJ=`\n return vec4(lessThanEqual(a, b));\n`,yJ=nt({opSnippet:gJ,packedOpSnippet:xJ,cpuKernelImpl:vR,dtype:\"bool\"}),aF={kernelName:En,backendName:\"webgl\",kernelFunc:yJ};function bJ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=kR(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var iF={kernelName:Rn,backendName:\"webgl\",kernelFunc:bJ};var CJ=Do+`\n return x < 0.0 ? 0./0. : log(x);\n`,wJ=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,SJ=ge({opSnippet:CJ,packedOpSnippet:wJ,cpuKernelImpl:NR}),uF={kernelName:Dn,backendName:\"webgl\",kernelFunc:SJ};var IJ=Do+`\n return log(1.0 + x);\n`,vJ=ge({opSnippet:IJ}),pF={kernelName:An,backendName:\"webgl\",kernelFunc:vJ};var kJ=\"return float(a >= 1.0 && b >= 1.0);\",NJ=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,TJ=nt({opSnippet:kJ,packedOpSnippet:NJ,dtype:\"bool\"}),cF={kernelName:Fn,backendName:\"webgl\",kernelFunc:TJ};var _J=\"return float(!(x >= 1.0));\",$J=ge({opSnippet:_J}),lF={kernelName:Pn,backendName:\"webgl\",kernelFunc:$J};var EJ=\"return float(a >= 1.0 || b >= 1.0);\",RJ=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,DJ=nt({opSnippet:EJ,packedOpSnippet:RJ,dtype:\"bool\"}),mF={kernelName:On,backendName:\"webgl\",kernelFunc:DJ};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var ag=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var AJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=P().getBool(\"WEBGL_PACK_NORMALIZATION\")?new ag(n.shape,s,a,i,p):new sg(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},dF={kernelName:Mn,backendName:\"webgl\",kernelFunc:AJ};var ig=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var FJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new ig(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},fF={kernelName:Ua,backendName:\"webgl\",kernelFunc:FJ};function hF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=qr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function pv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=C.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let w=t.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Ms(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var yF={kernelName:zn,backendName:\"webgl\",kernelFunc:LJ};function BJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new xu(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var bF={kernelName:ra,backendName:\"webgl\",kernelFunc:BJ};var ug=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},pg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function zJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,l,u,c),d=new xu(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new pg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var CF={kernelName:Li,backendName:\"webgl\",kernelFunc:zJ};function VJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Ps([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=C.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Ms(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new ug(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var wF={kernelName:Hp,backendName:\"webgl\",kernelFunc:VJ};function SF(r,e,t,o){let n=new Ms(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Ms(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var IF={kernelName:Bi,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(o.shape,n,s,u,a),[l,m]=SF(o,i,c,p);return[l,m]}};function vF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=qr(i,\"float32\",\"mean\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var kF={kernelName:Vn,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=C.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rc[0]+e[l]+c[1]);let n=e.length,s=Re(n),a=t.map(c=>c[0]).join(\",\"),i=t.map((c,l)=>c[0]+e[l]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n),u=o===\"reflect\"?0:1;if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${u};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${u};\n }\n setOutput(getX(outC - start));\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; i < ${n}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${u};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n }\n }\n ${s} coords = outC - start;\n setOutput(getX(${p}));\n }\n `}};var lg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=Re(n),a=t.map(f=>f[0]).join(\",\"),i=t.map((f,h)=>f[0]+e[h]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=o===\"reflect\"?0:1,d=\"\";if(n===1){let f=`\n ${s} source = rc;\n if (source < start) {\n source = start * 2 - source - ${m};\n } else if (source >= end) {\n source = (end - 1) * 2 - source + ${m};\n }\n source -= start;\n `;d=`\n ${s} rc = outputLoc;\n ${f}\n result[0] = getChannel(getX(${u.join()}), ${l});\n ${p[n-1]} += 1;\n if(${c}) {\n ${f}\n result[1] = getChannel(getX(${u.join()}), ${l});\n }\n `}else{let f=`\n ${s} source = rc;\n ${s} lt = ${s}(lessThan(source, start));\n ${s} gte = ${s}(greaterThanEqual(source, end));\n ${s} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${m}) +\n gte * ((end - 1) * 2 - source + ${m});\n source -= start;\n `;d=`\n ${s} rc = outputLoc;\n ${f}\n result[0] = getChannel(getX(${u.join()}), ${l});\n ${p[n-1]} += 1;\n if(${c}) {\n ${f}\n result[1] = getChannel(getX(${u.join()}), ${l});\n }\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {\n ${f}\n result[2] = getChannel(getX(${u.join()}), ${l});\n ${p[n-1]} += 1;\n if(${c}) {\n ${f}\n result[3] = getChannel(getX(${u.join()}), ${l});\n }\n }\n `}this.userCode=`\n const ${s} start = ${s}(${a});\n const ${s} end = ${s}(${i});\n\n void main() {\n ${s} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${d}\n setOutput(result);\n }\n `}};var KJ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=P().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new lg(o.shape,n,s):new cg(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},_F={kernelName:Gn,backendName:\"webgl\",kernelFunc:KJ};var qJ=`if (b == 0.0) return NAN;\n return mod(a, b);`,jJ=`\n vec4 result = mod(a, b);\n bvec4 isNaN = equal(b, vec4(0.0));\n `+Kr+`\n return result;\n`,XJ=nt({opSnippet:qJ,packedOpSnippet:jJ}),$F={kernelName:Ga,backendName:\"webgl\",kernelFunc:XJ};var mg=class{constructor(e,t,o){this.variableNames=[\"probs\"],this.customUniforms=[{name:\"seed\",type:\"float\"}],this.outputShape=[e,o],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; 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Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=nee(c,l,a,i,p,u);return[t.makeTensorInfo([m.length],\"int32\",new Int32Array(m)),t.makeTensorInfo([],\"int32\",new Int32Array([d]))]}var MF={kernelName:Ha,backendName:\"webgl\",kernelFunc:see};var aee=Wt.nonMaxSuppressionV5Impl;function iee(r){C.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=aee(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var LF={kernelName:Xn,backendName:\"webgl\",kernelFunc:iee};var dg=class{constructor(e,t,o,n){this.variableNames=[\"indices\"],this.outputShape=[e,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${n}), float(${o}),\n float(index == coords.y)));\n }\n `}};var uee=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new dg(u,a,i,p),l=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},BF={kernelName:Yn,backendName:\"webgl\",kernelFunc:uee};function rm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=gi({inputs:{input:o},backend:t}),s=rm({inputs:{x:n},backend:t}),a=wp({inputs:{input:o},backend:t}),i=rm({inputs:{x:a},backend:t}),p=Fr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return xi({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var zF={kernelName:fa,backendName:\"webgl\",kernelFunc:rm};function VF(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var UF={kernelName:sa,backendName:\"webgl\",kernelFunc:pee};var fg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Re(n),a=t.map(u=>u[0]).join(\",\"),i=t.map((u,c)=>u[0]+e[c]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n);if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${p}));\n }\n }\n `}};var hg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return xi({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=P().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new hg(n.shape,s,a):new fg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},GF={kernelName:Qn,backendName:\"webgl\",kernelFunc:dv};var cee=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,lee=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+Kr+`\n return result;\n`,mee=nt({opSnippet:cee,packedOpSnippet:lee}),HF={kernelName:Zn,backendName:\"webgl\",kernelFunc:mee};function dee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=C.getAxesPermutation(c,i),m=n;l!=null&&(m=bt({inputs:{x:n},backend:t,attrs:{perm:l}}),c=C.getInnerMostAxes(c.length,i),p.push(m)),C.assertAxesAreInnerMostDims(\"prod\",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=AR(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=C.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=Za(n.dtype),w=qr(x,b,\"prod\",t);d=te({inputs:{x:w},backend:t,attrs:{shape:f}}),p.push(x),p.push(w)}if(a){p.push(d);let f=C.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var KF={kernelName:es,backendName:\"webgl\",kernelFunc:dee};function fee(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=FR(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var qF={kernelName:Kp,backendName:\"webgl\",kernelFunc:fee};function hee(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=PR(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var jF={kernelName:qp,backendName:\"webgl\",kernelFunc:hee};function gee(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=OR(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var XF={kernelName:jp,backendName:\"webgl\",kernelFunc:gee};var fv=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=MR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},YF={kernelName:aa,backendName:\"webgl\",kernelFunc:fv};var xee=\"return 1.0 / x;\",yee=ge({opSnippet:xee}),QF={kernelName:ts,backendName:\"webgl\",kernelFunc:yee};var bee=Ut+`\n return (x < 0.0) ? 0.0 : x;\n`,Cee=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,wee=ge({opSnippet:bee,packedOpSnippet:Cee}),ZF={kernelName:rs,backendName:\"webgl\",kernelFunc:wee};var See=Ut+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Iee=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,vee=ge({opSnippet:See,packedOpSnippet:Iee}),JF={kernelName:ss,backendName:\"webgl\",kernelFunc:vee};var gg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var xg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function kee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=P().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new xg(n.shape,p,u,s,a):new gg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var e3={kernelName:ns,backendName:\"webgl\",kernelFunc:kee};var yg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Nee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new yg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var t3={kernelName:qa,backendName:\"webgl\",kernelFunc:Nee};var bg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Tee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=P().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var r3={kernelName:os,backendName:\"webgl\",kernelFunc:Tee};var wg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function _ee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var o3={kernelName:Ka,backendName:\"webgl\",kernelFunc:_ee};var Sg=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var Ig=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,w)=>d(w,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function $ee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=P().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Ig(n.shape,i):new Sg(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var n3={kernelName:as,backendName:\"webgl\",kernelFunc:$ee};var vg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var s3={kernelName:_s,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new vg(o.shape,s),[u,c]=C.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Eee=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,Ree=ge({opSnippet:Eee}),a3={kernelName:is,backendName:\"webgl\",kernelFunc:Ree};var Dee=\"return inversesqrt(x);\",Aee=ge({opSnippet:Dee,cpuKernelImpl:LR}),i3={kernelName:us,backendName:\"webgl\",kernelFunc:Aee};var yu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var kg=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\",b=t>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${e}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${t}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${t}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function Fee(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=C.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g;P().getBool(\"WEBGL_PACK\")?g=new kg(p,i,d.shape.length,f.shape.length,c,m):g=new yu(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var u3={kernelName:ps,backendName:\"webgl\",kernelFunc:Fee};var Ng=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=P().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Pee(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Ng(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],\"int32\",p)}var p3={kernelName:ls,backendName:\"webgl\",kernelFunc:Pee};var Tg=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Oee(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new Tg(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var c3={kernelName:ua,backendName:\"webgl\",kernelFunc:Oee};var Mee=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${C.SELU_SCALEALPHA};\n float scale = ${C.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Lee=ge({opSnippet:Mee}),l3={kernelName:ms,backendName:\"webgl\",kernelFunc:Lee};var Bee=Do+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,zee=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Vee=ge({opSnippet:Bee,packedOpSnippet:zee,cpuKernelImpl:zR}),m3={kernelName:hs,backendName:\"webgl\",kernelFunc:Vee};var Wee=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Uee=ge({opSnippet:Wee}),d3={kernelName:fs,backendName:\"webgl\",kernelFunc:Uee};var Gee=Do+`\n return sin(x);\n`,Hee=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Kr}\n return result;\n`,Kee=ge({opSnippet:Gee,packedOpSnippet:Hee}),f3={kernelName:ds,backendName:\"webgl\",kernelFunc:Kee};var qee=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,jee=ge({opSnippet:qee}),h3={kernelName:ja,backendName:\"webgl\",kernelFunc:jee};var Xee=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Yee=ge({opSnippet:Xee}),g3={kernelName:gs,backendName:\"webgl\",kernelFunc:Yee};var Qee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},x3={kernelName:ca,backendName:\"webgl\",kernelFunc:Qee};function Zee(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=WR(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var y3={kernelName:Vi,backendName:\"webgl\",kernelFunc:Zee};function Jee(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=UR(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var b3={kernelName:Xa,backendName:\"webgl\",kernelFunc:Jee};function ete(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=uh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var C3={kernelName:Wi,backendName:\"webgl\",kernelFunc:ete};function tte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=uh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var w3={kernelName:Ui,backendName:\"webgl\",kernelFunc:tte};function rte(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=C.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=t.bufferSync(n),b=t.bufferSync(s),w=y.decodeString(t.readSync(a.dataId)[0]),S=BR(x,b,i,m,c,u,p,l,w,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new yu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var S3={kernelName:Cs,backendName:\"webgl\",kernelFunc:rte};function ote(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Ls({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var I3={kernelName:la,backendName:\"webgl\",kernelFunc:ote};var v3=\"return sqrt(x);\",nte=ge({opSnippet:v3,packedOpSnippet:v3,cpuKernelImpl:GR}),k3={kernelName:xs,backendName:\"webgl\",kernelFunc:nte};var ste=\"return x * x;\",ate=ge({opSnippet:ste}),N3={kernelName:Gi,backendName:\"webgl\",kernelFunc:ate};var T3=\"return (a - b) * (a - b);\",ite=nt({opSnippet:T3,packedOpSnippet:T3}),_3={kernelName:ws,backendName:\"webgl\",kernelFunc:ite};function ute(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=t.readSync(n.dataId),a=C.fromUint8ToStringArray(s),i=HR(a,\"string\",o);return t.makeTensorInfo(n.shape,\"string\",i)}var $3={kernelName:_u,backendName:\"webgl\",kernelFunc:ute};function pte({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Ut+`\n return x > 0.0 ? 1.0 : float(${e.alpha});\n `,s=new tr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var E3={kernelName:yo,backendName:\"webgl\",kernelFunc:pte};var _g=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function cte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=ct.computeOutShape(b,w,S),R=Ls({inputs:{x:n},backend:t,attrs:{begin:b,size:E}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=me(n.shape,n.dtype,R),F=KR(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,F.values)}else{let R=new _g(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var R3={kernelName:Ss,backendName:\"webgl\",kernelFunc:cte};function lte(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=qR(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var D3={kernelName:ma,backendName:\"webgl\",kernelFunc:lte};function mte(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=jR(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var A3={kernelName:Hi,backendName:\"webgl\",kernelFunc:mte};function dte(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=XR(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var F3={kernelName:Ki,backendName:\"webgl\",kernelFunc:dte};var fte=\"return tan(x);\",hte=ge({opSnippet:fte}),P3={kernelName:vs,backendName:\"webgl\",kernelFunc:hte};var gte=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,xte=ge({opSnippet:gte}),O3={kernelName:ks,backendName:\"webgl\",kernelFunc:xte};function yte(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=C.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new yu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var M3={kernelName:cs,backendName:\"webgl\",kernelFunc:yte};var $g=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=QR(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new $g(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var L3={kernelName:so,backendName:\"webgl\",kernelFunc:hv};var Eg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Rg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function Ip(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function B3(r){let e=1;for(;ep){let F=t.readSync(n.dataId),[O,M]=ZR(F,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,xi({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&Ip(t,d);let x=B3(s),b=B3(c),w=null,S=()=>w===null?[g,g]:[g,w],k=(F,O,M)=>{let L=S(),B=new Eg(M),U=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[F],[O]],j=w;w=t.runWebGLProgram(B,L,\"int32\",U),Ip(t,j)};for(let F=1;F=1;M/=2)k(O,M,[h,b])}for(let F=b;F>x;F/=2){let O=S(),M=new Rg([h,F/2]),B=[[c],[w===null?1:0],[x]],z=w;w=t.runWebGLProgram(M,O,\"int32\",B),Ip(t,z);let U=x/2,j=U*2;for(let H=U;H>=1;H/=2)k(j,H,w.shape)}let _=w;w=Ls({inputs:{x:w},backend:t,attrs:{begin:0,size:[h,s]}}),Ip(t,_);let E=uv({inputs:{x:g,indices:w},backend:t,attrs:{axis:1,batchDims:1}});Ip(t,g);let R=u.slice(0,-1);R.push(s),_=w,w=te({inputs:{x:w},attrs:{shape:R},backend:t}),Ip(t,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:t}),Ip(t,D),[E,w]}var z3={kernelName:Ns,backendName:\"webgl\",kernelFunc:Cte};var Dg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${t}));\n float mapY = mapCoord(inY, float(${e}));\n\n if (${i} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function wte(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Dg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var V3={kernelName:Ts,backendName:\"webgl\",kernelFunc:wte};function Ste(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Ps(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=JR(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var W3={kernelName:qi,backendName:\"webgl\",kernelFunc:Ste};function Ite(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var U3={kernelName:da,backendName:\"webgl\",kernelFunc:Ite};var Ag=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function vte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=C.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=C.getInnerMostAxes(1,i)[0]);let m=C.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=Za(n.dtype),g=(S,k,_,E,R)=>{let D=S.shape[0],F=S.shape[1],O=C.segment_util.segOpComputeOptimalWindowSize(F,R),M={windowSize:O,inSize:F,batchSize:D,numSegments:R},L=new Ag(M,k),B=t.compileAndRun(L,[S,_],E);if(p.push(B),B.shape[1]===R)return B;let z=fv({backend:t,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=hv({inputs:{x:z},backend:t,attrs:{reps:[F/O]}});return p.push(z),p.push(U),g(B,k,U,E,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:t,attrs:{shape:m}}),w=b;if(c!=null){p.push(b);let S=C.getUndoAxesPermutation(c);w=bt({inputs:{x:w},backend:t,attrs:{perm:S}})}return p.forEach(S=>t.disposeIntermediateTensorInfo(S)),w}var G3={kernelName:ji,backendName:\"webgl\",kernelFunc:vte};var kte=[TD,$D,ED,RD,AD,FD,PD,OD,BD,zD,VD,WD,UD,GD,HD,KD,qD,jD,XD,YD,QD,JD,eA,tA,sA,iA,uA,xD,cA,mA,dA,fA,hA,gA,xA,yA,bA,CA,wA,vA,kA,NA,TA,_A,$A,EA,RA,DA,AA,FA,PA,OA,MA,LA,BA,VA,WA,UA,GA,KA,qA,jA,XA,YA,QA,ZA,JA,eF,gD,tF,lA,rF,oF,nF,yD,sF,aF,iF,uF,pF,cF,lF,mF,dF,fF,gF,xF,yF,bF,CF,wF,IF,kF,NF,TF,_F,$F,FF,wD,PF,OF,MF,LF,rA,BF,WF,UF,GF,HF,bD,KF,qF,jF,XF,YF,oA,EF,QF,ZF,JF,ID,e3,t3,r3,o3,n3,s3,a3,i3,u3,p3,c3,l3,m3,d3,f3,h3,ZD,AF,g3,x3,y3,b3,C3,w3,S3,I3,k3,N3,_3,$3,E3,R3,D3,A3,F3,DF,kD,P3,O3,M3,L3,z3,V3,ND,W3,U3,G3,zF];for(let r of kte)Ya(r);var we;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(we||(we={}));var bu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(bu||(bu={}));var H3;function Nte(r){H3=r.wasm.cwrap(bo,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Tte(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=bu[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=p?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],w=Sr.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)),S=t.makeOutput([...w,x,b],n.dtype),k=t.dataIdMap.get(S.dataId).id,_=new Uint8Array(new Int32Array(n.shape).buffer),E=new Uint8Array(new Int32Array(s.shape).buffer);return H3(m,_,n.shape.length,d,E,s.shape.length,p,u,g,f,h,l||0,k),S}var K3={kernelName:bo,backendName:\"wasm\",setupFunc:Nte,kernelFunc:Tte};function Ce(r,e){let t;function o(s){t=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function n(s){let{backend:a,inputs:{x:i}}=s,p=a.dataIdMap.get(i.dataId).id,u=a.makeOutput(i.shape,e||i.dtype),c=a.dataIdMap.get(u.dataId).id;return y.sizeFromShape(u.shape)===0||t(p,we[i.dtype],c),u}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:n}}var q3=Ce(Gs);var j3=Ce(zo);var X3=Ce(Vo);function Je(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(a){let{backend:i,inputs:p}=a,{a:u,b:c}=p,l=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,d=t!=null?t:u.dtype,f=C.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(f,d);if(y.sizeFromShape(f)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id;return(()=>o(l,g,u.shape.length,m,x,c.shape.length,we[u.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:s}}var _te=!0,Y3=Je(no,_te);var Q3;function $te(r){Q3=r.wasm.cwrap(Wo,null,[\"array\",\"number\",\"number\",\"number\"])}function Ete(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return Q3(s,n.length,we[o.dtype],a),o}var Z3={kernelName:Wo,backendName:\"wasm\",setupFunc:$te,kernelFunc:Ete};function vp(r){let{inputs:{x:e},backend:t}=r;if(e.dtype===\"string\")return ir(t.readSync(e.dataId),e.shape,e.dtype);let o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var J3={kernelName:xo,backendName:\"wasm\",kernelFunc:vp};var eP;function Rte(r){eP=r.wasm.cwrap(ao,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function mo(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=Ate(e.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var tP={kernelName:ao,backendName:\"wasm\",kernelFunc:mo,setupFunc:Rte};function Tr(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=C.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let c=new Array(n);for(let d=0;d`new shape: ${a}, old shape: ${o.shape}. 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e.dtype===\"string\"?l.stringBytes=p.slice(f,f+y.sizeFromShape(a)):n.typedArrayFromHeap(u).set(p.subarray(f,f+y.sizeFromShape(a))),u}if(e.dtype===\"string\"){let f=ip(p,s,a,e.shape,e.dtype);return l.stringBytes=f,u}let m=n.typedArrayFromHeap(u),d=e.shape.length;if(d===2)Kte(p,c[0],m,s,a);else if(d===3)qte(p,c[0],c[1],m,s,a);else if(d===4)jte(p,c[0],c[1],c[2],m,s,a);else{let f=ip(p,s,a,e.shape,e.dtype);m.set(f)}return u}function Kte(r,e,t,o,n){let s=0,a=o[0],i=o[1],p=a+n[0];for(let u=a;ux*b),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=zt({inputs:{x:n},backend:t,attrs:{shape:p}}),f=mo({inputs:{x:d},backend:t,attrs:{perm:u}}),h=zt({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ao({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(d.dataId),g}var IP={kernelName:js,backendName:\"wasm\",kernelFunc:Xte};var vP;function Yte(r){vP=r.wasm.cwrap(Zo,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function Qte(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=s.shape.reduce((l,m)=>l*m,1)!==0,p=n.shape.length===1?[a]:[n.shape[0],a],u=e.makeOutput(p,s.dtype);function c(l){return e.dataIdMap.get(l.dataId).id}return vP(c(n),a,i,c(s),we[s.dtype],c(u)),u}var kP={kernelName:Zo,backendName:\"wasm\",setupFunc:Yte,kernelFunc:Qte};function Zte(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.typedArrayFromHeap(o),a=t.typedArrayFromHeap(n),i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeOutput([i.length],\"int32\",void 0,new Int32Array(i))}var NP={kernelName:Xs,backendName:\"wasm\",kernelFunc:Zte};function Pr(r){let{inputs:{x:e},attrs:{dtype:t},backend:o}=r,n=o.makeOutput(e.shape,t),s=o.typedArrayFromHeap(e);return o.typedArrayFromHeap(n).set(s),n}var TP={kernelName:ho,backendName:\"wasm\",kernelFunc:Pr};var _P=Ce(Jo);var $P;function Jte(r){$P=r.wasm.cwrap(go,null,[\"number\",\"number\",\"number\",\"number\"])}function ere(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i=t.dataIdMap.get(n.dataId).id,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(p.dataId).id;return $P(i,s,a,u),p}var EP={kernelName:go,backendName:\"wasm\",setupFunc:Jte,kernelFunc:ere};function gv(r){let{inputs:e,backend:t}=r,o=y.parseAxisParam(r.attrs.axis,e[0].shape)[0],n=e.map(d=>d.shape);C.assertParamsConsistent(n,o);let s=C.computeOutShape(e.map(d=>d.shape),o),a=e.filter(d=>y.sizeFromShape(d.shape)>0);if(a.length===1)return vp({inputs:{x:a[0]},backend:t});let i=t.makeOutput(s,e[0].dtype);if(y.sizeFromShape(s)===0)return i;if(a[0].dtype===\"string\"){let d=a.map(w=>{let k=[-1,y.sizeFromShape(w.shape.slice(o))];return zt({inputs:{x:w},backend:t,attrs:{shape:k}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape}));s=C.computeOutShape(d.map(w=>w.shape),1);let h=d[0].shape[0]===1,g=np(f,s,e[0].dtype,h),x=C.computeOutShape(a.map(w=>w.shape),o);i.shape=x;let b=t.dataIdMap.get(i.dataId);return b.stringBytes=C.fromStringArrayToUint8(g),d.forEach(w=>t.disposeData(w.dataId)),i}let p=y.sizeFromShape(a[0].shape.slice(0,o)),u=0,c=a.map(d=>{let f=y.sizeFromShape(d.shape.slice(o));return u+=f,f}),l=a.map(d=>t.typedArrayFromHeap(d)),m=t.typedArrayFromHeap(i);for(let d=0;d`cumprod does not support ${n.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],p),c=n;u!==null&&(c=mo({inputs:{x:n},attrs:{perm:u},backend:t}));let l=C.getInnerMostAxes(1,p)[0];C.assertAxesAreInnerMostDims(\"cumprod\",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;KP(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let 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Cre(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=e.makeOutput(f,\"float32\"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),w=new Uint8Array(new Int32Array(f).buffer),S=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=e.dataIdMap.get(h.dataId).id;return ZP(x,s,a===\"NHWC\"?1:0,b,n.shape.length-1,w,S,f.length,k),h}var JP={kernelName:cn,backendName:\"wasm\",setupFunc:bre,kernelFunc:Cre};var eO;function wre(r){eO=r.wasm.cwrap(ln,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=C.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,w=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,F=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let O=o.makeOutput(d.outShape,\"float32\"),M=o.dataIdMap.get(O.dataId).id;return eO(a,n.shape[0],n.shape[1],n.shape[2],i,f,h,g,x,b,w,F,S,k,_,E,R,D,M),O}var tO={kernelName:ln,backendName:\"wasm\",setupFunc:wre,kernelFunc:Sre};var rO;function Ire(r){rO=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function vre(r){let{inputs:e,backend:t}=r,{x:o}=e,n=y.sizeFromShape(o.shape),s=t.makeOutput([...o.shape,...o.shape],o.dtype);return rO(t.dataIdMap.get(o.dataId).id,we[o.dtype],n,t.dataIdMap.get(s.dataId).id),s}var oO={kernelName:Zs,backendName:\"wasm\",setupFunc:Ire,kernelFunc:vre};var nO;function kre(r){nO=r.wasm.cwrap(mn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o;if(n.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=C.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(s.shape,s.dtype);return aO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var iO={kernelName:Ai,backendName:\"wasm\",setupFunc:Tre,kernelFunc:_re};var uO;function $re(r){uO=r.wasm.cwrap(Di,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ere(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=C.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(n.shape,n.dtype);return uO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var pO={kernelName:Di,backendName:\"wasm\",setupFunc:$re,kernelFunc:Ere};var cO=Ce(fn);var lO;function Rre(r){lO=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\"])}function Dre(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,\"float32\"),a=i=>t.dataIdMap.get(i.dataId).id;return lO(a(n),a(o),a(s)),s}var mO={kernelName:Va,backendName:\"wasm\",setupFunc:Rre,kernelFunc:Dre};var Are=!1,dO=Je(hn,Are,\"bool\");var fO=Ce(gn,\"float32\");function Pg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var hO={kernelName:Js,backendName:\"wasm\",kernelFunc:Pg};var gO=Ce(xn,\"float32\");function yv(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var xO={kernelName:ea,backendName:\"wasm\",kernelFunc:yv};var yO;function Fre(r){yO=r.wasm.cwrap(yn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Pre(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return yO(s,i,p,u,c,a),n}var bO={kernelName:yn,backendName:\"wasm\",kernelFunc:Pre,setupFunc:Fre};var CO=Ce(bn);var Ore=!1,wO=Je(Cn,Ore);var SO;function Mre(r){SO=r.wasm.cwrap(wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Lre(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return SO(c,l,m,d,f,n,g),h}var IO={kernelName:wn,backendName:\"wasm\",setupFunc:Mre,kernelFunc:Lre};var vO;function Bre(r){vO=r.wasm.cwrap(Co,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=C.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=bu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,w=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,F=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,H=h.inHeight,X=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return vO(x,j,H,X,b,k,_,S,E,R,D,F,U,O,M,L,B,z,w,g,ne,f||0,re),J}var kO={kernelName:Co,backendName:\"wasm\",setupFunc:Bre,kernelFunc:zre};var NO;function Vre(r){NO=r.wasm.cwrap(wo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Wre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=C.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=bu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,w=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,F=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,H=h.inHeight,X=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return NO(x,j,H,X,b,k,_,S,E,R,D,F,U,O,M,L,B,z,w,g,ne,f||0,re),J}var TO={kernelName:wo,backendName:\"wasm\",setupFunc:Vre,kernelFunc:Wre};var _O;function Ure(r){_O=r.wasm.cwrap(Sn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Gre(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=of.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return _O(d,we[o.dtype],h,a,l,i,g,x),u}var $O={kernelName:Sn,backendName:\"wasm\",setupFunc:Ure,kernelFunc:Gre};var EO;function Hre(r){EO=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Kre(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${F} is not in [0, ${c-1}]`)}let l=C.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,w=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return EO(w,we[n.dtype],E,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var RO={kernelName:ta,backendName:\"wasm\",setupFunc:Hre,kernelFunc:Kre};var qre=!1,DO=Je(In,qre,\"bool\");var jre=!1,AO=Je(vn,jre,\"bool\");var FO=Ce(kn,\"bool\");var PO=Ce(Nn,\"bool\");var OO=Ce(Tn,\"bool\");var MO;function Xre(r){MO=r.wasm.cwrap(_n,null,[\"number\",\"number\",\"number\",\"number\"])}function Yre(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,\"float32\");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;MO(n,we[e.dtype],t,a)}return s}var LO={kernelName:_n,backendName:\"wasm\",setupFunc:Xre,kernelFunc:Yre};var Qre=!1,BO=Je($n,Qre,\"bool\");var Zre=!1,zO=Je(En,Zre,\"bool\");var VO;function Jre(r){VO=r.wasm.cwrap(Rn,null,[\"number\",\"number\",\"number\",\"number\"])}function eoe(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],\"float32\");return VO(t.dataIdMap.get(i.dataId).id,o,n,a),i}var WO={kernelName:Rn,backendName:\"wasm\",setupFunc:Jre,kernelFunc:eoe};var UO=Ce(Dn);var GO=Ce(An);var toe=!1,HO=Je(Fn,toe,\"bool\");var KO=Ce(Pn);var roe=!1,qO=Je(On,roe,\"bool\");var ooe=!1,jO=Je(m0,ooe,\"bool\");var XO;function noe(r){XO=r.wasm.cwrap(Mn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function soe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=t.makeOutput(n.shape,n.dtype);return XO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var YO={kernelName:Mn,backendName:\"wasm\",setupFunc:noe,kernelFunc:soe};var QO;function aoe(r){QO=r.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=t.makeOutput(n.shape,n.dtype);return QO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var ZO={kernelName:Ua,backendName:\"wasm\",setupFunc:aoe,kernelFunc:ioe};var JO;function uoe(r){JO=r.wasm.cwrap(Ln,null,[\"number\",\"number\",\"number\",\"number\"])}function poe(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let w=e.dataIdMap.get(c.dataId).id;u=c,p=w}let f=u.shape.length;C.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=C.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;JO(p,we[a.dtype],x,w)}if(d&&e.disposeData(c.dataId),s){let w=C.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var eM={kernelName:Ln,backendName:\"wasm\",setupFunc:uoe,kernelFunc:poe};var coe=!1,tM=Je(Bn,coe);var rM;function loe(r){rM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function moe(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=C.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get(E.dataId).id;return rM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,w,S,k,_,R),E}var oM={kernelName:zn,backendName:\"wasm\",setupFunc:loe,kernelFunc:moe};var nM;function doe(r){nM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function foe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=C.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return nM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var sM={kernelName:ra,backendName:\"wasm\",setupFunc:doe,kernelFunc:foe};var aM;function hoe(r){aM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function goe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=C.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return aM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var iM={kernelName:Li,backendName:\"wasm\",setupFunc:hoe,kernelFunc:goe};var uM;function xoe(r){uM=r.wasm.cwrap(Vn,null,[\"number, number, number\"])}function yoe(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=C.getInnerMostAxes(f.length,u.shape.length))}C.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=C.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Pr({backend:e,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=e.dataIdMap.get(b.dataId).id);let w=e.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(w.dataId).id;uM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=C.expandShapeToKeepDim(w.shape,m);w.shape=S}return u.dtype!==\"float32\"&&e.disposeData(b.dataId),w}var pM={kernelName:Vn,backendName:\"wasm\",setupFunc:xoe,kernelFunc:yoe};var cM;function boe(r){cM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\"])}function Coe(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,p=w)}let f=u.shape.length;C.assertAxesAreInnerMostDims(\"min\",l,f);let[h,g]=C.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;cM(p,we[a.dtype],x,w)}if(d&&e.disposeData(c.dataId),s){let w=C.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var lM={kernelName:Wn,backendName:\"wasm\",setupFunc:boe,kernelFunc:Coe};var woe=!1,mM=Je(Un,woe);var bv;(function(r){r[r.reflect=0]=\"reflect\",r[r.symmetric=1]=\"symmetric\"})(bv||(bv={}));var dM;function Soe(r){dM=r.wasm.cwrap(Gn,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Ioe(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new 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p;let{sliceRank:u,numUpdates:c,sliceSize:l,strides:m,outputSize:d}=C.calculateShapes(s,n,i),f=e.dataIdMap.get(n.dataId).id,h=e.dataIdMap.get(s.dataId).id,g=e.dataIdMap.get(a.dataId).id,x=new Uint8Array(new Int32Array(m).buffer),b=e.dataIdMap.get(p.dataId).id;return IL(f,h,s.shape.length,g,we[a.dtype],u,c,l,x,d,b),p}var vL={kernelName:Cs,backendName:\"wasm\",setupFunc:wne,kernelFunc:Sne};function Ine(r){let{inputs:e,attrs:t,backend:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=t,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=new Array(n.shape.length).fill(0),c=n.shape.slice();return p.map(l=>{let m=[...c];m[i]=l;let d=Ao({inputs:{x:n},attrs:{begin:u,size:m},backend:o});return u[i]+=l,d})}var kL={kernelName:la,backendName:\"wasm\",kernelFunc:Ine};var NL=Ce(xs);var TL=Ce(Gi);var vne=!0,_L=Je(ws,vne);var $L;function kne(r){$L=r.wasm.cwrap(yo,null,[\"number\",\"number\",\"number\",\"number\"])}function 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s=mB(o),a=e*t*s,i=lB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e,t,o,n,s){if(this.freeTextures.size===0)return;let a=lB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=mB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function lB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function mB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function dB(r,e){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let t=r.length,o=\"xyzwuv\",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Bs=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast(${e}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${e});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var xB=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=nse(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"}),u=P().get(\"WEBGPU_PRINT_SHADER\");if(u!==\"\"){u=u.toLowerCase();let c=u.split(\",\");(u===\"all\"||c.some(l=>n.toLowerCase().includes(l)))&&(console.group(n),console.debug(a),console.groupEnd())}return p},Ae=(r,e=\"f32\")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function Nt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Po(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function K(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function fB(r,e){let t;return t=`\n ${ose(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function ose(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function nse(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${bB(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.isFromPixels){o.push(`\n struct Uniform {\n size : i32,\n numChannels : i32,\n outShapeStrides : vec2,\n };\n\n @group(0) @binding(0) var result: array<${kp(e.dtype,t.outputComponent)}>;\n @group(0) @binding(2) var uniforms: Uniform;\n `);let f=gB(t);return[hB,o.join(`\n`),Dv(e.shape),t.getUserCode(),fB(f,t)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Nt(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=Nt(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=Nt(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=Nt(s),i+=`\n outShapeStrides: ${a}, `,t.size&&(i+=\"size : i32, \"),t.uniforms&&(i+=t.uniforms),i+=\"};\",i=mse(i),o.push(i),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${kp(e.dtype,t.outputComponent)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?kp(r[h].dtype,t.variableComponents[h]):kp(r[h].dtype,t.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=pse(e.shape,t.dispatchLayout),c=[hB,o.join(`\n`)+sse,Dv(e.shape),u,cse(e.shape.length)];t.atomic||c.push(lse(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${Dv(r[h].shape,f)}`)});let l=r.map((f,h)=>use(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=gB(t);return c.push(fB(m,t)),c.join(`\n`)}function yB(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>C.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=bB(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var hB=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n var res: i32 = a / b;\n let modulo: i32 = a % b;\n if (sign < 0. && modulo != 0) {\n res = res - 1;\n }\n return res;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,sse=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function Dv(r,e=\"\"){let t=r.length,o=e!==\"\"?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=e!==\"\"?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:\"outShapeStrides\";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=Nt(t),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Po(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Po(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Po(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function ase(r,e){let t=r.name,o=r.shape.length,n=Nt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(e)} {\n return ${Ae(e)}(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(e)} {\n return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${e===1?\"\":` / ${e}`}]);\n }\n `}function ise(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Nt(p);if(y.arraysEqual(r.shape,e)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${t===1?\"\":` / ${t}`}]);\n }\n `;let c=C.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Po(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Nt(i),x=r.shape.map((b,w)=>`coords.${Po(w+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n`}function use(r,e,t,o){let n=ase(r,t);return r.shape.length<=e.length&&(n+=ise(r,e,t,o)),n}function pse(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Nt(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function cse(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function bB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function kp(r,e=1){if(r===\"float32\")return Ae(e,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(e,\"i32\");throw new Error(`type ${r} is not supported.`)}function lse(r,e,t){let o=r.length,n=kp(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Nt(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n `}return s}function mse(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function gB(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Fv={};He(Fv,{GPUBytesPerElement:()=>Hg,MatMulProgramType:()=>Oo,assertNotComplex:()=>cm,computeDispatch:()=>q,computeWorkPerThreadForConv2d:()=>um,computeWorkgroupInfoForMatMul:()=>Av,computeWorkgroupSizeForConv2d:()=>im,flatDispatchLayout:()=>Z,isWebGPUSupported:()=>pm,tilesFitEvenlyIntoShape:()=>fse});var Np=r=>{let e=1;for(let t=0;tt%r[o]===0)}function q(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Np(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Np(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Np(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Av(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function im(r,e,t=!1){if(t)return[8,8,1];let o=Np(r.x.map(s=>e[s])),n=Np(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function um(r,e,t=!1){if(t)return[4,4,1];let o=Np(r.x.map(s=>e[s])),n=Np(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Z(r){return{x:r.map((e,t)=>t)}}function Hg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function pm(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function cm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Oo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Oo||(Oo={}));var hse=P().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),gse=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Cu=class extends ro{nextDataId(){return Cu.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!pm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new Wg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ug(this.device),this.textureManager=new Gg(this.device),this.tensorMap=new Lo(this,ur()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),P().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),P().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype===\"string\")return o;if(t.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(e,x),x}let s=[\"opaque\",\"premultiplied\"],a=t.resourceInfo,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.ensureComputePassEnded(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,F)=>{this.ensureCommandEncoderReady(),this.currentCommandEncoder.copyBufferToTexture({buffer:a.buffer,bytesPerRow:x,offset:F},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,F,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=C.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=y.convertBackendValuesAndArrayBuffer(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,o){let n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=Hg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=Hg(t.dtype)*y.sizeFromShape(t.shape),n;if(t.values){if(n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage(),!0),n.mapState===\"unmapped\"){let s=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o),this.stagingPendingDisposal.push({size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s})}else{let s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n}}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(u=>{u.data.length===0&&(u.data=[1]);let c;switch(u.data.length){case 1:c=4;break;case 2:c=8;break;case 3:c=16;break;case 4:c=16;break;case 5:c=16;break;case 6:c=16;break;default:y.assert(!1,()=>`Unsupported ${u.data.length}D shape`)}(o===5||o===6)&&(c=16),c>s&&(s=c),t=Math.ceil(t/c)*c,o=u.data.length,n.push(t),t+=u.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((u,c)=>{let l=n[c];u.type===\"int32\"?new Int32Array(a,l,u.data.length).set(u.data):u.type===\"uint32\"?new Uint32Array(a,l,u.data.length).set(u.data):new Float32Array(a,l,u.data.length).set(u.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,a,0,t);let p={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(p),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=gse(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(g=>g.shape);let h=\"int32\";if(i.map(g=>{a.push({type:h,data:g});let x=y.computeStrides(g);a.push({type:h,data:x})}),e.size){let g=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.outputComponent?g/e.outputComponent:g]})}}let p=t.map((h,g)=>{if(h.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=yB(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=xB(this.device,e,p,s,u),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),P().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=hse){return P().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){let r={powerPreference:P().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize,maxBufferSize:o.maxBufferSize,maxComputeWorkgroupSizeX:o.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:o.maxComputeInvocationsPerWorkgroup};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Cu(n,s)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.GREATER=7]=\"GREATER\",r[r.GREATER_EQUAL=8]=\"GREATER_EQUAL\",r[r.INT_DIV=9]=\"INT_DIV\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var xse=`\n resultTemp = select(resultTemp, valueForNaN, isNaN | isnan(a) | isnan(b));`,yse=`\n resultTemp = select(\n resultTemp, vec4(valueForNaN),\n vec4(isNaN) | isnanVec4(a) | isnanVec4(b));\n `,bse=\"return a + b;\",Cse=\"var resultTemp = atan2(a, b);\",wse=\"return areal * breal - aimag * bimag;\",Sse=\"return areal * bimag + aimag * breal;\",Ise=\"return a / b;\",vse=\"return select(a * (b + 1.0), a, b >= 0.);\",kse=\"return select(a * (b + vec4(1.0)), a, b >= vec4(0.));\",Nse=\"return f32(a == b);\",Tse=\"return vec4(a == b);\",_se=\"return f32(a > b);\",$se=\"return vec4(a > b);\",Ese=\"return f32(a >= b);\",Rse=\"return vec4(a >= b);\",Dse=`\n let s = sign(a) * sign(b);\n let ia = i32(round(a));\n let ib = i32(round(b));\n return f32(idiv(ia, ib, s));\n`,Ase=`\n let ia = vec4(round(a));\n let ib = vec4(round(b));\n let cond = ib != vec4(0);\n var resultTemp = vec4(0);\n let s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(resultTemp);\n`,Fse=\"return f32(a < b);\",Pse=\"return vec4(a < b);\",Ose=\"return f32(a <= b);\",Mse=\"return vec4(a <= b);\",Lse=\"return f32(a >= 1.0 && b >= 1.0);\",Bse=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,zse=\"return f32(a >= 1.0 || b >= 1.0);\",Vse=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Wse=\"var resultTemp = max(a, b);\",Use=\"var resultTemp = min(a, b);\",Gse=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Hse=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Kse=\"return a * b;\",qse=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,jse=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Xse=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,Yse=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,Qse=\"if (a < 0.0) { return b * a; } return a;\",Zse=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Jse=\"return (a - b) * (a - b);\",eae=\"return a - b;\";function Kc(r,e){do{let t;switch(r){case fe.ATAN2:t=Cse;break;case fe.MAX:t=Wse;break;case fe.MIN:t=Use;break;case fe.MOD:t=e?Hse:Gse;break;case fe.NOT_EQUAL:t=e?jse:qse;break;case fe.POW:t=e?Yse:Xse;break;default:continue}return`\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n ${t}\n ${e?yse:xse}\n return resultTemp;\n }\n `}while(!1);switch(r){case fe.ADD:return bse;case fe.COMPLEX_MULTIPLY_IMAG:return Sse;case fe.COMPLEX_MULTIPLY_REAL:return wse;case fe.DIV:return Ise;case fe.ELU_DER:return e?kse:vse;case fe.EQUAL:return e?Tse:Nse;case fe.GREATER:return e?$se:_se;case fe.GREATER_EQUAL:return e?Rse:Ese;case fe.INT_DIV:return e?Ase:Dse;case fe.LESS:return e?Pse:Fse;case fe.LESS_EQUAL:return e?Mse:Ose;case fe.LOGICAL_AND:return e?Bse:Lse;case fe.LOGICAL_OR:return e?Vse:zse;case fe.MUL:return Kse;case fe.PRELU:return e?Zse:Qse;case fe.SQUARED_DIFFERENCE:return Jse;case fe.SUB:return eae;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Q||(Q={}));var tae=\"return abs(a);\",rae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,oae=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,nae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,sae=\"return asinh(a);\",aae=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,iae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,uae=\"return ceil(a);\",pae=\"return cos(a);\",cae=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,lae=\"return exp(a) - 1.0;\",mae=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",dae=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,fae=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${C.ERF_P};\n let a1 = ${C.ERF_A1};\n let a2 = ${C.ERF_A2};\n let a3 = ${C.ERF_A3};\n let a4 = ${C.ERF_A4};\n let a5 = ${C.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,hae=\"return exp(a);\",gae=\"return floor(a);\",xae=\"return f32(!isnan(a) && !isinf(a));\",yae=\"return f32(isinf(a));\",bae=\"return f32(isnan(a));\",Cae=\"return a;\",wae=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,Sae=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Iae=\"return f32(!(a >= 1.0));\",vae=\"return -a;\",kae=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Nae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Tae=\"return 1.0 / a;\",_ae=\"return select(a, 0.0, a < 0.0);\",$ae=\"return clamp(a, 0.0, 6.0);\",Eae=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",Rae=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Dae=\"return round(a);\",Aae=\"return inverseSqrt(a);\",Fae=`\n if (a >= 0.0) {\n return ${C.SELU_SCALE} * a;\n } else {\n return ${C.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,Pae=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Oae=\"return sign(a);\",Mae=\"return sin(a);\",Lae=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Bae=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,zae=\"return sqrt(a);\",Vae=\"return a * a;\",Wae=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,Uae=\"return tan(a);\",Gae=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Hae=\"return f32(i32((a)));\";function yi(r,e){switch(r){case Q.ABS:return tae;case Q.ACOS:return rae;case Q.ACOSH:return oae;case Q.ASIN:return nae;case Q.ASINH:return sae;case Q.ATAN:return aae;case Q.ATANH:return iae;case Q.COS:return pae;case Q.COSH:return cae;case Q.CEIL:return uae;case Q.ELU:return e?dae:mae;case Q.ERF:return fae;case Q.EXP:return hae;case Q.EXPM1:return lae;case Q.FLOOR:return gae;case Q.IS_FINITE:return xae;case Q.IS_INF:return yae;case Q.IS_NAN:return bae;case Q.LINEAR:return Cae;case Q.LOG:return wae;case Q.LOG1P:return Sae;case Q.LOGICAL_NOT:return Iae;case Q.NEG:return vae;case Q.LEAKYRELU:return e?Nae:kae;case Q.RECIPROCAL:return Tae;case Q.RELU:return e?Rae:_ae;case Q.RELU6:return e?Eae:$ae;case Q.ROUND:return Dae;case Q.RSQRT:return Aae;case Q.SELU:return Fae;case Q.SIGMOID:return Pae;case Q.SIGN:return Oae;case Q.SIN:return Mae;case Q.SINH:return Lae;case Q.SOFTPLUS:return Bae;case Q.SQRT:return zae;case Q.SQUARE:return Vae;case Q.STEP:return Wae;case Q.TAN:return Uae;case Q.TANH:return Gae;case Q.TO_INT:return Hae;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=yi(Q.LINEAR);else if(r===\"relu\")n=yi(Q.RELU,t);else if(r===\"elu\")n=yi(Q.ELU,t);else if(r===\"relu6\")n=yi(Q.RELU6,t);else if(r===\"prelu\")n=Kc(fe.PRELU,t);else if(r===\"sigmoid\")n=yi(Q.SIGMOID,t);else if(r===\"leakyrelu\")n=yi(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function jr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function Pv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n let col = colIn * ${s};\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} {\n let col = colIn * ${s};\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function lm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${Pv(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ae(i)}) {\n let col = colIn * ${i};\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${jr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Kae=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart / ${e} + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart / ${e} + inputCol);\n `,qae=(r,e,t)=>r?`\n let ACached0 = mm_Asub[k * ${e}][localRow];\n let ACached1 = mm_Asub[k * ${e} + 1][localRow];\n let ACached2 = mm_Asub[k * ${e} + 2][localRow];\n ${e===3?\"\":`let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}\n for (var i = 0; i < ${t}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n acc[i] = fma(BCached1, vec4(ACached1[i]), acc[i]);\n acc[i] = fma(BCached2, vec4(ACached2[i]), acc[i]);\n ${e===3?\"\":\"acc[i] = fma(BCached3, vec4(ACached3[i]), acc[i]);\"}\n }`:`\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = fma(BCached0, vec4(ACached.x), acc[i]);\n acc[i] = fma(BCached1, vec4(ACached.y), acc[i]);\n acc[i] = fma(BCached2, vec4(ACached.z), acc[i]);\n ${e===3?\"\":\"acc[i] = fma(BCached3, vec4(ACached.w), acc[i]);\"}\n }`;function Tp(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${K()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x);\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Kae(t,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${o/l}; k++) {\n let BCached0 = mm_Bsub[k * ${l}][tileCol];\n let BCached1 = mm_Bsub[k * ${l} + 1][tileCol];\n let BCached2 = mm_Bsub[k * ${l} + 2][tileCol];\n ${l===3?\"\":`let BCached3 = mm_Bsub[k * ${l} + 3][tileCol];`}\n\n ${qae(t,l,d)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var CB=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,jae=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function _p(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${CB(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${CB(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${jae(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${K()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Xae=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function Yae(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${K()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Xae(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Kg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Av(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${lm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Yae(this.workgroupSize,this.transposeA):_p(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function Qae(r){return`\n var sumValues : array;\n ${K()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var qg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${lm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Qae(this.workgroupSize[0])}\n `}};function Zae(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${K()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var jg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${lm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Zae(this.workgroupSize)}\n `}};var Xg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=q(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return`\n ${Pv(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Ae(e)}) {\n let col = colIn * ${e};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Bs(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${e===4?Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):_p(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Yg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${jr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Qg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function Vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Qg(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var wB={kernelName:ea,backendName:\"webgpu\",kernelFunc:Vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var SB={kernelName:ia,backendName:\"webgpu\",kernelFunc:pe};function $p({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],F=Math.max(x,b),O=[E,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[F,d,f],U=P().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let H=P().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),X=H>0?H:n.thresholdToIncreaseWorkgroups,J=F*Math.ceil(d/32)*Math.ceil(f/32);J<=X||d<=8&&J<=X*2?F*d*f<=128?U=Oo.MatMulReduceProgram:F===1&&m>=2e3?U=Oo.MatMulSplitKProgram:U=Oo.MatMulSmallOutputSizeProgram:U=Oo.MatMulPackedProgram}switch(U){case Oo.MatMulReduceProgram:L=new qg(z,t,o,s,p,a);break;case Oo.MatMulSplitKProgram:{if(B=Vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Xg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let X=new Yg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],X.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(X,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Oo.MatMulSmallOutputSizeProgram:L=new jg(k,_,z,t,o,s,p,a);break;case Oo.MatMulPackedProgram:let H=n.adapterInfo.isIntel();L=new Kg(k,z,t,o,s,p,a,H);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let H of D)n.disposeData(H.dataId);return j}function Jae(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return $p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var IB={kernelName:bo,backendName:\"webgpu\",kernelFunc:Jae};var mm=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,o),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Kc(this.op,!1)}\n }\n\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var bi=class{constructor(e,t,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=C.assertAndGetBroadcastShape(t,o),this.dispatchLayout=Z(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Kc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${K(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${t}(getAByOutputCoords(coords));\n let b = ${t}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var vB={kernelName:xo,backendName:\"webgpu\",kernelFunc:At};function fo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var kB={kernelName:Ti,backendName:\"webgpu\",kernelFunc:fo};var Xr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${yi(this.op,!1)}\n }\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function xe({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Xr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,w={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new bi(r,a.shape,i.shape);return p.runWebGPUProgram(k,[w,S],dt(x.dtype,b.dtype))});else{let g=new mm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new mm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=fo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?C.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?C.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new bi(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:NB,castImpl:TB,ceilImpl:_B,concatImpl:$B,equalImpl:EB,expImpl:RB,expm1Impl:DB,floorImpl:AB,floorDivImpl:FB,gatherNdImpl:PB,gatherV2Impl:OB,greaterEqualImpl:MB,greaterImpl:LB,lessEqualImpl:BB,lessImpl:zB,logImpl:VB,maxImpl:WB,maximumImpl:UB,minimumImpl:GB,multiplyImpl:HB,negImpl:KB,notEqualImpl:qB,prodImpl:jB,rangeImpl:XB,rsqrtImpl:YB,scatterImpl:QB,simpleAbsImpl:ZB,sliceImpl:JB,stridedSliceImpl:ez,stringNGramsImpl:tz,subImpl:rz,tileImpl:oz,topKImpl:nz,transposeImpl:sz,uniqueImpl:o3t}=Sc;var eie=xe({opType:Q.ABS,cpuKernelImpl:ZB}),az={kernelName:Gs,backendName:\"webgpu\",kernelFunc:eie};var tie=xe({opType:Q.ACOS}),iz={kernelName:zo,backendName:\"webgpu\",kernelFunc:tie};var rie=xe({opType:Q.ACOSH}),uz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:rie};var oie=et({opType:fe.ADD,cpuKernelImpl:NB,supportsComplex:!0}),pz={kernelName:no,backendName:\"webgpu\",kernelFunc:oie};var Zg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${K(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function nie(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Zg(s);return t.runWebGPUProgram(a,o,n)}var cz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:nie};var Jg=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${K()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var ex=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${K(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};function Yr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=C.getAxesPermutation(p,s),c=r;u!=null&&(c=rr({inputs:{x:r},attrs:{perm:u},backend:n}),p=C.getInnerMostAxes(p.length,s),a.push(c)),C.assertAxesAreInnerMostDims(o,p,s);let[l,m]=C.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=C.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=WB(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:w}=jB(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,w,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},w=o===\"mean\"?\"float32\":Za(r.dtype),S=[{type:\"int32\",data:[h]}],k=new tx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],w,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function aie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Yr(n,a,s,\"all\",t)}var mz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:aie};function iie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Yr(n,a,s,\"any\",t)}var dz={kernelName:Go,backendName:\"webgpu\",kernelFunc:iie};var qc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=C.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=Z(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=q(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Po(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${K(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function uie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=rr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new qc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var fz={kernelName:Hs,backendName:\"webgpu\",kernelFunc:uie};function pie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=rr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new qc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var hz={kernelName:Ks,backendName:\"webgpu\",kernelFunc:pie};var cie=xe({opType:Q.ASIN}),gz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:cie};var lie=xe({opType:Q.ASINH}),xz={kernelName:Ko,backendName:\"webgpu\",kernelFunc:lie};var mie=xe({opType:Q.ATAN}),yz={kernelName:qo,backendName:\"webgpu\",kernelFunc:mie};var die=et({opType:fe.ATAN2}),bz={kernelName:Xo,backendName:\"webgpu\",kernelFunc:die};var fie=xe({opType:Q.ATANH}),Cz={kernelName:jo,backendName:\"webgpu\",kernelFunc:fie};var rx=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Da=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${e}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}},wu=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue += value; count += 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${e}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}};function Ov(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Yr(n,s,a,\"max\",t)}var wz={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Ov};function Mv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Yr(n,a,s,\"mean\",t)}var Sz={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Mv};function ox(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=Mv({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Ov({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new rx(e):(t===\"avg\"?n=new Da(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Da(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function hie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=C.computePool2DInfo(n.shape,s,a,u,i,p);return ox(n,c,\"avg\",t)}var Iz={kernelName:Yo,backendName:\"webgpu\",kernelFunc:hie};function gie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var vz={kernelName:qs,backendName:\"webgpu\",kernelFunc:gie};var nx=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},sx=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function xie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=C.computePool3DInfo(a.shape,i,p,1,u,c),m=new sx(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var kz={kernelName:Ni,backendName:\"webgpu\",kernelFunc:xie};function yie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;cm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=C.computePool2DInfo(a.shape,i,p,1,u),l=new nx(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var Nz={kernelName:Gp,backendName:\"webgpu\",kernelFunc:yie};function bie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return $p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var Tz={kernelName:Qo,backendName:\"webgpu\",kernelFunc:bie};var ax=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Nt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Nt(this.rank),t=Cie(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${Lv[a]} = uniforms.start.${Po(a)} + coords.${Lv[a]};`),`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},Lv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Cie(r){if(r===1)return\"sourceLoc\";if(r<=6)return Lv.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function zs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=JB(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new ax(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var _z={kernelName:pa,backendName:\"webgpu\",kernelFunc:zs};var wie=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,w)=>b*w),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=rr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=zs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},$z={kernelName:js,backendName:\"webgpu\",kernelFunc:wie};var Sie=`\n fn bincount_write(index: i32, value: f32) {\n ${Bs(\"&result[index]\",\"value\",\"float32\")}\n }\n`,Iie=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,jc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Iie:Sie}\n ${K(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function vie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=Vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new jc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var Ez={kernelName:Zo,backendName:\"webgpu\",kernelFunc:vie};var ix=class{constructor(e){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function kie(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=C.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new ix(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return t.runWebGPUProgram(p,[o,n],\"int32\",u)}var Rz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:kie};var Bv=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:qB}),Dz={kernelName:qn,backendName:\"webgpu\",kernelFunc:Bv};function Ci(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var Az={kernelName:zi,backendName:\"webgpu\",kernelFunc:Ci};function Fz(r,e){let t=new Xr(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function zv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:t});let a=Wr(n.shape),i=zv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=fo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Ci({inputs:{input:n},backend:t}),i=zv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=TB(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return Fz(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Bv({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var Pz={kernelName:ho,backendName:\"webgpu\",kernelFunc:zv};var Nie=xe({opType:Q.CEIL,cpuKernelImpl:_B}),Oz={kernelName:Jo,backendName:\"webgpu\",kernelFunc:Nie};var ux=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var px=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function Tie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new ux(n.shape):i=new px(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var Mz={kernelName:go,backendName:\"webgpu\",kernelFunc:Tie};var cx=class{constructor(e){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function Lz(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function _ie(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new cx(o.shape),a=[Lz(o,n.complexTensorInfos.real),Lz(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var Bz={kernelName:_i,backendName:\"webgpu\",kernelFunc:_ie};var lx=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sCi({inputs:{input:w},backend:t})),h=r.map(w=>Ep({inputs:{input:w},backend:t})),g=Xc(f,e,t),x=Xc(h,e,t),b=fo({inputs:{real:g,imag:x},backend:t});return f.forEach(w=>t.disposeData(w.dataId)),h.forEach(w=>t.disposeData(w.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=C.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=$B(h,g,o,x),w=C.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(w,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new lx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function $ie(r,e,t){let o=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function Vv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Xc(p,s,t)}var Vz={kernelName:Ys,backendName:\"webgpu\",kernelFunc:Vv};function Eie(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,w=r?e&&o?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&t?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${r?w:S}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {\n ${r?S:w}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${jr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var mx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=im(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=um(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Tp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):_p(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Eie(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var dx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${jr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${K(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var fx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${K(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function hx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Rie({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=hx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=hx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=$p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Die({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,w=b===\"channelsLast\",S=p*u*c,k=h*f,_=w?[t.batchSize,k,S]:[t.batchSize,S,k],E=new fx(_,w),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),F=[];F.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(F.push(O),s!=null){let U=hx(s.shape,w);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),F.push(s))}if(n!=null){let U=hx(n.shape,w);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),F.push(n))}let B=$p({a:w?D:O,b:w?O:D,transposeA:!w,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});F.push(B);for(let U of F)o.disposeData(U.dataId);return z}function gx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=P().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return Rie({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=P().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(P().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Die({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new dx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new mx(t,_,E,R,p,i,u,D)}let w=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),w.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),w.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of w)o.disposeData(_.dataId);return k}function Aie(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return gx({x:n,filter:s,convInfo:m,backend:o})}var Wz={kernelName:en,backendName:\"webgpu\",kernelFunc:Aie};var xx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${K()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},yx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},bx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Cx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Fie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new yx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var Uz={kernelName:$i,backendName:\"webgpu\",kernelFunc:Fie};function Pie(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ae(r)} {\n let col = colIn * ${r};\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ae(r)} {\n let col = colIn * ${r};\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Ae(r)}) {\n let col = colIn * ${r};\n if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var wx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=im(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=um(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Tp(this.elementsPerThread,this.workgroupSize):_p(this.elementsPerThread,this.workgroupSize);return`\n ${Pie(this.isVec4?4:1)}\n ${e}\n `}};function Oie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(P().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new xx(m);else{f=new wx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var Gz={kernelName:tn,backendName:\"webgpu\",kernelFunc:Oie};var Sx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Mie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new Sx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var Hz={kernelName:rn,backendName:\"webgpu\",kernelFunc:Mie};function Lie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=C.computeConv3DInfo(n.shape,p,a,1,i),c=new bx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var Kz={kernelName:za,backendName:\"webgpu\",kernelFunc:Lie};function Bie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=C.computeConv3DInfo(p,s.shape,a,1,i),c=new Cx(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var qz={kernelName:on,backendName:\"webgpu\",kernelFunc:Bie};var zie=xe({opType:Q.COS}),jz={kernelName:nn,backendName:\"webgpu\",kernelFunc:zie};var Vie=xe({opType:Q.COSH}),Xz={kernelName:sn,backendName:\"webgpu\",kernelFunc:Vie};var Ix=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Wie=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Ix(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},Yz={kernelName:pn,backendName:\"webgpu\",kernelFunc:Wie};var Rp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Rp||(Rp={}));var dm=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Rp.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${Qz(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${Zz(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${Zz(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${Qz(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function Qz(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zz(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function vx(r,e,t,o,n,s){let a=e.shape.length,i=C.getAxesPermutation([o],a),p=e;i!=null&&(p=rr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new dm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new dm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=C.getUndoAxesPermutation(i),d=rr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function Uie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return vx(Rp.Prod,n,t,s,a,i)}var Jz={kernelName:an,backendName:\"webgpu\",kernelFunc:Uie};function Gie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return vx(Rp.Sum,n,t,s,a,i)}var eV={kernelName:un,backendName:\"webgpu\",kernelFunc:Gie};function Hie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=Vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new jc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var tV={kernelName:Qs,backendName:\"webgpu\",kernelFunc:Hie};var kx=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Kie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new kx(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var rV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Kie};var Nx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${K()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${K()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${jr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var Qc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${jr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function qie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=C.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Nx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Yc(d):(g=new Qc(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var oV={kernelName:ln,backendName:\"webgpu\",kernelFunc:qie};var Tx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},_x=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function jie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=C.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Tx(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],\"float32\",d)}var nV={kernelName:Ei,backendName:\"webgpu\",kernelFunc:jie};function Xie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=C.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new _x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var sV={kernelName:Ri,backendName:\"webgpu\",kernelFunc:Xie};var $x=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Yie(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new $x(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var aV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:Yie};var Ex=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function Qie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Ex(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var iV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Qie};var Rx=class{constructor(e,t){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(e.outShape),this.dispatch=q(this.dispatchLayout,e.outShape,this.workgroupSize),t!==\"float32\"&&t!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${t} type.`);this.type=t,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Bs(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Dx=class{constructor(e,t,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(e.outShape),this.dispatch=q(this.dispatchLayout,e.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Bs(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Zie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=C.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Dx(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var uV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:Zie};function Jie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=C.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Rx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var pV={kernelName:Di,backendName:\"webgpu\",kernelFunc:Jie};var Wv=et({opType:fe.MUL,cpuKernelImpl:HB,supportsComplex:!0}),cV={kernelName:Kn,backendName:\"webgpu\",kernelFunc:Wv};function Uv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Yr(n,s,a,\"sum\",t)}var lV={kernelName:ys,backendName:\"webgpu\",kernelFunc:Uv};function eue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=C.decodeEinsumEquation(n,s.length);C.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=C.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Uv({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var mV={kernelName:Fi,backendName:\"webgpu\",kernelFunc:eue};var tue=xe({opType:Q.ELU}),dV={kernelName:fn,backendName:\"webgpu\",kernelFunc:tue};var rue=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new bi(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},fV={kernelName:Va,backendName:\"webgpu\",kernelFunc:rue};var oue=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:EB}),hV={kernelName:hn,backendName:\"webgpu\",kernelFunc:oue};var nue=xe({opType:Q.ERF}),gV={kernelName:Wa,backendName:\"webgpu\",kernelFunc:nue};var sue=xe({opType:Q.EXP,cpuKernelImpl:RB,dtype:\"float32\"}),xV={kernelName:gn,backendName:\"webgpu\",kernelFunc:sue};function Ax(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var yV={kernelName:Js,backendName:\"webgpu\",kernelFunc:Ax};var aue=xe({opType:Q.EXPM1,cpuKernelImpl:DB}),bV={kernelName:xn,backendName:\"webgpu\",kernelFunc:aue};var fm=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Fx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new fm(\"real\",u),l=new fm(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=fo({inputs:{real:g,imag:x},backend:t});i.push(b);let w=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),w}function iue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Fx(o,!1,t)}var CV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:iue};var Px=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var wV={kernelName:yn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Px(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var uue=xe({opType:Q.FLOOR,cpuKernelImpl:AB}),SV={kernelName:bn,backendName:\"webgpu\",kernelFunc:uue};var pue=et({opType:fe.INT_DIV,cpuKernelImpl:FB,dtype:\"int32\"}),IV={kernelName:Cn,backendName:\"webgpu\",kernelFunc:pue};var Ox=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${K(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var vV={kernelName:$u,backendName:\"webgpu\",kernelFunc:cue},Zc,Gv=P().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function cue(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d)b={width:c,height:l,format:null,usage:null,texture:t.device.importExternalTexture({source:n})};else{if(f){let L=P().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Zc==null||L!==Gv)&&(Gv=L,Zc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Gv})),Zc.canvas.width=c,Zc.canvas.height=l,Zc.drawImage(n,0,0,c,l),n=Zc.canvas}let F=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O=\"rgba8unorm\",M=t.textureManager.acquireTexture(m[1],m[0],O,F);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:O,usage:F,texture:M}}let w=y.sizeFromShape(m),S=y.computeStrides(m),k=new Ox(m,s,d),_=[{type:\"uint32\",data:[w]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],E=t.makeTensorInfo([l,c],\"int32\"),R=t.tensorMap.get(E.dataId);R.resourceInfo=b;let D=t.runWebGPUProgram(k,[E],\"int32\",_);return t.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,w=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var kV={kernelName:wn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Mx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function lue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return gx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var NV={kernelName:Co,backendName:\"webgpu\",kernelFunc:lue};function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(C.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=C.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let w=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?S=new Yc(h,x,m,b):(S=new Qc(h,x,m,b),w.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(w.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(S,g,\"float32\",w)}var TV={kernelName:wo,backendName:\"webgpu\",kernelFunc:mue};var Lx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Nt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function due(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=C.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),w=t.bufferSync(o),S=PB(b,w,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Lx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var _V={kernelName:Sn,backendName:\"webgpu\",kernelFunc:due};var Bx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=fue(this.aShape);return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function fue(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Bx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var $V={kernelName:ta,backendName:\"webgpu\",kernelFunc:Hv};var hue=et({opType:fe.GREATER,cpuKernelImpl:LB,dtype:\"bool\"}),EV={kernelName:In,backendName:\"webgpu\",kernelFunc:hue};var gue=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:MB}),RV={kernelName:vn,backendName:\"webgpu\",kernelFunc:gue};function xue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Fx(o,!0,t)}var DV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:xue};var yue=xe({opType:Q.IS_FINITE,dtype:\"bool\"}),AV={kernelName:kn,backendName:\"webgpu\",kernelFunc:yue};var bue=xe({opType:Q.IS_INF,dtype:\"bool\"}),FV={kernelName:Nn,backendName:\"webgpu\",kernelFunc:bue};var Cue=xe({opType:Q.IS_NAN,dtype:\"bool\"}),PV={kernelName:Tn,backendName:\"webgpu\",kernelFunc:Cue};function wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Xr(n.shape,Q.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var OV={kernelName:_n,backendName:\"webgpu\",kernelFunc:wue};var Sue=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:zB}),MV={kernelName:$n,backendName:\"webgpu\",kernelFunc:Sue};var Iue=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:BB}),LV={kernelName:En,backendName:\"webgpu\",kernelFunc:Iue};var zx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function vue(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new zx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var BV={kernelName:Rn,backendName:\"webgpu\",kernelFunc:vue};var kue=xe({opType:Q.LOG,cpuKernelImpl:VB}),zV={kernelName:Dn,backendName:\"webgpu\",kernelFunc:kue};var Nue=xe({opType:Q.LOG1P}),VV={kernelName:An,backendName:\"webgpu\",kernelFunc:Nue};var Tue=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),WV={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Tue};var _ue=xe({opType:Q.LOGICAL_NOT}),UV={kernelName:Pn,backendName:\"webgpu\",kernelFunc:_ue};var $ue=et({opType:fe.LOGICAL_OR}),GV={kernelName:On,backendName:\"webgpu\",kernelFunc:$ue};var HV=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Vx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${HV}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Wx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${K()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${HV}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function Eue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Vx(n.shape):u=new Wx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var KV={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Eue};var Ux=class{constructor(e){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function Rue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Ux(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var qV={kernelName:Ua,backendName:\"webgpu\",kernelFunc:Rue};var Due=et({opType:fe.MAX,cpuKernelImpl:UB}),jV={kernelName:Bn,backendName:\"webgpu\",kernelFunc:Due};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=C.computePool2DInfo(n.shape,s,a,u,i,p);return ox(n,c,\"max\",t)}var XV={kernelName:zn,backendName:\"webgpu\",kernelFunc:Aue};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var YV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Fue};var Gx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Hx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,l,u,c),d=new wu(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],\"int32\",f),g=new Hx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var QV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;cm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=C.computePool2DInfo(i.shape,p,u,1,c,l),d=new Da(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],\"int32\",f),g=new Gx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var ZV={kernelName:Hp,backendName:\"webgpu\",kernelFunc:Oue};function Mue(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Da(c,\"max\",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Da(c,\"max\",!0,!0,i);let f=t.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var JV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Mue};function Lue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Yr(n,s,a,\"min\",t)}var eW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Lue};var Bue=et({opType:fe.MIN,cpuKernelImpl:GB}),tW={kernelName:Un,backendName:\"webgpu\",kernelFunc:Bue};var Kx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Nt(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var rW={kernelName:Gn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Kx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var zue=et({opType:fe.MOD}),oW={kernelName:Ga,backendName:\"webgpu\",kernelFunc:zue};var qx=class{constructor(e,t){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var jx=class{constructor(e){this.variableNames=[\"logits\"],this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${K(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function Kv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new jx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var nW={kernelName:bs,backendName:\"webgpu\",kernelFunc:Kv};function Vue(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Kv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new qx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=t.runWebGPUProgram(l,[p],\"int32\",m);return i||t.disposeData(p.dataId),d}var sW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=KB(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Xr(o.shape,Q.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var aW={kernelName:oa,backendName:\"webgpu\",kernelFunc:Wue};function Uue(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var iW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Uue};function Gue(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var uW={kernelName:Xn,backendName:\"webgpu\",kernelFunc:Gue};var Xx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${K(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Xx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var pW={kernelName:Yn,backendName:\"webgpu\",kernelFunc:Hue};function hm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=Ci({inputs:{input:o},backend:t}),s=hm({inputs:{x:n},backend:t}),a=Ep({inputs:{input:o},backend:t}),i=hm({inputs:{x:a},backend:t}),p=fo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var cW={kernelName:fa,backendName:\"webgpu\",kernelFunc:hm};function lW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Ci({inputs:{input:o},backend:t}),s=lW({inputs:{x:n},backend:t}),a=Ep({inputs:{input:o},backend:t}),i=hm({inputs:{x:a},backend:t}),p=fo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var mW={kernelName:na,backendName:\"webgpu\",kernelFunc:lW};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Ax({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Ax({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=Vv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var dW={kernelName:sa,backendName:\"webgpu\",kernelFunc:Kue};var Yx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Nt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let start = ${s};\n let end = ${a};\n let outC = getCoordsFromIndex(index);\n\n if (${i} || ${p}) {\n setOutputAtIndex(index, uniforms.constantValue);\n } else {\n let coords = outC - start;\n setOutputAtIndex(index, getX(${u}));\n }\n }\n }\n `}};var qv=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new Yx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},fW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:qv};var que=et({opType:fe.POW}),hW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:que};function jue(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new bi(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var gW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:jue};function Xue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Yr(n,s,a,\"prod\",t)}var xW={kernelName:es,backendName:\"webgpu\",kernelFunc:Xue};var Yue=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=XB(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},yW={kernelName:aa,backendName:\"webgpu\",kernelFunc:Yue};var Que=et({opType:fe.DIV}),bW={kernelName:dn,backendName:\"webgpu\",kernelFunc:Que};var Zue=xe({opType:Q.RECIPROCAL}),CW={kernelName:ts,backendName:\"webgpu\",kernelFunc:Zue};var Jue=xe({opType:Q.RELU}),wW={kernelName:rs,backendName:\"webgpu\",kernelFunc:Jue};var epe=xe({opType:Q.RELU6}),SW={kernelName:ss,backendName:\"webgpu\",kernelFunc:epe};var Qx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function tpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new Qx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var IW={kernelName:ns,backendName:\"webgpu\",kernelFunc:tpe};var Zx=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function rpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,w=new Zx(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(w,[s],s.dtype,S)}var vW={kernelName:qa,backendName:\"webgpu\",kernelFunc:rpe};var Jx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function ope(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new Jx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var kW={kernelName:os,backendName:\"webgpu\",kernelFunc:ope};var ey=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function npe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,w=new ey(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(w,[s],s.dtype,S)}var NW={kernelName:Ka,backendName:\"webgpu\",kernelFunc:npe};var ty=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function spe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new ty(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var TW={kernelName:as,backendName:\"webgpu\",kernelFunc:spe};var ry=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var _W={kernelName:_s,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new ry(o.shape,s),[u,c]=C.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var ape=xe({opType:Q.ROUND}),$W={kernelName:is,backendName:\"webgpu\",kernelFunc:ape};var ipe=xe({opType:Q.RSQRT,cpuKernelImpl:YB}),EW={kernelName:us,backendName:\"webgpu\",kernelFunc:ipe};var Aa=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=Z(e),this.dispatch=q(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Nt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${K(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${kp(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Bs(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function upe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=C.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=Vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],w=new Aa(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(w,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var RW={kernelName:ps,backendName:\"webgpu\",kernelFunc:upe};var oy=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function ppe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new oy([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var DW={kernelName:ls,backendName:\"webgpu\",kernelFunc:ppe};var ny=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function cpe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new ny(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var AW={kernelName:ua,backendName:\"webgpu\",kernelFunc:cpe};var lpe=xe({opType:Q.SELU}),FW={kernelName:ms,backendName:\"webgpu\",kernelFunc:lpe};var mpe=xe({opType:Q.SIGMOID}),PW={kernelName:hs,backendName:\"webgpu\",kernelFunc:mpe};var dpe=xe({opType:Q.SIGN}),OW={kernelName:fs,backendName:\"webgpu\",kernelFunc:dpe};var fpe=xe({opType:Q.SIN}),MW={kernelName:ds,backendName:\"webgpu\",kernelFunc:fpe};var hpe=xe({opType:Q.SINH}),LW={kernelName:ja,backendName:\"webgpu\",kernelFunc:hpe};var gpe=xe({opType:Q.SOFTPLUS}),BW={kernelName:gs,backendName:\"webgpu\",kernelFunc:gpe};var xpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},zW={kernelName:ca,backendName:\"webgpu\",kernelFunc:xpe};var sy=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=oz(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new sy(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var VW={kernelName:so,backendName:\"webgpu\",kernelFunc:gm};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=C.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=t.bufferSync(n),D=t.bufferSync(s),F=y.decodeString(t.readSync(a.dataId)[0]),O=QB(R,D,i,m,c,u,p,l,F,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),w=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=gm({inputs:{x:w},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new Aa([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new Aa([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new Aa([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(w.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),E}var WW={kernelName:Cs,backendName:\"webgpu\",kernelFunc:bpe};function Cpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=zs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var UW={kernelName:la,backendName:\"webgpu\",kernelFunc:Cpe};var wpe=xe({opType:Q.SQRT}),GW={kernelName:xs,backendName:\"webgpu\",kernelFunc:wpe};var HW={kernelName:Gi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Xr(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Spe=et({opType:fe.SQUARED_DIFFERENCE}),KW={kernelName:ws,backendName:\"webgpu\",kernelFunc:Spe};function Ipe({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Xr(o.shape,Q.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var qW={kernelName:yo,backendName:\"webgpu\",kernelFunc:Ipe};var ay=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Nt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function vpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ct.computeOutShape(b,w,S),E=zs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=ez(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let E=new ay(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=t.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var jW={kernelName:Ss,backendName:\"webgpu\",kernelFunc:vpe};function kpe(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=tz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var XW={kernelName:ma,backendName:\"webgpu\",kernelFunc:kpe};var Npe=et({opType:fe.SUB,cpuKernelImpl:rz,supportsComplex:!0}),YW={kernelName:Is,backendName:\"webgpu\",kernelFunc:Npe};var Tpe=xe({opType:Q.TAN}),QW={kernelName:vs,backendName:\"webgpu\",kernelFunc:Tpe};var _pe=xe({opType:Q.TANH}),ZW={kernelName:ks,backendName:\"webgpu\",kernelFunc:_pe};function $pe(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=C.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=gm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new Aa([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),w=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[w]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach(E=>t.disposeData(E.dataId)),_}var JW={kernelName:cs,backendName:\"webgpu\",kernelFunc:$pe};var iy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},uy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function Jc(r,e){e!==null&&r.disposeData(e.dataId)}function eU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new iy(E),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,\"int32\",O),Jc(t,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new uy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],F=f;f=t.runWebGPUProgram(E,_,\"int32\",D),Jc(t,F);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=zs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),Jc(t,x);let b=Hv({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});Jc(t,l);let w=i.slice(0,-1);w.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:w},backend:t}),Jc(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:w},backend:t}),Jc(t,S),[b,f]}var tU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:Epe};var py=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${K(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new py(g),b=a===\"nearest\"?1:2,w;switch(i){case\"constant\":w=1;break;case\"reflect\":w=2;break;case\"wrap\":w=3;break;case\"nearest\":w=4;break;default:w=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[w]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",S)}var rU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var oU={kernelName:da,backendName:\"webgpu\",kernelFunc:Dpe};var cy=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=Z(e),this.dispatch=q(this.dispatchLayout,e,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${K(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Bs(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=C.getAxesPermutation([u],i),l=n;c!=null&&(l=rr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=C.getInnerMostAxes(1,i)[0]);let m=C.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=Vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new cy(f.shape,g,h),w=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,w,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=C.getUndoAxesPermutation(c);_=rr({inputs:{x:_},backend:t,attrs:{perm:E}})}return p.forEach(E=>t.disposeData(E.dataId)),_}var nU={kernelName:ji,backendName:\"webgpu\",kernelFunc:Ape};var Fpe=[IB,az,iz,uz,pz,cz,mz,dz,fz,hz,gz,xz,yz,bz,Cz,Iz,vz,kz,Nz,Tz,$z,Ez,Rz,Pz,Oz,Mz,kB,Bz,Vz,Wz,Uz,Gz,Hz,Kz,qz,jz,Xz,Yz,Jz,eV,tV,rV,nV,sV,oV,aV,iV,uV,pV,mV,dV,fV,hV,gV,xV,yV,bV,CV,wB,wV,vV,SV,IV,kV,NV,TV,_V,$V,EV,RV,vB,DV,zz,AV,FV,PV,OV,MV,LV,BV,VV,zV,WV,UV,GV,KV,qV,wz,jV,XV,ZV,YV,QV,JV,Sz,eW,tW,rW,oW,sW,cV,aW,iW,uW,Dz,pW,mW,dW,fW,hW,gW,xW,yW,Az,bW,CW,wW,SW,SB,IW,vW,kW,NW,TW,_W,$W,EW,RW,DW,AW,FW,PW,OW,MW,LW,_z,qW,jW,XW,nW,BW,zW,WW,UW,GW,HW,KW,YW,lV,QW,ZW,JW,VW,tU,rU,lz,oU,nU,cW];for(let r of Fpe)Ya(r);var sU=\"4.5.0\",Ppe=\"4.5.0\",Ope=\"4.5.0\",Mpe=\"4.5.0\",Lpe=\"4.5.0\",Bpe=\"0.0.1-alpha.20\",zpe={tfjs:sU,\"tfjs-core\":sU,\"tfjs-converter\":Ppe,\"tfjs-backend-cpu\":Ope,\"tfjs-backend-webgl\":Mpe,\"tfjs-backend-wasm\":Lpe,\"tfjs-backend-webgpu\":Bpe};export{Gs as Abs,zo as Acos,Vo as Acosh,Yu as AdadeltaOptimizer,Qu as AdagradOptimizer,Zu as AdamOptimizer,Ju as AdamaxOptimizer,no as Add,Wo as AddN,Uo as All,Go as Any,Hs as ArgMax,Ks as ArgMin,Ho as Asin,Ko as Asinh,qo as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,qs as AvgPool3D,Ni as AvgPool3DGrad,Gp as AvgPoolGrad,am as BackendWasm,Qo as BatchMatMul,js as BatchToSpaceND,Zo as Bincount,ml as BitwiseAnd,Xs as BroadcastArgs,Kpe as BroadcastTo,ho as Cast,Jo as Ceil,go as ClipByValue,Ti as Complex,_i as ComplexAbs,Ys as Concat,en as Conv2D,$i as Conv2DBackpropFilter,tn as Conv2DBackpropInput,rn as Conv3D,za as Conv3DBackpropFilterV2,on as Conv3DBackpropInputV2,nn as Cos,sn as Cosh,pn as CropAndResize,an as Cumprod,un as Cumsum,Lo as DataStorage,Qs as DenseBincount,cn as DepthToSpace,ln as DepthwiseConv2dNative,Ei as DepthwiseConv2dNativeBackpropFilter,Ri as DepthwiseConv2dNativeBackpropInput,Zs as Diag,mn as Dilation2D,Ai as Dilation2DBackpropFilter,Di as Dilation2DBackpropInput,WC as ENV,Fi as Einsum,fn as Elu,Va as EluGrad,cl as Environment,hn as Equal,Wa as Erf,gn as Exp,Js as ExpandDims,xn as Expm1,Pi as FFT,ea as Fill,yn as FlipLeftRight,bn as Floor,Cn as FloorDiv,$u as FromPixels,wn as FusedBatchNorm,Co as FusedConv2D,wo as FusedDepthwiseConv2D,xp as GPGPUContext,Sn as GatherNd,ta as GatherV2,Ol as GraphModel,In as Greater,vn as GreaterEqual,Oi as IFFT,xo as Identity,Mi as Imag,kn as IsFinite,Nn as IsInf,Tn as IsNan,ro as KernelBackend,Mn as LRN,Ua as LRNGrad,_n as LeakyRelu,$n as Less,En as LessEqual,Rn as LinSpace,Dn as Log,An as Log1p,qpe as LogSoftmax,Fn as LogicalAnd,Pn as LogicalNot,On as LogicalOr,m0 as LogicalXor,jpe as LowerBound,lu as MathBackendCPU,hu as MathBackendWebGL,Xpe as MatrixBandPart,Ln as Max,zn as MaxPool,ra as MaxPool3D,Li as MaxPool3DGrad,Hp as MaxPoolGrad,Bi as MaxPoolWithArgmax,Bn as Maximum,Vn as Mean,Wn as Min,Un as Minimum,Gn as MirrorPad,Ga as Mod,ep as MomentumOptimizer,Hn as Multinomial,Kn as Multiply,oa as Neg,jn as NonMaxSuppressionV3,Ha as NonMaxSuppressionV4,Xn as NonMaxSuppressionV5,qn as NotEqual,pw as OP_SCOPE_SUFFIX,Yn as OneHot,na as OnesLike,kr as Optimizer,Dl as OptimizerConstructors,sa as Pack,Qn as PadV2,Ype as Pool,Zn as Pow,Jn as Prelu,es as Prod,tp as RMSPropOptimizer,Kp as RaggedGather,qp as RaggedRange,jp as RaggedTensorToTensor,aa as Range,JC as Rank,zi as Real,dn as RealDiv,ts as Reciprocal,Et as Reduction,rs as Relu,ss as Relu6,ia as Reshape,ns as ResizeBilinear,qa as ResizeBilinearGrad,os as ResizeNearestNeighbor,Ka as ResizeNearestNeighborGrad,as as Reverse,_s as RotateWithOffset,is as Round,us as Rsqrt,ii as SGDOptimizer,ps as ScatterNd,ls as SearchSorted,ua as Select,ms as Selu,hs as Sigmoid,fs as Sign,ds as Sin,ja as Sinh,pa as Slice,bs as Softmax,gs as Softplus,ca as SpaceToBatchND,Vi as SparseFillEmptyRows,Xa as SparseReshape,Wi as SparseSegmentMean,Ui as SparseSegmentSum,Cs as SparseToDense,la as SplitV,xs as Sqrt,Gi as Square,ws as SquaredDifference,_u as StaticRegexReplace,yo as Step,Ss as StridedSlice,ma as StringNGrams,Hi as StringSplit,Ki as StringToHashBucketFast,Is as Sub,ys as Sum,vs as Tan,ks as Tanh,pt as Tensor,tt as TensorBuffer,cs as TensorScatterUpdate,so as Tile,Ns as TopK,Ts as Transform,ao as Transpose,qi as Unique,da as Unpack,ji as UnsortedSegmentSum,Qpe as UpperBound,Qa as Variable,Cu as WebGPUBackend,fa as ZerosLike,bo as _FusedMatMul,Zt as abs,ik as acos,uk as acosh,be as add,pk as addN,ck as all,lk as any,mk as argMax,dk as argMin,fk as asin,hk as asinh,gk as atan,xk as atan2,yk as atanh,cd as avgPool,wk as avgPool3d,Tme as backend,C as backend_util,Sk as basicLSTMCell,tu as batchNorm,vk as batchNorm2d,kk as batchNorm3d,Nk as batchNorm4d,ld as batchToSpaceND,md as bincount,Tk as bitwiseAnd,qq as booleanMaskAsync,_k as broadcastArgs,ru as broadcastTo,Sr as broadcast_util,MN as browser,me as buffer,Ye as cast,$k as ceil,Ek as clipByValue,Vr as clone,$r as complex,yt as concat,Rk as concat1d,Dk as concat2d,Ak as concat3d,Fk as concat4d,Pk as conv1d,ou as conv2d,Ok as conv2dTranspose,Mk as conv3d,Bk as conv3dTranspose,sce as copyRegisteredKernels,zk as cos,Vk as cosh,_l as cosineWindow,Wk as cumprod,Uk as cumsum,Ir as customGrad,Gk as denseBincount,bw as deprecationWarn,Hk as depthToSpace,ac as depthwiseConv2d,KX as deregisterOp,Zi as device_util,Kk as diag,qk as dilation2d,gme as disableDeprecationWarnings,Ot as dispose,xme as disposeVariables,Ke as div,Xk as divNoNan,Yk as dot,s6 as dropout,Qk as einsum,gd as elu,hme as enableDebugMode,fme as enableProdMode,Pw as enclosingPowerOfTwo,ur as engine,Zk as ensureShape,P as env,hd as equal,Jk as erf,r2 as euclideanNorm,ko as exp,oi as expandDims,o2 as expm1,xd as eye,pc as fft,Sa as fill,kme as findBackend,Nme as findBackendFactory,yd as floor,pd as floorDiv,hD as forceHalfFloat,Ow as fused,bd as gather,o6 as gatherND,of as gather_util,Ime as getBackend,HC as getGradient,fl as getKernel,Km as getKernelsForBackend,ese as getThreadsCount,qI as gpgpu_util,YH as grad,QH as grads,Bu as greater,Cd as greaterEqual,Hu as ifft,su as imag,uj as image,i6 as inTopKAsync,pi as io,Wd as irfft,n2 as isFinite,s2 as isInf,a2 as isNaN,Er as keep,Wt as kernel_impls,wd as leakyRelu,kl as less,ic as lessEqual,pj as linalg,i2 as linspace,W5 as loadGraphModel,U5 as loadGraphModelSync,u2 as localResponseNormalization,ni as log,Sd as log1p,p2 as logSigmoid,c2 as logSoftmax,kd as logSumExp,zu as logicalAnd,Nd as logicalNot,Td as logicalOr,l2 as logicalXor,cj as losses,m2 as lowerBound,Qe as matMul,PN as math,Ia as max,$d as maxPool,d2 as maxPool3d,f2 as maxPoolWithArgmax,Ed as maximum,Vu as mean,yme as memory,h2 as meshgrid,vl as min,Wu as minimum,g2 as mirrorPad,x2 as mod,y2 as moments,Yq as movingAverage,se as mul,b2 as multiRNNCell,C2 as multinomial,pr as neg,Kw as nextFrame,Lu as norm,Rd as notEqual,Tl as oneHot,va as ones,w2 as onesLike,N as op,S2 as outerProduct,ka as pad,I2 as pad1d,v2 as pad2d,k2 as pad3d,N2 as pad4d,T2 as pool,ri as pow,Ad as prelu,ud as print,_2 as prod,bme as profile,$2 as raggedGather,E2 as raggedRange,R2 as raggedTensorToTensor,D2 as rand,J2 as randomGamma,Bd as randomNormal,e1 as randomStandardNormal,uc as randomUniform,t1 as randomUniformInt,au as range,Sme as ready,si as real,r1 as reciprocal,eu as registerBackend,rce as registerGradient,Ya as registerKernel,HX as registerOp,iu as relu,zd as relu6,vme as removeBackend,W as reshape,uo as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,cc as rfft,Vd as round,i1 as rsqrt,ke as scalar,Zq as scatterND,pu as scatter_util,Nl as searchSorted,u1 as selu,p1 as separableConv2d,vN as serialization,wme as setBackend,_me as setPlatform,Jne as setThreadsCount,Qne as setWasmPath,Zne as setWasmPaths,iI as setWebGLContext,c1 as setdiff1dAsync,Sc as shared,wa as sigmoid,l1 as sign,ij as signal,m1 as sin,d1 as sinh,qe as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ct as slice_util,y1 as softmax,vd as softplus,Dd as spaceToBatchND,lj as sparse,t6 as sparseToDense,aj as spectral,ai as split,Rr as sqrt,Jt as square,Ud as squaredDifference,lc as squeeze,vr as stack,Gd as step,b1 as stridedSlice,mj as string,Te as sub,ot as sum,Za as sumOutType,C1 as tan,Il as tanh,ir as tensor,xr as tensor1d,uu as tensor2d,Hd as tensor3d,w1 as tensor4d,S1 as tensor5d,I1 as tensor6d,k1 as tensorScatterUpdate,M0 as tensor_util,Z2 as test_util,De as tidy,nu as tile,Cme as time,N1 as topk,CUe as train,dc as transpose,T1 as truncatedNormal,_1 as unique,nce as unregisterGradient,oce as unregisterKernel,$1 as unsortedSegmentSum,po as unstack,dt as upcastType,E1 as upperBound,y as util,ZH as valueAndGrad,JH as valueAndGrads,R1 as variable,vw as variableGrads,zpe as version,H5 as version_converter,Vj as version_core,I8 as version_cpu,tse as version_wasm,xZ as version_webgl,cst as webgl,_c as webgl_util,Fv as webgpu_util,io as where,qd as whereAsync,Wr as zeros,Ht as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam', 'no devices');\n return;\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam', 'no stream');\n return;\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.0.6\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": {\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.node.js\",\n \"module\": \"./dist/human.node.js\"\n },\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.18.0\",\n \"@html-eslint/parser\": \"^0.18.0\",\n \"@microsoft/api-extractor\": \"^7.34.8\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"0.0.1-alpha.20\",\n \"@tensorflow/tfjs-converter\": \"^4.5.0\",\n \"@tensorflow/tfjs-core\": \"^4.5.0\",\n \"@tensorflow/tfjs-data\": \"^4.5.0\",\n \"@tensorflow/tfjs-layers\": \"^4.5.0\",\n \"@tensorflow/tfjs-node\": \"^4.5.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.5.0\",\n \"@types/node\": \"^20.1.0\",\n \"@types/offscreencanvas\": \"^2019.7.0\",\n \"@typescript-eslint/eslint-plugin\": \"^5.59.2\",\n \"@typescript-eslint/parser\": \"^5.59.2\",\n \"@vladmandic/build\": \"^0.9.2\",\n \"@vladmandic/pilogger\": \"^0.4.8\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^2.11.2\",\n \"esbuild\": \"^0.17.18\",\n \"eslint\": \"8.40.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^7.1.0\",\n \"eslint-plugin-import\": \"^2.27.5\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-markdown\": \"^3.0.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"rimraf\": \"^5.0.0\",\n \"tslib\": \"^2.5.0\",\n \"typedoc\": \"0.24.7\",\n \"typescript\": \"5.0.4\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 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0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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cw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(cw||(cw={}));var lw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(lw||(lw={}));var mw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(mw||(mw={}));var QG={float32:lw,int32:pw,bool:cw,complex64:mw};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return QG[r][e]}function ti(r){return dt(r,\"int32\")}function ed(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function td(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function dw(r,e){$(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function ZG(r,e){return e.some(t=>t.id===r.id)}function bl(r){let e=[];return H0(r,e,new Set),e}function H0(r,e,t){if(r==null)return;if(r instanceof ut){e.push(r);return}if(!JG(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),H0(s,e,t))}}function JG(r){return Array.isArray(r)||typeof r==\"object\"}function fw(r){return r.kernelName!=null}var rd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},tu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new rd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){jm(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof so)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return tu.nextTensorId++}nextVariableId(){return tu.nextVariableId++}clone(e){let t=T.runKernel(Co,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(fl(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=fw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(fw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=fl(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=fw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=ew(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&zo(e[0])&&(s=e.map(p=>eu(p)));let a=n.write(s,t,o),i=new ut(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=YC(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new ut(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new ei(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Hp(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof ei||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Hp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let 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s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));$(s instanceof ut,()=>\"The result y returned by f() must be a tensor.\");let a=M0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?e4(s.shape):o,L0(i,a,u=>this.tidy(u),t4);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return $(Ks(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{$(t.every(i=>i instanceof ut),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),$(o.value instanceof ut,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),$(Ks(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),$(c.every(m=>m instanceof ut),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Lu(),o=await this.backend.time(e);return o.wallMs=Lu()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new rd;for(let 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a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(Za,a);return{outputIndices:i[0],outputShape:i[1]}}var TN=N({sparseReshape_:kj});function Nj(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(Ki,a)}var _N=N({sparseSegmentMean_:Nj});function Tj(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but 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${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(Xi,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var RN=N({stringSplit_:$j});function Ej(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Yi,n,o)}var DN=N({stringToHashBucketFast_:Ej});function Rj(r,e,t,o=!0){let n=v(r,\"input\",\"staticRegexReplace\",\"string\"),s={pattern:e,rewrite:t,replaceGlobal:o};return T.runKernel(Du,{x:n},s)}var AN=N({staticRegexReplace_:Rj});var Dj={fft:cc,ifft:Xu,rfft:lc,irfft:Gd},Aj={hammingWindow:Y1,hannWindow:Yd,frame:Qd,stft:Q1},Fj={flipLeftRight:J1,grayscaleToRGB:eN,resizeNearestNeighbor:lN,resizeBilinear:cN,rotateWithOffset:tN,cropAndResize:Z1,nonMaxSuppression:rN,nonMaxSuppressionAsync:sN,nonMaxSuppressionWithScore:aN,nonMaxSuppressionWithScoreAsync:iN,nonMaxSuppressionPadded:uN,nonMaxSuppressionPaddedAsync:pN,threshold:mN,transform:dN},Pj={bandPart:fN,gramSchmidt:hN,qr:xN},Oj={absoluteDifference:yN,computeWeightedLoss:cr,cosineDistance:bN,hingeLoss:CN,huberLoss:wN,logLoss:SN,meanSquaredError:IN,sigmoidCrossEntropy:vN,softmaxCrossEntropy:kN},Mj={sparseFillEmptyRows:NN,sparseReshape:TN,sparseSegmentMean:_N,sparseSegmentSum:$N},Lj={stringNGrams:EN,stringSplit:RN,stringToHashBucketFast:DN,staticRegexReplace:AN};var FN={};Ke(FN,{Serializable:()=>$l,SerializationMap:()=>Ra,registerClass:()=>qw});var $l=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Ra=class{constructor(){this.classNameMap={}}static getMap(){return Ra.instance==null&&(Ra.instance=new Ra),Ra.instance}static register(e){Ra.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function qw(r){$(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),$(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),$(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),Ra.register(r)}var Nr=class extends $l{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Aw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Nr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var ep=class extends Nr{static get className(){return\"Adadelta\"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(er(i),1-this.rho)),l=se(je(Dr(Ce(u,this.epsilon)),Dr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(er(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var tp=class extends Nr{static get className(){return\"Adagrad\"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ta(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,er(a));i.assign(p);let u=Ce(se(je(a,Dr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var rp=class extends Nr{static get className(){return\"Adam\"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(er(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Dr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(si(this.beta1,this.iterations_+1)),this.accBeta2.assign(si(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var op=class extends Nr{static get className(){return\"Adamax\"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Jt(u),h=Dd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var ci=class extends Nr{static get className(){return\"SGD\"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Rr(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var np=class extends ci{static get className(){return\"Momentum\"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var sp=class extends Nr{static get className(){return\"RMSProp\"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(er(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Dr(Te(c,Ce(er(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(er(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Dr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var Bj=[ep,tp,rp,op,np,sp,ci];function PN(){for(let r of Bj)qw(r)}var mi={};Ke(mi,{CompositeArrayBuffer:()=>jt,browserFiles:()=>MN,browserHTTPRequest:()=>zN,concatenateArrayBuffers:()=>J0,copyModel:()=>dk,decodeWeights:()=>nd,encodeWeights:()=>Y0,fromMemory:()=>VN,fromMemorySync:()=>Jw,getLoadHandlers:()=>ok,getModelArtifactsForJSON:()=>oc,getModelArtifactsForJSONSync:()=>ww,getModelArtifactsInfoForJSON:()=>wa,getSaveHandlers:()=>rk,getWeightSpecs:()=>ad,http:()=>rf,isHTTPScheme:()=>tf,listModels:()=>lk,loadWeights:()=>LN,moveModel:()=>fk,registerLoadRouter:()=>tk,registerSaveRouter:()=>ek,removeModel:()=>mk,weightsLoaderFactory:()=>Qw,withSaveHandler:()=>WN,withSaveHandlerSync:()=>UN});var zj=\"model\",Vj=\".json\",Wj=\".weights.bin\";function ON(r){return new Promise(e=>setTimeout(e)).then(r)}var li=class{constructor(e){if(!A().getBool(\"IS_BROWSER\"))throw new Error(\"browserDownloads() cannot proceed because the current environment is not a 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mT=(r,e,t,o=Je)=>{switch(r.op){case\"BiasAdd\":case\"AddV2\":case\"Add\":return[o.add(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"AddN\":return[o.addN(I(\"tensors\",r,e,t))];case\"FloorMod\":case\"Mod\":return[o.mod(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Mul\":return[o.mul(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"RealDiv\":case\"Div\":return[o.div(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"DivNoNan\":return[o.divNoNan(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"FloorDiv\":return[o.floorDiv(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Sub\":return[o.sub(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Minimum\":return[o.minimum(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Maximum\":return[o.maximum(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Pow\":return[o.pow(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"SquaredDifference\":return[o.squaredDifference(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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TypeError(`Node type ${r.op} is not implemented`)}};function Hr(r,e,t=\"\"){if(!(typeof r==\"number\"||typeof e==\"number\")){y.assert(r.length===e.length,()=>t+` Shapes ${r} and ${e} must match`);for(let o=0;ot+` Shapes ${r} and ${e} must match`)}}}function fT(r){return!(typeof r==\"number\"||r.some(e=>e<0))}function hc(r,e,t){let o=Cf(r,t),n=!fT(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=Cf(s.shape,o)}),!fT(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function Cf(r,e){if(typeof r==\"number\")return e;if(typeof e==\"number\")return r;if(r.length!==e.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);let t=[];for(let o=0;o=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);t[o]=n>=0?n:s}return t}var wf=class{constructor(e,t,o,n,s,a,i){this.name=e,this.dtype=t,this.maxSize=o,this.elementShape=n,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Rr(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let o=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},\n because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Hr(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),o.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(o.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);o.tensor=t,Rr(t),o.written=!0,this.tensors[e]=o}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((o,n)=>this.write(o,t[n]))}gather(e,t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let n=0;n=this.maxSize)throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`);this.writeMany(e,mo(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let o=0,n=e.map(p=>(o+=p,o));if(o!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${o}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size 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n=hc(this.elementShape,this.tensors,e);return De(()=>{let s=this.tensors.map(a=>W(a,n));return kr(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error(\"Trying to pop from an empty list.\");let o=hc(this.elementShape,this.tensors,e),n=this.tensors.pop();return n.kept=!1,Hr(n.shape,e,\"TensorList shape mismatch: \"),W(n,o)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Hr(e.shape,this.elementShape,\"TensorList shape mismatch: \"),this.maxNumElements===this.size())throw new Error(\"Trying to push element into a full list.\");Rr(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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\"),Rr(t),this.tensors[e]!=null&&(this.tensors[e].kept=!1),this.tensors[e]=t}gather(e,t,o){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);Hr(this.elementShape,o,\"TensorList shape mismatch: \"),e=e.slice(0,this.size());let n=hc(this.elementShape,this.tensors,o);return e.length===0?ir([],[0].concat(n)):De(()=>{let s=e.map(a=>W(this.tensors[a],n));return kr(s,0)})}concat(e,t){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Hr(this.elementShape,t,\"TensorList shape mismatch: \");let o=hc(this.elementShape,this.tensors,t);return this.size()===0?ir([],[0].concat(o)):De(()=>{let n=this.tensors.map(s=>W(s,o));return bt(n,0)})}};function hT(r,e,t){let o=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==t)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t}`);let n=r.shape.slice(1);Hr(n,e,\"TensorList shape mismatch: \");let s=mo(r);return new di(s,e,o)}function gT(r,e,t,o){return new di([],r,e,o)}function xT(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n} vs. ${o})`);let s=new di([],t,r.dtype,o),a=mo(r,0);return e.forEach((i,p)=>{s.setItem(i,a[p])}),s}function yT(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=Cf(s,t),i=o===0?0:r.size/o,p=De(()=>{let c=[];r=W(r,[1,o,i]);for(let l=0;l{switch(r.op){case\"If\":case\"StatelessIf\":{let o=I(\"thenBranch\",r,e,t),n=I(\"elseBranch\",r,e,t),s=I(\"cond\",r,e,t),a=I(\"args\",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case\"While\":case\"StatelessWhile\":{let o=I(\"body\",r,e,t),n=I(\"cond\",r,e,t),s=I(\"args\",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let l=u.map(d=>d.id);c.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);p=await m[0].data(),m.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()})}return u}case\"LoopCond\":{let o=I(\"pred\",r,e,t);return[Ms(o)]}case\"Switch\":{let o=I(\"pred\",r,e,t),n=I(\"data\",r,e,t);return n.kept||(n=Ms(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case\"Merge\":{let o=r.inputNames.find(n=>Bt(n,e,t)!==void 0);if(o){let n=Bt(o,e,t);return[Ms(n)]}return}case\"Enter\":{let o=I(\"frameName\",r,e,t),n=I(\"tensor\",r,e,t);return t.enterFrame(o),[Ms(n)]}case\"Exit\":{let o=I(\"tensor\",r,e,t);return t.exitFrame(),[Ms(o)]}case\"NextIteration\":{let o=I(\"tensor\",r,e,t);return t.nextIteration(),[Ms(o)]}case\"TensorArrayV3\":{let o=I(\"size\",r,e,t),n=I(\"dtype\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"dynamicSize\",r,e,t),i=I(\"clearAfterRead\",r,e,t),p=I(\"identicalElementShapes\",r,e,t),u=I(\"name\",r,e,t),c=new wf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,ke(1)]}case\"TensorArrayWriteV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case\"TensorArrayReadV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t);return[t.getTensorArray(o.id).read(n)]}case\"TensorArrayGatherV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"dtype\",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case\"TensorArrayScatterV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case\"TensorArrayConcatV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id),s=I(\"dtype\",r,e,t);return[n.concat(s)]}case\"TensorArraySplitV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"lengths\",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case\"TensorArraySizeV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id);return[ke(n.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case\"TensorListSetItem\":{let o=I(\"tensorListId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case\"TensorListGetItem\":{let o=I(\"tensorListId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let o=I(\"indices\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"numElements\",r,e,t),i=xT(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let o=I(\"elementShape\",r,e,t),n=I(\"elementDType\",r,e,t),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let a=I(s,r,e,t),i=r.op===\"TensorListReserve\"?-1:a,p=gT(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case\"TensorListGather\":{let o=I(\"tensorListId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case\"TensorListStack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=hT(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=yT(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function CT(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=Al(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var wT=(r,e,t,o=Je)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=Al(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=CT(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=CT(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let 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n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ST=(r,e,t,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"seed\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function TS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var IT=async(r,e,t,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=TS(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=TS(r,e,t),c=I(\"padToMaxOutputSize\",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=TS(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,e,t),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,e,t),I(\"y\",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vT=(r,e,t,o=Je)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kT=(r,e,t,o=Je)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Bt(r.name,e,t)||n];case\"Placeholder\":return[Bt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[Ms(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>Ms(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[Ms(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=mo(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new Sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var TT=(r,e,t,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _T=(r,e,t,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $T=(r,e,t,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,e,t),I(\"numLower\",r,e,t),I(\"numUpper\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ET=(r,e,t,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RT=(r,e,t,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DT=(r,e,t,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AT=(r,e,t,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"tensor\",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FT=(r,e,t,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PT=(r,e,t,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OT=(r,e,t,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,e,t),I(\"pattern\",r,e,t),I(\"rewrite\",r,e,t),I(\"replaceGlobal\",r,e,t))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,e,t,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function _S(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>mT(a,i,p));case\"basic_math\":return n(()=>dT(a,i,p));case\"control\":return bT(a,i,p);case\"convolution\":return n(()=>wT(a,i,p));case\"creation\":return n(()=>ST(a,i,p));case\"dynamic\":return IT(a,i,p);case\"evaluation\":return n(()=>vT(a,i,p));case\"image\":return n(()=>TT(a,i,p));case\"graph\":return n(()=>kT(a,i,p));case\"logical\":return n(()=>_T(a,i,p));case\"matrices\":return n(()=>$T(a,i,p));case\"normalization\":return n(()=>ET(a,i,p));case\"ragged\":return n(()=>RT(a,i,p));case\"reduction\":return n(()=>DT(a,i,p));case\"slice_join\":return n(()=>AT(a,i,p));case\"sparse\":return n(()=>FT(a,i,p));case\"spectral\":return n(()=>PT(a,i,p));case\"string\":return n(()=>OT(a,i,p));case\"transformation\":return n(()=>MT(a,i,p));case\"hash_table\":return NT(a,i,p,o);case\"custom\":let u=af(a.op);if(u&&u.customExecutor)return u.customExecutor(new bf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Pl=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function $S(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Tr(m)[0]));o=o||[];let c=new Set(o.map(m=>Tr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||l8(m)||m8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function LT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Tr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=a8(f,p);return i8(h,p),h}function a8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function i8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function BT(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new ip(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(e,t){let o=$S(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=LT(this.graph,o),p=BT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Rr(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Tr(m)[0]]),s=t.map(m=>Tr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Tr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=_S(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=aS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=aS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Pl(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Tr(S)[0]]),i=o.map(S=>Tr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=$S(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Tr(S),E=[];E[_]=e[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Os(l.node.name,o)),n[l.node.name]==null){let d=_S(l.node,n,o,this._resourceManager);m||([m]=Os(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Os(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Tr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let 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s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ip(Fl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(e.modelInitializer);this.initializer=new ip(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than 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c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,C=c.dilationWidth,S=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,E=S-1-c.padInfo.front,R=_-1-c.padInfo.left,D=k-1-c.padInfo.top,P=me(s.shape,\"float32\"),O=1/(f*h*g),M=t.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=c.outHeight||Math.floor(ie)!==ie))for(let le=0;le<_;le+=C){let be=(J+le)/d;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;let _e=M.get(L,ee,ie,be,B);re+=_e}}}P.set(re*O,L,z,U,j,B)}return t.makeTensorInfo(P.shape,P.dtype,P.values)}var j_={kernelName:Ei,backendName:\"cpu\",kernelFunc:fY};function 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l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat===\"channelsLast\",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let lt=le+Pe*_[1],Ue=be+st*D,mt=lt;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],lt=le+Pe*u.inChannels,Ue=st;for(let mt=0;mtMath.cos(r)),c$={kernelName:sn,backendName:\"cpu\",kernelFunc:TY};var _Y=Ie(an,r=>Math.cosh(r)),l$={kernelName:an,backendName:\"cpu\",kernelFunc:_Y};function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,\"NHWC\",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var S$={kernelName:Mi,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var I$={kernelName:Oi,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Oi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function MY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=t.data.get(n.dataId).values,g=n.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${E}.`)}else if(n.dtype===\"int32\"&&(E<0||E>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${E}.`);f===1?(S[0]=E*g,S[1]=E*g,S[2]=E*g):S[_]=E*g}let k=C*4;x[k+0]=Math.round(S[0]),x[k+1]=Math.round(S[1]),x[k+2]=Math.round(S[2]),x[k+3]=Math.round(S[3])}s.width=d,s.height=m;let b=new ImageData(x,d,m);return l.putImageData(b,0,0),n}var v$={kernelName:qm,backendName:\"cpu\",kernelFunc:MY};function fi(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Q(n,\"sum\");let i;n.dtype===\"bool\"?i=Ro({inputs:{x:n},backend:t,attrs:{dtype:\"int32\"}}):i=lr({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=w.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=It({inputs:{x:i},backend:t,attrs:{perm:c}}),l=w.getInnerMostAxes(l.length,p)),w.assertAxesAreInnerMostDims(\"sum\",l,m.shape.length);let[d,f]=w.computeOutAndReduceShapes(m.shape,l),h=w.upcastType(m.dtype,\"int32\"),g=xc(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,C=t.data.get(m.dataId).values;for(let S=0;S=0&&(m=fi({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var N$={kernelName:Li,backendName:\"cpu\",kernelFunc:LY};function BY(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;Q([o,n],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=0?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,\"float32\",s)}var T$={kernelName:qa,backendName:\"cpu\",kernelFunc:BY};var zY=w.ERF_P,VY=w.ERF_A1,WY=w.ERF_A2,UY=w.ERF_A3,GY=w.ERF_A4,HY=w.ERF_A5,KY=Ie(gn,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+zY*t);return e*(1-((((HY*o+GY)*o+UY)*o+WY)*o+VY)*o*Math.exp(-t*t))}),_$={kernelName:gn,backendName:\"cpu\",kernelFunc:KY};function kc(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Ve({inputs:{x:n},backend:t,attrs:{shape:i}})}var $$={kernelName:oa,backendName:\"cpu\",kernelFunc:kc};var qY=ze((r,e)=>r/e),zl=Ye(fn,qY),Vl={kernelName:fn,backendName:\"cpu\",kernelFunc:zl};function Bf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&C=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=w.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Ve({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Ve({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),C=Nf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,C.dtype,C.values)}var O$={kernelName:sa,backendName:\"cpu\",kernelFunc:r7};function o7(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Ve({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=Bf(i,!0,t),u=Ve({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var M$={kernelName:zi,backendName:\"cpu\",kernelFunc:o7};var n7=Ie(Tn,r=>Number.isFinite(r)?1:0,\"bool\"),L$={kernelName:Tn,backendName:\"cpu\",kernelFunc:n7};var s7=Ie(_n,r=>Math.abs(r)===1/0?1:0,\"bool\"),B$={kernelName:_n,backendName:\"cpu\",kernelFunc:s7};var a7=Ie($n,r=>Number.isNaN(r)?1:0,\"bool\"),z$={kernelName:$n,backendName:\"cpu\",kernelFunc:a7};function i7(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=Tf(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var V$={kernelName:An,backendName:\"cpu\",kernelFunc:i7};var u7=Ie(Pn,r=>Math.log1p(r)),W$={kernelName:Pn,backendName:\"cpu\",kernelFunc:u7};var p7=ze((r,e)=>r&&e),c7=Ye(On,p7,null,\"bool\"),U$={kernelName:On,backendName:\"cpu\",kernelFunc:c7};var l7=Ie(Mn,r=>r?0:1,\"bool\"),G$={kernelName:Mn,backendName:\"cpu\",kernelFunc:l7};var m7=ze((r,e)=>r||e),d7=Ye(Ln,m7,null,\"bool\"),H$={kernelName:Ln,backendName:\"cpu\",kernelFunc:d7};function f7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;Q(n,\"LRN\");let u=n.shape[3],c=u-1,l=t.data.get(n.dataId).values,m=y.sizeFromShape(n.shape),d=new Float32Array(m);function f(h){let g=h%u,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),C=0;for(;x<=b;x++){let S=l[x];C+=S*S}return C}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p),l;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))l=lr({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,d=y.computeStrides(n.shape),f=vc(m,n.shape,n.dtype,d,c,\"max\");l=t.makeTensorInfo(c.outShape,n.dtype,f.values)}return l}var X$={kernelName:Wn,backendName:\"cpu\",kernelFunc:g7};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o;Q(n,\"maxPool3d\");let c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.data.get(n.dataId).values,m=Lf(l,n.shape,n.dtype,y.computeStrides(n.shape),c,\"max\");return t.makeTensorInfo(m.shape,\"float32\",m.values)}var Y$={kernelName:aa,backendName:\"cpu\",kernelFunc:x7};function y7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o;Q([n,s],\"maxPool3DGrad\");let c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.bufferSync(s),m=H_(l,c),d=c.strideDepth,f=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,C=c.effectiveFilterDepth,S=c.effectiveFilterHeight,k=c.effectiveFilterWidth,_=C-1-c.padInfo.front,E=k-1-c.padInfo.left,R=S-1-c.padInfo.top,D=me(s.shape,\"float32\"),P=t.bufferSync(n);for(let O=0;O=c.outDepth||Math.floor(re)!==re))for(let ne=0;ne=c.outHeight||Math.floor(ee)!==ee))for(let oe=0;oe=c.outWidth||Math.floor(ie)!==ie)continue;let le=C*S*k-1-m.get(O,re,ee,ie,M),be=J*S*k+ne*k+oe,_e=le===be?1:0;if(_e===0)continue;let ve=P.get(O,re,ee,ie,M);Y+=ve*_e}}}D.set(Y,O,L,B,z,M)}return t.makeTensorInfo(D.shape,D.dtype,D.values)}var Q$={kernelName:Ui,backendName:\"cpu\",kernelFunc:y7};function b7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Q([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=t.data.get(i.dataId).values,f=me(m.outShape,i.dtype,Mf(d,i.shape,i.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,C=m.effectiveFilterHeight,S=m.effectiveFilterWidth,k=S-1-m.padInfo.left,_=C-1-m.padInfo.top,E=me(i.shape,\"float32\"),R=t.data.get(n.dataId).values,D=me(n.shape,\"float32\",R);for(let P=0;P=m.outHeight||Math.floor(q)!==q))for(let Y=0;Y=m.outWidth||Math.floor(J)!==J)continue;let re=C*S-1-f.get(P,q,J,O),ne=j*S+Y,ee=re===ne?1:0;if(ee===0)continue;let oe=D.get(P,q,J,O);U+=oe*ee}}E.set(U,P,M,L,O)}return t.makeTensorInfo(E.shape,E.dtype,E.values)}var Z$={kernelName:Wi,backendName:\"cpu\",kernelFunc:b7};function J$(r,e,t,o,n){let s=y.computeStrides(e),a=vc(r,e,t,s,n,\"max\"),i=Mf(r,e,t,n,!0,o);return[a.values,i.values]}var eE={kernelName:ia,backendName:\"cpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;Q(o,\"MaxPoolWithArgmax\");let u=p.data.get(o.dataId).values,c=w.computePool2DInfo(o.shape,n,s,[1,1],a),[l,m]=J$(u,o.shape,o.dtype,i,c),d=p.write(l,c.outShape,o.dtype),f=p.write(m,c.outShape,o.dtype);return[{dataId:d,shape:c.outShape,dtype:o.dtype},{dataId:f,shape:c.outShape,dtype:\"int32\"}]}};function C7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=y.parseAxisParam(s,n.shape),u=w.computeOutAndReduceShapes(n.shape,i)[1],c=y.sizeFromShape(u),l=[],m=t.makeTensorInfo([],\"float32\",new Float32Array([c]));l.push(m);let d=Ro({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}});l.push(d);let f=zl({inputs:{a:d,b:m},backend:t});l.push(f);let h=fi({inputs:{x:f},backend:t,attrs:{axis:s,keepDims:a}});return l.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}var tE={kernelName:Un,backendName:\"cpu\",kernelFunc:C7};function w7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Q(n,\"min\");let i=y.parseAxisParam(s,n.shape),p=i,u=w.getAxesPermutation(p,n.shape.length),c=n;u!=null&&(c=It({inputs:{x:n},backend:t,attrs:{perm:u}}),p=w.getInnerMostAxes(p.length,n.shape.length)),w.assertAxesAreInnerMostDims(\"min\",p,c.shape.length);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=y.sizeFromShape(m),f=y.makeZerosTypedArray(y.sizeFromShape(l),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;xC[0]+n.shape[S]+C[1]),p=s.map(C=>C[0]),u=s.map((C,S)=>C[0]+n.shape[S]),c=a===\"reflect\"?0:1,l=t.data.get(n.dataId).values,m=n.shape.length,d=y.computeStrides(n.shape),f=y.sizeFromShape(i),h=i.length,g=y.computeStrides(i),x=y.getTypedArrayFromDType(n.dtype,f);for(let C=0;C=u[_]&&(S[_]=(u[_]-1)*2-S[_]+c);S=S.map((_,E)=>_-p[E]);let k=y.locToIndex(S,m,d);x[C]=l[k]}return{dataId:t.write(x,i,n.dtype),shape:i,dtype:n.dtype}}var oE={kernelName:Kn,backendName:\"cpu\",kernelFunc:S7};var I7=ze((r,e)=>{let t=r%e;return r<0&&e<0||r>=0&&e>=0?t:(t+e)%e}),v7=Ye(qn,I7),nE={kernelName:qn,backendName:\"cpu\",kernelFunc:v7};var aE=Up(zw());function fI(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=n.shape.length,i=s;if(i===-1&&(i=a-1),i!==a-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);let p=y.parseAxisParam([i],n.shape),u=dI({inputs:{x:n},backend:t,attrs:{reductionIndices:p,keepDims:!1}}),c=w.expandShapeToKeepDim(u.shape,p),l=Ve({inputs:{x:u},backend:t,attrs:{shape:c}}),m=Ll({inputs:{a:n,b:l},backend:t}),d=LS({inputs:{x:m},backend:t}),f=fi({inputs:{x:d},backend:t,attrs:{axis:p,keepDims:!1}}),h=Ve({inputs:{x:f},backend:t,attrs:{shape:c}}),g=zl({inputs:{a:d,b:h},backend:t});return t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var sE={kernelName:Is,backendName:\"cpu\",kernelFunc:fI};function k7(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;Q(n,\"multinomial\");let 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Yf(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.lengthr[t]).join(\", \")}function CR(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=gR(n,a,e),p=wI(r.gl,i),u=r.createProgram(p);return A().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(u),Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},BI(r,e,u)))}function BI(r,e,t){let o=[],n=[],s,a,i,p=null,u=null;u=r.getUniformLocation(t,\"NAN\",!1),A().getNumber(\"WEBGL_VERSION\")===1&&(p=r.getUniformLocation(t,\"INFINITY\",!1));let c=!1;for(let l of e.variableNames){let 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k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function pt(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=vt();this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?bp([\"r\",\"c\",\"d\"],e):zs([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${t.output} = result;\n }\n `}};var Zf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=vt();this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?bp([\"r\",\"c\",\"d\"],e):zs([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var Jf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=mr.DOWNLOAD;let t=vt();this.outputShape=e,this.userCode=`\n ${Xf}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var eh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=vt();this.outputShape=e,this.userCode=`\n ${Xf}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}};var 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i=0;ijI,createBufferFromOutputTexture:()=>QI,createFloat16MatrixTexture:()=>GI,createFloat16PackedMatrixTexture:()=>qI,createFloat32MatrixTexture:()=>UI,createIndexBuffer:()=>WI,createPackedMatrixTexture:()=>KI,createUnsignedBytesMatrixTexture:()=>HI,createVertexBuffer:()=>VI,createVertexShader:()=>zI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>JI,downloadFloat32MatrixFromBuffer:()=>ZI,downloadMatrixFromPackedOutputTexture:()=>tv,downloadPackedMatrixFromBuffer:()=>ev,getInternalFormatForFloat16MatrixTexture:()=>oh,getInternalFormatForFloat16PackedMatrixTexture:()=>ah,getInternalFormatForFloat32MatrixTexture:()=>rh,getInternalFormatForPackedMatrixTexture:()=>sh,getInternalFormatForUnsignedBytesMatrixTexture:()=>nh,uploadDenseMatrixToTexture:()=>XI,uploadPixelDataToTexture:()=>YI});function zI(r){let e=vt(),t=`${e.version}\n precision highp float;\n ${e.attribute} vec3 clipSpacePos;\n ${e.attribute} vec2 uv;\n ${e.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function ZI(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function JI(r,e,t,o){let[n,s]=yp(e,t),a=4,i=new Uint8Array(uR(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function ev(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(pR(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function tv(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var wp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let 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o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),A().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Nc(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Nc(this.gl,a);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Nc(this.gl,n);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",qr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(qr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=VI(this.gl),this.indexBuffer=WI(this.gl),this.framebuffer=_I(this.gl),this.textureConfig=Kl(this.gl,this.textureHalfFloatExtension)}get debug(){return A().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),qI(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),KI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(qf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>JI(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return ev(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return ZI(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=QI(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(A().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let 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t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),jI(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ql(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?$I(this.gl,e,t):EI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),RI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Pa(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ql(this.gl,this.program),Tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and 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A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),jl(this.gl,e,this.framebuffer),this.debug&&Tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(jl(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tc(this.gl)):qf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;jl(n,e,this.framebuffer),this.debug&&Tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function RZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:ov(r,e)}function fD(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var Mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${DZ(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Rc():Ec(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function DZ(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?fR([\"r\",\"c\",\"d\"],\"inputShape\"):zs([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var lh=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=gD(t,o),s=xD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=hD(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===tr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===tr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===tr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===tr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===tr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=gD(o,n),a=xD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=hD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function AZ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function hD(r,e,t,o,n){let s=FZ(e,o),a;if(n){let[p,u]=Pa(r[0],r[1]);a=p*u}else{let[p,u]=yp(r[0],r[1]);a=p*u}let i=AZ(t,s);return a*i}function FZ(r,e){switch(r){case tr.PACKED_2X2_FLOAT32:return sh(e);case tr.PACKED_2X2_FLOAT16:return ah(e);case tr.UNPACKED_FLOAT32:return rh(e);case tr.UNPACKED_FLOAT16:return oh(e);case tr.PACKED_4X1_UNSIGNED_BYTE:return nh(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function PZ(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?tr.PACKED_2X2_FLOAT32:tr.UNPACKED_FLOAT32:r?tr.PACKED_2X2_FLOAT16:tr.UNPACKED_FLOAT16}function gD(r,e){if(r===mr.UPLOAD)return tr.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return PZ(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return tr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function xD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var rr=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Ut=\"if (isnan(x)) return x;\",yD=\"return x;\",nv=\"return abs(x);\";var bD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",CD=Ut+`\n return (x < 0.0) ? 0.0 : x;\n`,wD=Ut+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Oa=\"return x;\",SD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var vD=\"return x;\",kD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,ND=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,TD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,_D=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var mh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length);let t=e.length,o=Rt(\"rc\",t),n=Re(t),s=fD(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var MZ=Wt.whereImpl,LZ=1e-7,BZ=1e-4,dh={};function zZ(r){return r in dh||(dh[r]={}),dh[r]}var VZ=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),WZ=600;function UZ(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*WZ/1024/1024}var bu=class extends so{nextDataId(){return bu.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof wp)t=e;else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"),e);t=new wp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"));t=new wp(o),this.binaryCache=zZ(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new lh(this.gpgpu),this.numMBBeforeWarning=UZ(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=_c(t),c=new Xl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Fr(i,Oa):m=new rr(i,Oa);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Fr(n,Oa):f=new rr(n,Oa);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Hl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Fr(s,Oa):d=new rr(s,Oa);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=VZ){return A().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new mh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ch(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[gi(e.shape),...xi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[gi(t),...xi(t)],a=new Mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=_c(s),p;n?p=new Zf(i):p=new Qf(i);let u=!0,c=[t!=null?t:Hl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===xu.DENSE){let x=a!=null?a:Hl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!yu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=SR(e,c,l),d=this.getAndSaveBinary(m,()=>CR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||wR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=A().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?LZ:BZ}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=DI(o,p),t.texShape=l),s!=null){let m=_c(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Pa(l[0],l[1])),p?d=new th(m,g):d=new Xl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=GZ(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await tS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Hf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=BI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};bu.nextDataId=0;function GZ(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew bu,2);var tat={forceHalfFloat:$D};var Lc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Pr=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jr=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=pt(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var ED={kernelName:Co,backendName:\"webgl\",kernelFunc:Dt};function Or(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var RD={kernelName:Ri,backendName:\"webgl\",kernelFunc:Or};var sv=\"return (a < 0.) ? b * a : a;\",av=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(av,n.shape,a.shape):new Pr(sv,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var DD={kernelName:En,backendName:\"webgl\",kernelFunc:KZ};var iv=\"return (a < 0.) ? b * a : a;\",uv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function qZ(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(uv,o.shape,n.shape):new Pr(iv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var AD={kernelName:rs,backendName:\"webgl\",kernelFunc:qZ};var Fo=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Fr(a.shape,e):c=new rr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new jr(e,p.shape,u.shape,t):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function yi(r,e=!1){if(r===\"linear\")return e?vD:yD;if(r===\"relu\")return e?ND:CD;if(r===\"elu\")return e?kD:bD;if(r===\"relu6\")return e?TD:wD;if(r===\"prelu\")return e?uv:iv;if(r===\"leakyrelu\")return e?av:sv;if(r===\"sigmoid\")return e?_D:SD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Bc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=pt(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!yu(n.shape,p)&&!(c.texture!==null&&yu(c.shape,p))?OD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var MD={kernelName:ma,backendName:\"webgl\",kernelFunc:te};var Jl=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var fh=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function XZ(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=w.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Yr(r,e,t,o){let n=XZ(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=ov(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes 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ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function QZ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return vp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var VD={kernelName:So,backendName:\"webgl\",kernelFunc:QZ};var WD=\"return abs(x);\";function ZZ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=uh(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Fr(o.shape,WD):n=new rr(o.shape,WD),t.runWebGLProgram(n,[o],o.dtype)}var UD={kernelName:js,backendName:\"webgl\",kernelFunc:ZZ};var JZ=Ut+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,e9=xe({opSnippet:JZ}),GD={kernelName:Vo,backendName:\"webgl\",kernelFunc:e9};var t9=Ut+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,r9=xe({opSnippet:t9}),HD={kernelName:Wo,backendName:\"webgl\",kernelFunc:r9};var KD=\"return a + b;\",o9=nt({opSnippet:KD,packedOpSnippet:KD,supportsComplex:!0,cpuKernelImpl:IR}),qD={kernelName:io,backendName:\"webgl\",kernelFunc:o9};var xh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var yh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function bh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=bh({inputs:o.slice(0,p),backend:t}),c=bh({inputs:o.slice(p),backend:t});return bh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new yh(o[0].shape,s):new xh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var jD={kernelName:Uo,backendName:\"webgl\",kernelFunc:bh};function n9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"all\",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var XD={kernelName:Go,backendName:\"webgl\",kernelFunc:n9};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"any\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"any\",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var YD={kernelName:Ho,backendName:\"webgl\",kernelFunc:s9};var Ch=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var wh=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Rt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = ${R}(${c.join()}, 0);\n ++${c[p-1]};\n ${R} sourceLocG = ${R}(${c.join()}, 0);\n ++${c[p-2]};\n ${R} sourceLocA = ${R}(${c.join()}, 0);\n --${c[p-1]};\n ${R} sourceLocB = ${R}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(R=>\"int \"+R),g=Rt(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Rt(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Rt(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=Rt(\"sourceLocA\",m-1).concat(\"inIdx.a\"),S=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,E=n?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${E}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n ${l}\n ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n sourceLocB${f}, sourceLocA${f}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${k}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${S}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function QD(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=w.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new Ch(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=QD(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function ZD(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=w.computeOptimalWindowSize(s),i=new wh(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=ZD(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Sh(r,e,t,o){let n=[t];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=QD(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return ZD(r,e,o)}function a9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=Sh(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var JD={kernelName:Xs,backendName:\"webgl\",kernelFunc:a9};function i9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=Sh(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var eA={kernelName:Ys,backendName:\"webgl\",kernelFunc:i9};var u9=Ut+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,p9=xe({opSnippet:u9}),tA={kernelName:Ko,backendName:\"webgl\",kernelFunc:p9};var c9=Ut+\"return log(x + sqrt(x * x + 1.0));\",l9=xe({opSnippet:c9}),rA={kernelName:qo,backendName:\"webgl\",kernelFunc:l9};var m9=Ut+`\n return atan(x);\n`,d9=xe({opSnippet:m9}),oA={kernelName:jo,backendName:\"webgl\",kernelFunc:d9};var f9=Lc+`\n return atan(a, b);\n`,h9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xr+`\n return result;\n`,g9=nt({opSnippet:f9,packedOpSnippet:h9}),nA={kernelName:Yo,backendName:\"webgl\",kernelFunc:g9};var x9=Ut+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,y9=xe({opSnippet:x9}),sA={kernelName:Xo,backendName:\"webgl\",kernelFunc:y9};var Vs=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,E=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${E}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${E}\n }\n }\n setOutput(${S});\n }\n `}},wu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${E}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${E};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function b9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Bs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Vs(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var aA={kernelName:Qo,backendName:\"webgl\",kernelFunc:b9};function C9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new wu(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var iA={kernelName:Qs,backendName:\"webgl\",kernelFunc:C9};var Ih=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},vh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function w9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new vh(m);return t.runWebGLProgram(d,[n],a.dtype)}var uA={kernelName:Ei,backendName:\"webgl\",kernelFunc:w9};function S9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Bs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new Ih(c);return t.runWebGLProgram(l,[n],a.dtype)}var pA={kernelName:$i,backendName:\"webgl\",kernelFunc:S9};function I9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return vp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var cA={kernelName:Zo,backendName:\"webgl\",kernelFunc:I9};var kh=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var Nh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var v9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Nh(o.shape,n.shape,s.shape,c,l,p):new kh(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},lA={kernelName:In,backendName:\"webgl\",kernelFunc:v9};var Th=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=k9(this.rank),n,s=e.map((a,i)=>`sourceLoc.${lv[i]} = start[${i}] + coords.${lv[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},lv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function k9(r){if(r===1)return\"sourceLoc\";if(r<=6)return lv.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var _h=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function N9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ct.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Ws(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=tD(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new _h(p):new Th(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),N9(n,i,p,t)}var mA={kernelName:fa,backendName:\"webgl\",kernelFunc:Ws};var T9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Ct({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ws({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},dA={kernelName:Zs,backendName:\"webgl\",kernelFunc:T9};function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ih(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var fA={kernelName:Jo,backendName:\"webgl\",kernelFunc:_9};var $9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,E9=`\n return float(int(a.r) & int(b.r));\n`;function R9(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[c,l]=kR(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(l,o.dtype),d=t.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr($9,o.shape,n.shape,!1):i=new Pr(E9,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var hA={kernelName:Ha,backendName:\"webgl\",kernelFunc:R9};function D9(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var gA={kernelName:Js,backendName:\"webgl\",kernelFunc:D9};var A9=\"return float(a != b);\",mv=nt({opSnippet:A9,cpuKernelImpl:KR,dtype:\"bool\"}),xA={kernelName:Yn,backendName:\"webgl\",kernelFunc:mv};function bi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var yA={kernelName:Gi,backendName:\"webgl\",kernelFunc:bi};var F9=\"return float(int(x));\";function bA(r,e){let t=new rr(r.shape,F9),o=e.runWebGLProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function dv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Gr(n.shape),i=dv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=Or({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=bi({inputs:{input:n},backend:t}),i=dv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=NR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return bA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=mv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var CA={kernelName:yo,backendName:\"webgl\",kernelFunc:dv};var wA=\"return ceil(x);\",P9=xe({opSnippet:wA,packedOpSnippet:wA,cpuKernelImpl:TR}),SA={kernelName:en,backendName:\"webgl\",kernelFunc:P9};var $h=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Eh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function O9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Eh(n.shape):i=new $h(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var IA={kernelName:bo,backendName:\"webgl\",kernelFunc:O9};var Rh=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function vA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function M9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Rh(o.shape),a=[vA(o,n.complexTensorInfos.real),vA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var kA={kernelName:Di,backendName:\"webgl\",kernelFunc:M9};var Dh=class{constructor(e){this.outputShape=[],this.outputShape=w.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) {\n return getChannel(\n getT${h}(${Ah(i,u,g)}),\n vec2(${Ah(c,u,g)}));\n }`}let d=p.length,f=p[p.length-1];m+=`\n return getChannel(\n getT${d}(${Ah(i,u,f)}),\n vec2(${Ah(c,u,f)}));`,this.userCode=`\n float getValue(${i.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n ${a[n-1]} = ${a[n-1]} + 1;\n if (${a[n-1]} < ${o[n-1]}) {\n result.g = getValue(${a});\n }\n\n ${a[n-2]} = ${a[n-2]} + 1;\n if (${a[n-2]} < ${o[n-2]}) {\n result.a = getValue(${a});\n }\n\n ${a[n-1]} = ${a[n-1]} - 1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function Ah(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function kp(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var NA={kernelName:Vi,backendName:\"webgl\",kernelFunc:kp};function zc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>bi({inputs:{input:b},backend:t})),f=r.map(b=>kp({inputs:{input:b},backend:t})),h=zc(d,e,t),g=zc(f,e,t),x=Or({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=_R(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new rr(r[0].shape,Oa):new Fr(r[0].shape,Oa);return t.runWebGLProgram(d,r,o)}let i=A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=L9(s,e,t),c=new Dh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function L9(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function fv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):zc(p,s,t)}var TA={kernelName:ea,backendName:\"webgl\",kernelFunc:fv};var Vc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,S=\"\",k=\"\";o&&(n?S=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?S=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:S=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${S}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Ph=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Mh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Mh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Mh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>cv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(yu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let R=vp({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=vp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Bh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Mh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Mh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Oh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?yi(i,!0):null,B=new Bc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function B9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Lh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Wc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=Bh({x:n,filter:s,convInfo:m,backend:t});else{let h=new Vc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var _A={kernelName:tn,backendName:\"webgl\",kernelFunc:B9};var zh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Vh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Wh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function z9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new zh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var $A={kernelName:Ai,backendName:\"webgl\",kernelFunc:z9};var Gh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=e.inShape,this.enableShapeUniforms=pt(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Gh(m);return t.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Vh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}}var EA={kernelName:rn,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ph(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var RA={kernelName:on,backendName:\"webgl\",kernelFunc:W9};function U9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Wh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var DA={kernelName:Ka,backendName:\"webgl\",kernelFunc:U9};function G9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Uh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var AA={kernelName:nn,backendName:\"webgl\",kernelFunc:G9};var H9=Fo+`\n return cos(x);\n`,K9=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,q9=xe({opSnippet:H9,packedOpSnippet:K9}),FA={kernelName:sn,backendName:\"webgl\",kernelFunc:q9};var j9=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,X9=xe({opSnippet:j9}),PA={kernelName:an,backendName:\"webgl\",kernelFunc:X9};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var Y9=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Hh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},OA={kernelName:cn,backendName:\"webgl\",kernelFunc:Y9};var Np;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Np||(Np={}));var em=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Np.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${MA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${LA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${LA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${MA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function MA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Kh(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=Ct({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new em(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new em(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=Ct({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function Q9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Kh(Np.Prod,n,t,s,a,i)}var BA={kernelName:un,backendName:\"webgl\",kernelFunc:Q9};function Z9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Kh(Np.Sum,n,t,s,a,i)}var zA={kernelName:pn,backendName:\"webgl\",kernelFunc:Z9};function J9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ih(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=vR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var VA={kernelName:ta,backendName:\"webgl\",kernelFunc:J9};var qh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function eJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new qh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var WA={kernelName:ln,backendName:\"webgl\",kernelFunc:eJ};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Gc(l):m=new Uc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var UA={kernelName:mn,backendName:\"webgl\",kernelFunc:tJ};var jh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Xh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function rJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new jh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var GA={kernelName:Fi,backendName:\"webgl\",kernelFunc:rJ};function oJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Xh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var HA={kernelName:Pi,backendName:\"webgl\",kernelFunc:oJ};var Yh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function nJ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Yh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var KA={kernelName:ra,backendName:\"webgl\",kernelFunc:nJ};var Qh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function sJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Qh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var qA={kernelName:dn,backendName:\"webgl\",kernelFunc:sJ};function aJ(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ip({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var jA={kernelName:Li,backendName:\"webgl\",kernelFunc:aJ};var iJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",uJ=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,pJ=xe({opSnippet:iJ,packedOpSnippet:uJ}),XA={kernelName:hn,backendName:\"webgl\",kernelFunc:pJ};var cJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",lJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,mJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(lJ,o.shape,n.shape):new Pr(cJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},YA={kernelName:qa,backendName:\"webgl\",kernelFunc:mJ};var dJ=`\n return vec4(equal(a, b));\n`,fJ=\"return float(a == b);\",hJ=nt({opSnippet:fJ,packedOpSnippet:dJ,dtype:\"bool\",cpuKernelImpl:$R}),QA={kernelName:xn,backendName:\"webgl\",kernelFunc:hJ};var gJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,xJ=xe({opSnippet:gJ}),ZA={kernelName:gn,backendName:\"webgl\",kernelFunc:xJ};var yJ=Fo+`\n return exp(x);\n`,bJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,hv=xe({opSnippet:yJ,packedOpSnippet:bJ,cpuKernelImpl:ER,dtype:\"float32\"}),JA={kernelName:yn,backendName:\"webgl\",kernelFunc:hv};function Zh(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var eF={kernelName:oa,backendName:\"webgl\",kernelFunc:Zh};var tF=\"return exp(x) - 1.0;\",CJ=xe({opSnippet:tF,packedOpSnippet:tF,cpuKernelImpl:RR}),rF={kernelName:bn,backendName:\"webgl\",kernelFunc:CJ};var tm=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function Jh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new tm(\"real\",p,e),c=new tm(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function wJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Jh(o,!1,t)}var oF={kernelName:Bi,backendName:\"webgl\",kernelFunc:wJ};var eg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ci(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new eg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var nF={kernelName:na,backendName:\"webgl\",kernelFunc:Ci};var tg=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var sF={kernelName:Cn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new tg(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var aF=\"return floor(x);\",SJ=xe({opSnippet:aF,packedOpSnippet:aF,cpuKernelImpl:DR}),iF={kernelName:wn,backendName:\"webgl\",kernelFunc:SJ};var IJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,vJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,kJ=nt({opSnippet:IJ,packedOpSnippet:vJ,dtype:\"int32\"}),uF={kernelName:Sn,backendName:\"webgl\",kernelFunc:kJ};var rg=class{constructor(e){this.variableNames=[\"A\"];let t=vt(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var og=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=vt(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var pF={kernelName:Au,backendName:\"webgl\",kernelFunc:NJ},Hc,gv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function NJ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Hc==null||h!==gv)&&(gv=h,Hc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:gv})),Hc.canvas.width=p,Hc.canvas.height=u,Hc.drawImage(n,0,0,p,u),n=Hc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=mr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new og(l):new rg(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:t,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Lh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?yi(d,!0):null,D=new Wc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=t.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=Bh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?yi(d,!1):null,D=new Vc(g,C,R,S,k),P=_();x=t.runWebGLProgram(D,P,\"float32\")}let E=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),E}var cF={kernelName:Io,backendName:\"webgl\",kernelFunc:TJ};function _J(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function $J(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=AR(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new ng(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var mF={kernelName:vn,backendName:\"webgl\",kernelFunc:$J};var sg=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=EJ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function EJ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),S=FR(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new sg(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var dF={kernelName:sa,backendName:\"webgl\",kernelFunc:xv};var RJ=\"return float(a > b);\",DJ=`\n return vec4(greaterThan(a, b));\n`,AJ=nt({opSnippet:RJ,packedOpSnippet:DJ,cpuKernelImpl:PR,dtype:\"bool\"}),fF={kernelName:kn,backendName:\"webgl\",kernelFunc:AJ};var FJ=\"return float(a >= b);\",PJ=`\n return vec4(greaterThanEqual(a, b));\n`,OJ=nt({opSnippet:FJ,packedOpSnippet:PJ,dtype:\"bool\",cpuKernelImpl:OR}),hF={kernelName:Nn,backendName:\"webgl\",kernelFunc:OJ};function MJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Jh(o,!0,t)}var gF={kernelName:zi,backendName:\"webgl\",kernelFunc:MJ};var LJ=\"return float(!isnan(x) && !isinf(x));\",BJ=xe({opSnippet:LJ,dtype:\"bool\"}),xF={kernelName:Tn,backendName:\"webgl\",kernelFunc:BJ};var zJ=\"return float(isinf(x));\",VJ=xe({opSnippet:zJ,dtype:\"bool\"}),yF={kernelName:_n,backendName:\"webgl\",kernelFunc:VJ};var WJ=\"return float(isnan(x));\",UJ=xe({opSnippet:WJ,dtype:\"bool\"}),bF={kernelName:$n,backendName:\"webgl\",kernelFunc:UJ};var GJ=\"return float(a < b);\",HJ=`\n return vec4(lessThan(a, b));\n`,KJ=nt({opSnippet:GJ,packedOpSnippet:HJ,cpuKernelImpl:MR,dtype:\"bool\"}),CF={kernelName:Rn,backendName:\"webgl\",kernelFunc:KJ};var qJ=\"return float(a <= b);\",jJ=`\n return vec4(lessThanEqual(a, b));\n`,XJ=nt({opSnippet:qJ,packedOpSnippet:jJ,cpuKernelImpl:LR,dtype:\"bool\"}),wF={kernelName:Dn,backendName:\"webgl\",kernelFunc:XJ};function YJ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=BR(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var SF={kernelName:An,backendName:\"webgl\",kernelFunc:YJ};var QJ=Fo+`\n return x < 0.0 ? 0./0. : log(x);\n`,ZJ=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,JJ=xe({opSnippet:QJ,packedOpSnippet:ZJ,cpuKernelImpl:zR}),IF={kernelName:Fn,backendName:\"webgl\",kernelFunc:JJ};var eee=Fo+`\n return log(1.0 + x);\n`,tee=xe({opSnippet:eee}),vF={kernelName:Pn,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(a >= 1.0 && b >= 1.0);\",oee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,nee=nt({opSnippet:ree,packedOpSnippet:oee,dtype:\"bool\"}),kF={kernelName:On,backendName:\"webgl\",kernelFunc:nee};var see=\"return float(!(x >= 1.0));\",aee=xe({opSnippet:see}),NF={kernelName:Mn,backendName:\"webgl\",kernelFunc:aee};var iee=\"return float(a >= 1.0 || b >= 1.0);\",uee=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,pee=nt({opSnippet:iee,packedOpSnippet:uee,dtype:\"bool\"}),TF={kernelName:Ln,backendName:\"webgl\",kernelFunc:pee};var ag=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var ig=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var cee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new ig(n.shape,s,a,i,p):new ag(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},_F={kernelName:Bn,backendName:\"webgl\",kernelFunc:cee};var ug=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var lee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new ug(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},$F={kernelName:ja,backendName:\"webgl\",kernelFunc:lee};function EF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function yv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Vs(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var AF={kernelName:Wn,backendName:\"webgl\",kernelFunc:hee};function gee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var FF={kernelName:aa,backendName:\"webgl\",kernelFunc:gee};var pg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},cg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function xee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new wu(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new cg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var PF={kernelName:Ui,backendName:\"webgl\",kernelFunc:xee};function yee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Bs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Vs(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new pg(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var OF={kernelName:Wi,backendName:\"webgl\",kernelFunc:yee};function MF(r,e,t,o){let n=new Vs(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Vs(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var LF={kernelName:ia,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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fg(u,a,i,p),l=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},e3={kernelName:Jn,backendName:\"webgl\",kernelFunc:Bee};function rm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=bi({inputs:{input:o},backend:t}),s=rm({inputs:{x:n},backend:t}),a=kp({inputs:{input:o},backend:t}),i=rm({inputs:{x:a},backend:t}),p=Or({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ci({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var t3={kernelName:ba,backendName:\"webgl\",kernelFunc:rm};function r3(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var n3={kernelName:ca,backendName:\"webgl\",kernelFunc:zee};var hg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Re(n),a=t.map(u=>u[0]).join(\",\"),i=t.map((u,c)=>u[0]+e[c]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n);if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${p}));\n }\n }\n `}};var gg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ci({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new gg(n.shape,s,a):new hg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},s3={kernelName:es,backendName:\"webgl\",kernelFunc:Sv};var Vee=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,Wee=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+Xr+`\n return result;\n`,Uee=nt({opSnippet:Vee,packedOpSnippet:Wee}),a3={kernelName:ts,backendName:\"webgl\",kernelFunc:Uee};function Gee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=Ct({inputs:{x:n},backend:t,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims(\"prod\",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=qR(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=ti(n.dtype),C=Yr(x,b,\"prod\",t);d=te({inputs:{x:C},backend:t,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var i3={kernelName:os,backendName:\"webgl\",kernelFunc:Gee};function Hee(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=jR(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var u3={kernelName:jp,backendName:\"webgl\",kernelFunc:Hee};function Kee(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=XR(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var p3={kernelName:Xp,backendName:\"webgl\",kernelFunc:Kee};function qee(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=YR(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var c3={kernelName:Yp,backendName:\"webgl\",kernelFunc:qee};var Iv=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=QR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},l3={kernelName:la,backendName:\"webgl\",kernelFunc:Iv};var jee=\"return 1.0 / x;\",Xee=xe({opSnippet:jee}),m3={kernelName:ns,backendName:\"webgl\",kernelFunc:Xee};var Yee=Ut+`\n return (x < 0.0) ? 0.0 : x;\n`,Qee=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Zee=xe({opSnippet:Yee,packedOpSnippet:Qee}),d3={kernelName:ss,backendName:\"webgl\",kernelFunc:Zee};var Jee=Ut+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,ete=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,tte=xe({opSnippet:Jee,packedOpSnippet:ete}),f3={kernelName:us,backendName:\"webgl\",kernelFunc:tte};var xg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var yg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function rte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new yg(n.shape,p,u,s,a):new xg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var h3={kernelName:is,backendName:\"webgl\",kernelFunc:rte};var bg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function ote(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new bg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var g3={kernelName:Qa,backendName:\"webgl\",kernelFunc:ote};var Cg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var wg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function nte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new wg(n.shape,p,u,s,a):new Cg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var x3={kernelName:as,backendName:\"webgl\",kernelFunc:nte};var Sg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function ste(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new Sg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var y3={kernelName:Ya,backendName:\"webgl\",kernelFunc:ste};var Ig=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var vg=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function ate(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new vg(n.shape,i):new Ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var b3={kernelName:ps,backendName:\"webgl\",kernelFunc:ate};var kg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var C3={kernelName:Ds,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new kg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var ite=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,ute=xe({opSnippet:ite}),w3={kernelName:cs,backendName:\"webgl\",kernelFunc:ute};var pte=\"return inversesqrt(x);\",cte=xe({opSnippet:pte,cpuKernelImpl:ZR}),S3={kernelName:ls,backendName:\"webgl\",kernelFunc:cte};var Su=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var Ng=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\",b=t>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${e}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${t}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${t}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function lte(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new Ng(p,i,d.shape.length,f.shape.length,c,m):g=new Su(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var I3={kernelName:ms,backendName:\"webgl\",kernelFunc:lte};var Tg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function mte(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Tg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],\"int32\",p)}var v3={kernelName:fs,backendName:\"webgl\",kernelFunc:mte};var _g=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function dte(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new _g(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var k3={kernelName:da,backendName:\"webgl\",kernelFunc:dte};var fte=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,hte=xe({opSnippet:fte}),N3={kernelName:hs,backendName:\"webgl\",kernelFunc:hte};var gte=Fo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,xte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,yte=xe({opSnippet:gte,packedOpSnippet:xte,cpuKernelImpl:eD}),T3={kernelName:bs,backendName:\"webgl\",kernelFunc:yte};var bte=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Cte=xe({opSnippet:bte}),_3={kernelName:ys,backendName:\"webgl\",kernelFunc:Cte};var wte=Fo+`\n return sin(x);\n`,Ste=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,Ite=xe({opSnippet:wte,packedOpSnippet:Ste}),$3={kernelName:gs,backendName:\"webgl\",kernelFunc:Ite};var vte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,kte=xe({opSnippet:vte}),E3={kernelName:xs,backendName:\"webgl\",kernelFunc:kte};var Nte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Tte=xe({opSnippet:Nte}),R3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Tte};var _te=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},D3={kernelName:ha,backendName:\"webgl\",kernelFunc:_te};function $te(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=rD(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var A3={kernelName:Hi,backendName:\"webgl\",kernelFunc:$te};function Ete(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=oD(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var F3={kernelName:Za,backendName:\"webgl\",kernelFunc:Ete};function Rte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=ph(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var P3={kernelName:Ki,backendName:\"webgl\",kernelFunc:Rte};function Dte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at 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t.disposeIntermediateTensorInfo(h),g}var M3={kernelName:vs,backendName:\"webgl\",kernelFunc:Ate};function Fte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Ws({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var L3={kernelName:ga,backendName:\"webgl\",kernelFunc:Fte};var B3=\"return sqrt(x);\",Pte=xe({opSnippet:B3,packedOpSnippet:B3,cpuKernelImpl:nD}),z3={kernelName:ws,backendName:\"webgl\",kernelFunc:Pte};var Ote=\"return x * x;\",Mte=xe({opSnippet:Ote}),V3={kernelName:ji,backendName:\"webgl\",kernelFunc:Mte};var W3=\"return (a - b) * (a - b);\",Lte=nt({opSnippet:W3,packedOpSnippet:W3}),U3={kernelName:ks,backendName:\"webgl\",kernelFunc:Lte};function Bte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=t.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=sD(a,\"string\",o);return t.makeTensorInfo(n.shape,\"string\",i)}var G3={kernelName:Du,backendName:\"webgl\",kernelFunc:Bte};function zte({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Ut+`\n return x > 0.0 ? 1.0 : float(${e.alpha});\n `,s=new rr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var H3={kernelName:wo,backendName:\"webgl\",kernelFunc:zte};var $g=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function Vte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=ct.computeOutShape(b,C,S),R=Ws({inputs:{x:n},backend:t,attrs:{begin:b,size:E}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=aD(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,P.values)}else{let R=new $g(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var K3={kernelName:Ns,backendName:\"webgl\",kernelFunc:Vte};function Wte(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=iD(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var q3={kernelName:xa,backendName:\"webgl\",kernelFunc:Wte};function Ute(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=uD(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var j3={kernelName:Xi,backendName:\"webgl\",kernelFunc:Ute};function Gte(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=pD(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var X3={kernelName:Yi,backendName:\"webgl\",kernelFunc:Gte};var Hte=\"return tan(x);\",Kte=xe({opSnippet:Hte}),Y3={kernelName:_s,backendName:\"webgl\",kernelFunc:Kte};var qte=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,jte=xe({opSnippet:qte}),Q3={kernelName:$s,backendName:\"webgl\",kernelFunc:jte};function Xte(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new Su(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var Z3={kernelName:ds,backendName:\"webgl\",kernelFunc:Xte};var Eg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=lD(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Eg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var J3={kernelName:uo,backendName:\"webgl\",kernelFunc:vv};var Rg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Dg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function Tp(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function eP(r){let e=1;for(;ep){let P=t.readSync(n.dataId),[O,M]=mD(P,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,Ci({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&Tp(t,d);let x=eP(s),b=eP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Rg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=t.runWebGLProgram(B,L,\"int32\",U),Tp(t,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Dg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,O,\"int32\",B),Tp(t,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Ws({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),Tp(t,_);let E=xv({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});Tp(t,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:t}),Tp(t,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:t}),Tp(t,D),[E,C]}var tP={kernelName:Es,backendName:\"webgl\",kernelFunc:Qte};var Ag=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Zte(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Ag(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var rP={kernelName:Rs,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Bs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=dD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var oP={kernelName:Qi,backendName:\"webgl\",kernelFunc:Jte};function ere(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var nP={kernelName:ya,backendName:\"webgl\",kernelFunc:ere};var Fg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function 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Iu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(Iu||(Iu={}));var aP;function ore(r){aP=r.wasm.cwrap(So,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function nre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Iu[c];if(g==null)throw new Error(`${c} 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roe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(s.shape,s.dtype);return vO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var kO={kernelName:Mi,backendName:\"wasm\",setupFunc:ioe,kernelFunc:uoe};var NO;function poe(r){NO=r.wasm.cwrap(Oi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function coe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(n.shape,n.dtype);return NO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var TO={kernelName:Oi,backendName:\"wasm\",setupFunc:poe,kernelFunc:coe};var _O=he(hn);var $O;function loe(r){$O=r.wasm.cwrap(qa,null,[\"number\",\"number\",\"number\"])}function moe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,\"float32\"),a=i=>t.dataIdMap.get(i.dataId).id;return $O(a(n),a(o),a(s)),s}var EO={kernelName:qa,backendName:\"wasm\",setupFunc:loe,kernelFunc:moe};var doe=!1,RO=We(xn,doe,\"bool\");var DO=he(gn);var AO=he(yn,\"float32\");function Og(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var FO={kernelName:oa,backendName:\"wasm\",kernelFunc:Og};var PO=he(bn,\"float32\");function Tv(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var OO={kernelName:na,backendName:\"wasm\",kernelFunc:Tv};var MO;function foe(r){MO=r.wasm.cwrap(Cn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function hoe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return MO(s,i,p,u,c,a),n}var LO={kernelName:Cn,backendName:\"wasm\",kernelFunc:hoe,setupFunc:foe};var BO=he(wn);var goe=!1,zO=We(Sn,goe);var VO;function xoe(r){VO=r.wasm.cwrap(In,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return VO(c,l,m,d,f,n,g),h}var WO={kernelName:In,backendName:\"wasm\",setupFunc:xoe,kernelFunc:yoe};var UO;function boe(r){UO=r.wasm.cwrap(Io,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Coe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=Iu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return UO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var GO={kernelName:Io,backendName:\"wasm\",setupFunc:boe,kernelFunc:Coe};var HO;function woe(r){HO=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Soe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=Iu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return HO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var KO={kernelName:vo,backendName:\"wasm\",setupFunc:woe,kernelFunc:Soe};var qO;function Ioe(r){qO=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function voe(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=nf.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return qO(d,we[o.dtype],h,a,l,i,g,x),u}var jO={kernelName:vn,backendName:\"wasm\",setupFunc:Ioe,kernelFunc:voe};var XO;function koe(r){XO=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Noe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return XO(C,we[n.dtype],E,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var YO={kernelName:sa,backendName:\"wasm\",setupFunc:koe,kernelFunc:Noe};var Toe=!1,QO=We(kn,Toe,\"bool\");var _oe=!1,ZO=We(Nn,_oe,\"bool\");var JO=he(Tn,\"bool\");var eM=he(_n,\"bool\");var tM=he($n,\"bool\");var rM;function $oe(r){rM=r.wasm.cwrap(En,null,[\"number\",\"number\",\"number\",\"number\"])}function Eoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,\"float32\");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;rM(n,we[e.dtype],t,a)}return s}var oM={kernelName:En,backendName:\"wasm\",setupFunc:$oe,kernelFunc:Eoe};var Roe=!1,nM=We(Rn,Roe,\"bool\");var Doe=!1,sM=We(Dn,Doe,\"bool\");var aM;function Aoe(r){aM=r.wasm.cwrap(An,null,[\"number\",\"number\",\"number\",\"number\"])}function Foe(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],\"float32\");return aM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var iM={kernelName:An,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var uM=he(Fn);var pM=he(Pn);var Poe=!1,cM=We(On,Poe,\"bool\");var lM=he(Mn);var Ooe=!1,mM=We(Ln,Ooe,\"bool\");var Moe=!1,dM=We(w0,Moe,\"bool\");var fM;function Loe(r){fM=r.wasm.cwrap(Bn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Boe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=t.makeOutput(n.shape,n.dtype);return fM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var hM={kernelName:Bn,backendName:\"wasm\",setupFunc:Loe,kernelFunc:Boe};var gM;function zoe(r){gM=r.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=t.makeOutput(n.shape,n.dtype);return gM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var xM={kernelName:ja,backendName:\"wasm\",setupFunc:zoe,kernelFunc:Voe};var yM;function Woe(r){yM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\"])}function Uoe(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;yM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var bM={kernelName:zn,backendName:\"wasm\",setupFunc:Woe,kernelFunc:Uoe};var Goe=!1,CM=We(Vn,Goe);var wM;function Hoe(r){wM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Koe(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get(E.dataId).id;return wM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var SM={kernelName:Wn,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:Koe};var IM;function qoe(r){IM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function joe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return IM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var vM={kernelName:aa,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var kM;function Xoe(r){kM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Yoe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return kM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var NM={kernelName:Ui,backendName:\"wasm\",setupFunc:Xoe,kernelFunc:Yoe};var TM;function Qoe(r){TM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Zoe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return TM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var _M={kernelName:Wi,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe};var $M;function Joe(r){$M=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ene(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,\"int32\");return $M(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var EM={kernelName:ia,backendName:\"wasm\",setupFunc:Joe,kernelFunc:ene};var RM;function tne(r){RM=r.wasm.cwrap(Un,null,[\"number, number, number\"])}function rne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;RM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&e.disposeData(b.dataId),C}var DM={kernelName:Un,backendName:\"wasm\",setupFunc:tne,kernelFunc:rne};var AM;function one(r){AM=r.wasm.cwrap(Gn,null,[\"number\",\"number\",\"number\",\"number\"])}function nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"min\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:Gn,backendName:\"wasm\",setupFunc:one,kernelFunc:nne};var sne=!1,PM=We(Hn,sne);var _v;(function(r){r[r.reflect=0]=\"reflect\",r[r.symmetric=1]=\"symmetric\"})(_v||(_v={}));var OM;function ane(r){OM=r.wasm.cwrap(Kn,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function ine(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new 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KM={kernelName:Qn,backendName:\"wasm\",setupFunc:mne,kernelFunc:dne};var qM;function fne(r){qM=r.wasm.cwrap(Xa,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function hne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=qM(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Kc(e,m);e.wasm._free(h);let x=e.makeOutput([f],\"int32\",d),b=e.makeOutput([],\"int32\",g);return[x,b]}var jM={kernelName:Xa,backendName:\"wasm\",setupFunc:fne,kernelFunc:hne};var XM;function gne(r){XM=r.wasm.cwrap(Zn,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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t.typedArrayFromHeap(o).fill(1),o}var eL={kernelName:pa,backendName:\"wasm\",kernelFunc:wne};function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Og({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Og({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=kv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var tL={kernelName:ca,backendName:\"wasm\",kernelFunc:Sne};var rL;function Ine(r){rL=r.wasm.cwrap(es,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function vne(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,constantValue:n}}=r,s=o.map((h,g)=>h[0]+e.shape[g]+h[1]);if(y.sizeFromShape(e.shape)===0)return 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sL={kernelName:rs,backendName:\"wasm\",setupFunc:Nne,kernelFunc:Tne};var aL;function _ne(r){aL=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\"])}function $ne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;aL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var iL={kernelName:os,backendName:\"wasm\",setupFunc:_ne,kernelFunc:$ne};var Ene=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cp(o,n,s,a),p=e.makeOutput([i.length],a);return 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g=e.dataIdMap.get(n.dataId),x;g.dtype!==\"float32\"&&(x=Mr({backend:e,inputs:{x:n},attrs:{dtype:\"float32\"}}),g=e.dataIdMap.get(x.dataId));let b=g.id,C=e.dataIdMap.get(h.dataId).id;return xL(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&e.disposeData(x.dataId),h}var yL={kernelName:as,backendName:\"wasm\",setupFunc:One,kernelFunc:Mne};var bL;function Lne(r){bL=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\",\"array\",\"array\",\"boolean\"])}function Bne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=t.makeOutput(n.shape,\"float32\"),p=t.dataIdMap.get(n.dataId),u;return p.dtype!==\"float32\"&&(u=Mr({backend:t,inputs:{x:n},attrs:{dtype:\"float32\"}}),p=t.dataIdMap.get(u.dataId)),bL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var CL={kernelName:Ya,backendName:\"wasm\",setupFunc:Lne,kernelFunc:Bne};var wL;function zne(r){wL=r.wasm.cwrap(ps,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function Vne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return _p({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);wL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var SL={kernelName:ps,backendName:\"wasm\",kernelFunc:Vne,setupFunc:zne};var IL;function Wne(r){IL=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function Une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{radians:s,fillValue:a,center:i}=o,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a==\"number\"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return IL(u,l,m,d,f,s,h,g,S,C.length,c),p}var vL={kernelName:Ds,backendName:\"wasm\",kernelFunc:Une,setupFunc:Wne};var kL=he(cs);var NL=he(ls);var TL;function Gne(r){TL=r.wasm.cwrap(ms,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function Hne(r){let{backend:e,inputs:t,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,i=e.makeOutput(a,s.dtype);if(y.sizeFromShape(a)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=du.calculateShapes(s,n,a),f=e.dataIdMap.get(n.dataId).id,g=e.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(l).buffer),b=e.dataIdMap.get(i.dataId).id;return TL(f,g,we[s.dtype],p,u,c,x,m,b),i}var _L={kernelName:ms,backendName:\"wasm\",setupFunc:Gne,kernelFunc:Hne};var $L;function Kne(r){$L=r.wasm.cwrap(fs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\",\"number\"])}function qne(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o;if(n.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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1:{S=w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:S=w.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1],C[2]);break;case 3:S=w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1],C[2],C[3]);break;default:S=\"\"}if(t.disposeData(x.dataId),S)throw t.disposeData(h.dataId),new Error(S);return h}function ose(r){return Bg(r,!0)}var KL={kernelName:Ki,backendName:\"wasm\",setupFunc:Lg,kernelFunc:ose};function nse(r){return Bg(r,!1)}var qL={kernelName:qi,backendName:\"wasm\",setupFunc:Lg,kernelFunc:nse};var jL;function sse(r){jL=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function ase(r){let{backend:e,inputs:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,p=e.makeOutput(i,a.dtype);if(y.sizeFromShape(i)===0)return 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pB={kernelName:Ss,backendName:\"wasm\",setupFunc:xse,kernelFunc:yse};var cB=he(_s);var lB=he($s);var mB;function bse(r){mB=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function Cse(r){let{backend:e,inputs:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,i=e.makeOutput(n.shape,n.dtype);if(y.sizeFromShape(n.shape)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=du.calculateShapes(a,s,n.shape),f=e.dataIdMap.get(s.dataId).id,g=e.dataIdMap.get(a.dataId).id,b=e.dataIdMap.get(n.dataId).id,C=new Uint8Array(new Int32Array(l).buffer),S=e.dataIdMap.get(i.dataId).id;return mB(f,g,we[a.dtype],p,u,c,C,m,S,b),i}var dB={kernelName:ds,backendName:\"wasm\",setupFunc:bse,kernelFunc:Cse};var fB;function wse(r){fB=r.wasm.cwrap(uo,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function 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t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),\"PThread\"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,o,n){let s;if(o==null)s=this.write(n!=null?n:null,e,t);else{let a=this.dataIdNextNumber++;s={id:a},this.dataIdMap.set(s,{id:a,memoryOffset:o,shape:e,dtype:t,refCount:1});let i=y.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,i,o)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:o}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(o),a=y.sizeFromShape(e);switch(t){case\"float32\":return new Float32Array(n,s,a);case\"int32\":return new Int32Array(n,s,a);case\"bool\":return new Uint8Array(n,s,a);default:throw new Error(`Unknown dtype ${t}`)}}};function Dse(r){return(e,t)=>(y.fetch(r,{credentials:\"same-origin\"}).then(o=>{o.ok||e.env.a(`failed to load 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go=A();go.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);go.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);go.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\",()=>-1);go.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!0);go.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);go.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);go.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);go.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\",()=>!0);go.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\",()=>!1);go.registerFlag(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\",()=>0);go.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);go.registerFlag(\"WEBGPU_PRINT_SHADER\",()=>\"\");go.registerFlag(\"WEBGPU_ENGINE_COMPILE_ONLY\",()=>!1);var Ug=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith(\"gen\"))return Number(this.architecture.match(/\\d+/));if(this.architecture.startsWith(\"xe\"))return 12}return 0}isIntel(){return this.vendor===\"intel\"}};var Gg=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t,o=!1,n=!0){let s,a=PB(e,t);return n?(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).length>0?(s=this.freeBuffers.get(a).pop(),this.numFreeBuffers--):(s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o}),this.numBytesAllocated+=e)):(s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o}),this.numBytesAllocated+=e),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.usedBuffers.get(a).push(s),this.numUsedBuffers++,this.numBytesUsed+=e,s}releaseBuffer(e,t=!0){if(this.freeBuffers.size===0)return;let o=e.size,n=e.usage,s=PB(o,n),a=this.usedBuffers.get(s),i=a.indexOf(e);if(i<0)throw new Error(\"Cannot find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,t?(this.freeBuffers.get(s).push(e),this.numFreeBuffers++):(e.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function PB(r,e){return`${r}_${e}`}var Hg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=MB(o),a=e*t*s,i=OB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=OB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=MB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function OB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function MB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function LB(r,e){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let t=r.length,o=\"xyzwuv\",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Us=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast(${e}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${e});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var WB=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=Wse(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>e.shaderKey.toLowerCase().includes(c)))&&(console.group(e.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})},Ae=(r,e=\"f32\")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function ht(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function H(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function BB(r,e){let t;return t=`\n ${Vse(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function Vse(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function Wse(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${GB(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.isFromPixels){o.push(`\n struct Uniform {\n size : i32,\n numChannels : i32,\n outShapeStrides : vec2,\n };\n\n @group(0) @binding(0) var result: array<${$p(e.dtype,t.outputComponent)}>;\n @group(0) @binding(2) var uniforms: Uniform;\n `);let f=VB(t);return[zB,o.join(`\n`),im(e.shape),t.getUserCode(),BB(f,t)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=ht(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ht(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ht(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=ht(s),i+=`\n outShapeStrides: ${a}, `,t.size&&(i+=\"size : i32, \"),t.uniforms&&(i+=t.uniforms),i+=\"};\",i=Yse(i),o.push(i),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${$p(e.dtype,t.outputComponent)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?$p(r[h].dtype,t.variableComponents[h]):$p(r[h].dtype,t.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=qse(e.shape,t.dispatchLayout),c=[zB,o.join(`\n`)+Use,im(e.shape),u,jse(e.shape.length)];t.atomic||c.push(Xse(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${im(r[h].shape,f)}`)});let l=r.map((f,h)=>Kse(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=VB(t);return c.push(BB(m,t)),c.join(`\n`)}function UB(r,e,t){let o=r.shaderKey;if(r.isFromPixels)return o;let n=[],s=[];e.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(t.shape),s.push(t.dtype);let a=e.map(c=>w.getBroadcastDims(c.shape,t.shape)),i=e.map(c=>y.arraysEqual(c.shape,t.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=GB(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var zB=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n var res: i32 = a / b;\n let modulo: i32 = a % b;\n if (sign < 0. && modulo != 0) {\n res = res - 1;\n }\n return res;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,Use=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function im(r,e=\"\"){let t=r.length,o=e!==\"\"?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=e!==\"\"?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:\"outShapeStrides\";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ht(t),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function Gse(r,e){let t=r.name,o=r.shape.length,n=ht(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(e)} {\n return ${Ae(e)}(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(e)} {\n return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${e===1?\"\":` / ${e}`}]);\n }\n `}function Hse(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=ht(p);if(y.arraysEqual(r.shape,e)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${t===1?\"\":` / ${t}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ht(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n`}function Kse(r,e,t,o){let n=Gse(r,t);return r.shape.length<=e.length&&(n+=Hse(r,e,t,o)),n}function qse(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${ht(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function jse(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function GB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function $p(r,e=1){if(r===\"float32\")return Ae(e,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(e,\"i32\");throw new Error(`type ${r} is not supported.`)}function Xse(r,e,t){let o=r.length,n=$p(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ht(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n `}return s}function Yse(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function VB(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Wv={};Ke(Wv,{GPUBytesPerElement:()=>Kg,MatMulProgramType:()=>Mo,assertNotComplex:()=>lm,computeDispatch:()=>K,computeWorkPerThreadForConv2d:()=>pm,computeWorkgroupInfoForMatMul:()=>Vv,computeWorkgroupSizeForConv2d:()=>um,flatDispatchLayout:()=>X,isWebGPUSupported:()=>cm,tilesFitEvenlyIntoShape:()=>Zse});var Ep=r=>{let e=1;for(let t=0;tt%r[o]===0)}function K(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ep(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ep(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ep(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Vv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function um(r,e,t=!1){if(t)return[8,8,1];let o=Ep(r.x.map(s=>e[s])),n=Ep(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function pm(r,e,t=!1){if(t)return[4,4,1];let o=Ep(r.x.map(s=>e[s])),n=Ep(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function Kg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function cm(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function lm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Mo||(Mo={}));var Jse=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),eae=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},vu=class extends so{nextDataId(){return vu.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!cm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Ug(t),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Gg(this.device),this.textureManager=new Hg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype===\"string\")return o;if(t.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(e,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=t.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=Kg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=Kg(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=eae(this.device,e);let a=t.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:e.variableNames[u]}});e.shaderKey=UB(e,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=WB(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[];if(!e.isFromPixels){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(t).map(m=>m.shape);let l=\"int32\";if(a.map(m=>{s.push({type:l,data:m});let d=y.computeStrides(m);s.push({type:l,data:d})}),e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:l,data:[e.outputComponent?m/e.outputComponent:m]})}}n&&(s=[...s,...n]);let i=[this.tensorToBinding(t),...o.map(l=>this.tensorToBinding(l)),this.makeUniforms(s)];o.forEach(l=>{this.commandQueueOwnedIds.add(l.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let p=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:i.map((l,m)=>({binding:m,resource:l}))}),u=this.activeTimers!=null;this.ensureCommandEncoderReady();let c={};u&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),c.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:\"beginning\"},{querySet:this.querySet,queryIndex:1,location:\"end\"}],this.computePassEncoder=this.commandEncoder.beginComputePass(c)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(c)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,p),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(u||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass)&&(this.endComputePassEncoder(),u?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=Jse){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query\")&&(t.requiredFeatures=[\"timestamp-query\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize,maxBufferSize:o.maxBufferSize,maxComputeWorkgroupSizeX:o.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:o.maxComputeInvocationsPerWorkgroup};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new vu(n,s)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.GREATER=7]=\"GREATER\",r[r.GREATER_EQUAL=8]=\"GREATER_EQUAL\",r[r.INT_DIV=9]=\"INT_DIV\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var tae=\"return a + b;\",rae=\"var resultTemp = atan2(a, b);\",oae=\"return areal * breal - aimag * bimag;\",nae=\"return areal * bimag + aimag * breal;\",sae=\"return a / b;\",aae=\"return select(a * (b + 1.0), a, b >= 0.);\",iae=\"return select(a * (b + vec4(1.0)), a, b >= vec4(0.));\",uae=\"return f32(a == b);\",pae=\"return vec4(a == b);\",cae=\"return f32(a > b);\",lae=\"return vec4(a > b);\",mae=\"return f32(a >= b);\",dae=\"return vec4(a >= b);\",fae=`\n let s = sign(a) * sign(b);\n let ia = i32(round(a));\n let ib = i32(round(b));\n return f32(idiv(ia, ib, s));\n`,hae=`\n let ia = vec4(round(a));\n let ib = vec4(round(b));\n let cond = ib != vec4(0);\n var resultTemp = vec4(0);\n let s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(resultTemp);\n`,gae=\"return f32(a < b);\",xae=\"return vec4(a < b);\",yae=\"return f32(a <= b);\",bae=\"return vec4(a <= b);\",Cae=\"return f32(a >= 1.0 && b >= 1.0);\",wae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,Sae=\"return f32(a >= 1.0 || b >= 1.0);\",Iae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,vae=\"var resultTemp = max(a, b);\",kae=\"var resultTemp = min(a, b);\",Nae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Tae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,_ae=\"return a * b;\",$ae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Eae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Rae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,Dae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,Aae=\"if (a < 0.0) { return b * a; } return a;\",Fae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Pae=\"return (a - b) * (a - b);\",Oae=\"return a - b;\";function qc(r,e){let t;do{switch(r){case fe.ATAN2:t=rae;break;case fe.MAX:t=vae;break;case fe.MIN:t=kae;break;case fe.MOD:t=e?Tae:Nae;break;case fe.NOT_EQUAL:t=e?Eae:$ae;break;case fe.POW:t=e?Dae:Rae;break;default:continue}let o,n,s;return e?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${t}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:return tae;case fe.COMPLEX_MULTIPLY_IMAG:return nae;case fe.COMPLEX_MULTIPLY_REAL:return oae;case fe.DIV:return sae;case fe.ELU_DER:return e?iae:aae;case fe.EQUAL:return e?pae:uae;case fe.GREATER:return e?lae:cae;case fe.GREATER_EQUAL:return e?dae:mae;case fe.INT_DIV:return e?hae:fae;case fe.LESS:return e?xae:gae;case fe.LESS_EQUAL:return e?bae:yae;case fe.LOGICAL_AND:return e?wae:Cae;case fe.LOGICAL_OR:return e?Iae:Sae;case fe.MUL:return _ae;case fe.PRELU:return e?Fae:Aae;case fe.SQUARED_DIFFERENCE:return Pae;case fe.SUB:return Oae;default:}return`\n ${t}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Mae=\"return abs(a);\",Lae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Bae=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,zae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,Vae=\"return asinh(a);\",Wae=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,Uae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,Gae=\"return ceil(a);\",Hae=\"return cos(a);\",Kae=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,qae=\"return exp(a) - 1.0;\",jae=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",Xae=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,Yae=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,Qae=\"return exp(a);\",Zae=\"return floor(a);\",Jae=\"return f32(!isnan(a) && !isinf(a));\",eie=\"return f32(isinf(a));\",tie=\"return f32(isnan(a));\",rie=\"return a;\",oie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,nie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,sie=\"return f32(!(a >= 1.0));\",aie=\"return -a;\",iie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",uie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,pie=\"return 1.0 / a;\",cie=\"return select(a, 0.0, a < 0.0);\",lie=\"return clamp(a, 0.0, 6.0);\",mie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",die=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,fie=\"return round(a);\",hie=\"return inverseSqrt(a);\",gie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,xie=\"return 1.0 / (1.0 + exp(-1.0 * a));\",yie=\"return sign(a);\",bie=\"return sin(a);\",Cie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,wie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,Sie=\"return sqrt(a);\",Iie=\"return a * a;\",vie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,kie=\"return tan(a);\",Nie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Tie=\"return f32(i32((a)));\";function wi(r,e){switch(r){case Z.ABS:return Mae;case Z.ACOS:return Lae;case Z.ACOSH:return Bae;case Z.ASIN:return zae;case Z.ASINH:return Vae;case Z.ATAN:return Wae;case Z.ATANH:return Uae;case Z.COS:return Hae;case Z.COSH:return Kae;case Z.CEIL:return Gae;case Z.ELU:return e?Xae:jae;case Z.ERF:return Yae;case Z.EXP:return Qae;case Z.EXPM1:return qae;case Z.FLOOR:return Zae;case Z.IS_FINITE:return Jae;case Z.IS_INF:return eie;case Z.IS_NAN:return tie;case Z.LINEAR:return rie;case Z.LOG:return oie;case Z.LOG1P:return nie;case Z.LOGICAL_NOT:return sie;case Z.NEG:return aie;case Z.LEAKYRELU:return e?uie:iie;case Z.RECIPROCAL:return pie;case Z.RELU:return e?die:cie;case Z.RELU6:return e?mie:lie;case Z.ROUND:return fie;case Z.RSQRT:return hie;case Z.SELU:return gie;case Z.SIGMOID:return xie;case Z.SIGN:return yie;case Z.SIN:return bie;case Z.SINH:return Cie;case Z.SOFTPLUS:return wie;case Z.SQRT:return Sie;case Z.SQUARE:return Iie;case Z.STEP:return vie;case Z.TAN:return kie;case Z.TANH:return Nie;case Z.TO_INT:return Tie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=wi(Z.LINEAR);else if(r===\"relu\")n=wi(Z.RELU,t);else if(r===\"elu\")n=wi(Z.ELU,t);else if(r===\"relu6\")n=wi(Z.RELU6,t);else if(r===\"prelu\")n=qc(fe.PRELU,t);else if(r===\"sigmoid\")n=wi(Z.SIGMOID,t);else if(r===\"leakyrelu\")n=wi(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Qr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function Uv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n let col = colIn * ${s};\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} {\n let col = colIn * ${s};\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function mm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${Uv(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ae(i)}) {\n let col = colIn * ${i};\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Qr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var _ie=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart / ${e} + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart / ${e} + inputCol);\n `,$ie=(r,e,t)=>r?`\n let ACached0 = mm_Asub[k * ${e}][localRow];\n let ACached1 = mm_Asub[k * ${e} + 1][localRow];\n let ACached2 = mm_Asub[k * ${e} + 2][localRow];\n ${e===3?\"\":`let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}\n for (var i = 0; i < ${t}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n acc[i] = fma(BCached1, vec4(ACached1[i]), acc[i]);\n acc[i] = fma(BCached2, vec4(ACached2[i]), acc[i]);\n ${e===3?\"\":\"acc[i] = fma(BCached3, vec4(ACached3[i]), acc[i]);\"}\n }`:`\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = fma(BCached0, vec4(ACached.x), acc[i]);\n acc[i] = fma(BCached1, vec4(ACached.y), acc[i]);\n acc[i] = fma(BCached2, vec4(ACached.z), acc[i]);\n ${e===3?\"\":\"acc[i] = fma(BCached3, vec4(ACached.w), acc[i]);\"}\n }`;function Rp(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${H()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x);\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${_ie(t,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${o/l}; k++) {\n let BCached0 = mm_Bsub[k * ${l}][tileCol];\n let BCached1 = mm_Bsub[k * ${l} + 1][tileCol];\n let BCached2 = mm_Bsub[k * ${l} + 2][tileCol];\n ${l===3?\"\":`let BCached3 = mm_Bsub[k * ${l} + 3][tileCol];`}\n\n ${$ie(t,l,d)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var HB=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Eie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Dp(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${HB(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${HB(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Eie(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${H()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Rie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function Die(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${H()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Rie(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var qg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Vv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${mm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Rp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Die(this.workgroupSize,this.transposeA):Dp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function Aie(r){return`\n var sumValues : array;\n ${H()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var jg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${mm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Aie(this.workgroupSize[0])}\n `}};function Fie(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${H()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Xg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${mm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Fie(this.workgroupSize)}\n `}};var Yg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=K(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return`\n ${Uv(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Ae(e)}) {\n let col = colIn * ${e};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Us(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${e===4?Rp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Dp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Qg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Qr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Zg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function Vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Zg(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var KB={kernelName:na,backendName:\"webgpu\",kernelFunc:Vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var qB={kernelName:ma,backendName:\"webgpu\",kernelFunc:pe};function Ap({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Ir.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new jg(z,t,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=Vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Yg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Qg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Xg(k,_,z,t,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new qg(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Pie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Ap({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var jB={kernelName:So,backendName:\"webgpu\",kernelFunc:Pie};var dm=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${qc(this.op,!1)}\n }\n\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Si=class{constructor(e,t,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${qc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${H(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${t}(getAByOutputCoords(coords));\n let b = ${t}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var XB={kernelName:Co,backendName:\"webgpu\",kernelFunc:At};function xo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var YB={kernelName:Ri,backendName:\"webgpu\",kernelFunc:xo};var Zr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${wi(this.op,!1)}\n }\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Zr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new dm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new dm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:QB,castImpl:ZB,ceilImpl:JB,concatImpl:ez,equalImpl:tz,expImpl:rz,expm1Impl:oz,floorImpl:nz,floorDivImpl:sz,gatherNdImpl:az,gatherV2Impl:iz,greaterEqualImpl:uz,greaterImpl:pz,lessEqualImpl:cz,lessImpl:lz,logImpl:mz,maxImpl:dz,maximumImpl:fz,minimumImpl:hz,multiplyImpl:gz,negImpl:xz,notEqualImpl:yz,prodImpl:bz,rangeImpl:Cz,rsqrtImpl:wz,scatterImpl:Sz,simpleAbsImpl:Iz,sliceImpl:vz,stridedSliceImpl:kz,stringNGramsImpl:Nz,subImpl:Tz,tileImpl:_z,topKImpl:$z,transposeImpl:Ez,uniqueImpl:NPt}=Ic;var Oie=ye({opType:Z.ABS,cpuKernelImpl:Iz}),Rz={kernelName:js,backendName:\"webgpu\",kernelFunc:Oie};var Mie=ye({opType:Z.ACOS}),Dz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Mie};var Lie=ye({opType:Z.ACOSH}),Az={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Lie};var Bie=et({opType:fe.ADD,cpuKernelImpl:QB,supportsComplex:!0}),Fz={kernelName:io,backendName:\"webgpu\",kernelFunc:Bie};var Jg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${H(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function zie(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Jg(s);return t.runWebGPUProgram(a,o,n)}var Pz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:zie};var ex=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${H()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var tx=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${H(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};function Jr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=yr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=dz(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=bz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":ti(r.dtype),S=[{type:\"int32\",data:[h]}],k=new rx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Vie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Jr(n,a,s,\"all\",t)}var Mz={kernelName:Go,backendName:\"webgpu\",kernelFunc:Vie};function Wie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Jr(n,a,s,\"any\",t)}var Lz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Wie};var jc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=K(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${H(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function Uie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var Bz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:Uie};function Gie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var zz={kernelName:Ys,backendName:\"webgpu\",kernelFunc:Gie};var Hie=ye({opType:Z.ASIN}),Vz={kernelName:Ko,backendName:\"webgpu\",kernelFunc:Hie};var Kie=ye({opType:Z.ASINH}),Wz={kernelName:qo,backendName:\"webgpu\",kernelFunc:Kie};var qie=ye({opType:Z.ATAN}),Uz={kernelName:jo,backendName:\"webgpu\",kernelFunc:qie};var jie=et({opType:fe.ATAN2}),Gz={kernelName:Yo,backendName:\"webgpu\",kernelFunc:jie};var Xie=ye({opType:Z.ATANH}),Hz={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Xie};var ox=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Ma=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${e}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}},ku=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue += value; count += 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${e}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}};function Hv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Jr(n,s,a,\"max\",t)}var Kz={kernelName:zn,backendName:\"webgpu\",kernelFunc:Hv};function Kv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Jr(n,a,s,\"mean\",t)}var qz={kernelName:Un,backendName:\"webgpu\",kernelFunc:Kv};function nx(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=Kv({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Hv({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new ox(e):(t===\"avg\"?n=new Ma(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Ma(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Yie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return nx(n,c,\"avg\",t)}var jz={kernelName:Qo,backendName:\"webgpu\",kernelFunc:Yie};function Qie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new ku(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var Xz={kernelName:Qs,backendName:\"webgpu\",kernelFunc:Qie};var sx=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ax=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Zie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ax(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var Yz={kernelName:Ei,backendName:\"webgpu\",kernelFunc:Zie};function Jie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;lm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new sx(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var Qz={kernelName:$i,backendName:\"webgpu\",kernelFunc:Jie};function eue(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Ap({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var Zz={kernelName:Zo,backendName:\"webgpu\",kernelFunc:eue};var ix=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ht(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=ht(this.rank),t=tue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${qv[a]} = uniforms.start.${Oo(a)} + coords.${qv[a]};`),`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},qv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function tue(r){if(r===1)return\"sourceLoc\";if(r<=6)return qv.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Gs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=vz(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new ix(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var Jz={kernelName:fa,backendName:\"webgpu\",kernelFunc:Gs};var rue=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=yr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},eV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:rue};var oue=`\n fn bincount_write(index: i32, value: f32) {\n ${Us(\"&result[index]\",\"value\",\"float32\")}\n }\n`,nue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Xc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?nue:oue}\n ${H(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function sue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=Vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Xc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var tV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:sue};var ux=class{constructor(e){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function aue(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new ux(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return t.runWebGPUProgram(p,[o,n],\"int32\",u)}var rV={kernelName:Js,backendName:\"webgpu\",kernelFunc:aue};var jv=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:yz}),oV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:jv};function Ii(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var nV={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Ii};function sV(r,e){let t=new Zr(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Xv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:t});let a=Gr(n.shape),i=Xv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=xo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Ii({inputs:{input:n},backend:t}),i=Xv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=ZB(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return sV(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=jv({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var aV={kernelName:yo,backendName:\"webgpu\",kernelFunc:Xv};var iue=ye({opType:Z.CEIL,cpuKernelImpl:JB}),iV={kernelName:en,backendName:\"webgpu\",kernelFunc:iue};var px=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var cx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function uue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new px(n.shape):i=new cx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var uV={kernelName:bo,backendName:\"webgpu\",kernelFunc:uue};var lx=class{constructor(e){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function pV(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function pue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new lx(o.shape),a=[pV(o,n.complexTensorInfos.real),pV(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var cV={kernelName:Di,backendName:\"webgpu\",kernelFunc:pue};var mx=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sIi({inputs:{input:C},backend:t})),h=r.map(C=>Fp({inputs:{input:C},backend:t})),g=Yc(f,e,t),x=Yc(h,e,t),b=xo({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=ez(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new mx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function cue(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function Yv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Yc(p,s,t)}var mV={kernelName:ea,backendName:\"webgpu\",kernelFunc:Yv};function lue(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&t?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Qr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var dx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=um(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Rp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Dp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${lue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var fx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Qr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${H(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var hx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${H(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function gx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function mue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=gx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=gx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Ap({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function due({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[t.batchSize,k,S]:[t.batchSize,S,k],E=new hx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=gx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=gx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=Ap({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function xx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return mue({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return due({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new fx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new dx(t,_,E,R,p,i,u,D)}let C=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function fue(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return xx({x:n,filter:s,convInfo:m,backend:o})}var dV={kernelName:tn,backendName:\"webgpu\",kernelFunc:fue};var yx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${H()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},bx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Cx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function hue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new bx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var fV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:hue};function gue(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ae(r)} {\n let col = colIn * ${r};\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ae(r)} {\n let col = colIn * ${r};\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Ae(r)}) {\n let col = colIn * ${r};\n if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var Sx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=um(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Rp(this.elementsPerThread,this.workgroupSize):Dp(this.elementsPerThread,this.workgroupSize);return`\n ${gue(this.isVec4?4:1)}\n ${e}\n `}};function xue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new yx(m);else{f=new Sx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var hV={kernelName:rn,backendName:\"webgpu\",kernelFunc:xue};var Ix=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function yue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new Ix(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var gV={kernelName:on,backendName:\"webgpu\",kernelFunc:yue};function bue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Cx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var xV={kernelName:Ka,backendName:\"webgpu\",kernelFunc:bue};function Cue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new wx(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var yV={kernelName:nn,backendName:\"webgpu\",kernelFunc:Cue};var wue=ye({opType:Z.COS}),bV={kernelName:sn,backendName:\"webgpu\",kernelFunc:wue};var Sue=ye({opType:Z.COSH}),CV={kernelName:an,backendName:\"webgpu\",kernelFunc:Sue};var vx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Iue=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new vx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},wV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Iue};var Pp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Pp||(Pp={}));var fm=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Pp.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${SV(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${IV(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${IV(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${SV(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function SV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function IV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function kx(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=yr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new fm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new fm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=yr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return kx(Pp.Prod,n,t,s,a,i)}var vV={kernelName:un,backendName:\"webgpu\",kernelFunc:vue};function kue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return kx(Pp.Sum,n,t,s,a,i)}var kV={kernelName:pn,backendName:\"webgpu\",kernelFunc:kue};function Nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=Vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Xc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var NV={kernelName:ta,backendName:\"webgpu\",kernelFunc:Nue};var Nx=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Tue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new Nx(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var TV={kernelName:ln,backendName:\"webgpu\",kernelFunc:Tue};var Tx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${H()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=K(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${H(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Qr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var Zc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Qr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function _ue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Tx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Qc(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new Zc(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var _V={kernelName:mn,backendName:\"webgpu\",kernelFunc:_ue};var _x=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},$x=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function $ue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new _x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],\"float32\",d)}var $V={kernelName:Fi,backendName:\"webgpu\",kernelFunc:$ue};function Eue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new $x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var EV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:Eue};var Ex=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Rue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Ex(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var RV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Rue};var Rx=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function Due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Rx(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var DV={kernelName:dn,backendName:\"webgpu\",kernelFunc:Due};var Dx=class{constructor(e,t){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=K(this.dispatchLayout,e.outShape,this.workgroupSize),t!==\"float32\"&&t!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${t} type.`);this.type=t,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Us(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Ax=class{constructor(e,t,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=K(this.dispatchLayout,e.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Us(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Ax(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var AV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Aue};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Dx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var FV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Fue};var Qv=et({opType:fe.MUL,cpuKernelImpl:gz,supportsComplex:!0}),PV={kernelName:Xn,backendName:\"webgpu\",kernelFunc:Qv};function Zv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Jr(n,s,a,\"sum\",t)}var OV={kernelName:Ss,backendName:\"webgpu\",kernelFunc:Zv};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Zv({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var MV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Pue};var Oue=ye({opType:Z.ELU}),LV={kernelName:hn,backendName:\"webgpu\",kernelFunc:Oue};var Mue=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Si(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},BV={kernelName:qa,backendName:\"webgpu\",kernelFunc:Mue};var Lue=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:tz}),zV={kernelName:xn,backendName:\"webgpu\",kernelFunc:Lue};var Bue=ye({opType:Z.ERF}),VV={kernelName:gn,backendName:\"webgpu\",kernelFunc:Bue};var zue=ye({opType:Z.EXP,cpuKernelImpl:rz,dtype:\"float32\"}),WV={kernelName:yn,backendName:\"webgpu\",kernelFunc:zue};function Fx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var UV={kernelName:oa,backendName:\"webgpu\",kernelFunc:Fx};var Vue=ye({opType:Z.EXPM1,cpuKernelImpl:oz}),GV={kernelName:bn,backendName:\"webgpu\",kernelFunc:Vue};var hm=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Px(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new hm(\"real\",u),l=new hm(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function Wue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Px(o,!1,t)}var HV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Wue};var Ox=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var KV={kernelName:Cn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Uue=ye({opType:Z.FLOOR,cpuKernelImpl:nz}),qV={kernelName:wn,backendName:\"webgpu\",kernelFunc:Uue};var Gue=et({opType:fe.INT_DIV,cpuKernelImpl:sz,dtype:\"int32\"}),jV={kernelName:Sn,backendName:\"webgpu\",kernelFunc:Gue};var Mx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${H(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var XV={kernelName:Au,backendName:\"webgpu\",kernelFunc:Hue},Jc,Jv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d)b=t.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Jc==null||L!==Jv)&&(Jv=L,Jc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Jv})),Jc.canvas.width=c,Jc.canvas.height=l,Jc.drawImage(n,0,0,c,l),n=Jc.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O=\"rgba8unorm\",M=t.textureManager.acquireTexture(m[1],m[0],O,P);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new Mx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],E=t.makeTensorInfo([l,c],\"int32\"),R=t.tensorMap.get(E.dataId);R.resource=b;let D=t.runWebGPUProgram(k,[E],\"int32\",_);return t.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var YV={kernelName:In,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Lx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return xx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var QV={kernelName:Io,backendName:\"webgpu\",kernelFunc:Kue};function que(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Qc(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new Zc(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(S,g,\"float32\",C)}var ZV={kernelName:vo,backendName:\"webgpu\",kernelFunc:que};var Bx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ht(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function jue(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),S=az(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Bx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var JV={kernelName:vn,backendName:\"webgpu\",kernelFunc:jue};var zx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Xue(this.aShape);return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function Xue(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new zx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var eW={kernelName:sa,backendName:\"webgpu\",kernelFunc:e0};var Yue=et({opType:fe.GREATER,cpuKernelImpl:pz,dtype:\"bool\"}),tW={kernelName:kn,backendName:\"webgpu\",kernelFunc:Yue};var Que=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:uz}),rW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:Que};function Zue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Px(o,!0,t)}var oW={kernelName:zi,backendName:\"webgpu\",kernelFunc:Zue};var Jue=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),nW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:Jue};var epe=ye({opType:Z.IS_INF,dtype:\"bool\"}),sW={kernelName:_n,backendName:\"webgpu\",kernelFunc:epe};var tpe=ye({opType:Z.IS_NAN,dtype:\"bool\"}),aW={kernelName:$n,backendName:\"webgpu\",kernelFunc:tpe};function rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Zr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var iW={kernelName:En,backendName:\"webgpu\",kernelFunc:rpe};var ope=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:lz}),uW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:ope};var npe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:cz}),pW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:npe};var Vx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Vx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var cW={kernelName:An,backendName:\"webgpu\",kernelFunc:spe};var ape=ye({opType:Z.LOG,cpuKernelImpl:mz}),lW={kernelName:Fn,backendName:\"webgpu\",kernelFunc:ape};var ipe=ye({opType:Z.LOG1P}),mW={kernelName:Pn,backendName:\"webgpu\",kernelFunc:ipe};var upe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),dW={kernelName:On,backendName:\"webgpu\",kernelFunc:upe};var ppe=ye({opType:Z.LOGICAL_NOT}),fW={kernelName:Mn,backendName:\"webgpu\",kernelFunc:ppe};var cpe=et({opType:fe.LOGICAL_OR}),hW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:cpe};var gW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Wx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${gW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Ux=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${H()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${gW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function lpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Wx(n.shape):u=new Ux(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var xW={kernelName:Bn,backendName:\"webgpu\",kernelFunc:lpe};var Gx=class{constructor(e){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function mpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Gx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var yW={kernelName:ja,backendName:\"webgpu\",kernelFunc:mpe};var dpe=et({opType:fe.MAX,cpuKernelImpl:fz}),bW={kernelName:Vn,backendName:\"webgpu\",kernelFunc:dpe};function fpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return nx(n,c,\"max\",t)}var CW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:fpe};function hpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new ku(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var wW={kernelName:aa,backendName:\"webgpu\",kernelFunc:hpe};var Hx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Kx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function gpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new ku(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],\"int32\",f),g=new Kx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var SW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:gpe};function xpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;lm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ma(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],\"int32\",f),g=new Hx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var IW={kernelName:Wi,backendName:\"webgpu\",kernelFunc:xpe};function ype(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ma(c,\"max\",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ma(c,\"max\",!0,!0,i);let f=t.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var vW={kernelName:ia,backendName:\"webgpu\",kernelFunc:ype};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Jr(n,s,a,\"min\",t)}var kW={kernelName:Gn,backendName:\"webgpu\",kernelFunc:bpe};var Cpe=et({opType:fe.MIN,cpuKernelImpl:hz}),NW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Cpe};var qx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=ht(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var TW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new qx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var wpe=et({opType:fe.MOD}),_W={kernelName:qn,backendName:\"webgpu\",kernelFunc:wpe};var jx=class{constructor(e,t){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Xx=class{constructor(e){this.variableNames=[\"logits\"],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${H(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Xx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var $W={kernelName:Is,backendName:\"webgpu\",kernelFunc:t0};function Spe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:t0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new jx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=t.runWebGPUProgram(l,[p],\"int32\",m);return i||t.disposeData(p.dataId),d}var EW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Spe};function Ipe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=xz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Zr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var RW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ipe};function vpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var DW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:vpe};function kpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var AW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:kpe};var Yx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Npe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Yx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var FW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Npe};function gm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=Ii({inputs:{input:o},backend:t}),s=gm({inputs:{x:n},backend:t}),a=Fp({inputs:{input:o},backend:t}),i=gm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var PW={kernelName:ba,backendName:\"webgpu\",kernelFunc:gm};function OW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Ii({inputs:{input:o},backend:t}),s=OW({inputs:{x:n},backend:t}),a=Fp({inputs:{input:o},backend:t}),i=gm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var MW={kernelName:pa,backendName:\"webgpu\",kernelFunc:OW};function Tpe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Fx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Fx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=Yv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var LW={kernelName:ca,backendName:\"webgpu\",kernelFunc:Tpe};function r0(r,e=!1){let t=r.length,o=ht(t),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:\"\"}`).join(\",\"),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=t>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${e?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var Qx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${r0(this.xShape)}\n }\n }\n `}};var _pe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new Qx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},BW={kernelName:es,backendName:\"webgpu\",kernelFunc:_pe};var $pe=et({opType:fe.POW}),zW={kernelName:ts,backendName:\"webgpu\",kernelFunc:$pe};function Epe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Si(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var VW={kernelName:rs,backendName:\"webgpu\",kernelFunc:Epe};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Jr(n,s,a,\"prod\",t)}var WW={kernelName:os,backendName:\"webgpu\",kernelFunc:Rpe};var Dpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Cz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},UW={kernelName:la,backendName:\"webgpu\",kernelFunc:Dpe};var Ape=et({opType:fe.DIV}),GW={kernelName:fn,backendName:\"webgpu\",kernelFunc:Ape};var Fpe=ye({opType:Z.RECIPROCAL}),HW={kernelName:ns,backendName:\"webgpu\",kernelFunc:Fpe};var Ppe=ye({opType:Z.RELU}),KW={kernelName:ss,backendName:\"webgpu\",kernelFunc:Ppe};var Ope=ye({opType:Z.RELU6}),qW={kernelName:us,backendName:\"webgpu\",kernelFunc:Ope};var Zx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Mpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new Zx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var jW={kernelName:is,backendName:\"webgpu\",kernelFunc:Mpe};var Jx=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function Lpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new Jx(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var XW={kernelName:Qa,backendName:\"webgpu\",kernelFunc:Lpe};var ey=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new ey(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var YW={kernelName:as,backendName:\"webgpu\",kernelFunc:Bpe};var ty=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function zpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ty(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var QW={kernelName:Ya,backendName:\"webgpu\",kernelFunc:zpe};var ry=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function Vpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new ry(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var ZW={kernelName:ps,backendName:\"webgpu\",kernelFunc:Vpe};var oy=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var JW={kernelName:Ds,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new oy(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Wpe=ye({opType:Z.ROUND}),eU={kernelName:cs,backendName:\"webgpu\",kernelFunc:Wpe};var Upe=ye({opType:Z.RSQRT,cpuKernelImpl:wz}),tU={kernelName:ls,backendName:\"webgpu\",kernelFunc:Upe};var La=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=K(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=ht(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${H(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${$p(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Us(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=Vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new La(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var rU={kernelName:ms,backendName:\"webgpu\",kernelFunc:Gpe};var ny=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function Hpe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new ny([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var oU={kernelName:fs,backendName:\"webgpu\",kernelFunc:Hpe};var sy=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function Kpe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new sy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var nU={kernelName:da,backendName:\"webgpu\",kernelFunc:Kpe};var qpe=ye({opType:Z.SELU}),sU={kernelName:hs,backendName:\"webgpu\",kernelFunc:qpe};var jpe=ye({opType:Z.SIGMOID}),aU={kernelName:bs,backendName:\"webgpu\",kernelFunc:jpe};var Xpe=ye({opType:Z.SIGN}),iU={kernelName:ys,backendName:\"webgpu\",kernelFunc:Xpe};var Ype=ye({opType:Z.SIN}),uU={kernelName:gs,backendName:\"webgpu\",kernelFunc:Ype};var Qpe=ye({opType:Z.SINH}),pU={kernelName:xs,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.SOFTPLUS}),cU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:Zpe};var ay=class{constructor(e,t,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ht(this.outputShape.length),t=Gv(this.newDim);return`\n ${im(this.paddedXShape,\"PaddedX\")}\n ${H(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${r0(this.xShape,!0)}\n }\n }\n `}};var Jpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new ay(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},lU={kernelName:ha,backendName:\"webgpu\",kernelFunc:Jpe};var iy=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=_z(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new iy(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var mU={kernelName:uo,backendName:\"webgpu\",kernelFunc:xm};function tce(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=t.bufferSync(n),D=t.bufferSync(s),P=y.decodeString(t.readSync(a.dataId)[0]),O=Sz(R,D,i,m,c,u,p,l,P,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=xm({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new La([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new La([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new La([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),E}var dU={kernelName:vs,backendName:\"webgpu\",kernelFunc:tce};function rce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var fU={kernelName:ga,backendName:\"webgpu\",kernelFunc:rce};var oce=ye({opType:Z.SQRT}),hU={kernelName:ws,backendName:\"webgpu\",kernelFunc:oce};var gU={kernelName:ji,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Zr(t.shape,Z.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var nce=et({opType:fe.SQUARED_DIFFERENCE}),xU={kernelName:ks,backendName:\"webgpu\",kernelFunc:nce};function sce({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Zr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var yU={kernelName:wo,backendName:\"webgpu\",kernelFunc:sce};var uy=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ht(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function ace(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ct.computeOutShape(b,C,S),E=Gs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=kz(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let E=new uy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=t.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var bU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:ace};function ice(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Nz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var CU={kernelName:xa,backendName:\"webgpu\",kernelFunc:ice};var uce=et({opType:fe.SUB,cpuKernelImpl:Tz,supportsComplex:!0}),wU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:uce};var pce=ye({opType:Z.TAN}),SU={kernelName:_s,backendName:\"webgpu\",kernelFunc:pce};var cce=ye({opType:Z.TANH}),IU={kernelName:$s,backendName:\"webgpu\",kernelFunc:cce};function lce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=xm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new La([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach(E=>t.disposeData(E.dataId)),_}var vU={kernelName:ds,backendName:\"webgpu\",kernelFunc:lce};var py=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},cy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function el(r,e){e!==null&&r.disposeData(e.dataId)}function kU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new py(E),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,\"int32\",O),el(t,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new cy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=t.runWebGPUProgram(E,_,\"int32\",D),el(t,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Gs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),el(t,x);let b=e0({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});el(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:t}),el(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:t}),el(t,S),[b,f]}var NU={kernelName:Es,backendName:\"webgpu\",kernelFunc:mce};var ly=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${H(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function dce(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new ly(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",S)}var TU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:dce};function fce(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var _U={kernelName:ya,backendName:\"webgpu\",kernelFunc:fce};var my=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=K(this.dispatchLayout,e,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${H(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Us(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function hce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=yr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=Vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new my(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=yr({inputs:{x:_},backend:t,attrs:{perm:E}})}return p.forEach(E=>t.disposeData(E.dataId)),_}var $U={kernelName:Zi,backendName:\"webgpu\",kernelFunc:hce};var gce=[jB,Rz,Dz,Az,Fz,Pz,Mz,Lz,Bz,zz,Vz,Wz,Uz,Gz,Hz,jz,Xz,Yz,Qz,Zz,eV,tV,rV,aV,iV,uV,YB,cV,mV,dV,fV,hV,gV,xV,yV,bV,CV,wV,vV,kV,NV,TV,$V,EV,_V,RV,DV,AV,FV,MV,LV,BV,zV,VV,WV,UV,GV,HV,KB,KV,XV,qV,jV,YV,QV,ZV,JV,eW,tW,rW,XB,oW,lV,nW,sW,aW,iW,uW,pW,cW,mW,lW,dW,fW,hW,xW,yW,Kz,bW,CW,IW,wW,SW,vW,qz,kW,NW,TW,_W,EW,PV,RW,DW,AW,oV,FW,MW,LW,BW,zW,VW,WW,UW,nV,GW,HW,KW,qW,qB,jW,XW,YW,QW,ZW,JW,eU,tU,rU,oU,nU,sU,aU,iU,uU,pU,Jz,yU,bU,CU,$W,cU,lU,dU,fU,hU,gU,xU,wU,OV,SU,IU,vU,mU,NU,TU,Oz,_U,$U,PW];for(let r of gce)Ja(r);var EU=\"4.7.0\",xce=\"4.7.0\",yce=\"4.7.0\",bce=\"4.7.0\",Cce=\"4.7.0\",wce=\"4.7.0\",Sce={tfjs:EU,\"tfjs-core\":EU,\"tfjs-converter\":xce,\"tfjs-backend-cpu\":yce,\"tfjs-backend-webgl\":bce,\"tfjs-backend-wasm\":Cce,\"tfjs-backend-webgpu\":wce};export{js as Abs,Vo as Acos,Wo as Acosh,ep as AdadeltaOptimizer,tp as AdagradOptimizer,rp as AdamOptimizer,op as AdamaxOptimizer,io as Add,Uo as AddN,Go as All,Ho as Any,Xs as ArgMax,Ys as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Qs as AvgPool3D,Ei as AvgPool3DGrad,$i as AvgPoolGrad,am as BackendWasm,Zo as BatchMatMul,Zs as BatchToSpaceND,Jo as Bincount,Ha as BitwiseAnd,Js as BroadcastArgs,_ce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Ri as Complex,Di as ComplexAbs,ea as Concat,tn as Conv2D,Ai as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,Ka as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ta as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Fi as DepthwiseConv2dNativeBackpropFilter,Pi as DepthwiseConv2dNativeBackpropInput,ra as Diag,dn as Dilation2D,Mi as Dilation2DBackpropFilter,Oi as Dilation2DBackpropInput,qm as Draw,QC as ENV,Li as Einsum,hn as Elu,qa as EluGrad,ll as Environment,xn as Equal,gn as Erf,yn as Exp,oa as ExpandDims,bn as Expm1,Bi as FFT,na as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Au as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,wp as GPGPUContext,vn as GatherNd,sa as GatherV2,Ol as GraphModel,kn as Greater,Nn as GreaterEqual,zi as IFFT,Co as Identity,Vi as Imag,Tn as IsFinite,_n as IsInf,$n as IsNan,so as KernelBackend,Bn as LRN,ja as LRNGrad,En as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,$ce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,w0 as LogicalXor,Ece as LowerBound,hu as MathBackendCPU,bu as MathBackendWebGL,Rce as MatrixBandPart,zn as Max,Wn as MaxPool,aa as MaxPool3D,Ui as MaxPool3DGrad,Wi as MaxPoolGrad,ia as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,np as MomentumOptimizer,jn as Multinomial,Xn as Multiply,ua as Neg,Qn as NonMaxSuppressionV3,Xa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,yw as OP_SCOPE_SUFFIX,Jn as OneHot,pa as OnesLike,Nr as Optimizer,Dl as OptimizerConstructors,ca as Pack,es as PadV2,Dce as Pool,ts as Pow,rs as Prelu,os as Prod,sp as RMSPropOptimizer,jp as RaggedGather,Xp as RaggedRange,Yp as RaggedTensorToTensor,la as Range,uw as Rank,Gi as Real,fn as RealDiv,ns as Reciprocal,Et as Reduction,ss as Relu,us as Relu6,ma as Reshape,is as ResizeBilinear,Qa as ResizeBilinearGrad,as as ResizeNearestNeighbor,Ya as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,ci as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,da as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,fa as Slice,Is as Softmax,Cs as Softplus,ha as SpaceToBatchND,Hi as SparseFillEmptyRows,Za as SparseReshape,Ki as SparseSegmentMean,qi as SparseSegmentSum,vs as SparseToDense,ga as SplitV,ws as Sqrt,ji as Square,ks as SquaredDifference,Du as StaticRegexReplace,wo as Step,Ns as StridedSlice,xa as StringNGrams,Xi as StringSplit,Yi as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,$s as Tanh,ut as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,uo as Tile,Es as TopK,Rs as Transform,po as Transpose,Qi as Unique,ya as Unpack,Zi as UnsortedSegmentSum,Ace as UpperBound,ei as Variable,vu as WebGPUBackend,ba as ZerosLike,So as _FusedMatMul,Jt as abs,xk as acos,yk as acosh,Ce as add,bk as addN,Ck as all,wk as any,Sk as argMax,Ik as argMin,vk as asin,kk as asinh,Nk as atan,Tk as atan2,_k as atanh,md as avgPool,Rk as avgPool3d,dde as backend,w as backend_util,Dk as basicLSTMCell,su as batchNorm,Fk as batchNorm2d,Pk as batchNorm3d,Ok as batchNorm4d,dd as batchToSpaceND,fd as bincount,Mk as bitwiseAnd,b6 as booleanMaskAsync,Lk as broadcastArgs,au as broadcastTo,Ir as broadcast_util,XN as browser,me as buffer,qe as cast,Bk as ceil,zk as clipByValue,Ur as clone,Er as complex,bt as concat,Vk as concat1d,Wk as concat2d,Uk as concat3d,Gk as concat4d,Hk as conv1d,iu as conv2d,Kk as conv2dTranspose,qk as conv3d,Xk as conv3dTranspose,Vce as copyRegisteredKernels,Yk as cos,Qk as cosh,_l as cosineWindow,Zk as cumprod,Jk as cumsum,vr as customGrad,e2 as denseBincount,_w as deprecationWarn,t2 as depthToSpace,ic as depthwiseConv2d,b5 as deregisterOp,ru as device_util,r2 as diag,o2 as dilation2d,rde as disableDeprecationWarnings,Ot as dispose,ode as disposeVariables,je as div,s2 as divNoNan,a2 as dot,R6 as dropout,i2 as einsum,yd as elu,tde as enableDebugMode,ede as enableProdMode,Gw as enclosingPowerOfTwo,ur as engine,u2 as ensureShape,A as env,xd as equal,p2 as erf,m2 as euclideanNorm,_o as exp,ai as expandDims,d2 as expm1,bd as eye,cc as fft,Ta as fill,lde as findBackend,mde as findBackendFactory,Cd as floor,ld as floorDiv,$D as forceHalfFloat,Hw as fused,wd as gather,$6 as gatherND,nf as gather_util,pde as getBackend,ew as getGradient,fl as getKernel,jm as getKernelsForBackend,Lse as getThreadsCount,rv as gpgpu_util,SK as grad,IK as grads,Uu as greater,Sd as greaterEqual,Xu as ifft,pu as imag,Fj as image,A6 as inTopKAsync,mi as io,Gd as irfft,f2 as isFinite,h2 as isInf,g2 as isNaN,Rr as keep,Wt as kernel_impls,Id as leakyRelu,kl as less,uc as lessEqual,Pj as linalg,x2 as linspace,h8 as loadGraphModel,g8 as loadGraphModelSync,y2 as localResponseNormalization,ii as log,vd as log1p,b2 as logSigmoid,C2 as logSoftmax,Td as logSumExp,Gu as logicalAnd,_d as logicalNot,$d as logicalOr,w2 as logicalXor,Oj as losses,S2 as lowerBound,Ze as matMul,HN as math,_a as max,Rd as maxPool,I2 as maxPool3d,v2 as maxPoolWithArgmax,Dd as maximum,Hu as mean,nde as memory,k2 as meshgrid,vl as min,Ku as minimum,N2 as mirrorPad,T2 as mod,_2 as moments,S6 as movingAverage,se as mul,$2 as multiRNNCell,E2 as multinomial,pr as neg,tS as nextFrame,Wu as norm,Ad as notEqual,Tl as oneHot,$a as ones,R2 as onesLike,N as op,D2 as outerProduct,Ea as pad,A2 as pad1d,F2 as pad2d,P2 as pad3d,O2 as pad4d,M2 as pool,si as pow,Pd as prelu,cd as print,L2 as prod,sde as profile,B2 as raggedGather,z2 as raggedRange,V2 as raggedTensorToTensor,W2 as rand,p1 as randomGamma,Vd as randomNormal,c1 as randomStandardNormal,pc as randomUniform,l1 as randomUniformInt,cu as range,ude as ready,ui as real,m1 as reciprocal,nu as registerBackend,Lce as registerGradient,Ja as registerKernel,y5 as registerOp,lu as relu,Wd as relu6,cde as removeBackend,W as reshape,lo as reverse,d1 as reverse1d,f1 as reverse2d,h1 as reverse3d,g1 as reverse4d,lc as rfft,Ud as round,x1 as rsqrt,ke as scalar,v6 as scatterND,du as scatter_util,Nl as searchSorted,y1 as selu,b1 as separableConv2d,FN as serialization,ide as setBackend,fde as setPlatform,Mse as setThreadsCount,Pse as setWasmPath,Ose as setWasmPaths,gI as setWebGLContext,C1 as setdiff1dAsync,Ic as shared,Na as sigmoid,w1 as sign,Aj as signal,S1 as sin,I1 as sinh,Xe as slice,v1 as slice1d,k1 as slice2d,N1 as slice3d,T1 as slice4d,ct as slice_util,_1 as softmax,Nd as softplus,Fd as spaceToBatchND,Mj as sparse,T6 as sparseToDense,Dj as spectral,pi as split,Dr as sqrt,er as square,Hd as squaredDifference,mc as squeeze,kr as stack,Kd as step,$1 as stridedSlice,Lj as string,Te as sub,ot as sum,ti as sumOutType,E1 as tan,Il as tanh,ir as tensor,xr as tensor1d,mu as tensor2d,qd as tensor3d,R1 as tensor4d,D1 as tensor5d,A1 as tensor6d,P1 as tensorScatterUpdate,K0 as tensor_util,u1 as test_util,De as tidy,uu as tile,ade as time,O1 as topk,uGe as train,fc as transpose,M1 as truncatedNormal,L1 as unique,zce as unregisterGradient,Bce as unregisterKernel,B1 as unsortedSegmentSum,mo as unstack,dt as upcastType,z1 as upperBound,y as util,vK as valueAndGrad,kK as valueAndGrads,V1 as variable,Aw as variableGrads,Sce as version,y8 as version_converter,fX as version_core,Y8 as version_cpu,Bse as version_wasm,HZ as version_webgl,tat as webgl,$c as webgl_util,Wv as webgpu_util,co as where,Xd as whereAsync,Gr as zeros,Ht as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.0.7\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": {\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.node.js\",\n \"module\": \"./dist/human.node.js\"\n },\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.19.0\",\n \"@html-eslint/parser\": \"^0.19.0\",\n \"@microsoft/api-extractor\": \"^7.35.2\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.7.0\",\n \"@tensorflow/tfjs-converter\": \"^4.7.0\",\n \"@tensorflow/tfjs-core\": \"^4.7.0\",\n \"@tensorflow/tfjs-data\": \"^4.7.0\",\n \"@tensorflow/tfjs-layers\": \"^4.7.0\",\n \"@tensorflow/tfjs-node\": \"^4.7.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.7.0\",\n \"@types/node\": \"^20.3.0\",\n \"@types/offscreencanvas\": \"^2019.7.0\",\n \"@typescript-eslint/eslint-plugin\": \"^5.59.9\",\n \"@typescript-eslint/parser\": \"^5.59.9\",\n \"@vladmandic/build\": \"^0.9.2\",\n \"@vladmandic/pilogger\": \"^0.4.8\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^2.11.2\",\n \"esbuild\": \"^0.18.1\",\n \"eslint\": \"8.42.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^7.1.0\",\n \"eslint-plugin-import\": \"^2.27.5\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-markdown\": \"^3.0.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"rimraf\": \"^5.0.1\",\n \"tslib\": \"^2.5.3\",\n \"typedoc\": \"0.24.8\",\n \"typescript\": \"5.1.3\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n 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0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:Pe(()=>Qa(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:Pe(()=>Qa(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;Pe(()=>{let l=ke(te(i,this.rho),te(Cn(s),1-this.rho)),u=te(ve(er(ke(o,this.epsilon)),er(ke(i,this.epsilon))),s),p=ke(te(o,this.rho),te(Cn(u),1-this.rho));i.assign(l),o.assign(p);let c=ke(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},z3=class extends ls{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:Pe(()=>sr(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;Pe(()=>{let i=ke(s,Cn(r));s.assign(i);let o=ke(te(ve(r,er(ke(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},L3=class extends ls{static get className(){return"Adam"}constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Pe(()=>{this.accBeta1=Ue(t).variable(),this.accBeta2=Ue(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);Pe(()=>{let a=ye(1,this.accBeta1),n=ye(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Pe(()=>Qa(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:Pe(()=>Qa(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=ke(te(u,this.beta1),te(l,1-this.beta1)),d=ke(te(p,this.beta2),te(Cn(l),1-this.beta2)),h=ve(c,a),f=ve(d,n);u.assign(c),p.assign(d);let m=ke(te(ve(h,ke(er(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Pe(()=>{this.accBeta1.assign(zl(this.beta1,this.iterations_+1)),this.accBeta2.assign(zl(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},W3=class extends ls{static get className(){return"Adamax"}constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Pe(()=>{this.iteration=Ue(0).variable(),this.accBeta1=Ue(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);Pe(()=>{let a=ye(1,this.accBeta1),n=ve(-this.learningRate,ke(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Qa(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Qa(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,c=ke(te(u,this.beta1),te(l,1-this.beta1)),d=te(p,this.beta2),h=Za(l),f=y3(d,h);u.assign(c),p.assign(f);let m=ke(te(ve(n,a),ve(c,ke(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ke(this.iteration,1)),this.accBeta1.assign(te(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&J(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},qh=class extends ls{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];Pe(()=>{let s=ke(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=zn(Ue(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}},B3=class extends qh{static get 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n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:De(()=>Qa(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:De(()=>Qa(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;De(()=>{let l=we(te(i,this.rho),te(Tn(s),1-this.rho)),u=te(ve(Qn(we(o,this.epsilon)),Qn(we(i,this.epsilon))),s),p=we(te(o,this.rho),te(Tn(u),1-this.rho));i.assign(l),o.assign(p);let c=we(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},j3=class extends us{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:De(()=>ir(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;De(()=>{let i=we(s,Tn(r));s.assign(i);let o=we(te(ve(r,Qn(we(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},q3=class extends us{static get className(){return"Adam"}constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=Ue(t).variable(),this.accBeta2=Ue(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=ye(1,this.accBeta1),n=ye(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:De(()=>Qa(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:De(()=>Qa(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=we(te(p,this.beta2),te(Tn(l),1-this.beta2)),h=ve(c,a),m=ve(d,n);u.assign(c),p.assign(d);let f=we(te(ve(h,we(Qn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await 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t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=ye(1,this.accBeta1),n=ve(-this.learningRate,we(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Qa(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Qa(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=te(p,this.beta2),h=Za(l),m=S3(d,h);u.assign(c),p.assign(m);let f=we(te(ve(n,a),ve(c,we(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(we(this.iteration,1)),this.accBeta1.assign(te(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&J(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},Kh=class extends us{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];De(()=>{let s=we(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=zn(Ue(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}},K3=class extends Kh{static get className(){return"Momentum"}constructor(e,t,a=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=a,this.accumulations=[],this.m=Ue(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulations[a]==null&&(this.accumulations[a]={originalName:`${t}/momentum`,variable:De(()=>Qa(n).variable(!1))});let r=this.accumulations[a].variable,s=Array.isArray(e)?e[a].tensor:e[t];s!=null&&De(()=>{let i,o=we(te(this.m,r),s);this.useNesterov?i=we(te(this.c,we(s,te(o,this.m))),n):i=we(te(this.c,o),n),r.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&J(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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TypeError(`Node type ${e.op} is not implemented`)}};function q5(e,t,a){let[n,r]=k("fusedOps",e,t,a),s=n==="biasadd",i=!s,o=r==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,a);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let p=k("strides",e,t,a),c=qc(e,t,a),d=k("dataFormat",e,t,a).toUpperCase(),h=k("dilations",e,t,a),[f,m]=k("args",e,t,a);i&&(m=f,f=void 0);let g=k("leakyreluAlpha",e,t,a);return{stride:p,pad:c,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var CP=(e,t,a,n=Qt)=>{switch(e.op){case"Conv1D":{let 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ue(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let a=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Pe(()=>{let n=Ra(t),r=a.length,s=n.length;v.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let n=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=n.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,a),i=k("valueDType",e,t,a),o=new MP(s,i);return n.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,a,n),s=k("keys",e,t,a),i=k("values",e,t,a);return[await n.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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r=k("images",e,t,a),s=k("transforms",e,t,a),i=k("outputShape",e,t,a),o=k("fillValue",e,t,a),l=k("interpolation",e,t,a),u=k("fillMode",e,t,a);return[n.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$P=(e,t,a,n=Qt)=>{switch(e.op){case"Equal":return[n.equal(k("a",e,t,a),k("b",e,t,a))];case"NotEqual":return[n.notEqual(k("a",e,t,a),k("b",e,t,a))];case"Greater":return[n.greater(k("a",e,t,a),k("b",e,t,a))];case"GreaterEqual":return[n.greaterEqual(k("a",e,t,a),k("b",e,t,a))];case"Less":return[n.less(k("a",e,t,a),k("b",e,t,a))];case"LessEqual":return[n.lessEqual(k("a",e,t,a),k("b",e,t,a))];case"LogicalAnd":return[n.logicalAnd(k("a",e,t,a),k("b",e,t,a))];case"LogicalNot":return[n.logicalNot(k("a",e,t,a))];case"LogicalOr":return[n.logicalOr(k("a",e,t,a),k("b",e,t,a))];case"Select":case"SelectV2":return[n.where(k("condition",e,t,a),k("a",e,t,a),k("b",e,t,a))];case"BitwiseAnd":return[n.bitwiseAnd(k("a",e,t,a),k("b",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},DP=(e,t,a,n=Qt)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(k("a",e,t,a),k("b",e,t,a),k("transposeA",e,t,a),k("transposeB",e,t,a))];case"Einsum":return[n.einsum(k("equation",e,t,a),...k("tensors",e,t,a))];case"Transpose":return[n.transpose(k("x",e,t,a),k("perm",e,t,a))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,a),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,a),u=k("leakyreluAlpha",e,t,a);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,c]=k("args",e,t,a);return[n.fused.matMul({a:k("a",e,t,a),b:k("b",e,t,a),transposeA:k("transposeA",e,t,a),transposeB:k("transposeB",e,t,a),bias:p,activation:s,preluActivationWeights:c,leakyreluAlpha:u})];case"MatrixBandPart":return[n.linalg.bandPart(k("a",e,t,a),k("numLower",e,t,a),k("numUpper",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},PP=(e,t,a,n=Qt)=>{switch(e.op){case"EuclideanNorm":return[n.euclideanNorm(k("x",e,t,a),k("axis",e,t,a),k("keepDims",e,t,a))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"FusedBatchNormV3":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"LRN":return[n.localResponseNormalization(k("x",e,t,a),k("radius",e,t,a),k("bias",e,t,a),k("alpha",e,t,a),k("beta",e,t,a))];case"Softmax":return[n.softmax(k("x",e,t,a))];case"LogSoftmax":return[n.logSoftmax(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},OP=(e,t,a,n=Qt)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=n.raggedGather(k("paramsNestedSplits",e,t,a),k("paramsDenseValues",e,t,a),k("indices",e,t,a),k("outputRaggedRank",e,t,a));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=n.raggedRange(k("starts",e,t,a),k("limits",e,t,a),k("splits",e,t,a));return[r,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(k("shape",e,t,a),k("values",e,t,a),k("defaultValue",e,t,a),k("rowPartitionTensors",e,t,a),k("rowPartitionTypes",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zP=(e,t,a,n=Qt)=>{switch(e.op){case"Max":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.max(k("x",e,t,a),o,l)]}case"Mean":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.mean(k("x",e,t,a),o,l)]}case"Min":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.min(k("x",e,t,a),o,l)]}case"Sum":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.sum(k("x",e,t,a),o,l)]}case"All":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.all(k("x",e,t,a),o,l)]}case"Any":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.any(k("x",e,t,a),o,l)]}case"ArgMax":{let o=k("axis",e,t,a);return[n.argMax(k("x",e,t,a),o)]}case"ArgMin":{let o=k("axis",e,t,a);return[n.argMin(k("x",e,t,a),o)]}case"Prod":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.prod(k("x",e,t,a),o,l)]}case"Cumprod":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumprod(k("x",e,t,a),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumsum(k("x",e,t,a),o,l,u)]}case"Bincount":let r=k("x",e,t,a),s=k("weights",e,t,a),i=k("size",e,t,a);return[n.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,a),l=k("weights",e,t,a),u=k("size",e,t,a),p=k("binaryOutput",e,t,a);return[n.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},LP=(e,t,a,n=Qt)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,a),s=k("axis",e,t,a),i=k("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,a),s=k("batchDims",e,t,a),i=k("x",e,t,a),o=k("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,a),s=[];for(let o=0;o{let r=k("axis",e,t,a),s=k("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,a),s=k("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=k("reps",e,t,a);return[n.tile(k("x",e,t,a),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,a),s=k("numOrSizeSplits",e,t,a),i=k("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,a),s=k("outputShape",e,t,a),i=k("sparseValues",e,t,a),o=k("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("tensor",e,t,a);return[n.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},WP=(e,t,a,n=Qt)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(k("indices",e,t,a),k("values",e,t,a),k("denseShape",e,t,a),k("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(k("inputIndices",e,t,a),k("inputShape",e,t,a),k("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},BP=(e,t,a,n=Qt)=>{switch(e.op){case"FFT":return[n.fft(k("x",e,t,a))];case"IFFT":return[n.ifft(k("x",e,t,a))];case"RFFT":return[n.rfft(k("x",e,t,a))];case"IRFFT":return[n.irfft(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},VP=(e,t,a,n=Qt)=>{switch(e.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(k("input",e,t,a),k("pattern",e,t,a),k("rewrite",e,t,a),k("replaceGlobal",e,t,a))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(k("data",e,t,a),k("dataSplits",e,t,a),k("separator",e,t,a),k("nGramWidths",e,t,a),k("leftPad",e,t,a),k("rightPad",e,t,a),k("padWidth",e,t,a),k("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(k("input",e,t,a),k("delimiter",e,t,a),k("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(k("input",e,t,a),k("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},UP=(e,t,a,n=Qt)=>{switch(e.op){case"Cast":return[n.cast(k("x",e,t,a),k("dtype",e,t,a))];case"ExpandDims":{let r=k("axis",e,t,a);return[n.expandDims(k("x",e,t,a),r)]}case"Squeeze":{let r=k("axis",e,t,a);return[n.squeeze(k("x",e,t,a),r)]}case"Reshape":return[n.reshape(k("x",e,t,a),k("shape",e,t,a))];case"EnsureShape":return[n.ensureShape(k("x",e,t,a),k("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(k("x",e,t,a),k("padding",e,t,a),k("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(k("x",e,t,a),k("padding",e,t,a),k("constantValue",e,t,a))];case"SpaceToBatchND":{let r=k("blockShape",e,t,a),s=k("paddings",e,t,a);return[n.spaceToBatchND(k("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,a),s=k("crops",e,t,a);return[n.batchToSpaceND(k("x",e,t,a),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,a),s=k("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(k("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(k("x",e,t,a),k("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(k("s0",e,t,a),k("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function X5(e,t,a,n,r=Pe){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>xP(i,o,l));case"basic_math":return r(()=>AP(i,o,l));case"control":return SP(i,o,l);case"convolution":return r(()=>CP(i,o,l));case"creation":return r(()=>TP(i,o,l));case"dynamic":return NP(i,o,l);case"evaluation":return r(()=>RP(i,o,l));case"image":return r(()=>_P(i,o,l));case"graph":return r(()=>EP(i,o,l));case"logical":return r(()=>$P(i,o,l));case"matrices":return r(()=>DP(i,o,l));case"normalization":return r(()=>PP(i,o,l));case"ragged":return r(()=>OP(i,o,l));case"reduction":return r(()=>zP(i,o,l));case"slice_join":return r(()=>LP(i,o,l));case"sparse":return r(()=>WP(i,o,l));case"spectral":return r(()=>BP(i,o,l));case"string":return r(()=>VP(i,o,l));case"transformation":return r(()=>UP(i,o,l));case"hash_table":return FP(i,o,l,n);case"custom":let u=b7(i.op);if(u&&u.customExecutor)return u.customExecutor(new yP(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var K5=class{constructor(e={},t={},a={},n={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Y5(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(d=>Ya(d)[0]));n=n||[];let p=new Set(n.map(d=>Ya(d.name)[0])),c=[...t];for(;c.length>0;){let d=c.pop();if((zs(d)||ZP(d)||JP(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&!u.has(d.name)&&!p.has(d.name)){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function GP(e,t){let{usedNodes:a,inputs:n}=t,r=Object.keys(n).map(g=>Ya(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>a.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(y=>[y.name,y])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let y of g.children)i(y)||(c[y.name]=Number.POSITIVE_INFINITY),c[y.name]=(c[y.name]||0)+1}let d=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),h=[...d];for(;d.length>0;){let g=d.pop(),y=p.get(g);for(let x of y.children.filter(i))--c[x.name]===0&&(h.push(x.name),d.push(x.name))}let f=h.map(g=>p.get(g)),m=HP(f,l);return jP(m,l),m}function HP(e,t){let a=new Map(e.map(s=>[s.name,s])),n=t.map(s=>s.name),r=new Set(n);for(;n.length>0;){let s=n.pop(),i=a.get(s);for(let o of i.children)!a.has(o.name)||r.has(o.name)||(r.add(o.name),n.push(o.name))}return e.filter(s=>r.has(s.name))}var Wc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function jP(e,t){let a=new Map(e.map((o,l)=>[o.name,l])),n=new Set(t.map(o=>o.name)),r=o=>n.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!a.has(l.name))throw new Wc(`Child ${l.name} of node ${o.name} is unreachable.`);if(a.get(o.name)>a.get(l.name))throw new Wc(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!a.has(l.name))throw new Wc(`Input ${l.name} of node ${o.name} is unreachable.`);if(a.get(l.name)>a.get(o.name))throw new Wc(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function qP(e){let t=new Map(e.map((o,l)=>[o.name,l])),a=Number.MAX_SAFE_INTEGER,n=e.map((o,l)=>zs(o)?a:l),r=o=>{let l=n[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),n[l])),i=new Map;for(let o=0;oe[a].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new y1(e.functions[a],this)})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let a=Y5(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${n}]`)}let i=GP(this.graph,a),o=qP(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return zn(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(c=>this.graph.nodes[Ya(c)[0]]),r=t.map(c=>Ya(c)[0]),s=new Set(r),i=r.map(c=>this.graph.nodes[c]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(n,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let u={},p={};return Pe(()=>{let c=new K5(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[y,x]=Ya(g,c),A=[];A[x]=e[g],d[y]=A,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(A))});let h=this.getFrozenTensorIds(d),{orderedNodes:f,nodeLiveUntilMap:m}=l;for(let g of f){if(d[g.name])continue;let y=X5(g,d,c,this._resourceManager);if(v.isPromise(y))throw new Error(`The execution of the op '${g.op}' returned a promise. 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You can use model.execute() instead.");let x=l.filter(A=>!zs(A)&&!ua(A.name,f,t)).map(A=>A.name);if(x.length>0){let A="";throw c!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${r}]. Consider providing the following inputs: [${p}]. ${A}`)}return f}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&k("isConstant",p.node,n,a)&&([c]=Ar(p.node.name,a)),n[p.node.name]==null){let d=X5(p.node,n,a,this._resourceManager);c||([c]=Ar(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(f=>(n[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),f))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=Ar(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ua(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ua(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=Ya(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=Ya(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=Ya(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},QP=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},eO="?tfjs-format=file",tO="model.json",Lp=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},a=Kn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new QP}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new y1(G5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=G5.Instance.transformGraph(e.modelInitializer);this.initializer=new y1(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof gt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof gt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,a=Object.keys(t);for(let n=0;n1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=await this.executor.executeAsync(e,t);return a.length>1?a:a[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,a)=>(t[a]=[e[a]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&J(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function Y3(e,t={},a=Kn){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=nO(e));let n=new Lp(e,t,a);return await n.load(),n}function aO(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[n,r]=e;if(!n)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=Kn.getWeightSpecs(n.weightsManifest),i=Kn.getModelArtifactsForJSONSync(n,s,r);t=Kn.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=Kn.fromMemorySync(e);else throw new Error("Unknown model format");let a=new Lp(t);return a.load(),a}function nO(e){return e.endsWith("/")||(e=e+"/"),`${e}${tO}${eO}`}var rO="4.5.0";function Ie(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var sO=Nn.whereImpl,Xh=class extends Kl{nextDataId(){return Xh.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Zd(this,It())}write(e,t,a){this.firstUse&&(this.firstUse=!1,W().get("IS_NODE")&&S.warn(` + ${n}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=I1(s,a),o=n===0?0:e.size/n,l=De(()=>{let p=[];e=Q(e,[1,n,o]);for(let c=0;c{switch(e.op){case"If":case"StatelessIf":{let n=k("thenBranch",e,t,a),r=k("elseBranch",e,t,a),s=k("cond",e,t,a),i=k("args",e,t,a);return(await s.data())[0]?a.functionMap[n].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap):a.functionMap[r].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap)}case"While":case"StatelessWhile":{let n=k("body",e,t,a),r=k("cond",e,t,a),s=k("args",e,t,a),i=await a.functionMap[r].executeFunctionAsync(s,a.tensorArrayMap,a.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await a.functionMap[n].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);let c=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()});let d=await a.functionMap[r].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let n=k("pred",e,t,a);return[vr(n)]}case"Switch":{let n=k("pred",e,t,a),r=k("data",e,t,a);return r.kept||(r=vr(r)),(await n.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let n=e.inputNames.find(r=>ua(r,t,a)!==void 0);if(n){let r=ua(n,t,a);return[vr(r)]}return}case"Enter":{let n=k("frameName",e,t,a),r=k("tensor",e,t,a);return a.enterFrame(n),[vr(r)]}case"Exit":{let n=k("tensor",e,t,a);return a.exitFrame(),[vr(n)]}case"NextIteration":{let n=k("tensor",e,t,a);return a.nextIteration(),[vr(n)]}case"TensorArrayV3":{let n=k("size",e,t,a),r=k("dtype",e,t,a),s=k("elementShape",e,t,a),i=k("dynamicSize",e,t,a),o=k("clearAfterRead",e,t,a),l=k("identicalElementShapes",e,t,a),u=k("name",e,t,a),p=new OD(u,r,n,s,l,i,o);return a.addTensorArray(p),[p.idTensor,Ue(1)]}case"TensorArrayWriteV3":{let n=k("tensorArrayId",e,t,a),r=k("index",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let n=k("tensorArrayId",e,t,a),r=k("index",e,t,a);return[a.getTensorArray(n.id).read(r)]}case"TensorArrayGatherV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("dtype",e,t,a);return[a.getTensorArray(n.id).gather(r,s)]}case"TensorArrayScatterV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id),s=k("dtype",e,t,a);return[r.concat(s)]}case"TensorArraySplitV3":{let 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n=k("elementShape",e,t,a),r=k("elementDType",e,t,a),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,a),o=e.op==="TensorListReserve"?-1:i,l=LD(n,r,i,o);return a.addTensorList(l),[l.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,a),r=k("indices",e,t,a),s=k("elementShape",e,t,a),i=k("elementDType",e,t,a);return[a.getTensorList(n.id).gather(r,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=k("numElements",e,t,a);return[a.getTensorList(n.id).stack(r,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=zD(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id),s=k("dtype",e,t,a),i=k("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,a),r=k("tensor",e,t,a),s=a.getTensorList(n.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a);return[a.getTensorList(n.id).popBack(r,s)]}case"TensorListSplit":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("lengths",e,t,a),i=BD(n,s,r);return a.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id);return[Ue(r.size(),"int32")]}case"TensorListResize":{let n=k("tensorListId",e,t,a),r=k("size",e,t,a),s=a.getTensorList(n.id).resize(r);return a.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function nx(e,t,a){let[n,r]=k("fusedOps",e,t,a),s=n==="biasadd",i=!s,o=r==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,a);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one 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r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a),o=k("includeBatchInIndex",e,t,a),{result:l,indexes:u}=n.maxPoolWithArgmax(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.avgPool3d(k("x",e,t,a),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.maxPool3d(k("x",e,t,a),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("dilations",e,t,a),o=r[1],l=r[2],u=i[1],p=i[2];return[n.dilation2d(k("x",e,t,a),k("filter",e,t,a),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},GD=(e,t,a,n=ea)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,a),s=k("dtype",e,t,a),i=k("value",e,t,a);return[n.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("num",e,t,a);return[n.linspace(r,s,i)]}case"Multinomial":{let r=k("logits",e,t,a),s=k("numSamples",e,t,a),i=k("seed",e,t,a);return[n.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,a),s=k("depth",e,t,a),i=k("onValue",e,t,a),o=k("offValue",e,t,a),l=k("dtype",e,t,a);return[n.oneHot(r,s,i,o,l)]}case"Ones":return[n.ones(k("shape",e,t,a),k("dtype",e,t,a))];case"OnesLike":return[n.onesLike(k("x",e,t,a))];case"RandomStandardNormal":return[n.randomStandardNormal(k("shape",e,t,a),k("dtype",e,t,a),k("seed",e,t,a))];case"RandomUniform":return[n.randomUniform(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("dtype",e,t,a))];case"RandomUniformInt":return[n.randomUniformInt(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("seed",e,t,a))];case"Range":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("step",e,t,a);return[n.range(r,s,i,k("dtype",e,t,a))]}case"TruncatedNormal":{let 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ue(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let a=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=Ra(t),r=a.length,s=n.length;v.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let n=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=n.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,a),i=k("valueDType",e,t,a),o=new XD(s,i);return n.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,a,n),s=k("keys",e,t,a),i=k("values",e,t,a);return[await n.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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r=k("images",e,t,a),s=k("transforms",e,t,a),i=k("outputShape",e,t,a),o=k("fillValue",e,t,a),l=k("interpolation",e,t,a),u=k("fillMode",e,t,a);return[n.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ZD=(e,t,a,n=ea)=>{switch(e.op){case"Equal":return[n.equal(k("a",e,t,a),k("b",e,t,a))];case"NotEqual":return[n.notEqual(k("a",e,t,a),k("b",e,t,a))];case"Greater":return[n.greater(k("a",e,t,a),k("b",e,t,a))];case"GreaterEqual":return[n.greaterEqual(k("a",e,t,a),k("b",e,t,a))];case"Less":return[n.less(k("a",e,t,a),k("b",e,t,a))];case"LessEqual":return[n.lessEqual(k("a",e,t,a),k("b",e,t,a))];case"LogicalAnd":return[n.logicalAnd(k("a",e,t,a),k("b",e,t,a))];case"LogicalNot":return[n.logicalNot(k("a",e,t,a))];case"LogicalOr":return[n.logicalOr(k("a",e,t,a),k("b",e,t,a))];case"Select":case"SelectV2":return[n.where(k("condition",e,t,a),k("a",e,t,a),k("b",e,t,a))];case"BitwiseAnd":return[n.bitwiseAnd(k("a",e,t,a),k("b",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JD=(e,t,a,n=ea)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(k("a",e,t,a),k("b",e,t,a),k("transposeA",e,t,a),k("transposeB",e,t,a))];case"Einsum":return[n.einsum(k("equation",e,t,a),...k("tensors",e,t,a))];case"Transpose":return[n.transpose(k("x",e,t,a),k("perm",e,t,a))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,a),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,a),u=k("leakyreluAlpha",e,t,a);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,c]=k("args",e,t,a);return[n.fused.matMul({a:k("a",e,t,a),b:k("b",e,t,a),transposeA:k("transposeA",e,t,a),transposeB:k("transposeB",e,t,a),bias:p,activation:s,preluActivationWeights:c,leakyreluAlpha:u})];case"MatrixBandPart":return[n.linalg.bandPart(k("a",e,t,a),k("numLower",e,t,a),k("numUpper",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},QD=(e,t,a,n=ea)=>{switch(e.op){case"EuclideanNorm":return[n.euclideanNorm(k("x",e,t,a),k("axis",e,t,a),k("keepDims",e,t,a))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"FusedBatchNormV3":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"LRN":return[n.localResponseNormalization(k("x",e,t,a),k("radius",e,t,a),k("bias",e,t,a),k("alpha",e,t,a),k("beta",e,t,a))];case"Softmax":return[n.softmax(k("x",e,t,a))];case"LogSoftmax":return[n.logSoftmax(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},eO=(e,t,a,n=ea)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=n.raggedGather(k("paramsNestedSplits",e,t,a),k("paramsDenseValues",e,t,a),k("indices",e,t,a),k("outputRaggedRank",e,t,a));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=n.raggedRange(k("starts",e,t,a),k("limits",e,t,a),k("splits",e,t,a));return[r,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(k("shape",e,t,a),k("values",e,t,a),k("defaultValue",e,t,a),k("rowPartitionTensors",e,t,a),k("rowPartitionTypes",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tO=(e,t,a,n=ea)=>{switch(e.op){case"Max":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.max(k("x",e,t,a),o,l)]}case"Mean":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.mean(k("x",e,t,a),o,l)]}case"Min":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.min(k("x",e,t,a),o,l)]}case"Sum":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.sum(k("x",e,t,a),o,l)]}case"All":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.all(k("x",e,t,a),o,l)]}case"Any":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.any(k("x",e,t,a),o,l)]}case"ArgMax":{let o=k("axis",e,t,a);return[n.argMax(k("x",e,t,a),o)]}case"ArgMin":{let o=k("axis",e,t,a);return[n.argMin(k("x",e,t,a),o)]}case"Prod":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.prod(k("x",e,t,a),o,l)]}case"Cumprod":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumprod(k("x",e,t,a),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumsum(k("x",e,t,a),o,l,u)]}case"Bincount":let r=k("x",e,t,a),s=k("weights",e,t,a),i=k("size",e,t,a);return[n.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,a),l=k("weights",e,t,a),u=k("size",e,t,a),p=k("binaryOutput",e,t,a);return[n.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},aO=(e,t,a,n=ea)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,a),s=k("axis",e,t,a),i=k("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,a),s=k("batchDims",e,t,a),i=k("x",e,t,a),o=k("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,a),s=[];for(let o=0;o{let r=k("axis",e,t,a),s=k("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,a),s=k("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=k("reps",e,t,a);return[n.tile(k("x",e,t,a),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,a),s=k("numOrSizeSplits",e,t,a),i=k("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,a),s=k("outputShape",e,t,a),i=k("sparseValues",e,t,a),o=k("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("tensor",e,t,a);return[n.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},nO=(e,t,a,n=ea)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(k("indices",e,t,a),k("values",e,t,a),k("denseShape",e,t,a),k("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(k("inputIndices",e,t,a),k("inputShape",e,t,a),k("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},rO=(e,t,a,n=ea)=>{switch(e.op){case"FFT":return[n.fft(k("x",e,t,a))];case"IFFT":return[n.ifft(k("x",e,t,a))];case"RFFT":return[n.rfft(k("x",e,t,a))];case"IRFFT":return[n.irfft(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},sO=(e,t,a,n=ea)=>{switch(e.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(k("input",e,t,a),k("pattern",e,t,a),k("rewrite",e,t,a),k("replaceGlobal",e,t,a))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(k("data",e,t,a),k("dataSplits",e,t,a),k("separator",e,t,a),k("nGramWidths",e,t,a),k("leftPad",e,t,a),k("rightPad",e,t,a),k("padWidth",e,t,a),k("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(k("input",e,t,a),k("delimiter",e,t,a),k("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(k("input",e,t,a),k("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},iO=(e,t,a,n=ea)=>{switch(e.op){case"Cast":return[n.cast(k("x",e,t,a),k("dtype",e,t,a))];case"ExpandDims":{let r=k("axis",e,t,a);return[n.expandDims(k("x",e,t,a),r)]}case"Squeeze":{let r=k("axis",e,t,a);return[n.squeeze(k("x",e,t,a),r)]}case"Reshape":return[n.reshape(k("x",e,t,a),k("shape",e,t,a))];case"EnsureShape":return[n.ensureShape(k("x",e,t,a),k("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(k("x",e,t,a),k("padding",e,t,a),k("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(k("x",e,t,a),k("padding",e,t,a),k("constantValue",e,t,a))];case"SpaceToBatchND":{let r=k("blockShape",e,t,a),s=k("paddings",e,t,a);return[n.spaceToBatchND(k("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,a),s=k("crops",e,t,a);return[n.batchToSpaceND(k("x",e,t,a),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,a),s=k("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(k("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(k("x",e,t,a),k("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(k("s0",e,t,a),k("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function rx(e,t,a,n,r=De){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>FD(i,o,l));case"basic_math":return r(()=>DD(i,o,l));case"control":return VD(i,o,l);case"convolution":return r(()=>UD(i,o,l));case"creation":return r(()=>GD(i,o,l));case"dynamic":return HD(i,o,l);case"evaluation":return r(()=>jD(i,o,l));case"image":return r(()=>YD(i,o,l));case"graph":return r(()=>qD(i,o,l));case"logical":return r(()=>ZD(i,o,l));case"matrices":return r(()=>JD(i,o,l));case"normalization":return r(()=>QD(i,o,l));case"ragged":return r(()=>eO(i,o,l));case"reduction":return r(()=>tO(i,o,l));case"slice_join":return r(()=>aO(i,o,l));case"sparse":return r(()=>nO(i,o,l));case"spectral":return r(()=>rO(i,o,l));case"string":return r(()=>sO(i,o,l));case"transformation":return r(()=>iO(i,o,l));case"hash_table":return KD(i,o,l,n);case"custom":let u=M7(i.op);if(u&&u.customExecutor)return u.customExecutor(new $D(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var sx=class{constructor(e={},t={},a={},n={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function ix(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(d=>Ya(d)[0]));n=n||[];let p=new Set(n.map(d=>Ya(d.name)[0])),c=[...t];for(;c.length>0;){let d=c.pop();if((Ls(d)||mO(d)||fO(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&!u.has(d.name)&&!p.has(d.name)){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function oO(e,t){let{usedNodes:a,inputs:n}=t,r=Object.keys(n).map(g=>Ya(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>a.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(y=>[y.name,y])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let y of g.children)i(y)||(c[y.name]=Number.POSITIVE_INFINITY),c[y.name]=(c[y.name]||0)+1}let d=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),h=[...d];for(;d.length>0;){let g=d.pop(),y=p.get(g);for(let x of y.children.filter(i))--c[x.name]===0&&(h.push(x.name),d.push(x.name))}let m=h.map(g=>p.get(g)),f=lO(m,l);return uO(f,l),f}function lO(e,t){let a=new Map(e.map(s=>[s.name,s])),n=t.map(s=>s.name),r=new Set(n);for(;n.length>0;){let s=n.pop(),i=a.get(s);for(let o of i.children)!a.has(o.name)||r.has(o.name)||(r.add(o.name),n.push(o.name))}return e.filter(s=>r.has(s.name))}var Gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function uO(e,t){let a=new Map(e.map((o,l)=>[o.name,l])),n=new Set(t.map(o=>o.name)),r=o=>n.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!a.has(l.name))throw new Gc(`Child ${l.name} of node ${o.name} is unreachable.`);if(a.get(o.name)>a.get(l.name))throw new Gc(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!a.has(l.name))throw new Gc(`Input ${l.name} of node ${o.name} is unreachable.`);if(a.get(l.name)>a.get(o.name))throw new Gc(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function dO(e){let t=new Map(e.map((o,l)=>[o.name,l])),a=Number.MAX_SAFE_INTEGER,n=e.map((o,l)=>Ls(o)?a:l),r=o=>{let l=n[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),n[l])),i=new Map;for(let o=0;oe[a].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new S1(e.functions[a],this)})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let a=ix(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${n}]`)}let i=oO(this.graph,a),o=dO(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return zn(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(c=>this.graph.nodes[Ya(c)[0]]),r=t.map(c=>Ya(c)[0]),s=new Set(r),i=r.map(c=>this.graph.nodes[c]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(n,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let u={},p={};return De(()=>{let c=new sx(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[y,x]=Ya(g,c),A=[];A[x]=e[g],d[y]=A,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(A))});let h=this.getFrozenTensorIds(d),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(d[g.name])continue;let y=rx(g,d,c,this._resourceManager);if(v.isPromise(y))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);d[g.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(g,d,c,h,s,f.get(g.name))}return this.parent==null&&c.dispose(h),t.map(g=>ua(g,d,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){if(!(Ls(t)||s.has(e))){for(let o of a[e])o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length);for(let o of t.inputs){if(Ls(o))continue;let l=Q5(o.name,a,n);if(l!=null)for(let u of l){if(!u||u.kept||r.has(u.id))continue;let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,a,n,r,s){function i(o){return Ls(o)||r.has(o.name)}if(!(Ls(e)||s==null))for(let o of s){if(i(o))continue;let l=Q5(o.name,t,a);for(let u of l)!u||u.kept||n.has(u.id)||u.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new sx(this.weightMap,n,r,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>ua(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(A=>this.graph.nodes[Ya(A)[0]]),i=a.map(A=>Ya(A)[0]),o=new Set(i),l=i.map(A=>this.graph.nodes[A]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:p,dynamicNode:c,syncInputs:d}=ix(e,l,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),m=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[b,w]=Ya(A),I=[];I[w]=e[A],m[b]=I});let f={},g=this.getFrozenTensorIds(m),y={};for(;h.length>0;){let A=this.processStack(s,h,t,m,y,g,o,f,u);await Promise.all(A)}c==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=l.filter(A=>!Ls(A)&&!ua(A.name,m,t)).map(A=>A.name);if(x.length>0){let A="";throw c!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${r}]. 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u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=br(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ua(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ua(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=Ya(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=Ya(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=Ya(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},gO=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new S1(ex.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=ex.Instance.transformGraph(e.modelInitializer);this.initializer=new S1(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let 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iL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,w=$e(i.shape,"float32"),C=1/(h*f),T=a.data.get(r.dataId).values,N=$e(r.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(U)!==U))for(let G=0;G=p.outWidth||Math.floor(q)!==q)continue;let H=N.get(M,U,q,F);B+=H}}w.set(B*C,M,E,I,F)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var oL={kernelName:kh,backendName:"cpu",kernelFunc:iL};function lL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,x)=>y*x),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=At({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Ua({inputs:{x:h},backend:a,attrs:{perm:u}}),m=At({inputs:{x:f},backend:a,attrs:{shape:p}}),g=Xs({inputs:{x:m},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),g}var pL={kernelName:tu,backendName:"cpu",kernelFunc:dL};function cL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=J3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var hL={kernelName:pi,backendName:"cpu",kernelFunc:cL};function fL(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var mL={kernelName:au,backendName:"cpu",kernelFunc:fL},gL=ct(rs,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;um.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(m=>v.sizeFromShape(m.shape)>0);if(l.length===1)return ar({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let m=l.map(b=>qs({inputs:{input:b},backend:a})),g=l.map(b=>Hl({inputs:{input:b},backend:a})),y=jl({inputs:m,backend:a,attrs:{axis:s}}),x=jl({inputs:g,backend:a,attrs:{axis:s}}),A=Ja({inputs:{real:y,imag:x},backend:a});return m.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(x),A}let u=l.map(m=>{let g=[-1,v.sizeFromShape(m.shape.slice(s))];return At({inputs:{x:m},backend:a,attrs:{shape:g}})}),p=u.map(m=>({vals:a.data.get(m.dataId).values,shape:m.shape}));o=S.computeOutShape(u.map(m=>m.shape),1);let c=u[0].shape[0]===1,d=Q3(p,o,t[0].dtype,c),h=S.computeOutShape(l.map(m=>m.shape),s),f=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var vL={kernelName:nu,backendName:"cpu",kernelFunc:jl};function O6(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ie([r,s],"conv2d");let c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new Wt(d.outShape,r.dtype),w=v.computeStrides(r.shape),C=v.computeStrides(s.shape),T=w[0],N=A?w[1]:w[2],M=A?w[2]:1,F=A?1:w[1],E=b.strides[0],I=A?b.strides[1]:b.strides[2],_=A?b.strides[2]:1,O=A?1:b.strides[1],B=a.data.get(r.dataId).values,$=a.data.get(s.dataId).values,U=b.values;for(let G=0;G=d.inHeight)continue;let me=re*C[0],ie=q+ee*N;for(let Ae=0;Ae=d.inWidth)continue;let mt=me+Le*C[1],dt=ie+qe*M,st=mt;for(let it=0;it=u.inDepth)continue;let G=$*M[0],q=E+U*N[1];for(let H=0;H=u.inHeight)continue;let ee=G+X*M[1],me=q+re*N[2];for(let ie=0;ie=u.inWidth)continue;let qe=ee+Re*M[2],mt=me+Le*u.inChannels,dt=qe;for(let st=0;stMath.cos(e)),$L={kernelName:xi,backendName:"cpu",kernelFunc:_L},DL=ct(Ai,e=>Math.cosh(e)),PL={kernelName:Ai,backendName:"cpu",kernelFunc:DL};function OL(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,f=s.shape[0],[m,g]=o,y=$e([f,m,g,h],"float32"),x=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),C=v.computeStrides(y.shape);for(let T=0;T=p)continue;let O=m>1?(E-M)*(c-1)/(m-1):0,B=g>1?(I-F)*(d-1)/(g-1):0;for(let $=0;$1?M*(c-1)+$*O:.5*(M+E)*(c-1);if(U<0||U>c-1){for(let G=0;G1?F*(d-1)+V*B:.5*(F+I)*(d-1);if(Z<0||Z>d-1){for(let me=0;me1?F*(d-1)+G*B:.5*(F+I)*(d-1);if(q<0||q>d-1){for(let Z=0;Zy+f-x-1:(y,x)=>y+x;for(let y=0;yy+f-x-1:(y,x)=>y+x;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),f=a.data.get(r.dataId).values,m=new Float32Array(o*c*d*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=S.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,C=new Wt(h.outShape,r.dtype),T=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,M=C.values;for(let F=0;F=h.inHeight)continue;let G=$*c[0],q=E+U*p[1];for(let H=0;H=h.inWidth)continue;let ee=G+X*c[1],me=q+re*h.inChannels,ie=V,Ae=ee;for(let Ce=0;Ce{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:C,filterWidth:T,dilationHeight:N,dilationWidth:M,outShape:F}=S.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),E=v.sizeFromShape(F),I=F.length,_=v.getArrayFromDType(n.dtype,E);for(let O=0;O=0&&X=0&&eeH&&(H=Ae)}}}let V=v.locToIndex([O,B,U,q],I,v.computeStrides(F));_[V]=H}}}return{dataId:l.write(v.toTypedArray(_,n.dtype),F,n.dtype),shape:F,dtype:n.dtype}}},tW={kernelName:Pl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:C,dilationHeight:T,dilationWidth:N,outShape:M}=S.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Pl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let F=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let I=0;I=0&&Z=0&&reG&&(G=ee,q=V,H=X)}}}E[q][H][U]+=F[I][_][B][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},aW={kernelName:Dl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:C,dilationHeight:T,dilationWidth:N,outShape:M}=S.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Dl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let F=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let I=0;I=0&&Z=0&&reG&&(G=ee,q=Z,H=re)}}}E[I][q][H][U]+=F[I][_][B][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Wp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"sum");let o;r.dtype==="bool"?o=ts({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=ar({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=S.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Ua({inputs:{x:o},backend:a,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,l)),S.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,f]=S.computeOutAndReduceShapes(d.shape,c),m=S.upcastType(d.dtype,"int32"),g=uh(a,h,m),y=v.sizeFromShape(f),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=Wp({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var sW={kernelName:ip,backendName:"cpu",kernelFunc:rW};function iW(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ie([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var oW={kernelName:ou,backendName:"cpu",kernelFunc:iW},lW=S.ERF_P,uW=S.ERF_A1,dW=S.ERF_A2,pW=S.ERF_A3,cW=S.ERF_A4,hW=S.ERF_A5,fW=ct(lu,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+lW*a);return t*(1-((((hW*n+cW)*n+pW)*n+dW)*n+uW)*n*Math.exp(-a*a))}),mW={kernelName:lu,backendName:"cpu",kernelFunc:fW};function ch(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),At({inputs:{x:r},backend:a,attrs:{shape:o}})}var gW={kernelName:uu,backendName:"cpu",kernelFunc:ch},yW=$t((e,t)=>e/t),dg=Xt(Ci,yW),A1={kernelName:Ci,backendName:"cpu",kernelFunc:dg};function L6(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&x=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=S.segment_util.collectGatherOpShapeInfo(r,s,l,c),f=At({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=At({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],y=a.bufferSync(m),x=a.bufferSync(f),A=a6(x,y,g);return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var _W={kernelName:pu,backendName:"cpu",kernelFunc:FW};function $W(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=At({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=L6(o,!0,a),u=At({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var DW={kernelName:lp,backendName:"cpu",kernelFunc:$W},PW=ct(Li,e=>Number.isFinite(e)?1:0,"bool"),OW={kernelName:Li,backendName:"cpu",kernelFunc:PW},zW=ct(Wi,e=>Math.abs(e)===1/0?1:0,"bool"),LW={kernelName:Wi,backendName:"cpu",kernelFunc:zW},WW=ct(Bi,e=>Number.isNaN(e)?1:0,"bool"),BW={kernelName:Bi,backendName:"cpu",kernelFunc:WW};function VW(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=o6(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var UW={kernelName:Hi,backendName:"cpu",kernelFunc:VW},GW=ct(qi,e=>Math.log1p(e)),HW={kernelName:qi,backendName:"cpu",kernelFunc:GW},jW=$t((e,t)=>e&&t),qW=Xt(Xi,jW,null,"bool"),XW={kernelName:Xi,backendName:"cpu",kernelFunc:qW},KW=ct(Ki,e=>e?0:1,"bool"),YW={kernelName:Ki,backendName:"cpu",kernelFunc:KW},ZW=$t((e,t)=>e||t),JW=Xt(Yi,ZW,null,"bool"),QW={kernelName:Yi,backendName:"cpu",kernelFunc:JW};function eB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ie(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function f(m){let g=m%u,y=m-g+Math.max(0,g-s),x=m-g+Math.min(g+s,p),A=0;for(;y<=x;y++){let b=c[y];A+=b*b}return A}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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kL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,w=$e(i.shape,"float32"),I=1/(h*m),T=a.data.get(r.dataId).values,N=$e(r.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(U)!==U))for(let G=0;G=p.outWidth||Math.floor(q)!==q)continue;let H=N.get(M,U,q,P);B+=H}}w.set(B*I,M,E,C,P)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var IL={kernelName:rp,backendName:"cpu",kernelFunc:kL};function SL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,x)=>y*x),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=At({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Ua({inputs:{x:h},backend:a,attrs:{perm:u}}),f=At({inputs:{x:m},backend:a,attrs:{shape:p}}),g=Ks({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var NL={kernelName:su,backendName:"cpu",kernelFunc:TL};function RL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=ig(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var EL={kernelName:hi,backendName:"cpu",kernelFunc:RL};function ML(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var _L={kernelName:ou,backendName:"cpu",kernelFunc:ML},PL=ct(ss,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;uf.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return tr({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let f=l.map(b=>Xs({inputs:{input:b},backend:a})),g=l.map(b=>Yl({inputs:{input:b},backend:a})),y=Zl({inputs:f,backend:a,attrs:{axis:s}}),x=Zl({inputs:g,backend:a,attrs:{axis:s}}),A=Ja({inputs:{real:y,imag:x},backend:a});return f.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(x),A}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return At({inputs:{x:f},backend:a,attrs:{shape:g}})}),p=u.map(f=>({vals:a.data.get(f.dataId).values,shape:f.shape}));o=S.computeOutShape(u.map(f=>f.shape),1);let c=u[0].shape[0]===1,d=og(p,o,t[0].dtype,c),h=S.computeOutShape(l.map(f=>f.shape),s),m=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var zL={kernelName:lu,backendName:"cpu",kernelFunc:Zl};function X6(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ie([r,s],"conv2d");let c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new Bt(d.outShape,r.dtype),w=v.computeStrides(r.shape),I=v.computeStrides(s.shape),T=w[0],N=A?w[1]:w[2],M=A?w[2]:1,P=A?1:w[1],E=b.strides[0],C=A?b.strides[1]:b.strides[2],_=A?b.strides[2]:1,O=A?1:b.strides[1],B=a.data.get(r.dataId).values,$=a.data.get(s.dataId).values,U=b.values;for(let G=0;G=d.inHeight)continue;let fe=re*I[0],ie=q+ee*N;for(let be=0;be=d.inWidth)continue;let gt=fe+Le*I[1],dt=ie+Xe*M,st=gt;for(let it=0;it=u.inDepth)continue;let G=$*M[0],q=E+U*N[1];for(let H=0;H=u.inHeight)continue;let ee=G+X*M[1],fe=q+re*N[2];for(let ie=0;ie=u.inWidth)continue;let Xe=ee+Re*M[2],gt=fe+Le*u.inChannels,dt=Xe;for(let st=0;stMath.cos(e)),ZL={kernelName:bi,backendName:"cpu",kernelFunc:YL},JL=ct(vi,e=>Math.cosh(e)),QL={kernelName:vi,backendName:"cpu",kernelFunc:JL};function eW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=$e([m,f,g,h],"float32"),x=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),I=v.computeStrides(y.shape);for(let T=0;T=p)continue;let O=f>1?(E-M)*(c-1)/(f-1):0,B=g>1?(C-P)*(d-1)/(g-1):0;for(let $=0;$1?M*(c-1)+$*O:.5*(M+E)*(c-1);if(U<0||U>c-1){for(let G=0;G1?P*(d-1)+V*B:.5*(P+C)*(d-1);if(Z<0||Z>d-1){for(let fe=0;fe1?P*(d-1)+G*B:.5*(P+C)*(d-1);if(q<0||q>d-1){for(let Z=0;Zy+m-x-1:(y,x)=>y+x;for(let y=0;yy+m-x-1:(y,x)=>y+x;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),m=a.data.get(r.dataId).values,f=new Float32Array(o*c*d*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=S.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,I=new Bt(h.outShape,r.dtype),T=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,M=I.values;for(let P=0;P=h.inHeight)continue;let G=$*c[0],q=E+U*p[1];for(let H=0;H=h.inWidth)continue;let ee=G+X*c[1],fe=q+re*h.inChannels,ie=V,be=ee;for(let Ce=0;Ce{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:N,dilationWidth:M,outShape:P}=S.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),E=v.sizeFromShape(P),C=P.length,_=v.getArrayFromDType(n.dtype,E);for(let O=0;O=0&&X=0&&eeH&&(H=be)}}}let V=v.locToIndex([O,B,U,q],C,v.computeStrides(P));_[V]=H}}}return{dataId:l.write(v.toTypedArray(_,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}},xW={kernelName:Bl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=S.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Bl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let P=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let C=0;C=0&&Z=0&&reG&&(G=ee,q=V,H=X)}}}E[q][H][U]+=P[C][_][B][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},AW={kernelName:Wl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=S.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Wl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let P=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let C=0;C=0&&Z=0&&reG&&(G=ee,q=Z,H=re)}}}E[C][q][H][U]+=P[C][_][B][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function bW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let c=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(c==null)throw new Error(`Could not get the context with ${p} type.`);let[d,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=a.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,y=new Uint8ClampedArray(h*d*4);for(let A=0;A1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${T}.`)}else if(r.dtype==="int32"&&(T<0||T>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${T}.`);m===1?(b[0]=T*g,b[1]=T*g,b[2]=T*g):b[I]=T*g}let w=A*4;y[w+0]=Math.round(b[0]),y[w+1]=Math.round(b[1]),y[w+2]=Math.round(b[2]),y[w+3]=Math.round(b[3])}s.width=h,s.height=d;let x=new ImageData(y,h,d);return c.putImageData(x,0,0),r}var vW={kernelName:Y1,backendName:"cpu",kernelFunc:bW};function Hp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"sum");let o;r.dtype==="bool"?o=as({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=tr({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=S.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Ua({inputs:{x:o},backend:a,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,l)),S.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,m]=S.computeOutAndReduceShapes(d.shape,c),f=S.upcastType(d.dtype,"int32"),g=mh(a,h,f),y=v.sizeFromShape(m),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=Hp({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var IW={kernelName:pp,backendName:"cpu",kernelFunc:kW};function SW(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ie([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var CW={kernelName:cu,backendName:"cpu",kernelFunc:SW},TW=S.ERF_P,NW=S.ERF_A1,RW=S.ERF_A2,EW=S.ERF_A3,MW=S.ERF_A4,_W=S.ERF_A5,PW=ct(Ei,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+TW*a);return t*(1-((((_W*n+MW)*n+EW)*n+RW)*n+NW)*n*Math.exp(-a*a))}),$W={kernelName:Ei,backendName:"cpu",kernelFunc:PW};function yh(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),At({inputs:{x:r},backend:a,attrs:{shape:o}})}var FW={kernelName:hu,backendName:"cpu",kernelFunc:yh},DW=Ft((e,t)=>e/t),xg=Yt(Ni,DW),T1={kernelName:Ni,backendName:"cpu",kernelFunc:xg};function Y6(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&x=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=S.segment_util.collectGatherOpShapeInfo(r,s,l,c),m=At({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=At({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],y=a.bufferSync(f),x=a.bufferSync(m),A=h6(x,y,g);return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var JW={kernelName:fu,backendName:"cpu",kernelFunc:ZW};function QW(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=At({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=Y6(o,!0,a),u=At({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var eB={kernelName:hp,backendName:"cpu",kernelFunc:QW},tB=ct(Vi,e=>Number.isFinite(e)?1:0,"bool"),aB={kernelName:Vi,backendName:"cpu",kernelFunc:tB},nB=ct(Ui,e=>Math.abs(e)===1/0?1:0,"bool"),rB={kernelName:Ui,backendName:"cpu",kernelFunc:nB},sB=ct(Gi,e=>Number.isNaN(e)?1:0,"bool"),iB={kernelName:Gi,backendName:"cpu",kernelFunc:sB};function oB(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=x6(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var lB={kernelName:Xi,backendName:"cpu",kernelFunc:oB},uB=ct(Yi,e=>Math.log1p(e)),dB={kernelName:Yi,backendName:"cpu",kernelFunc:uB},pB=Ft((e,t)=>e&&t),cB=Yt(Zi,pB,null,"bool"),hB={kernelName:Zi,backendName:"cpu",kernelFunc:cB},mB=ct(Ji,e=>e?0:1,"bool"),fB={kernelName:Ji,backendName:"cpu",kernelFunc:mB},gB=Ft((e,t)=>e||t),yB=Yt(Qi,gB,null,"bool"),xB={kernelName:Qi,backendName:"cpu",kernelFunc:yB};function AB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ie(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),x=f-g+Math.min(g+s,p),A=0;for(;y<=x;y++){let b=c[y];A+=b*b}return A}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=tr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=yg(d,r.shape,r.dtype,h,p,"max");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var SB={kernelName:no,backendName:"cpu",kernelFunc:IB};function CB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"maxPool3d");let p=S.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=q6(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"max");return a.makeTensorInfo(d.shape,"float32",d.values)}var TB={kernelName:yu,backendName:"cpu",kernelFunc:CB};function NB(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"maxPool3DGrad");let p=S.computePool3DInfo(s.shape,i,o,1,l,u),c=a.bufferSync(s),d=gL(c,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,A=p.effectiveFilterDepth,b=p.effectiveFilterHeight,w=p.effectiveFilterWidth,I=A-1-p.padInfo.front,T=w-1-p.padInfo.left,N=b-1-p.padInfo.top,M=$e(s.shape,"float32"),P=a.bufferSync(r);for(let E=0;E=p.outDepth||Math.floor(V)!==V))for(let Z=0;Z=p.outHeight||Math.floor(X)!==X))for(let re=0;re=p.outWidth||Math.floor(ee)!==ee)continue;let fe=A*b*w-1-d.get(E,V,X,ee,C),ie=H*b*w+Z*w+re,be=fe===ie?1:0;if(be===0)continue;let Ce=P.get(E,V,X,ee,C);q+=Ce*be}}}M.set(q,E,_,O,B,C)}return a.makeTensorInfo(M.shape,M.dtype,M.values)}var RB={kernelName:gp,backendName:"cpu",kernelFunc:NB};function EB(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=a.data.get(o.dataId).values,m=$e(d.outShape,o.dtype,j6(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,x=d.dilationWidth,A=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,I=A-1-d.padInfo.top,T=$e(o.shape,"float32"),N=a.data.get(r.dataId).values,M=$e(r.shape,"float32",N);for(let P=0;P=d.outHeight||Math.floor(G)!==G))for(let q=0;q=d.outWidth||Math.floor(H)!==H)continue;let V=A*b-1-m.get(P,G,H,E),Z=U*b+q,X=V===Z?1:0;if(X===0)continue;let re=M.get(P,G,H,E);$+=re*X}}T.set($,P,C,_,E)}return a.makeTensorInfo(T.shape,T.dtype,T.values)}var MB={kernelName:fp,backendName:"cpu",kernelFunc:EB};function _B(e,t,a,n,r){let s=v.computeStrides(t),i=yg(e,t,a,s,r,"max"),o=j6(e,t,a,r,!0,n);return[i.values,o.values]}var PB={kernelName:xu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;Ie(n,"MaxPoolWithArgmax");let u=l.data.get(n.dataId).values,p=S.computePool2DInfo(n.shape,r,s,[1,1],i),[c,d]=_B(u,n.shape,n.dtype,o,p),h=l.write(c,p.outShape,n.dtype),m=l.write(d,p.outShape,n.dtype);return[{dataId:h,shape:p.outShape,dtype:n.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function $B(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=v.parseAxisParam(s,r.shape),l=S.computeOutAndReduceShapes(r.shape,o)[1],u=v.sizeFromShape(l),p=[],c=a.makeTensorInfo([],"float32",new Float32Array([u]));p.push(c);let d=as({inputs:{x:r},backend:a,attrs:{dtype:"float32"}});p.push(d);let h=xg({inputs:{a:d,b:c},backend:a});p.push(h);let m=Hp({inputs:{x:h},backend:a,attrs:{axis:s,keepDims:i}});return p.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var FB={kernelName:ro,backendName:"cpu",kernelFunc:$B};function DB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"min");let o=v.parseAxisParam(s,r.shape),l=o,u=S.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Ua({inputs:{x:r},backend:a,attrs:{perm:u}}),l=S.getInnerMostAxes(l.length,r.shape.length)),S.assertAxesAreInnerMostDims("min",l,p.shape.length);let[c,d]=S.computeOutAndReduceShapes(p.shape,l),h=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(c),p.dtype),f=a.data.get(p.dataId).values;for(let y=0;yx[0]+r.shape[A]+x[1]),l=s.map(x=>x[0]),u=s.map((x,A)=>x[0]+r.shape[A]),p=i==="reflect"?0:1,c=a.data.get(r.dataId).values,d=r.shape.length,h=v.computeStrides(r.shape),m=v.sizeFromShape(o),f=o.length,g=v.computeStrides(o),y=v.getTypedArrayFromDType(r.dtype,m);for(let x=0;x=u[w]&&(A[w]=(u[w]-1)*2-A[w]+p);A=A.map((w,I)=>w-l[I]);let b=v.locToIndex(A,d,h);y[x]=c[b]}return{dataId:a.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var LB={kernelName:oo,backendName:"cpu",kernelFunc:zB},WB=Ft((e,t)=>{let a=e%t;return e<0&&t<0||e>=0&&t>=0?a:(a+t)%t}),BB=Yt(lo,WB),VB={kernelName:lo,backendName:"cpu",kernelFunc:BB},UB=Jl(aA());function J6(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=v.parseAxisParam([o],r.shape),u=Z6({inputs:{x:r},backend:a,attrs:{reductionIndices:l,keepDims:!1}}),p=S.expandShapeToKeepDim(u.shape,l),c=At({inputs:{x:u},backend:a,attrs:{shape:p}}),d=fg({inputs:{a:r,b:c},backend:a}),h=l6({inputs:{x:d},backend:a}),m=Hp({inputs:{x:h},backend:a,attrs:{axis:l,keepDims:!1}}),f=At({inputs:{x:m},backend:a,attrs:{shape:p}}),g=xg({inputs:{a:h,b:f},backend:a});return a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var GB={kernelName:Lo,backendName:"cpu",kernelFunc:J6};function HB(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;Ie(r,"multinomial");let l=o?r:J6({inputs:{logits:r},backend:a,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],c=a.data.get(l.dataId).values,d=[u,s],h=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let m=0;m=0&&c[d]{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=yh({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=Zl({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var iV={kernelName:wu,backendName:"cpu",kernelFunc:ev};function oV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;Ie(r,"pad");let o=s.map((y,x)=>y[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=a.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),c=r.shape.length,d=v.computeStrides(r.shape),h=v.sizeFromShape(o),m=o.length,f=v.computeStrides(o),g=v.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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Se=W();Se.registerFlag("HAS_WEBGL",()=>Se.getNumber("WEBGL_VERSION")>0);Se.registerFlag("WEBGL_VERSION",()=>M1(2)?2:M1(1)?1:0);Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Se.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Se.get("WEBGL_VERSION")===2);Se.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Se.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Se.registerFlag("WEBGL_PACK",()=>Se.getBool("HAS_WEBGL"));Se.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_CLIP",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_REDUCE",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_LAZILY_UNPACK",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_CONV_IM2COL",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>vv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>wv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Se.getNumber("WEBGL_VERSION");return e===0?0:kv(e)});Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Ep.isMobile());Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Iv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Se.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Sv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Cv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Se.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Ep.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Se.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Se.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Se.registerFlag("WEBGL_EXP_CONV",()=>!1);Se.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Se.getBool("IS_TEST"));Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Se.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Se.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function Ea(){let e,t,a,n,r,s,i,o,l,u;return W().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",a="out",n="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=W().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -100,15 +100,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function jo(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Jh(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function qU(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function XU(e,t,a="index"){let n=e.map((s,i)=>i),r=qU(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function fg(e){let t=v.computeStrides(e).map(a=>a.toString());return` + `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Zo(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function e0(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function cG(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function hG(e,t,a="index"){let n=e.map((s,i)=>i),r=cG(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function wg(e){let t=v.computeStrides(e).map(a=>a.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function mg(){return` +`}function kg(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var gv=` +`}var Tv=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -147,22 +147,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:yv}=S;function KU(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:f}=gg(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(` -`),s=e.map(d=>YU(d,t,a.packedInputs,a.enableShapeUniforms)).join(` -`),i=t.texShape,o=Ea(),l=QU(o),u,p,c=aG(o);return t.isPacked?(u=ZU(t.logicalShape,i,a.enableShapeUniforms),p=tG(o)):(u=JU(t.logicalShape,i,a.enableShapeUniforms),p=eG(o)),a.packedInputs&&(c+=iG),[c,l,p,r,u,s,a.userCode].join(` -`)}function Ou(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return xG(e,t);case 1:return bG(e,t);case 2:return wG(e,t);case 3:return IG(e,t);case 4:return CG(e,t);case 5:return TG(e);case 6:return NG(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function xv(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return yG(e);case 1:return AG(e,t);case 2:return vG(e,t);case 3:return kG(e,t);default:return SG(e,t)}}function YU(e,t,a=!1,n){let r="";a?r+=xv(e,n):r+=Ou(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=RG(e,t):r+=EG(e,t)),r}function ZU(e,t,a){switch(e.length){case 0:return Av();case 1:return oG(e,t,a);case 2:return mG(e,t,a);case 3:return uG(e,t,a);default:return pG(e,t,a)}}function JU(e,t,a){switch(e.length){case 0:return Av();case 1:return lG(e,t,a);case 2:return gG(e,t,a);case 3:return dG(e,t,a);case 4:return cG(e,t,a);case 5:return hG(e,t);case 6:return fG(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function QU(e){return` +`,{getBroadcastDims:Nv}=S;function mG(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:m}=Ig(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(` +`),s=e.map(d=>fG(d,t,a.packedInputs,a.enableShapeUniforms)).join(` +`),i=t.texShape,o=Ea(),l=xG(o),u,p,c=vG(o);return t.isPacked?(u=gG(t.logicalShape,i,a.enableShapeUniforms),p=bG(o)):(u=yG(t.logicalShape,i,a.enableShapeUniforms),p=AG(o)),a.packedInputs&&(c+=SG),[c,l,p,r,u,s,a.userCode].join(` +`)}function Wu(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return OG(e,t);case 1:return LG(e,t);case 2:return BG(e,t);case 3:return UG(e,t);case 4:return HG(e,t);case 5:return jG(e);case 6:return qG(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function Rv(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return DG(e);case 1:return zG(e,t);case 2:return WG(e,t);case 3:return VG(e,t);default:return GG(e,t)}}function fG(e,t,a=!1,n){let r="";a?r+=Rv(e,n):r+=Wu(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=XG(e,t):r+=KG(e,t)),r}function gG(e,t,a){switch(e.length){case 0:return Ev();case 1:return CG(e,t,a);case 2:return $G(e,t,a);case 3:return NG(e,t,a);default:return EG(e,t,a)}}function yG(e,t,a){switch(e.length){case 0:return Ev();case 1:return TG(e,t,a);case 2:return FG(e,t,a);case 3:return RG(e,t,a);case 4:return MG(e,t,a);case 5:return _G(e,t);case 6:return PG(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function xG(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function eG(e){return` + `}function AG(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function tG(e){return` + `}function bG(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function aG(e){return`${e.version} + `}function vG(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -217,10 +217,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${nG} - ${rG} - ${sG} - `}var nG=` + ${wG} + ${kG} + ${IG} + `}var wG=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -232,7 +232,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,rG=` +`,kG=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -240,7 +240,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,sG=` +`,IG=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -249,7 +249,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,iG=` +`,SG=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -260,11 +260,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function Av(){return` +`;function Ev(){return` int getOutputCoords() { return 0; } - `}function oG(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?` + `}function CG(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -293,7 +293,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${n[0]}, ${n[1]})); return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); } - `}function lG(e,t,a){return t[0]===1?a?` + `}function TG(e,t,a){return t[0]===1?a?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -321,7 +321,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function uG(e,t,a){if(a)return` + `}function NG(e,t,a){if(a)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -352,15 +352,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function dG(e,t,a){if(a)return` + `}function RG(e,t,a){if(a)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Jh(["r","c","d"],e)} + ${e0(["r","c","d"],e)} return ivec3(r, c, d); } -`;let n=jo(["r","c","d"],e);return` +`;let n=Zo(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -368,7 +368,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec3(r, c, d); } - `}function pG(e,t,a){if(a)return` + `}function EG(e,t,a){if(a)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -409,15 +409,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function cG(e,t,a){if(a)return` + `}function MG(e,t,a){if(a)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Jh(["r","c","d","d2"],e)} + ${e0(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let n=jo(["r","c","d","d2"],e);return` + `;let n=Zo(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -425,7 +425,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec4(r, c, d, d2); } - `}function hG(e,t){let a=jo(["r","c","d","d2","d3"],e);return` + `}function _G(e,t){let a=Zo(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function fG(e,t){let a=jo(["r","c","d","d2","d3","d4"],e);return` + `}function PG(e,t){let a=Zo(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -448,7 +448,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function mG(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return a?` + `}function $G(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return a?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -481,7 +481,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function gG(e,t,a){return v.arraysEqual(e,t)?a?` + `}function FG(e,t,a){return v.arraysEqual(e,t)?a?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -535,15 +535,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function qo(e){return`offset${e}`}function yG(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ea();return` + `}function Jo(e){return`offset${e}`}function DG(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ea();return` vec4 ${a}() { return ${n.texture2D}(${t}, halfCR); } - `}function xG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${a};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` + `}function OG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${a};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` float ${n}() { return sampleTexture(${a}, halfCR); } - `;let i=qo(a);if(t)return` + `;let i=Jo(a);if(t)return` float ${n}() { vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], ${i}); return sampleTexture(${a}, uv); @@ -553,7 +553,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${a}, uv); } - `}function AG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=e.shapeInfo.texShape,s=Ea();if(t)return` + `}function zG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=e.shapeInfo.texShape,s=Ea();if(t)return` vec4 ${n}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -566,15 +566,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${a}, uv); } - `}function bG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return` + `}function LG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return` float ${n}(int index) { - ${zu(e)} + ${Bu(e)} } `;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return` float ${n}(int index) { return sampleTexture(${a}, halfCR); } - `;let o=qo(a);return i===1?t?` + `;let o=Jo(a);return i===1?t?` float ${n}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${a}TexShape[0])); return sampleTexture(${a}, uv); @@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${a}, uv); } - `}function vG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Ea();if(s!=null&&v.arraysEqual(a,s))return t?` + `}function WG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Ea();if(s!=null&&v.arraysEqual(a,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); @@ -628,7 +628,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${n}, uv); } - `}function wG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(a,s)){if(t)return` + `}function BG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(a,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); return sampleTexture(${n}, uv); @@ -638,17 +638,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0); return sampleTexture(${n}, uv); } - `}let{newShape:i,keptDims:o}=v.squeezeShape(a),l=i;if(l.length=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` -`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${c[y+u]}`).join(", ");let h="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)h=` + `}function XG(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Nv(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` +`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${c[y+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); - `;else if(f&&!m)i===1?h=` + `;else if(m&&!f)i===1?h=` return vec4(outputValue.x, outputValue.x, 0., 0.); `:h=` return vec4(outputValue.x); @@ -965,20 +965,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${n}(${d}); ${h} } - `}function EG(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` + `}function KG(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` float ${r}() { return sampleTexture(${a}, resultUV); } - `;let u=ft(l),p=yv(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(m=>`coords.${h[m+c]} = 0;`).join(` -`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+c]}`).join(", "),` + `;let u=ft(l),p=Nv(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(f=>`coords.${h[f+c]} = 0;`).join(` +`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+c]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${d} - return get${n}(${f}); + return get${n}(${m}); } - `}function ft(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function gg(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.lengthe[a]).join(", ")}function MG(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=KU(r,i,t),l=K6(e.gl,o),u=e.createProgram(l);return W().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},bv(e,t,u)))}function bv(e,t,a){let n=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(a,"NAN",!1),W().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(a,"INFINITY",!1));let p=!1;for(let c of t.variableNames){let d={name:c,uniform:e.getUniformLocation(a,c,p),offset:e.getUniformLocation(a,`offset${c}`,p)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(a,`${c}Shape`,p),d.texShape=e.getUniformLocation(a,`${c}TexShape`,p)),n.push(d)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(a,"outShape",p),o=e.getUniformLocation(a,"outShapeStrides",p),i=e.getUniformLocation(a,"outTexShape",p)),t.customUniforms)for(let c of t.customUniforms)r.push(e.getUniformLocation(a,c.name,p));return{variablesLocations:n,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function nx(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function FG(e,t,a,n,r){t.program.enableShapeUniforms||(nx(t.inShapeInfos,a),nx([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),W().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=gg(e.packedInputs,i.shape,l),d="",h="",f="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=v.computeStrides(p);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=S.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&m===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${W().getNumber("WEBGL_VERSION")}`,s}function ya(e){return W().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var $G=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Ud.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}function ft(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ig(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.lengthe[a]).join(", ")}function YG(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=mG(r,i,t),l=iv(e.gl,o),u=e.createProgram(l);return W().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},Mv(e,t,u)))}function Mv(e,t,a){let n=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(a,"NAN",!1),W().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(a,"INFINITY",!1));let p=!1;for(let c of t.variableNames){let d={name:c,uniform:e.getUniformLocation(a,c,p),offset:e.getUniformLocation(a,`offset${c}`,p)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(a,`${c}Shape`,p),d.texShape=e.getUniformLocation(a,`${c}TexShape`,p)),n.push(d)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(a,"outShape",p),o=e.getUniformLocation(a,"outShapeStrides",p),i=e.getUniformLocation(a,"outTexShape",p)),t.customUniforms)for(let c of t.customUniforms)r.push(e.getUniformLocation(a,c.name,p));return{variablesLocations:n,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function hx(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function ZG(e,t,a,n,r){t.program.enableShapeUniforms||(hx(t.inShapeInfos,a),hx([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),W().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=Ig(e.packedInputs,i.shape,l),d="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=v.computeStrides(p);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=S.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&f===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${W().getNumber("WEBGL_VERSION")}`,s}function ya(e){return W().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var QG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=qd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Jh(["r","c","d"],e):jo(["r","c","d"],e)} + ${this.enableShapeUniforms?e0(["r","c","d"],e):Zo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},DG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ud.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}},eH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=qd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Jh(["r","c","d"],e):jo(["r","c","d"],e)} + ${this.enableShapeUniforms?e0(["r","c","d"],e):Zo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1016,26 +1016,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},PG=class{constructor(e){this.variableNames=["A"],this.outTexUsage=fn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=` - ${gv} + `}},tH=class{constructor(e){this.variableNames=["A"],this.outTexUsage=mn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=` + ${Tv} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},OG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=fn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=` - ${gv} + `}},aH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=` + ${Tv} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},zG={R:0,G:1,B:2,A:3},rx=class{constructor(e,t=!1,a="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iEv,createBufferFromOutputTexture:()=>_v,createFloat16MatrixTexture:()=>Cv,createFloat16PackedMatrixTexture:()=>Rv,createFloat32MatrixTexture:()=>Sv,createIndexBuffer:()=>Iv,createPackedMatrixTexture:()=>Nv,createUnsignedBytesMatrixTexture:()=>Tv,createVertexBuffer:()=>kv,createVertexShader:()=>wv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Dv,downloadFloat32MatrixFromBuffer:()=>$v,downloadMatrixFromPackedOutputTexture:()=>Ov,downloadPackedMatrixFromBuffer:()=>Pv,getInternalFormatForFloat16MatrixTexture:()=>xg,getInternalFormatForFloat16PackedMatrixTexture:()=>vg,getInternalFormatForFloat32MatrixTexture:()=>yg,getInternalFormatForPackedMatrixTexture:()=>bg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Ag,uploadDenseMatrixToTexture:()=>Mv,uploadPixelDataToTexture:()=>Fv});function wv(e){let t=Ea(),a=`${t.version} + `}},_v={};Ze(_v,{bindVertexProgramAttributeStreams:()=>Bv,createBufferFromOutputTexture:()=>Gv,createFloat16MatrixTexture:()=>Ov,createFloat16PackedMatrixTexture:()=>Wv,createFloat32MatrixTexture:()=>Dv,createIndexBuffer:()=>Fv,createPackedMatrixTexture:()=>Lv,createUnsignedBytesMatrixTexture:()=>zv,createVertexBuffer:()=>$v,createVertexShader:()=>Pv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>jv,downloadFloat32MatrixFromBuffer:()=>Hv,downloadMatrixFromPackedOutputTexture:()=>Xv,downloadPackedMatrixFromBuffer:()=>qv,getInternalFormatForFloat16MatrixTexture:()=>Cg,getInternalFormatForFloat16PackedMatrixTexture:()=>Rg,getInternalFormatForFloat32MatrixTexture:()=>Sg,getInternalFormatForPackedMatrixTexture:()=>Ng,getInternalFormatForUnsignedBytesMatrixTexture:()=>Tg,uploadDenseMatrixToTexture:()=>Vv,uploadPixelDataToTexture:()=>Uv});function Pv(e){let t=Ea(),a=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1107,7 +1107,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; - }`;return X6(e,a)}function kv(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return J6(e,t)}function Iv(e){let t=new Uint16Array([0,1,2,2,1,3]);return Q6(e,t)}function Vp(e,t,a,n,r,s){tv(t,a);let i=ev(e),o=e.TEXTURE_2D;return pe(e,()=>e.bindTexture(o,i)),pe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),pe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),pe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),pe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),W().getNumber("WEBGL_VERSION")===1?pe(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):pe(e,()=>e.texStorage2D(o,1,n,t,a)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function yg(e){return e.internalFormatFloat}function Sv(e,t,a,n){let[r,s]=Bp(t,a);return Vp(e,r,s,yg(n),n.textureFormatFloat,e.FLOAT)}function xg(e){return e.internalFormatHalfFloat}function Cv(e,t,a,n){let[r,s]=Bp(t,a);return Vp(e,r,s,xg(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Ag(e){return e.downloadTextureFormat}function Tv(e,t,a,n){let[r,s]=Bp(t,a);return Vp(e,r,s,Ag(n),e.RGBA,e.UNSIGNED_BYTE)}function bg(e){return e.internalFormatPackedFloat}function Nv(e,t,a,n){let[r,s]=Du(t,a);return Vp(e,r,s,bg(n),e.RGBA,e.FLOAT)}function vg(e){return e.internalFormatPackedHalfFloat}function Rv(e,t,a,n){let[r,s]=Du(t,a);return Vp(e,r,s,vg(n),e.RGBA,n.textureTypeHalfFloat)}function Ev(e,t,a){return pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),v1(e,t,"clipSpacePos",a,3,20,0)&&v1(e,t,"uv",a,2,20,12)}function Mv(e,t,a,n,r,s){pe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(a*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new 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t=this.gl;pe(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),Ev(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(pe(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Xc(this.gl,this.program),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?rv(this.gl,e,t):sv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;Kc(n,e,this.framebuffer),this.debug&&bd(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,a)),pe(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),pe(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function WG(e){let t=0;for(;t`${e}.${a}`)}function Ia(e,t){return t===1?[e]:Bv(e,t)}function MH(e,t){if(e===1)return"rc";let a="";for(let n=0;ne.bindTexture(o,i)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),W().getNumber("WEBGL_VERSION")===1?ce(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):ce(e,()=>e.texStorage2D(o,1,n,t,a)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function Sg(e){return e.internalFormatFloat}function Dv(e,t,a,n){let[r,s]=jp(t,a);return qp(e,r,s,Sg(n),n.textureFormatFloat,e.FLOAT)}function Cg(e){return e.internalFormatHalfFloat}function Ov(e,t,a,n){let[r,s]=jp(t,a);return qp(e,r,s,Cg(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Tg(e){return e.downloadTextureFormat}function zv(e,t,a,n){let[r,s]=jp(t,a);return qp(e,r,s,Tg(n),e.RGBA,e.UNSIGNED_BYTE)}function Ng(e){return e.internalFormatPackedFloat}function Lv(e,t,a,n){let[r,s]=zu(t,a);return 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ce(e,()=>e.readPixels(0,0,r,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function qv(e,t,a,n,r,s,i,o){let l=e,u=new Float32Array(tG(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Xv(e,t,a){let n=new Float32Array(t*a*4);return ce(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,n)),n}var zl=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=W().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,Qh(t,e)):this.gl=Wn(t),e=this.gl,W().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ce(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ce(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ce(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ce(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ce(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ce(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ce(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),W().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=kd(this.gl,r),fn(this.gl,s))this.textureHalfFloatExtension=kd(this.gl,s);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(a),fn(this.gl,n))this.colorBufferHalfFloatExtension=kd(this.gl,n);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",fn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(fn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=$v(this.gl),this.indexBuffer=Fv(this.gl),this.framebuffer=hv(this.gl),this.textureConfig=bg(this.gl,this.textureHalfFloatExtension)}get debug(){return W().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Dv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Ov(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),zv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Uv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),Vv(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Wv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Lv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(E1(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>jv(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return qv(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Hv(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=Gv(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(W().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let s=n.clientWaitSync(r,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=r}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),a=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):a=()=>!0;return{query:t,isFencePassed:a}}downloadMatrixFromPackedTexture(e,t,a){return this.downloadMatrixDriver(e,()=>Xv(this.gl,t,a))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=Pv(t));let a=ov(t);ce(t,()=>t.attachShader(a,this.vertexShader)),ce(t,()=>t.attachShader(a,e)),lv(t,a);let n=Object.assign(a,{vao:this.createVertexArray()});return this.debug&&Jc(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),Bv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Jc(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?fv(this.gl,e,t):gv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),yv(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=zu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Jc(this.gl,this.program),Id(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=kd(this.gl,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.createQuery();return a.beginQuery(n.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=sH(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in W().platform&&(a=W().platform.setTimeoutCustom.bind(W().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Qc(this.gl,e,this.framebuffer),this.debug&&Id(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Qc(this.gl,this.outputTexture,this.framebuffer),this.debug&&Id(this.gl)):E1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;Qc(n,e,this.framebuffer),this.debug&&Id(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,a)),ce(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function sH(e){let t=0;for(;t`${e}.${a}`)}function Ia(e,t){return t===1?[e]:Jv(e,t)}function ZH(e,t){if(e===1)return"rc";let a="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},Vv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},Qv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` ${r} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1146,8 +1146,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${n>0?"}":""} `}this.userCode=` - ${_H(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?mg():fg(e)} + ${QH(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?kg():wg(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1162,12 +1162,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function _H(e,t){return` + `}};function QH(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?XU(["r","c","d"],"inputShape"):jo(["r","c","d"],e)} + ${t?hG(["r","c","d"],"inputShape"):Zo(["r","c","d"],e)} return ivec3(r, c, d); } - `}var $H=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,a){let n=ix(t,a),r=ox(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=sx(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return n===da.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===da.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===da.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===da.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===da.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=ix(a,n),s=ox(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=sx(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function DH(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function sx(e,t,a,n,r){let s=PH(t,n),i;if(r){let[l,u]=Du(e[0],e[1]);i=l*u}else{let[l,u]=Bp(e[0],e[1]);i=l*u}let o=DH(a,s);return i*o}function PH(e,t){switch(e){case da.PACKED_2X2_FLOAT32:return bg(t);case da.PACKED_2X2_FLOAT16:return vg(t);case da.UNPACKED_FLOAT32:return yg(t);case da.UNPACKED_FLOAT16:return xg(t);case da.PACKED_4X1_UNSIGNED_BYTE:return Ag(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function OH(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?da.PACKED_2X2_FLOAT32:da.UNPACKED_FLOAT32:e?da.PACKED_2X2_FLOAT16:da.UNPACKED_FLOAT16}function ix(e,t){if(e===fn.UPLOAD)return da.PACKED_2X2_FLOAT32;if(e===fn.RENDER||e==null)return OH(t);if(e===fn.DOWNLOAD||e===fn.PIXELS)return da.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ox(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var Yn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}var ej=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,a){let n=gx(t,a),r=yx(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=fx(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return n===da.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===da.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===da.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===da.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===da.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=gx(a,n),s=yx(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=fx(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function tj(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function fx(e,t,a,n,r){let s=aj(t,n),i;if(r){let[l,u]=zu(e[0],e[1]);i=l*u}else{let[l,u]=jp(e[0],e[1]);i=l*u}let o=tj(a,s);return i*o}function aj(e,t){switch(e){case da.PACKED_2X2_FLOAT32:return Ng(t);case da.PACKED_2X2_FLOAT16:return Rg(t);case da.UNPACKED_FLOAT32:return Sg(t);case da.UNPACKED_FLOAT16:return Cg(t);case da.PACKED_4X1_UNSIGNED_BYTE:return Tg(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function nj(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?da.PACKED_2X2_FLOAT32:da.UNPACKED_FLOAT32:e?da.PACKED_2X2_FLOAT16:da.UNPACKED_FLOAT16}function gx(e,t){if(e===mn.UPLOAD)return da.PACKED_2X2_FLOAT32;if(e===mn.RENDER||e==null)return nj(t);if(e===mn.DOWNLOAD||e===mn.PIXELS)return da.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function yx(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var Kn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1178,11 +1178,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Rn="if (isnan(x)) return x;",zH="return x;",lx="return abs(x);",LH="return (x >= 0.0) ? x : (exp(x) - 1.0);",WH=Rn+` + `}},En="if (isnan(x)) return x;",rj="return x;",xx="return abs(x);",sj="return (x >= 0.0) ? x : (exp(x) - 1.0);",ij=En+` return (x < 0.0) ? 0.0 : x; -`,BH=Rn+` +`,oj=En+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Lr="return x;",VH="return 1.0 / (1.0 + exp(-1.0 * x));",UH="return x;",GH=` +`,Wr="return x;",lj="return 1.0 / (1.0 + exp(-1.0 * x));",uj="return x;",dj=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1191,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,HH=` +`,pj=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1201,7 +1201,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,jH=` +`,cj=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1211,7 +1211,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,qH="return 1.0 / (1.0 + exp(-1.0 * x));",Gr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` +`,hj="return 1.0 / (1.0 + exp(-1.0 * x));",Hr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1222,17 +1222,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},XH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.length,a=Ia("rc",t),n=ft(t),r=MH(t,a),s=a.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},mj=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.length,a=Ia("rc",t),n=ft(t),r=ZH(t,a),s=a.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${i})); } - `}},KH=Nn.whereImpl,YH=1e-7,ZH=1e-4,W2={};function JH(e){return e in W2||(W2[e]={}),W2[e]}var QH=W().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),ej=600;function tj(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*ej/1024/1024}var Bu=class extends Kl{nextDataId(){return Bu.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof _l)t=e;else{let a=Wn(W().getNumber("WEBGL_VERSION"),e);t=new _l(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Wn(W().getNumber("WEBGL_VERSION"));t=new _l(a),this.binaryCache=JH(W().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new $H(this.gpgpu),this.numMBBeforeWarning=tj(),this.texData=new Zd(this,It())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=vd(t),u=new rx(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:a,values:e,usage:fn.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,a,n,r){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:a,dtype:n,values:t,usage:fn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Gr(i,Lr):c=new Yn(i,Lr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=S.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Gr(n,Lr):h=new Yn(n,Lr);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(W().getBool("DEBUG")&&!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Vc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];p=S.mergeRealAndImagArrays(f,m)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;pe(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&It().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Gr(r,Lr):d=new Yn(r,Lr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=It().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return $e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=QH){return W().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return It().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new XH(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new FH(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Ks(e.shape),...Ys(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Ks(t),...Ys(t)],s=new Vv(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=vd(r),o;n?o=new DG(i):o=new $G(i);let l=!0,u=[t!=null?t:Vc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Ud.DENSE){let g=s!=null?s:Vc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Gd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=_G(e,u,p),d=this.getAndSaveBinary(c,()=>MG(this.gpgpu,e,u,p)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),W().get("ENGINE_COMPILE_ONLY")||FG(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=W().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Pe(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(Ue(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?YH:ZH}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=uv(a,o),t.texShape=p),r!=null){let c=vd(a),d,h=p[1],f=p[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=Du(p[0],p[1])),o?d=new LG(c,m):d=new rx(c,m);let g=m?[f,h]:p,y=this.makeTensorInfo(g,n),x=this.texData.get(y.dataId);m?x.usage=fn.PIXELS:x.usage=fn.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,w=this.runWebGLProgram(d,[y],n,A,b),C=this.texData.get(w.dataId);t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,W().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=C.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=aj(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await x7(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(hg(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=bv(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=It().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return It().makeTensorFromDataId(l,t,a,o)}};Bu.nextDataId=0;function aj(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew Bu,2);var rj={forceHalfFloat:Uv},kg=` + `}},fj=Rn.whereImpl,gj=1e-7,yj=1e-4,K2={};function xj(e){return e in K2||(K2[e]={}),K2[e]}var Aj=W().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),bj=600;function vj(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*bj/1024/1024}var Gu=class extends Ql{nextDataId(){return Gu.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof zl)t=e;else{let a=Wn(W().getNumber("WEBGL_VERSION"),e);t=new zl(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Wn(W().getNumber("WEBGL_VERSION"));t=new zl(a),this.binaryCache=xj(W().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new ej(this.gpgpu),this.numMBBeforeWarning=vj(),this.texData=new tp(this,It())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Sd(t),u=new mx(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:a,values:e,usage:mn.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,a,n,r){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:a,dtype:n,values:t,usage:mn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Hr(i,Wr):c=new Kn(i,Wr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=S.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Hr(n,Wr):h=new Kn(n,Wr);let m=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(a!=null)return this.convertAndCacheOnCPU(e);if(W().getBool("DEBUG")&&!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...jc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=S.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&It().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Hr(r,Wr):d=new Kn(r,Wr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=It().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return $e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Aj){return W().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return It().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new mj(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new JH(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Ys(e.shape),...Zs(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Ys(t),...Zs(t)],s=new Qv(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Sd(r),o;n?o=new eH(i):o=new QG(i);let l=!0,u=[t!=null?t:jc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===qd.DENSE){let g=s!=null?s:jc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Xd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=JG(e,u,p),d=this.getAndSaveBinary(c,()=>YG(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),W().get("ENGINE_COMPILE_ONLY")||ZG(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=W().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(Ue(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?gj:yj}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=bv(a,o),t.texShape=p),r!=null){let c=Sd(a),d,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=zu(p[0],p[1])),o?d=new rH(c,f):d=new mx(c,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,n),x=this.texData.get(y.dataId);f?x.usage=mn.PIXELS:x.usage=mn.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let A=[[m,h]],b=!0,w=this.runWebGLProgram(d,[y],n,A,b),I=this.texData.get(w.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,W().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=wj(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await R7(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(vg(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=Mv(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=It().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return It().makeTensorFromDataId(l,t,a,o)}};Gu.nextDataId=0;function wj(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew Gu,2);var Ij={forceHalfFloat:e8},Mg=` if (isnan(a)) return a; if (isnan(b)) return b; -`,ql=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` +`,Js=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1242,12 +1242,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},Xo=` + `}},Qo=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,Up=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=ya(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` +`,Hu=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=ya(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1291,22 +1291,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function en(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var sj={kernelName:zi,backendName:"webgl",kernelFunc:en};function ds(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=en({inputs:{x:n},backend:a}),l=en({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var ij={kernelName:tp,backendName:"webgl",kernelFunc:ds},Gv="return (a < 0.) ? b * a : a;",Hv=` + `}};function en(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Sj={kernelName:Bi,backendName:"webgl",kernelFunc:en};function ps(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=en({inputs:{x:n},backend:a}),l=en({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Cj={kernelName:ip,backendName:"webgl",kernelFunc:ps},t8="return (a < 0.) ? b * a : a;",a8=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function oj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Up(Hv,r.shape,i.shape):new ql(Gv,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var lj={kernelName:Vi,backendName:"webgl",kernelFunc:oj},jv="return (a < 0.) ? b * a : a;",qv=` +`;function Tj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Hu(a8,r.shape,i.shape):new Js(t8,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var Nj={kernelName:Hi,backendName:"webgl",kernelFunc:Tj},n8="return (a < 0.) ? b * a : a;",r8=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function uj(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Up(qv,n.shape,r.shape):new ql(jv,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var dj={kernelName:fo,backendName:"webgl",kernelFunc:uj},Vu="if (isnan(x)) return x;";function et({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Gr(i.shape,t):p=new Yn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function fa({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let f=p.texData.get(l.dataId),m=p.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,w]=A,C={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N=new ql(e,l.shape,u.shape);return p.runWebGLProgram(N,[C,T],pa(b.dtype,w.dtype))}),x=ds({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||pa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let f=p.texData.get(l.dataId).values,m=p.texData.get(u.dataId).values,g=l.dtype==="string"?S.fromUint8ToStringArray(f):f,y=l.dtype==="string"?S.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),w=p.texData.get(b.dataId);return w.values=x,b}let d=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Up(t,l.shape,u.shape,a):h=new ql(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function Hd(e,t=!1){if(e==="linear")return t?UH:zH;if(e==="relu")return t?HH:WH;if(e==="elu")return t?GH:LH;if(e==="relu6")return t?jH:BH;if(e==="prelu")return t?qv:jv;if(e==="leakyrelu")return t?Hv:Gv;if(e==="sigmoid")return t?qH:VH;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Xv=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=ya(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) { +`;function Rj(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Hu(r8,n.shape,r.shape):new Js(n8,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var Ej={kernelName:xo,backendName:"webgl",kernelFunc:Rj},ju="if (isnan(x)) return x;";function tt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Hr(i.shape,t):p=new Kn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function ma({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(A=>{let[b,w]=A,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N=new Js(e,l.shape,u.shape);return p.runWebGLProgram(N,[I,T],pa(b.dtype,w.dtype))}),x=ps({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||pa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?S.fromUint8ToStringArray(m):m,y=l.dtype==="string"?S.fromUint8ToStringArray(f):f,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),w=p.texData.get(b.dataId);return w.values=x,b}let d=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Hu(t,l.shape,u.shape,a):h=new Js(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function Kd(e,t=!1){if(e==="linear")return t?uj:rj;if(e==="relu")return t?pj:ij;if(e==="elu")return t?dj:sj;if(e==="relu6")return t?cj:oj;if(e==="prelu")return t?r8:n8;if(e==="leakyrelu")return t?a8:t8;if(e==="sigmoid")return t?hj:lj;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var s8=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=ya(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} - }`:l?m=`vec4 activation(vec4 a) { + }`:l?f=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} - }`:m=`vec4 activation(vec4 x) { + }`:f=`vec4 activation(vec4 x) { ${i} }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",A="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Gd(r.shape,l)&&!(p.texture!==null&&Gd(p.shape,l))?cj(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var hj={kernelName:bu,backendName:"webgl",kernelFunc:ue},cx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` + `}},vx="return a * b;";function _g(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=S.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new bx(Ax.REAL,n.shape,r.shape),p=new bx(Ax.IMAG,n.shape,r.shape),c=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=ps({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=TH(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Hu(vx,n.shape,r.shape):i=new Js(vx,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var Mj={kernelName:po,backendName:"webgl",kernelFunc:_g};function _j(e,t,a){let n=[Ys(e.shape),...Zs(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Ys(t),...Zs(t)],i=new Qv(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function de(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Xd(r.shape,l)&&!(p.texture!==null&&Xd(p.shape,l))?_j(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Pj={kernelName:Iu,backendName:"webgl",kernelFunc:de},wx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1402,7 +1402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},fj=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(a/4)*4,p=a%4,c=` + `}},$j=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(a/4)*4,p=a%4,c=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function mj(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=S.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function Ko(e,t,a,n){let r=mj(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=ft(this.rank),r=Bv("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=ft(this.rank),r=Jv("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,f,d]:[x,d,f],C=ue({inputs:{x:e},backend:r,attrs:{shape:b}}),T=ue({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[C,T],M=Math.max(y,x),F=a?C.shape[1]:C.shape[2],E=s!=null,I=i!=null,_=l==="leakyrelu",O=l!=null?Hd(l,!0):null,B=E||I||_||O!=null,$;if((h===1||f===1)&&F>Kv&&B===!1){let G=C,q=T;a&&(G=Ta({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Ta({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=f!==1,V=f===1,Z=G;H&&(Z=ue({inputs:{x:G},backend:r,attrs:{shape:[M,F,1]}}),N.push(Z));let X=f===1?2:1,re=q;V&&(re=ue({inputs:{x:q},backend:r,attrs:{shape:[M,1,F]}}),N.push(re));let ee=Ig({inputs:{a:Z,b:re},backend:r});$=e0({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=pa(e.dtype,t.dtype),q=new Xv(b,w,[M,h,f],a,n,E,O,I,_),H=[C,T];if(s!=null&&H.push(s),I&&H.push(i),_){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),N.push(V)}$=r.runWebGLProgram(q,H,G)}let U=ue({inputs:{x:$},backend:r,attrs:{shape:A}});N.push($);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function wj(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return fh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var kj={kernelName:Xr,backendName:"webgl",kernelFunc:wj},hx="return abs(x);";function Ij(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=Lv(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Gr(n.shape,hx):r=new Yn(n.shape,hx),a.runWebGLProgram(r,[n],n.dtype)}var Sj={kernelName:Zl,backendName:"webgl",kernelFunc:Ij},Cj=Rn+` + `}};function t0(e,t,a){let n=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zj(e.shape,t):new Dj(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function Lj(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=S.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=t0(e,l,n),o=S.getInnerMostAxes(o.length,s)),S.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=S.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=S.expandShapeToKeepDim(c,i));let m=v.sizeFromShape(d),f=v.sizeFromShape(e.shape)/m,g=de({inputs:{x:p},attrs:{shape:[f,m]},backend:n}),y=Rp(e.dtype),x=el(g,y,"sum",n),A=de({inputs:{x},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(p),A}function a0(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Lj(r,s,i,a)}var Wj={kernelName:zo,backendName:"webgl",kernelFunc:a0};function Ta(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=de({inputs:{x:e},backend:r,attrs:{shape:b}}),T=de({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),P=a?I.shape[1]:I.shape[2],E=s!=null,C=i!=null,_=l==="leakyrelu",O=l!=null?Kd(l,!0):null,B=E||C||_||O!=null,$;if((h===1||m===1)&&P>i8&&B===!1){let G=I,q=T;a&&(G=Ta({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Ta({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=m!==1,V=m===1,Z=G;H&&(Z=de({inputs:{x:G},backend:r,attrs:{shape:[M,P,1]}}),N.push(Z));let X=m===1?2:1,re=q;V&&(re=de({inputs:{x:q},backend:r,attrs:{shape:[M,1,P]}}),N.push(re));let ee=_g({inputs:{a:Z,b:re},backend:r});$=a0({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=pa(e.dtype,t.dtype),q=new s8(b,w,[M,h,m],a,n,E,O,C,_),H=[I,T];if(s!=null&&H.push(s),C&&H.push(i),_){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),N.push(V)}$=r.runWebGLProgram(q,H,G)}let U=de({inputs:{x:$},backend:r,attrs:{shape:A}});N.push($);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function Vj(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Ah({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Uj={kernelName:Kr,backendName:"webgl",kernelFunc:Vj},kx="return abs(x);";function Gj(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=Yv(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Hr(n.shape,kx):r=new Kn(n.shape,kx),a.runWebGLProgram(r,[n],n.dtype)}var Hj={kernelName:tu,backendName:"webgl",kernelFunc:Gj},jj=En+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,Tj=et({opSnippet:Cj}),Nj={kernelName:Qs,backendName:"webgl",kernelFunc:Tj},Rj=Rn+` +`,qj=tt({opSnippet:jj}),Xj={kernelName:ti,backendName:"webgl",kernelFunc:qj},Kj=En+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,Ej=et({opSnippet:Rj}),Mj={kernelName:ei,backendName:"webgl",kernelFunc:Ej},fx="return a + b;",Fj=fa({opSnippet:fx,packedOpSnippet:fx,supportsComplex:!0,cpuKernelImpl:BG}),_j={kernelName:ns,backendName:"webgl",kernelFunc:Fj},$j=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,Yj=tt({opSnippet:Kj}),Zj={kernelName:ai,backendName:"webgl",kernelFunc:Yj},Ix="return a + b;",Jj=ma({opSnippet:Ix,packedOpSnippet:Ix,supportsComplex:!0,cpuKernelImpl:iH}),Qj={kernelName:rs,backendName:"webgl",kernelFunc:Jj},eq=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${a.join(` `)} @@ -1531,7 +1531,7 @@ return log(x + sqrt(x * x - 1.0));`,Ej=et({opSnippet:Rj}),Mj={kernelName:ei,back float result = ${n}; setOutput(result); } - `}},Dj=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`vec4 v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},tq=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`vec4 v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${a.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,Ej=et({opSnippet:Rj}),Mj={kernelName:ei,back vec4 result = ${n}; setOutput(result); } - `}};function Jc(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return en({inputs:{x:n[0]},backend:a});if(n.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Jc({inputs:n.slice(0,o),backend:a}),u=Jc({inputs:n.slice(o),backend:a});return Jc({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=n.map(o=>o.shape),i=W().getBool("WEBGL_PACK")?new Dj(n[0].shape,s):new $j(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var Pj={kernelName:ti,backendName:"webgl",kernelFunc:Jc};function Oj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=S.getInnerMostAxes(u.length,o)),S.assertAxesAreInnerMostDims("all",u,o);let[d,h]=S.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=ue({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=Ko(m,m.dtype,"all",a),y;if(i){let x=S.expandShapeToKeepDim(d,l);y=ue({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=ue({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var zj={kernelName:ai,backendName:"webgl",kernelFunc:Oj};function Lj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=S.getInnerMostAxes(u.length,o)),S.assertAxesAreInnerMostDims("any",u,o);let[d,h]=S.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=ue({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=Ko(m,m.dtype,"any",a),y;if(i){let x=S.expandShapeToKeepDim(d,l);y=ue({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=ue({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Wj={kernelName:ni,backendName:"webgl",kernelFunc:Lj},Bj=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function ah(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return en({inputs:{x:n[0]},backend:a});if(n.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=ah({inputs:n.slice(0,o),backend:a}),u=ah({inputs:n.slice(o),backend:a});return ah({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=n.map(o=>o.shape),i=W().getBool("WEBGL_PACK")?new tq(n[0].shape,s):new eq(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var aq={kernelName:ni,backendName:"webgl",kernelFunc:ah};function nq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=S.getInnerMostAxes(u.length,o)),S.assertAxesAreInnerMostDims("all",u,o);let[d,h]=S.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=el(f,f.dtype,"all",a),y;if(i){let x=S.expandShapeToKeepDim(d,l);y=de({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=de({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var rq={kernelName:ri,backendName:"webgl",kernelFunc:nq};function sq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=S.getInnerMostAxes(u.length,o)),S.assertAxesAreInnerMostDims("any",u,o);let[d,h]=S.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=el(f,f.dtype,"any",a),y;if(i){let x=S.expandShapeToKeepDim(d,l);y=de({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=de({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var iq={kernelName:si,backendName:"webgl",kernelFunc:sq},oq=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1559,7 +1559,7 @@ return log(x + sqrt(x * x - 1.0));`,Ej=et({opSnippet:Rj}),Mj={kernelName:ei,back } setOutput(float(bestIndex)); } - `}},Vj=class{constructor(e,t,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${a.charAt(0).toUpperCase()+a.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),u=Ia("coords",o),p,c;if(s===1){c=o+1;let T=ft(c);p=` + `}},lq=class{constructor(e,t,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${a.charAt(0).toUpperCase()+a.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),u=Ia("coords",o),p,c;if(s===1){c=o+1;let T=ft(c);p=` ${T} sourceLocR = ${T}(${u.join()}, 0); ++${u[o-1]}; ${T} sourceLocG = ${T}(${u.join()}, 0); @@ -1575,24 +1575,24 @@ return log(x + sqrt(x * x - 1.0));`,Ej=et({opSnippet:Rj}),Mj={kernelName:ei,back ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; - --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],f=d.map(T=>"int "+T),m=Ia("sourceLocR",c-1).concat("inIdx.r"),g=Ia("sourceLocG",c-1).concat("inIdx.g"),y=Ia("sourceLocB",c-1).concat("inIdx.b"),x=Ia("sourceLocA",c-1).concat("inIdx.a"),A=a==="max"?"greaterThan":"lessThan",b=n?"":` - inIdx = round(vec4(getBestIndicesAChannel(${m.join()}), + --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],m=d.map(T=>"int "+T),f=Ia("sourceLocR",c-1).concat("inIdx.r"),g=Ia("sourceLocG",c-1).concat("inIdx.g"),y=Ia("sourceLocB",c-1).concat("inIdx.b"),x=Ia("sourceLocA",c-1).concat("inIdx.a"),A=a==="max"?"greaterThan":"lessThan",b=n?"":` + inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${y.join()}), getBestIndicesAChannel(${x.join()})));`,w=`vec4( - getAChannel(${m.join()}), + getAChannel(${f.join()}), hasNextCol ? getAChannel(${g.join()}) : 0., hasNextRow ? getAChannel(${y.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,C=n?"":` - float getBestIndicesAChannel(${f.join()}) { + hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,I=n?"":` + float getBestIndicesAChannel(${m.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); }`;this.userCode=` - float getAChannel(${f.join()}) { + float getAChannel(${m.join()}) { return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); } - ${C} + ${I} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,Ej=et({opSnippet:Rj}),Mj={kernelName:ei,back } setOutput(bestIndex); } - `}};function Yv(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new Bj(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=Yv(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function Zv(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=S.computeOptimalWindowSize(s),o=new Vj(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=Zv(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function Jv(e,t,a,n){let r=[a];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=S.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=ue({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=Yv(e,d,n);s.push(h);let f=ue({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return Zv(e,t,n)}function Uj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ta({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=Jv(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var Gj={kernelName:Jl,backendName:"webgl",kernelFunc:Uj};function Hj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ta({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=Jv(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var jj={kernelName:Ql,backendName:"webgl",kernelFunc:Hj},qj=Rn+` + `}};function o8(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new oq(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=o8(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function l8(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=S.computeOptimalWindowSize(s),o=new lq(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=l8(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function u8(e,t,a,n){let r=[a];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=S.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=de({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=o8(e,d,n);s.push(h);let m=de({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return l8(e,t,n)}function uq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ta({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=u8(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var dq={kernelName:au,backendName:"webgl",kernelFunc:uq};function pq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ta({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=u8(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var cq={kernelName:nu,backendName:"webgl",kernelFunc:pq},hq=En+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,Xj=et({opSnippet:qj}),Kj={kernelName:ri,backendName:"webgl",kernelFunc:Xj},Yj=Rn+"return log(x + sqrt(x * x + 1.0));",Zj=et({opSnippet:Yj}),Jj={kernelName:si,backendName:"webgl",kernelFunc:Zj},Qj=Rn+` +`,mq=tt({opSnippet:hq}),fq={kernelName:ii,backendName:"webgl",kernelFunc:mq},gq=En+"return log(x + sqrt(x * x + 1.0));",yq=tt({opSnippet:gq}),xq={kernelName:oi,backendName:"webgl",kernelFunc:yq},Aq=En+` return atan(x); -`,eq=et({opSnippet:Qj}),tq={kernelName:ii,backendName:"webgl",kernelFunc:eq},aq=kg+` +`,bq=tt({opSnippet:Aq}),vq={kernelName:li,backendName:"webgl",kernelFunc:bq},wq=Mg+` return atan(a, b); -`,nq=` +`,kq=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+Xo+` + `+Qo+` return result; -`,rq=fa({opSnippet:aq,packedOpSnippet:nq}),sq={kernelName:li,backendName:"webgl",kernelFunc:rq},iq=Rn+` +`,Iq=ma({opSnippet:wq,packedOpSnippet:kq}),Sq={kernelName:di,backendName:"webgl",kernelFunc:Iq},Cq=En+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelName:oi,backendName:"webgl",kernelFunc:oq},jd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),a){let T=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Tq=tt({opSnippet:Cq}),Nq={kernelName:ui,backendName:"webgl",kernelFunc:Tq},Yd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),a){let T=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${h}); @@ -1684,14 +1684,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam if (value ${T} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${n?r?m:g:`wR * ${c} + wC`}; + minMaxPosition = ${n?r?f:g:`wR * ${c} + wC`}; } } } setOutput(float(minMaxPosition)); } - `;return}let x="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / max(count, 1.0)");let b=Math.floor(s/4)*4,w=s%4,C=` - if (${f}) { + `;return}let x="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / max(count, 1.0)");let b=Math.floor(s/4)*4,w=s%4,I=` + if (${m}) { avgValue += dot(values, ones); } else { minMaxValue = ${x}(values, minMaxValue); @@ -1745,7 +1745,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam getValue(batch, xR, xC + 3 * ${u}, d) ); - ${C} + ${I} } int xC = xCCorner + ${b}; @@ -1757,7 +1757,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam initializationValue ); - ${C} + ${I} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), @@ -1766,7 +1766,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam initializationValue ); - ${C} + ${I} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), @@ -1775,15 +1775,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam initializationValue ); - ${C} + ${I} } } setOutput(${A}); } - `}},Sg=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){let M=">=";this.userCode=` + `}},Pg=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){let M=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); - const ivec3 pads = ivec3(${m}, ${g}, ${y}); + const ivec3 pads = ivec3(${f}, ${g}, ${y}); void main() { ivec5 coords = getOutputCoords(); @@ -1817,7 +1817,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam continue; } - for (int wC = 0; wC < ${f}; + for (int wC = 0; wC < ${m}; wC += ${c}) { int xC = xCCorner + wC; @@ -1834,15 +1834,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam if (value ${M} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${n?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} + - wR * ${f} + wC`}; + minMaxPosition = ${n?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + + wR * ${m} + wC`}; } } } } setOutput(float(minMaxPosition)); } - `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / max(count, 1.0)");let C=Math.floor(s/4)*4,T=s%4,N=` + `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / max(count, 1.0)");let I=Math.floor(s/4)*4,T=s%4,N=` if (${x}) { avgValue += dot(values, ones); } else { @@ -1851,7 +1851,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); - const ivec3 pads = ivec3(${m}, ${g}, ${y}); + const ivec3 pads = ivec3(${f}, ${g}, ${y}); const float initializationValue = ${A}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); @@ -1897,7 +1897,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam continue; } - for (int wC = 0; wC < ${C}; wC += 4) { + for (int wC = 0; wC < ${I}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( @@ -1910,7 +1910,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${N} } - int xC = xCCorner + ${C}; + int xC = xCCorner + ${I}; if (${T===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -1943,7 +1943,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(${w}); } - `}};function uq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Pu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(S.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new jd(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var dq={kernelName:ui,backendName:"webgl",kernelFunc:uq};function pq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new Sg(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var cq={kernelName:eu,backendName:"webgl",kernelFunc:pq},hq=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` + `}};function Rq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Lu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(S.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new Yd(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var Eq={kernelName:pi,backendName:"webgl",kernelFunc:Rq};function Mq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new Pg(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var _q={kernelName:ru,backendName:"webgl",kernelFunc:Mq},Pq=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${c}); @@ -1985,8 +1985,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},fq=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=p-1-e.padInfo.front,f=c-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*a*n);this.userCode=` - const ivec3 pads = ivec3(${h}, ${f}, ${m}); + `}},$q=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=c-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*a*n);this.userCode=` + const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); void main() { @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}};function mq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new fq(d);return a.runWebGLProgram(h,[r],i.dtype)}var gq={kernelName:ep,backendName:"webgl",kernelFunc:mq};function yq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Pu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=new hq(p);return a.runWebGLProgram(c,[r],i.dtype)}var xq={kernelName:kh,backendName:"webgl",kernelFunc:yq};function Aq(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return fh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var bq={kernelName:di,backendName:"webgl",kernelFunc:Aq},vq=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function Fq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new $q(d);return a.runWebGLProgram(h,[r],i.dtype)}var Dq={kernelName:sp,backendName:"webgl",kernelFunc:Fq};function Oq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Lu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=new Pq(p);return a.runWebGLProgram(c,[r],i.dtype)}var zq={kernelName:rp,backendName:"webgl",kernelFunc:Oq};function Lq(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Ah({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Wq={kernelName:ci,backendName:"webgl",kernelFunc:Lq},Bq=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2051,7 +2051,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},wq=class{constructor(e,t,a,n,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,a);let i="vec4(0.0)";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},Vq=class{constructor(e,t,a,n,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,a);let i="vec4(0.0)";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam setOutput((x - mean) * inv + offset); } - `}},kq=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=W().getBool("WEBGL_PACK_NORMALIZATION")?new wq(n.shape,r.shape,s.shape,p,c,l):new vq(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},Iq={kernelName:$i,backendName:"webgl",kernelFunc:kq},Sq=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=Cq(this.rank),n,r=e.map((s,i)=>`sourceLoc.${S1[i]} = start[${i}] + coords.${S1[i]};`);n=` + `}},Uq=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=W().getBool("WEBGL_PACK_NORMALIZATION")?new Vq(n.shape,r.shape,s.shape,p,c,l):new Bq(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},Gq={kernelName:Oi,backendName:"webgl",kernelFunc:Uq},Hq=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=jq(this.rank),n,r=e.map((s,i)=>`sourceLoc.${P1[i]} = start[${i}] + coords.${P1[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${n} setOutput(getSource(${a})); } - `}},S1=["x","y","z","w","u","v"];function Cq(e){if(e===1)return"sourceLoc";if(e<=6)return S1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Tq=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ft(this.rank),a=Ia("coords",this.rank),n=Ia("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` + `}},P1=["x","y","z","w","u","v"];function jq(e){if(e===1)return"sourceLoc";if(e<=6)return P1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var qq=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ft(this.rank),a=Ia("coords",this.rank),n=Ia("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` result.x = ${s}; if (++${a[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,7 +2103,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${o} setOutput(result); } - `}};function Nq(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=Nt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Uu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=xH(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=Nt.isSliceContinous(r.shape,o,l);if(u||!p){let c=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Tq(l):new Sq(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),Nq(r,o,l,a)}var Rq={kernelName:Iu,backendName:"webgl",kernelFunc:Uu},Eq=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=[],f=ue({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Ta({inputs:{x:f},backend:a,attrs:{perm:u}}),g=ue({inputs:{x:m},backend:a,attrs:{shape:p}}),y=Uu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},Mq={kernelName:tu,backendName:"webgl",kernelFunc:Eq};function Fq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=zv(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var _q={kernelName:pi,backendName:"webgl",kernelFunc:Fq};function $q(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Dq={kernelName:au,backendName:"webgl",kernelFunc:$q},Pq="return float(a != b);",Qv=fa({opSnippet:Pq,cpuKernelImpl:uH,dtype:"bool"}),Oq={kernelName:oo,backendName:"webgl",kernelFunc:Qv};function Gp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.real},backend:a})}var zq={kernelName:cp,backendName:"webgl",kernelFunc:Gp},Lq="return float(int(x));";function Wq(e,t){let a=new Yn(e.shape,Lq),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function C1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return en({inputs:{x:r},backend:a});let i=gn(r.shape),o=C1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ds({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Gp({inputs:{input:r},backend:a}),o=C1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=en({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=UG(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return Wq(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=Qv({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var Bq={kernelName:ci,backendName:"webgl",kernelFunc:C1},mx="return ceil(x);",Vq=et({opSnippet:mx,packedOpSnippet:mx,cpuKernelImpl:GG}),Uq={kernelName:hi,backendName:"webgl",kernelFunc:Vq},Gq=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function Xq(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=Nt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function qu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=zH(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=Nt.isSliceContinous(r.shape,o,l);if(u||!p){let c=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qq(l):new Hq(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),Xq(r,o,l,a)}var Kq={kernelName:Nu,backendName:"webgl",kernelFunc:qu},Yq=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=[],m=de({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Ta({inputs:{x:m},backend:a,attrs:{perm:u}}),g=de({inputs:{x:f},backend:a,attrs:{shape:p}}),y=qu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},Zq={kernelName:su,backendName:"webgl",kernelFunc:Yq};function Jq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=Kv(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var Qq={kernelName:hi,backendName:"webgl",kernelFunc:Jq},eX=` + int r = int(a.r) & int(b.r); + int g = int(a.g) & int(b.g); + int rb = int(a.b) & int(b.b); + int ra = int(a.a) & int(b.a); + return vec4(r, g, rb, ra); +`,tX=` + return float(int(a.r) & int(b.r)); +`;function aX(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=W().getNumber("WEBGL_VERSION");if(a.shouldExecuteOnCPU([n,r])||i===1){let l=a.texData.get(n.dataId).values,u=a.texData.get(r.dataId).values,[p,c]=lH(n.shape,r.shape,l,u,n.dtype),d=a.makeTensorInfo(c,n.dtype),h=a.texData.get(d.dataId);return h.values=p,d}let o;return s?o=new Hu(eX,n.shape,r.shape,!1):o=new Js(tX,n.shape,r.shape),a.runWebGLProgram(o,[n,r],n.dtype)}var nX={kernelName:iu,backendName:"webgl",kernelFunc:aX};function rX(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var sX={kernelName:ou,backendName:"webgl",kernelFunc:rX},iX="return float(a != b);",d8=ma({opSnippet:iX,cpuKernelImpl:RH,dtype:"bool"}),oX={kernelName:co,backendName:"webgl",kernelFunc:d8};function Xp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.real},backend:a})}var lX={kernelName:yp,backendName:"webgl",kernelFunc:Xp},uX="return float(int(x));";function dX(e,t){let a=new Kn(e.shape,uX),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function $1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return en({inputs:{x:r},backend:a});let i=gn(r.shape),o=$1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ps({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Xp({inputs:{input:r},backend:a}),o=$1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=en({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=uH(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return dX(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=d8({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var pX={kernelName:mi,backendName:"webgl",kernelFunc:$1},Sx="return ceil(x);",cX=tt({opSnippet:Sx,packedOpSnippet:Sx,cpuKernelImpl:dH}),hX={kernelName:fi,backendName:"webgl",kernelFunc:cX},mX=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2114,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}},Hq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}},fX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2125,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function jq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;W().getBool("WEBGL_PACK_CLIP")?o=new Hq(r.shape):o=new Gq(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var qq={kernelName:rs,backendName:"webgl",kernelFunc:jq},Xq=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function gX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;W().getBool("WEBGL_PACK_CLIP")?o=new fX(r.shape):o=new mX(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var yX={kernelName:ss,backendName:"webgl",kernelFunc:gX},xX=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2138,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function gx(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Kq(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new Xq(n.shape),i=[gx(n,r.complexTensorInfos.real),gx(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var Yq={kernelName:ap,backendName:"webgl",kernelFunc:Kq},Zq=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[f-1]}) { + }`;for(let m=1;m= ${o[m-1]}) { return getChannel( - getT${f}(${Gc(i,l,m)}), - vec2(${Gc(u,l,m)})); + getT${m}(${Xc(i,l,f)}), + vec2(${Xc(u,l,f)})); }`}let d=o.length,h=o[o.length-1];c+=` return getChannel( - getT${d}(${Gc(i,l,h)}), - vec2(${Gc(u,l,h)}));`,this.userCode=` - float getValue(${i.map(f=>"int "+f)}) { + getT${d}(${Xc(i,l,h)}), + vec2(${Xc(u,l,h)}));`,this.userCode=` + float getValue(${i.map(m=>"int "+m)}) { ${c} } @@ -2184,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(result); } - `}};function Gc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function t0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.imag},backend:a})}var Qq={kernelName:up,backendName:"webgl",kernelFunc:t0};function wd(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>Gp({inputs:{input:x},backend:a})),f=e.map(x=>t0({inputs:{input:x},backend:a})),m=wd(h,t,a),g=wd(f,t,a),y=ds({inputs:{real:m,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),f.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let w=[-1,v.sizeFromShape(b.shape.slice(t))];return ue({inputs:{x:b},backend:a,attrs:{shape:w}})}),f=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),m=S.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=HG(f,m,n,g),x=S.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new Yn(e[0].shape,Lr):new Gr(e[0].shape,Lr);return a.runWebGLProgram(h,e,n)}let o=W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let m=0;mf.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=eX(s,t,a),p=new Zq(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=ue({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function eX(e,t,a){let n=S.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ue({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function e8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?en({inputs:{x:l[0]},backend:a}):wd(l,s,a)}var tX={kernelName:nu,backendName:"webgl",kernelFunc:e8},t8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";a&&(n?A=`float activation(float a) { + `}};function Xc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function n0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.imag},backend:a})}var kX={kernelName:mp,backendName:"webgl",kernelFunc:n0};function Cd(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>Xp({inputs:{input:x},backend:a})),m=e.map(x=>n0({inputs:{input:x},backend:a})),f=Cd(h,t,a),g=Cd(m,t,a),y=ps({inputs:{real:f,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),m.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let w=[-1,v.sizeFromShape(b.shape.slice(t))];return de({inputs:{x:b},backend:a,attrs:{shape:w}})}),m=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),f=S.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=pH(m,f,n,g),x=S.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new Kn(e[0].shape,Wr):new Hr(e[0].shape,Wr);return a.runWebGLProgram(h,e,n)}let o=W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=IX(s,t,a),p=new vX(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=de({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function IX(e,t,a){let n=S.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>de({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function p8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?en({inputs:{x:l[0]},backend:a}):Cd(l,s,a)}var SX={kernelName:lu,backendName:"webgl",kernelFunc:p8},c8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,x=f?3:1,A="",b="";a&&(n?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?A=`float activation(float a) { @@ -2235,7 +2243,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam getW(wR, wC, d1 + 3, d2) ); - if (${m}) { + if (${f}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), @@ -2254,9 +2262,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } } - if (${f===1}) { + if (${m===1}) { - if (${m}) { + if (${f}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); @@ -2266,13 +2274,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam getW(wR, wC, ${h}, d2); } - } else if (${f===2}) { + } else if (${m===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); - if (${m}) { + if (${f}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) @@ -2286,14 +2294,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam dotProd += dot(xValues, wValues); } - } else if (${f===3}) { + } else if (${m===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); - if (${m}) { + if (${f}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), @@ -2318,7 +2326,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${b} setOutput(result); } - `}},aX=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,a=e.padInfo.top,n=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` + `}},CX=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,a=e.padInfo.top,n=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${a}, ${n}); @@ -2374,11 +2382,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam dotProd += dot(xValues, wValues); } - if (${f===1}) { + if (${m===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); - } else if (${f===2}) { + } else if (${m===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1) @@ -2388,7 +2396,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam getW(wF, wR, wC, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); - } else if (${f===3}) { + } else if (${m===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1), @@ -2406,25 +2414,25 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},a8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` + `}},h8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m=0 && xR < inDims[0]) { - `;for(let m=0;m<(p+1)/2;m++){let g=m*2;if(c+=` + `;for(let f=0;f<(p+1)/2;f++){let g=f*2;if(c+=` xC = xCCorner + ${g*o}; `,i===1){if(g=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function n8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(s!=null){let x=mh(s.shape,h);x!=null&&(s=ue({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=mh(r.shape,h);x!=null&&(r=ue({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>Kv)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Gd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let w=ue({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let C=fh({a:A,b:w,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(C.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=a.outShape,g=en({inputs:{x:C},backend:n}),g.shape=a.outShape,y.push(C)}else{let x=a.outHeight*a.outWidth,A=ue({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=ue({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=fh({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ue({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(w)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function r8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,f=h==="channelsLast",m=l*u*p,g=d*c,y=[a.batchSize,m,g],x=!0,A=!1,b=[];if(s!=null){let G=mh(s.shape,f);G!=null&&(s=ue({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=mh(r.shape,f);G!=null&&(r=ue({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let w=ue({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w);let C=new nX(y,a),T=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(C,[e],"float32",T),M=ue({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(M);let F=r!=null,E=s!=null,I=o==="leakyrelu",_=o?Hd(o,!0):null,O=new Xv(f?M.shape:w.shape,f?w.shape:M.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,F,_,E,I),B=f?[M,w]:[w,M];if(r&&B.push(r),E&&B.push(s),I){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));B.push(G),b.push(G)}let $=n.runWebGLProgram(O,B,"float32"),U=ue({inputs:{x:$},backend:n,attrs:{shape:a.outShape}});b.push($);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function rX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=n8({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&W().getBool("WEBGL_EXP_CONV")){let m=new a8(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(W().getBool("WEBGL_CONV_IM2COL"))h=r8({x:r,filter:s,convInfo:d,backend:a});else{let m=new t8(d);h=a.runWebGLProgram(m,[r,s],"float32")}let f=ue({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),f}var sX={kernelName:fi,backendName:"webgl",kernelFunc:rX},iX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function bh(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function m8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let x=bh(s.shape,h);x!=null&&(s=de({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=bh(r.shape,h);x!=null&&(r=de({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>i8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Xd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let w=de({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let I=Ah({a:A,b:w,backend:n,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=a.outShape,g=en({inputs:{x:I},backend:n}),g.shape=a.outShape,y.push(I)}else{let x=a.outHeight*a.outWidth,A=de({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=de({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=Ah({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:f,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=de({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(w)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function f8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,m=h==="channelsLast",f=l*u*p,g=d*c,y=[a.batchSize,f,g],x=!0,A=!1,b=[];if(s!=null){let G=bh(s.shape,m);G!=null&&(s=de({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=bh(r.shape,m);G!=null&&(r=de({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let w=de({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new TX(y,a),T=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(I,[e],"float32",T),M=de({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(M);let P=r!=null,E=s!=null,C=o==="leakyrelu",_=o?Kd(o,!0):null,O=new s8(m?M.shape:w.shape,m?w.shape:M.shape,m?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,P,_,E,C),B=m?[M,w]:[w,M];if(r&&B.push(r),E&&B.push(s),C){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));B.push(G),b.push(G)}let $=n.runWebGLProgram(O,B,"float32"),U=de({inputs:{x:$},backend:n,attrs:{shape:a.outShape}});b.push($);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function NX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=m8({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&W().getBool("WEBGL_EXP_CONV")){let f=new h8(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(f,[r,s],"float32",g)}else if(W().getBool("WEBGL_CONV_IM2COL"))h=f8({x:r,filter:s,convInfo:d,backend:a});else{let f=new c8(d);h=a.runWebGLProgram(f,[r,s],"float32")}let m=de({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),m}var RX={kernelName:gi,backendName:"webgl",kernelFunc:NX},EX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2686,7 +2694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},oX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` + `}},MX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2739,7 +2747,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},lX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},_X=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2781,7 +2789,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},uX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=a-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=` + `}},PX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=a-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2838,7 +2846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}};function dX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new iX(d);return a.runWebGLProgram(h,[r,s],"float32")}var pX={kernelName:np,backendName:"webgl",kernelFunc:dX},cX=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.filterHeight,a=e.filterWidth,n=t-1-e.padInfo.top,r=a-1-e.padInfo.left;this.userCode=` + `}};function $X(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new EX(d);return a.runWebGLProgram(h,[r,s],"float32")}var FX={kernelName:lp,backendName:"webgl",kernelFunc:$X},DX=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.filterHeight,a=e.filterWidth,n=t-1-e.padInfo.top,r=a-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${n}, ${r}); void main() { @@ -2912,18 +2920,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(result); } - `}};function hX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c);if(W().getBool("WEBGL_PACK")&&c==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],f=new cX(d);return a.runWebGLProgram(f,[r,s],"float32",h)}else{let h=new oX(d);return a.runWebGLProgram(h,[r,s],"float32")}}var fX={kernelName:mi,backendName:"webgl",kernelFunc:hX};function mX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new aX(u);return a.runWebGLProgram(p,[r,s],"float32")}var gX={kernelName:gi,backendName:"webgl",kernelFunc:mX};function yX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=S.computeConv3DInfo(r.shape,l,i,1,o),p=new lX(u);return a.runWebGLProgram(p,[r,s],"float32")}var xX={kernelName:ru,backendName:"webgl",kernelFunc:yX};function AX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,o,1,i),p=new uX(u);return a.runWebGLProgram(p,[r,s],"float32")}var bX={kernelName:yi,backendName:"webgl",kernelFunc:AX},vX=Vu+` + `}};function OX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c);if(W().getBool("WEBGL_PACK")&&c==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],m=new DX(d);return a.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new MX(d);return a.runWebGLProgram(h,[r,s],"float32")}}var zX={kernelName:yi,backendName:"webgl",kernelFunc:OX};function LX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new CX(u);return a.runWebGLProgram(p,[r,s],"float32")}var WX={kernelName:xi,backendName:"webgl",kernelFunc:LX};function BX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=S.computeConv3DInfo(r.shape,l,i,1,o),p=new _X(u);return a.runWebGLProgram(p,[r,s],"float32")}var VX={kernelName:uu,backendName:"webgl",kernelFunc:BX};function UX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,o,1,i),p=new PX(u);return a.runWebGLProgram(p,[r,s],"float32")}var GX={kernelName:Ai,backendName:"webgl",kernelFunc:UX},HX=ju+` return cos(x); -`,wX=` +`,jX=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Xo} + ${Qo} return result; -`,kX=et({opSnippet:vX,packedOpSnippet:wX}),IX={kernelName:xi,backendName:"webgl",kernelFunc:kX},SX=` +`,qX=tt({opSnippet:HX,packedOpSnippet:jX}),XX={kernelName:bi,backendName:"webgl",kernelFunc:qX},KX=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,CX=et({opSnippet:SX}),TX={kernelName:Ai,backendName:"webgl",kernelFunc:CX},NX=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` - const float height_ratio = float(${m}); +`,YX=tt({opSnippet:KX}),ZX={kernelName:vi,backendName:"webgl",kernelFunc:YX},JX=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` + const float height_ratio = float(${f}); const float width_ratio = float(${x}); void main() { ivec4 coords = getOutputCoords(); @@ -2953,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam return; } float in_x = ${b}; - if( in_x < 0.0 || in_x > ${f} ) { + if( in_x < 0.0 || in_x > ${m} ) { setOutput(float(${r})); return; } @@ -2983,20 +2991,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam setOutput(newValue); } } - `}},RX=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new NX(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},EX={kernelName:wi,backendName:"webgl",kernelFunc:RX},qd;(function(e){e.Prod="*",e.Sum="+"})(qd||(qd={}));var yx=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===qd.Prod?"1.0":"0.0",i=a?s:`getX(${xx(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` + `}},QX=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new JX(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},eK={kernelName:Ii,backendName:"webgl",kernelFunc:QX},Zd;(function(e){e.Prod="*",e.Sum="+"})(Zd||(Zd={}));var Tx=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Zd.Prod?"1.0":"0.0",i=a?s:`getX(${Nx(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${ft(r)} coords = getOutputCoords(); - int end = ${Ax(r,"coords",this.op)}; + int end = ${Rx(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${Ax(r,"coords",this.op)} = idx; - val ${this.op}= getX(${xx(r,"coords",this.op)}); + ${Rx(r,"coords",this.op)} = idx; + val ${this.op}= getX(${Nx(r,"coords",this.op)}); } setOutput(val); } - `}};function xx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Ax(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function s8(e,t,a,n,r,s){let i=t.shape.length,o=S.getAxesPermutation([n],i),l=t;o!=null&&(l=Ta({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=S.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=en({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new yx(e,l.shape,!1,s),f=[[d]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let d=new yx(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=Ta({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function MX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return s8(qd.Prod,r,a,s,i,o)}var FX={kernelName:bi,backendName:"webgl",kernelFunc:MX};function _X(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return s8(qd.Sum,r,a,s,i,o)}var $X={kernelName:vi,backendName:"webgl",kernelFunc:_X};function DX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=zv(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=VG(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var PX={kernelName:su,backendName:"webgl",kernelFunc:DX},OX=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` + `}};function Nx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Rx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function g8(e,t,a,n,r,s){let i=t.shape.length,o=S.getAxesPermutation([n],i),l=t;o!=null&&(l=Ta({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=S.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=en({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Tx(e,l.shape,!1,s),m=[[d]],f=c;c=a.runWebGLProgram(h,[c],c.dtype,m),a.disposeIntermediateTensorInfo(f)}if(r){let d=new Tx(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=Ta({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function tK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return g8(Zd.Prod,r,a,s,i,o)}var aK={kernelName:wi,backendName:"webgl",kernelFunc:tK};function nK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return g8(Zd.Sum,r,a,s,i,o)}var rK={kernelName:ki,backendName:"webgl",kernelFunc:nK};function sK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=Kv(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=oH(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var iK={kernelName:du,backendName:"webgl",kernelFunc:sK},oK=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3015,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function zX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=new OX(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var LX={kernelName:ki,backendName:"webgl",kernelFunc:zX},i8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function lK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=new oK(m,s,i);return a.runWebGLProgram(f,[r],r.dtype)}var uK={kernelName:Si,backendName:"webgl",kernelFunc:lK},y8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?l=`float activation(float a) { @@ -3068,7 +3076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${u} setOutput(result); } - `}},o8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` + `}},x8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=S.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;W().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new o8(c):d=new i8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var BX={kernelName:Ii,backendName:"webgl",kernelFunc:WX},VX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function dK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(S.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=S.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;W().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new x8(c):d=new y8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var pK={kernelName:Ci,backendName:"webgl",kernelFunc:dK},cK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3292,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},UX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=a-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + `}},hK=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=a-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { @@ -3337,13 +3345,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}};function GX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=S.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new VX(c);return a.runWebGLProgram(d,[r,s],"float32")}var HX={kernelName:rp,backendName:"webgl",kernelFunc:GX};function jX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=S.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new UX(c);return a.runWebGLProgram(d,[r,s],"float32")}var qX={kernelName:sp,backendName:"webgl",kernelFunc:jX},XX=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function mK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=S.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new cK(c);return a.runWebGLProgram(d,[r,s],"float32")}var fK={kernelName:up,backendName:"webgl",kernelFunc:mK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=S.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new hK(c);return a.runWebGLProgram(d,[r,s],"float32")}var yK={kernelName:dp,backendName:"webgl",kernelFunc:gK},xK=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function KX(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ue({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new XX(s),l=a.runWebGLProgram(o,[i],i.dtype),u=ue({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var YX={kernelName:iu,backendName:"webgl",kernelFunc:KX},ZX=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` + `}};function AK(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=de({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new xK(s),l=a.runWebGLProgram(o,[i],i.dtype),u=de({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var bK={kernelName:pu,backendName:"webgl",kernelFunc:AK},vK=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${c}); const float neg_infinity = -3.4e38; @@ -3381,7 +3389,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam float result = curVal; setOutput(result); } - `}};function JX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new ZX(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=ue({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var QX={kernelName:Si,backendName:"webgl",kernelFunc:JX};function eK(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=S.decodeEinsumEquation(r,s.length);S.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=S.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m=0&&(d=e0({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var tK={kernelName:ip,backendName:"webgl",kernelFunc:eK},aK="return (x >= 0.0) ? x : (exp(x) - 1.0);",nK=` + `}};function wK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new vK(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=de({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var kK={kernelName:Ti,backendName:"webgl",kernelFunc:wK};function IK(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=S.decodeEinsumEquation(r,s.length);S.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=S.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=a0({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var SK={kernelName:pp,backendName:"webgl",kernelFunc:IK},CK="return (x >= 0.0) ? x : (exp(x) - 1.0);",TK=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3390,12 +3398,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,rK=et({opSnippet:aK,packedOpSnippet:nK}),sK={kernelName:Ti,backendName:"webgl",kernelFunc:rK},iK="return (b >= 0.0) ? a : a * (b + 1.0);",oK=` +`,NK=tt({opSnippet:CK,packedOpSnippet:TK}),RK={kernelName:Ri,backendName:"webgl",kernelFunc:NK},EK="return (b >= 0.0) ? a : a * (b + 1.0);",MK=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,lK=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Up(oK,n.shape,r.shape):new ql(iK,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},uK={kernelName:ou,backendName:"webgl",kernelFunc:lK},dK=` +`,_K=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Hu(MK,n.shape,r.shape):new Js(EK,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},PK={kernelName:cu,backendName:"webgl",kernelFunc:_K},$K=` return vec4(equal(a, b)); -`,pK="return float(a == b);",cK=fa({opSnippet:pK,packedOpSnippet:dK,dtype:"bool",cpuKernelImpl:jG}),hK={kernelName:Ni,backendName:"webgl",kernelFunc:cK},fK=` +`,FK="return float(a == b);",DK=ma({opSnippet:FK,packedOpSnippet:$K,dtype:"bool",cpuKernelImpl:cH}),OK={kernelName:Mi,backendName:"webgl",kernelFunc:DK},zK=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3410,9 +3418,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,mK=et({opSnippet:fK}),gK={kernelName:lu,backendName:"webgl",kernelFunc:mK},yK=Vu+` +`,LK=tt({opSnippet:zK}),WK={kernelName:Ei,backendName:"webgl",kernelFunc:LK},BK=ju+` return exp(x); -`,xK=` +`,VK=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3421,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,l8=et({opSnippet:yK,packedOpSnippet:xK,cpuKernelImpl:qG,dtype:"float32"}),AK={kernelName:Ri,backendName:"webgl",kernelFunc:l8};function T1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ue({inputs:{x:s},backend:n,attrs:{shape:o}})}var bK={kernelName:uu,backendName:"webgl",kernelFunc:T1},bx="return exp(x) - 1.0;",vK=et({opSnippet:bx,packedOpSnippet:bx,cpuKernelImpl:XG}),wK={kernelName:Ei,backendName:"webgl",kernelFunc:vK},vx=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,A8=tt({opSnippet:BK,packedOpSnippet:VK,cpuKernelImpl:hH,dtype:"float32"}),UK={kernelName:_i,backendName:"webgl",kernelFunc:A8};function F1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),de({inputs:{x:s},backend:n,attrs:{shape:o}})}var GK={kernelName:hu,backendName:"webgl",kernelFunc:F1},Ex="return exp(x) - 1.0;",HK=tt({opSnippet:Ex,packedOpSnippet:Ex,cpuKernelImpl:mH}),jK={kernelName:Pi,backendName:"webgl",kernelFunc:HK},Mx=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3454,12 +3462,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function u8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ue({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new vx("real",l,t),p=new vx("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=ds({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let m=ue({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function kK(e){let{inputs:t,backend:a}=e,{input:n}=t;return u8(n,!1,a)}var IK={kernelName:op,backendName:"webgl",kernelFunc:kK},SK=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function b8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=de({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Mx("real",l,t),p=new Mx("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=ps({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let f=de({inputs:{x:m},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(m),f}function qK(e){let{inputs:t,backend:a}=e,{input:n}=t;return b8(n,!1,a)}var XK={kernelName:cp,backendName:"webgl",kernelFunc:qK},KK=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Hp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new SK(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var CK={kernelName:du,backendName:"webgl",kernelFunc:Hp},TK=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Kp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new KK(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var YK={kernelName:mu,backendName:"webgl",kernelFunc:Kp},ZK=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3473,7 +3481,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(outputValue); } - `}},NK={kernelName:Mi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new TK(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},wx="return floor(x);",RK=et({opSnippet:wx,packedOpSnippet:wx,cpuKernelImpl:KG}),EK={kernelName:Fi,backendName:"webgl",kernelFunc:RK},MK=` + `}},JK={kernelName:$i,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new ZK(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},_x="return floor(x);",QK=tt({opSnippet:_x,packedOpSnippet:_x,cpuKernelImpl:fH}),eY={kernelName:Fi,backendName:"webgl",kernelFunc:QK},tY=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3483,7 +3491,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } else { return NAN; } -`,FK=` +`,aY=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3504,7 +3512,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,_K=fa({opSnippet:MK,packedOpSnippet:FK,dtype:"int32"}),$K={kernelName:_i,backendName:"webgl",kernelFunc:_K},DK=class{constructor(e){this.variableNames=["A"];let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=` +`,nY=ma({opSnippet:tY,packedOpSnippet:aY,dtype:"int32"}),rY={kernelName:Di,backendName:"webgl",kernelFunc:nY},sY=class{constructor(e){this.variableNames=["A"];let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3526,7 +3534,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},PK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=` + `}},iY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,SY=` +`,KY=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3600,18 +3608,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,CY=et({opSnippet:IY,packedOpSnippet:SY,cpuKernelImpl:nH}),TY={kernelName:ji,backendName:"webgl",kernelFunc:CY},NY=Vu+` +`,YY=tt({opSnippet:XY,packedOpSnippet:KY,cpuKernelImpl:kH}),ZY={kernelName:Ki,backendName:"webgl",kernelFunc:YY},JY=ju+` return log(1.0 + x); -`,RY=et({opSnippet:NY}),EY={kernelName:qi,backendName:"webgl",kernelFunc:RY},MY="return float(a >= 1.0 && b >= 1.0);",FY=` +`,QY=tt({opSnippet:JY}),eZ={kernelName:Yi,backendName:"webgl",kernelFunc:QY},tZ="return float(a >= 1.0 && b >= 1.0);",aZ=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,_Y=fa({opSnippet:MY,packedOpSnippet:FY,dtype:"bool"}),$Y={kernelName:Xi,backendName:"webgl",kernelFunc:_Y},DY="return float(!(x >= 1.0));",PY=et({opSnippet:DY}),OY={kernelName:Ki,backendName:"webgl",kernelFunc:PY},zY="return float(a >= 1.0 || b >= 1.0);",LY=` +`,nZ=ma({opSnippet:tZ,packedOpSnippet:aZ,dtype:"bool"}),rZ={kernelName:Zi,backendName:"webgl",kernelFunc:nZ},sZ="return float(!(x >= 1.0));",iZ=tt({opSnippet:sZ}),oZ={kernelName:Ji,backendName:"webgl",kernelFunc:iZ},lZ="return float(a >= 1.0 || b >= 1.0);",uZ=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,WY=fa({opSnippet:zY,packedOpSnippet:LY,dtype:"bool"}),BY={kernelName:Yi,backendName:"webgl",kernelFunc:WY},VY=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,dZ=ma({opSnippet:lZ,packedOpSnippet:uZ,dtype:"bool"}),pZ={kernelName:Qi,backendName:"webgl",kernelFunc:dZ},cZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3630,7 +3638,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam float val = x * ${o}; setOutput(val); } - `}},UY=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},hZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3692,7 +3700,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},GY=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new UY(r.shape,s,i,o,l):new VY(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},HY={kernelName:Zi,backendName:"webgl",kernelFunc:GY},jY=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` + `}},mZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new hZ(r.shape,s,i,o,l):new cZ(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},fZ={kernelName:eo,backendName:"webgl",kernelFunc:mZ},gZ=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3747,16 +3755,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(result); } - `}},qY=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new jY(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},XY={kernelName:cu,backendName:"webgl",kernelFunc:qY};function KY(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ue({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=Ko(i,e.dtype,"max",n),l=ue({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function p8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let C=0;C{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new gZ(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},xZ={kernelName:gu,backendName:"webgl",kernelFunc:yZ};function AZ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=el(i,e.dtype,"max",n),l=de({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function w8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let I=0;I`Error in maxPool: Either strides or dilations must be 1. 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Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new Yd(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var CZ={kernelName:no,backendName:"webgl",kernelFunc:SZ};function TZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Pg(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var NZ={kernelName:yu,backendName:"webgl",kernelFunc:TZ},RZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3802,7 +3810,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}},iZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},EZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${c}, ${d}); void main() { @@ -3866,16 +3874,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam } setOutput(dotProd); } - `}};function oZ(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new Sg(d,"max",!0),f=a.runWebGLProgram(h,[i],i.dtype),m=new iZ(d),g=a.runWebGLProgram(m,[r,f],i.dtype);return a.disposeIntermediateTensorInfo(f),g}var lZ={kernelName:dp,backendName:"webgl",kernelFunc:oZ};function uZ(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Pu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,f=new jd(d,"max",h),m=a.runWebGLProgram(f,[o],o.dtype),g=new sZ(d),y=a.runWebGLProgram(g,[r,m],o.dtype);return a.disposeIntermediateTensorInfo(m),y}var dZ={kernelName:Sh,backendName:"webgl",kernelFunc:uZ};function pZ(e,t,a,n){let r=new jd(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new jd(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var cZ={kernelName:pp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${i}; @@ -3904,7 +3912,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${r} coords = outC - start; setOutput(getX(${o})); } - `}},wZ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let n=e.length,r=ft(n),s=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=Ia("rc",n),l=Ia("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=a==="reflect"?0:1,d="";if(n===1){let h=` + `}},jZ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let n=e.length,r=ft(n),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=Ia("rc",n),l=Ia("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=a==="reflect"?0:1,d="";if(n===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${c}; @@ -3960,13 +3968,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam ${d} setOutput(result); } - `}},kZ=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wZ(n.shape,r,s):new vZ(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},IZ={kernelName:ro,backendName:"webgl",kernelFunc:kZ},SZ=`if (b == 0.0) return NAN; - return mod(a, b);`,CZ=` + `}},qZ=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jZ(n.shape,r,s):new HZ(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},XZ={kernelName:oo,backendName:"webgl",kernelFunc:qZ},KZ=`if (b == 0.0) return NAN; + return mod(a, b);`,YZ=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+Xo+` + `+Qo+` return result; -`,TZ=fa({opSnippet:SZ,packedOpSnippet:CZ}),NZ={kernelName:fu,backendName:"webgl",kernelFunc:TZ},RZ=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` +`,ZZ=ma({opSnippet:KZ,packedOpSnippet:YZ}),JZ={kernelName:lo,backendName:"webgl",kernelFunc:ZZ},QZ=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3986,11 +3994,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,oq=et({opSnippet:iq}),lq={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},EZ=` + `}},eJ=` if (a == b) { return 1.0; }; -return a / b;`,MZ=` +return a / b;`,tJ=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4008,9 +4016,9 @@ return a / b;`,MZ=` } return result; -`,c8=fa({opSnippet:EZ,packedOpSnippet:MZ,checkOutOfBounds:!0}),FZ={kernelName:Ci,backendName:"webgl",kernelFunc:c8},kx="return a - b;",h8=fa({opSnippet:kx,packedOpSnippet:kx,supportsComplex:!0,cpuKernelImpl:TH}),_Z={kernelName:zo,backendName:"webgl",kernelFunc:h8};function f8(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=p8({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=S.expandShapeToKeepDim(o.shape,i),u=ue({inputs:{x:o},backend:a,attrs:{shape:l}}),p=h8({inputs:{a:r,b:u},backend:a}),c=l8({inputs:{x:p},backend:a}),d=e0({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=ue({inputs:{x:d},backend:a,attrs:{shape:l}}),f=c8({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}var $Z={kernelName:$o,backendName:"webgl",kernelFunc:f8};function DZ(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:f8({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new RZ(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var PZ={kernelName:so,backendName:"webgl",kernelFunc:DZ},OZ=Rn+` +`,k8=ma({opSnippet:eJ,packedOpSnippet:tJ,checkOutOfBounds:!0}),aJ={kernelName:Ni,backendName:"webgl",kernelFunc:k8},Px="return a - b;",I8=ma({opSnippet:Px,packedOpSnippet:Px,supportsComplex:!0,cpuKernelImpl:qH}),nJ={kernelName:Uo,backendName:"webgl",kernelFunc:I8};function S8(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=w8({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=S.expandShapeToKeepDim(o.shape,i),u=de({inputs:{x:o},backend:a,attrs:{shape:l}}),p=I8({inputs:{a:r,b:u},backend:a}),c=A8({inputs:{x:p},backend:a}),d=a0({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=de({inputs:{x:d},backend:a,attrs:{shape:l}}),m=k8({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}var rJ={kernelName:Lo,backendName:"webgl",kernelFunc:S8};function sJ(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:S8({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new QZ(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var iJ={kernelName:uo,backendName:"webgl",kernelFunc:sJ},oJ=En+` return -x; -`,zZ=` +`,lJ=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4020,14 +4028,14 @@ return a / b;`,MZ=` result.a = isNaN.a ? x.a : result.a; return result; -`;function LZ(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=lH(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Gr(n.shape,zZ):r=new Yn(n.shape,OZ),a.runWebGLProgram(r,[n],n.dtype)}var WZ={kernelName:mu,backendName:"webgl",kernelFunc:LZ},BZ=Nn.nonMaxSuppressionV3Impl;function VZ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=BZ(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var UZ={kernelName:lo,backendName:"webgl",kernelFunc:VZ},GZ=Nn.nonMaxSuppressionV4Impl;function HZ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=GZ(p,c,i,o,l,u);return[a.makeTensorInfo([d.length],"int32",new Int32Array(d)),a.makeTensorInfo([],"int32",new Int32Array([h]))]}var jZ={kernelName:gu,backendName:"webgl",kernelFunc:HZ},qZ=Nn.nonMaxSuppressionV5Impl;function XZ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=qZ(p,c,d,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var KZ={kernelName:uo,backendName:"webgl",kernelFunc:XZ},YZ=class{constructor(e,t,a,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function uJ(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=NH(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Hr(n.shape,lJ):r=new Kn(n.shape,oJ),a.runWebGLProgram(r,[n],n.dtype)}var dJ={kernelName:Au,backendName:"webgl",kernelFunc:uJ},pJ=Rn.nonMaxSuppressionV3Impl;function cJ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. 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r=Gp({inputs:{input:n},backend:a}),s=m8({inputs:{x:r},backend:a}),i=t0({inputs:{input:n},backend:a}),o=gh({inputs:{x:i},backend:a}),l=ds({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Hp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var eJ={kernelName:yu,backendName:"webgl",kernelFunc:m8};function tJ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return T1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=T1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=e8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var aJ={kernelName:xu,backendName:"webgl",kernelFunc:tJ},nJ=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=ft(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` + `}},vJ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new bJ(u,i,o,l),c=de({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],m=de({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),m},wJ={kernelName:fo,backendName:"webgl",kernelFunc:vJ};function vh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Xp({inputs:{input:n},backend:a}),s=vh({inputs:{x:r},backend:a}),i=n0({inputs:{input:n},backend:a}),o=vh({inputs:{x:i},backend:a}),l=ps({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Kp({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var kJ={kernelName:$u,backendName:"webgl",kernelFunc:vh};function C8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Xp({inputs:{input:n},backend:a}),s=C8({inputs:{x:r},backend:a}),i=n0({inputs:{input:n},backend:a}),o=vh({inputs:{x:i},backend:a}),l=ps({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Kp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var IJ={kernelName:vu,backendName:"webgl",kernelFunc:C8};function SJ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return F1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=F1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=p8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var CJ={kernelName:wu,backendName:"webgl",kernelFunc:SJ},TJ=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=ft(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4052,19 +4060,19 @@ return a / b;`,MZ=` setOutput(getX(${o})); } } - `}},rJ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let n=e.length,r=ft(n),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=Ia("rc",n),l=Ia("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=[`${r} rc = outputLoc;`,`${o[n-1]} += 1; + `}},NJ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,r=ft(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Ia("rc",n),l=Ia("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=[`${r} rc = outputLoc;`,`${o[n-1]} += 1; if(${u}) { `,n===1?"":`} rc = outputLoc; ${o[n-2]} += 1; if(${o[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${o[n-1]} += 1; - if(${u}) {`],d=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=n===1?2:4;f= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=n===1?2:4;m{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Hp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rJ(r.shape,s,i):new nJ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},sJ={kernelName:co,backendName:"webgl",kernelFunc:g8},iJ=` + `}},T8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Kp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new NJ(r.shape,s,i):new TJ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},RJ={kernelName:go,backendName:"webgl",kernelFunc:T8},EJ=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4085,7 +4093,7 @@ return a / b;`,MZ=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,oJ=` +`,MJ=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -4101,11 +4109,11 @@ return a / b;`,MZ=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+Xo+` + `+Qo+` return result; -`,lJ=fa({opSnippet:iJ,packedOpSnippet:oJ}),uJ={kernelName:ho,backendName:"webgl",kernelFunc:lJ};function dJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=S.getAxesPermutation(p,o),d=r;c!=null&&(d=Ta({inputs:{x:r},backend:a,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,o),l.push(d)),S.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let f=a.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=dH(d.shape,d.dtype,f,p);h=a.makeTensorInfo(g,y,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(m),y=ue({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Ip(r.dtype),A=Ko(y,x,"prod",a);h=ue({inputs:{x:A},backend:a,attrs:{shape:f}}),l.push(y),l.push(A)}if(i){l.push(h);let f=S.expandShapeToKeepDim(h.shape,u);h=ue({inputs:{x:h},backend:a,attrs:{shape:f}})}return l.forEach(f=>a.disposeIntermediateTensorInfo(f)),h}var pJ={kernelName:mo,backendName:"webgl",kernelFunc:dJ};function cJ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,f]=pH(l,u,p,s.shape,s.dtype,c,i.shape,o),m=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(f,s.dtype,h);return m.concat([g])}var hJ={kernelName:Ch,backendName:"webgl",kernelFunc:cJ};function fJ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=cH(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var mJ={kernelName:Th,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[f,m]=hH(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(f,s.dtype,m)}var yJ={kernelName:Nh,backendName:"webgl",kernelFunc:gJ},y8=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=fH(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},xJ={kernelName:Au,backendName:"webgl",kernelFunc:y8},AJ="return 1.0 / x;",bJ=et({opSnippet:AJ}),vJ={kernelName:go,backendName:"webgl",kernelFunc:bJ},wJ=Rn+` +`,_J=ma({opSnippet:EJ,packedOpSnippet:MJ}),PJ={kernelName:yo,backendName:"webgl",kernelFunc:_J};function $J(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=S.getAxesPermutation(p,o),d=r;c!=null&&(d=Ta({inputs:{x:r},backend:a,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,o),l.push(d)),S.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let m=a.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=EH(d.shape,d.dtype,m,p);h=a.makeTensorInfo(g,y,f)}else{let[m,f]=S.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(f),y=de({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Rp(r.dtype),A=el(y,x,"prod",a);h=de({inputs:{x:A},backend:a,attrs:{shape:m}}),l.push(y),l.push(A)}if(i){l.push(h);let m=S.expandShapeToKeepDim(h.shape,u);h=de({inputs:{x:h},backend:a,attrs:{shape:m}})}return l.forEach(m=>a.disposeIntermediateTensorInfo(m)),h}var FJ={kernelName:Ao,backendName:"webgl",kernelFunc:$J};function DJ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,m]=MH(l,u,p,s.shape,s.dtype,c,i.shape,o),f=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var OJ={kernelName:Nh,backendName:"webgl",kernelFunc:DJ};function zJ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=_H(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var LJ={kernelName:Rh,backendName:"webgl",kernelFunc:zJ};function WJ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=PH(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var BJ={kernelName:Eh,backendName:"webgl",kernelFunc:WJ},N8=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=$H(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},VJ={kernelName:ku,backendName:"webgl",kernelFunc:N8},UJ="return 1.0 / x;",GJ=tt({opSnippet:UJ}),HJ={kernelName:bo,backendName:"webgl",kernelFunc:GJ},jJ=En+` return (x < 0.0) ? 0.0 : x; -`,kJ=` +`,qJ=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4115,9 +4123,9 @@ return a / b;`,MZ=` result.a = isNaN.a ? x.a : result.a; return result; -`,IJ=et({opSnippet:wJ,packedOpSnippet:kJ}),SJ={kernelName:yo,backendName:"webgl",kernelFunc:IJ},CJ=Rn+` +`,XJ=tt({opSnippet:jJ,packedOpSnippet:qJ}),KJ={kernelName:vo,backendName:"webgl",kernelFunc:XJ},YJ=En+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,TJ=` +`,ZJ=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4127,7 +4135,7 @@ return a / b;`,MZ=` result.a = isNaN.a ? x.a : result.a; return result; -`,NJ=et({opSnippet:CJ,packedOpSnippet:TJ}),RJ={kernelName:bo,backendName:"webgl",kernelFunc:NJ},EJ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,JJ=tt({opSnippet:YJ,packedOpSnippet:ZJ}),QJ={kernelName:Io,backendName:"webgl",kernelFunc:JJ},eQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4160,7 +4168,7 @@ return a / b;`,MZ=` setOutput(newValue); } - `}},MJ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},tQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4237,7 +4245,7 @@ return a / b;`,MZ=` setOutput(newValue); } - `}};function FJ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new MJ(r.shape,l,u,s,i):new EJ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var _J={kernelName:Ao,backendName:"webgl",kernelFunc:FJ},$J=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=` + `}};function aQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new tQ(r.shape,l,u,s,i):new eQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var nQ={kernelName:ko,backendName:"webgl",kernelFunc:aQ},rQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4254,7 +4262,7 @@ return a / b;`,MZ=` const float invWidthScale = float(${d}); const int winHeight = int(${h}); - const int winWidth = int(${f}); + const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4318,7 +4326,7 @@ return a / b;`,MZ=` setOutput(accumulator); } - `}};function DJ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new $J(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var PJ={kernelName:wu,backendName:"webgl",kernelFunc:DJ},OJ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function sQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new rQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var iQ={kernelName:Cu,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4340,7 +4348,7 @@ return a / b;`,MZ=` setOutput(newValue); } - `}},zJ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},lQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4381,7 +4389,7 @@ return a / b;`,MZ=` setOutput(newValue); } - `}};function LJ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zJ(r.shape,l,u,s,i):new OJ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var WJ={kernelName:xo,backendName:"webgl",kernelFunc:LJ},BJ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=` + `}};function uQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new lQ(r.shape,l,u,s,i):new oQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var dQ={kernelName:wo,backendName:"webgl",kernelFunc:uQ},pQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4398,7 +4406,7 @@ return a / b;`,MZ=` const float invWidthScale = float(${d}); const int winHeight = int(${h}); - const int winWidth = int(${f}); + const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4451,7 +4459,7 @@ return a / b;`,MZ=` setOutput(accumulator); } - `}};function VJ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new BJ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var UJ={kernelName:vu,backendName:"webgl",kernelFunc:VJ},GJ=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` + `}};function cQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new pQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var hQ={kernelName:Su,backendName:"webgl",kernelFunc:cQ},mQ=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4461,7 +4469,7 @@ return a / b;`,MZ=` ${s} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},HJ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);this.outputShape=e;let n=Ia("rc",a),r=`${n[a-1]} + 1 < ${this.outputShape[a-1]}`,s=`${n[a-2]} + 1 < ${this.outputShape[a-2]}`,i=ft(a);a===1?this.userCode=` + `}},fQ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);this.outputShape=e;let n=Ia("rc",a),r=`${n[a-1]} + 1 < ${this.outputShape[a-1]}`,s=`${n[a-2]} + 1 < ${this.outputShape[a-2]}`,i=ft(a);a===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4489,7 +4497,7 @@ return a / b;`,MZ=` } setOutput(result); } - `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let f=e.map((y,x)=>d(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function jJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return en({inputs:{x:r},backend:a});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HJ(r.shape,o):new GJ(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var qJ={kernelName:vo,backendName:"webgl",kernelFunc:jJ},XJ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let m=e.map((y,x)=>d(x,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function gQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return en({inputs:{x:r},backend:a});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fQ(r.shape,o):new mQ(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var yQ={kernelName:So,backendName:"webgl",kernelFunc:gQ},xQ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4508,7 +4516,7 @@ return a / b;`,MZ=` } setOutput(outputValue); } - `}},KJ={kernelName:Uo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new XJ(n.shape,s),[u,p]=S.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},YJ=` + `}},AQ={kernelName:Xo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new xQ(n.shape,s),[u,p]=S.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},bQ=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4523,7 +4531,7 @@ return a / b;`,MZ=` return base + 1.0; } } -`,ZJ=et({opSnippet:YJ}),JJ={kernelName:wo,backendName:"webgl",kernelFunc:ZJ},QJ="return 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h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4541,9 +4549,9 @@ return a / b;`,MZ=` found = true; } } - setOutput(mix(${m}, sum, float(found))); + setOutput(mix(${f}, sum, float(found))); } - `}},aQ=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ft(r.length),u=ft(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,f="";o&&(f="coords[0], coords[1]");let m=`getDefaultValue(${f})`,g=t>1?"strides[j]":"strides",y=t>1?"strides[j + 1]":"strides";this.userCode=` + `}},CQ=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ft(r.length),u=ft(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",y=t>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4578,9 +4586,9 @@ return a / b;`,MZ=` } } } - setOutput(mix(${m}, sum, found)); + setOutput(mix(${f}, sum, found)); } - `}};function nQ(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ue({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=ue({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g;W().getBool("WEBGL_PACK")?g=new aQ(l,o,h.shape.length,f.shape.length,p,d):g=new Cg(l,o,h.shape.length,f.shape.length,p,d);let y=a.runWebGLProgram(g,[f,h,m],f.dtype),x=ue({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(m),x}var rQ={kernelName:Io,backendName:"webgl",kernelFunc:nQ},sQ=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=W().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` + `}};function TQ(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=de({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=de({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=a.makeTensorInfo([],"float32",new Float32Array([0])),g;W().getBool("WEBGL_PACK")?g=new CQ(l,o,h.shape.length,m.shape.length,p,d):g=new $g(l,o,h.shape.length,m.shape.length,p,d);let y=a.runWebGLProgram(g,[m,h,f],m.dtype),x=de({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(f),x}var NQ={kernelName:No,backendName:"webgl",kernelFunc:TQ},RQ=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=W().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4605,7 +4613,7 @@ return a / b;`,MZ=` setOutput(float(findBound(batch, value))); } - `}};function iQ(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new sQ(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var oQ={kernelName:Co,backendName:"webgl",kernelFunc:iQ},lQ=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,cQ=et({opSnippet:pQ}),hQ={kernelName:To,backendName:"webgl",kernelFunc:cQ},fQ=Vu+` +`,DQ=tt({opSnippet:FQ}),OQ={kernelName:Mo,backendName:"webgl",kernelFunc:DQ},zQ=ju+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,mQ=` +`,LQ=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4633,20 +4641,20 @@ return a / b;`,MZ=` result.a = isNaN.a ? x.a : result.a; return result; -`,gQ=et({opSnippet:fQ,packedOpSnippet:mQ,cpuKernelImpl:yH}),yQ={kernelName:Eo,backendName:"webgl",kernelFunc:gQ},xQ=` +`,WQ=tt({opSnippet:zQ,packedOpSnippet:LQ,cpuKernelImpl:OH}),BQ={kernelName:Fo,backendName:"webgl",kernelFunc:WQ},VQ=` if (isnan(x)) { return 0.0; } return sign(x); -`,AQ=et({opSnippet:xQ}),bQ={kernelName:Ro,backendName:"webgl",kernelFunc:AQ},vQ=Vu+` +`,UQ=tt({opSnippet:VQ}),GQ={kernelName:$o,backendName:"webgl",kernelFunc:UQ},HQ=ju+` return sin(x); -`,wQ=` +`,jQ=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Xo} + ${Qo} return result; -`,kQ=et({opSnippet:vQ,packedOpSnippet:wQ}),IQ={kernelName:No,backendName:"webgl",kernelFunc:kQ},SQ=` +`,qQ=tt({opSnippet:HQ,packedOpSnippet:jQ}),XQ={kernelName:_o,backendName:"webgl",kernelFunc:qQ},KQ=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,CQ=et({opSnippet:SQ}),TQ={kernelName:Su,backendName:"webgl",kernelFunc:CQ},NQ=` +`,YQ=tt({opSnippet:KQ}),ZQ={kernelName:Po,backendName:"webgl",kernelFunc:YQ},JQ=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4666,17 +4674,17 @@ return a / b;`,MZ=` result = log(exp_x + 1.0); } return result; -`,RQ=et({opSnippet:NQ}),EQ={kernelName:Mo,backendName:"webgl",kernelFunc:RQ},MQ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let y=1+s.length;ya.disposeIntermediateTensorInfo(y)),g},FQ={kernelName:Cu,backendName:"webgl",kernelFunc:MQ};function _Q(e){let{inputs:t,backend:a}=e,{indices:n,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: 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i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=Zv(i,n.shape,n.dtype,o,l);return a.makeTensorInfo(p,n.dtype,u)}var dee={kernelName:bp,backendName:"webgl",kernelFunc:uee};function pee(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=S.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=a.bufferSync(r),x=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=DH(y,x,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let m=new $g(u,l,r.shape.length,s.shape.length,c,[d,1],h),f=a.runWebGLProgram(m,[s,r,i],s.dtype),g=de({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(f),g}var cee={kernelName:Wo,backendName:"webgl",kernelFunc:pee};function hee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=S.prepareSplitSize(r,s,o),u=r.shape.length,p=new 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Yn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var tee={kernelName:is,backendName:"webgl",kernelFunc:eee},aee=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=ft(a.length),s=ft(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new Kn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var See={kernelName:os,backendName:"webgl",kernelFunc:Iee},Cee=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=ft(a.length),s=ft(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); @@ -4684,15 +4692,15 @@ return a / b;`,MZ=` ${s} coords = 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ree={kernelName:Oo,backendName:"webgl",kernelFunc:nee};function see(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=IH(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var iee={kernelName:Ru,backendName:"webgl",kernelFunc:see};function oee(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=SH(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new Int32Array(c))]}var lee={kernelName:xp,backendName:"webgl",kernelFunc:oee};function uee(e){let{inputs:t,backend:a,attrs:n}=e,{numBuckets:r}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=a.readSync(s.dataId),o=CH(i,r);return a.makeTensorInfo(s.shape,"int32",o)}var dee={kernelName:Ap,backendName:"webgl",kernelFunc:uee},pee="return tan(x);",cee=et({opSnippet:pee}),hee={kernelName:Lo,backendName:"webgl",kernelFunc:cee},fee=` + `}};function Tee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=de({inputs:{x:r},backend:a,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let 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xee={kernelName:So,backendName:"webgl",kernelFunc:yee},Aee=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=$e(r.shape,r.dtype,l),p=NH(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Aee(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var vee={kernelName:ss,backendName:"webgl",kernelFunc:x8},wee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function Gee(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=$e(r.shape,r.dtype,l),p=XH(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Uee(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var Hee={kernelName:is,backendName:"webgl",kernelFunc:R8},jee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4732,7 +4740,7 @@ return a / b;`,MZ=` setOutput(float(i1)); } } - `}},kee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + `}},qee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4766,7 +4774,7 @@ return a / b;`,MZ=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function _s(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Cx(e){let t=1;for(;tl){let M=a.readSync(r.dataId),[F,E]=RH(M,u,r.dtype,s,i);return[a.makeTensorInfo(F.shape,F.dtype,F.values),a.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Hp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,f=v.sizeFromShape(u)/p,m=ue({inputs:{x:h},attrs:{shape:[f,p]},backend:a});d&&_s(a,h);let g=Cx(s),y=Cx(p),x=null,A=()=>x===null?[m,m]:[m,x],b=(M,F,E)=>{let I=A(),_=new wee(E),O=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[F]],B=x;x=a.runWebGLProgram(_,I,"int32",O),_s(a,B)};for(let M=1;M=1;E/=2)b(F,E,[f,y])}for(let M=y;M>g;M/=2){let F=A(),E=new kee([f,M/2]),I=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(E,F,"int32",I),_s(a,_);let O=g/2,B=O*2;for(let $=O;$>=1;$/=2)b(B,$,x.shape)}let w=x;x=Uu({inputs:{x},backend:a,attrs:{begin:0,size:[f,s]}}),_s(a,w);let C=d8({inputs:{x:m,indices:x},backend:a,attrs:{axis:1,batchDims:1}});_s(a,m);let T=u.slice(0,-1);T.push(s),w=x,x=ue({inputs:{x},attrs:{shape:T},backend:a}),_s(a,w);let N=C;return C=ue({inputs:{x:C},attrs:{shape:T},backend:a}),_s(a,N),[C,x]}var See={kernelName:Bo,backendName:"webgl",kernelFunc:Iee},Cee=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function $s(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Dx(e){let t=1;for(;tl){let M=a.readSync(r.dataId),[P,E]=KH(M,u,r.dtype,s,i);return[a.makeTensorInfo(P.shape,P.dtype,P.values),a.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Kp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=de({inputs:{x:h},attrs:{shape:[m,p]},backend:a});d&&$s(a,h);let g=Dx(s),y=Dx(p),x=null,A=()=>x===null?[f,f]:[f,x],b=(M,P,E)=>{let C=A(),_=new jee(E),O=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[P]],B=x;x=a.runWebGLProgram(_,C,"int32",O),$s(a,B)};for(let M=1;M=1;E/=2)b(P,E,[m,y])}for(let M=y;M>g;M/=2){let 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y=S.getUndoAxesPermutation(u);g=as({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var dae={kernelName:bi,backendName:"wasm",setupFunc:lae,kernelFunc:uae},L8;function pae(e){L8=e.wasm.cwrap(vi,null,["number","number","number","number","number","number"])}function cae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=S.getAxesPermutation([s],l),p=r;u!==null&&(p=as({inputs:{x:r},attrs:{perm:u},backend:a}));let c=S.getInnerMostAxes(1,l)[0];S.assertAxesAreInnerMostDims("cumsum",[c],l);let d=a.makeOutput(p.shape,p.dtype),h=p.shape[c],f=a.dataIdMap.get(p.dataId).id,m=a.dataIdMap.get(d.dataId).id;L8(f,i?1:0,o?1:0,h,m,nt[r.dtype]);let g=d;if(u!==null){let y=S.getUndoAxesPermutation(u);g=as({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var hae={kernelName:vi,backendName:"wasm",setupFunc:pae,kernelFunc:cae},W8;function fae(e){W8=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function mae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i,binaryOutput:o}=n,l=s.shape.reduce((d,h)=>d*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function c(d){return t.dataIdMap.get(d.dataId).id}return W8(c(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,c(s),nt[s.dtype],o,c(p)),p}var gae={kernelName:su,backendName:"wasm",setupFunc:fae,kernelFunc:mae},B8;function yae(e){B8=e.wasm.cwrap(ki,null,["number","number","number","array","number","array","array","number","number"])}function xae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return B8(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,f.length,b),m}var Aae={kernelName:ki,backendName:"wasm",setupFunc:yae,kernelFunc:xae},V8;function bae(e){V8=e.wasm.cwrap(Ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vae(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=S.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,C=h.strideHeight,T=h.strideWidth,N=h.inChannels,M=h.outChannels,F=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=n.makeOutput(h.outShape,"float32"),I=n.dataIdMap.get(E.dataId).id;return V8(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,y,x,A,F,b,w,C,T,N,M,I),E}var wae={kernelName:Ii,backendName:"wasm",setupFunc:bae,kernelFunc:vae},U8;function kae(e){U8=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Iae(e){let{inputs:t,backend:a}=e,{x:n}=t,r=v.sizeFromShape(n.shape),s=a.makeOutput([...n.shape,...n.shape],n.dtype);return U8(a.dataIdMap.get(n.dataId).id,nt[n.dtype],r,a.dataIdMap.get(s.dataId).id),s}var Sae={kernelName:iu,backendName:"wasm",setupFunc:kae,kernelFunc:Iae},G8;function Cae(e){G8=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=a.makeOutput(u.outShape,r.dtype);return G8(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(p.dataId).id,nt[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var Nae={kernelName:Si,backendName:"wasm",setupFunc:Cae,kernelFunc:Tae},H8;function Rae(e){H8=e.wasm.cwrap(Pl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Eae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(s.shape,s.dtype);return H8(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Mae={kernelName:Pl,backendName:"wasm",setupFunc:Rae,kernelFunc:Eae},j8;function Fae(e){j8=e.wasm.cwrap(Dl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _ae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(r.shape,r.dtype);return j8(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var $ae={kernelName:Dl,backendName:"wasm",setupFunc:Fae,kernelFunc:_ae},Dae=at(Ti),q8;function Pae(e){q8=e.wasm.cwrap(ou,null,["number","number","number"])}function Oae(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=a.makeOutput(r.shape,"float32"),i=o=>a.dataIdMap.get(o.dataId).id;return q8(i(r),i(n),i(s)),s}var zae={kernelName:ou,backendName:"wasm",setupFunc:Pae,kernelFunc:Oae},Lae=!1,Wae=ea(Ni,Lae,"bool"),Bae=at(Ri,"float32");function R1(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ba({inputs:{x:r},backend:n,attrs:{shape:o}})}var Vae={kernelName:uu,backendName:"wasm",kernelFunc:R1},Uae=at(Ei,"float32");function X8(e){let{attrs:{shape:t,value:a,dtype:n},backend:r}=e,s=r.makeOutput(t,n);return r.typedArrayFromHeap(s).fill(a),s}var Gae={kernelName:du,backendName:"wasm",kernelFunc:X8},K8;function Hae(e){K8=e.wasm.cwrap(Mi,null,["number","number","number","number","number","number"])}function jae(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return K8(s,o,l,u,p,i),r}var qae={kernelName:Mi,backendName:"wasm",kernelFunc:jae,setupFunc:Hae},Xae=at(Fi),Kae=!1,Yae=ea(_i,Kae),Y8;function Zae(e){Y8=e.wasm.cwrap($i,null,["number","number","number","number","number","number","number"])}function Jae(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return Y8(p,c,d,h,f,r,g),m}var Qae={kernelName:$i,backendName:"wasm",setupFunc:Zae,kernelFunc:Jae},Z8;function ene(e){Z8=e.wasm.cwrap(Kr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=a,m=S.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=Xd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=m.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,N=m.padInfo.right,M=m.padInfo.bottom,F=m.padInfo.left,E=m.dilationHeight,I=m.dilationWidth,_=m.strideHeight,O=m.strideWidth,B=m.inChannels,$=m.padInfo.type==="SAME"?1:0,U=m.batchSize,G=m.inHeight,q=m.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(m.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return Z8(y,U,G,q,x,w,C,b,T,N,M,F,$,E,I,_,O,B,A,g,Z,f||0,V),H}var ane={kernelName:Kr,backendName:"wasm",setupFunc:ene,kernelFunc:tne},J8;function nne(e){J8=e.wasm.cwrap(Yr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=a,m=S.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!0),g=Xd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=m.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,N=m.padInfo.right,M=m.padInfo.bottom,F=m.padInfo.left,E=m.dilationHeight,I=m.dilationWidth,_=m.strideHeight,O=m.strideWidth,B=m.inChannels,$=m.padInfo.type==="SAME"?1:0,U=m.batchSize,G=m.inHeight,q=m.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(m.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return J8(y,U,G,q,x,w,C,b,T,N,M,F,$,E,I,_,O,B,A,g,Z,f||0,V),H}var sne={kernelName:Yr,backendName:"wasm",setupFunc:nne,kernelFunc:rne},Q8;function ine(e){Q8=e.wasm.cwrap(Di,null,["number","number","number","number","number","number","array","number"])}function one(e){let{backend:t,inputs:a}=e,{params:n,indices:r}=a,[s,i,o,l]=H3.prepareAndValidate(n,r),u=t.makeOutput(s,n.dtype);if(i===0)return u;let p=r.shape,c=p[p.length-1],d=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(r.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return Q8(d,nt[n.dtype],h,i,c,o,f,m),u}var lne={kernelName:Di,backendName:"wasm",setupFunc:ine,kernelFunc:one},ew;function une(e){ew=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function dne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,indices:s}=a,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let T=0;T=0,()=>`GatherV2: the index value ${N} is not in [0, ${p-1}]`)}let c=S.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=Ba({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),f=Ba({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),m=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,x=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(f.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),C=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return ew(x,nt[r.dtype],w,y,A,c.batchSize,C,b),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=c.outputShape,g}var pne={kernelName:pu,backendName:"wasm",setupFunc:une,kernelFunc:dne},cne=!1,hne=ea(Pi,cne,"bool"),fne=!1,mne=ea(Oi,fne,"bool"),gne=at(Li,"bool"),yne=at(Wi,"bool"),xne=at(Bi,"bool"),tw;function Ane(e){tw=e.wasm.cwrap(Vi,null,["number","number","number","number"])}function bne(e){let{inputs:{x:t},attrs:{alpha:a},backend:n}=e,r=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;tw(r,nt[t.dtype],a,i)}return s}var vne={kernelName:Vi,backendName:"wasm",setupFunc:Ane,kernelFunc:bne},wne=!1,kne=ea(Ui,wne,"bool"),Ine=!1,Sne=ea(Gi,Ine,"bool"),aw;function Cne(e){aw=e.wasm.cwrap(Hi,null,["number","number","number","number"])}function Tne(e){let{attrs:t,backend:a}=e,{start:n,stop:r,num:s}=t,i=Math.floor(s),o=a.makeOutput([i],"float32");return aw(a.dataIdMap.get(o.dataId).id,n,r,i),o}var Nne={kernelName:Hi,backendName:"wasm",setupFunc:Cne,kernelFunc:Tne},Rne=at(ji),Ene=at(qi),Mne=!1,Fne=ea(Xi,Mne,"bool"),_ne=at(Ki),$ne=!1,Dne=ea(Yi,$ne,"bool"),Pne=!1,One=ea(oA,Pne,"bool"),nw;function zne(e){nw=e.wasm.cwrap(Zi,null,["number","number","number","number","number","number","number"])}function Lne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=a.makeOutput(r.shape,r.dtype);return nw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Wne={kernelName:Zi,backendName:"wasm",setupFunc:zne,kernelFunc:Lne},rw;function Bne(e){rw=e.wasm.cwrap(cu,null,["number","number","number","number","number","number","number","number","number"])}function Vne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let c=a.makeOutput(r.shape,r.dtype);return rw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,i.shape[3],o,l,u,p),c}var Une={kernelName:cu,backendName:"wasm",setupFunc:Bne,kernelFunc:Vne},sw;function Gne(e){sw=e.wasm.cwrap(Ji,null,["number","number","number","number"])}function Hne(e){let{backend:t,inputs:a,attrs:n}=e,{reductionIndices:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:c,inputWasTransposed:d}=ps(i,r,t);if(d){let x=t.dataIdMap.get(u.dataId).id;l=u,o=x}let h=l.shape.length;S.assertAxesAreInnerMostDims("max",p,h);let[f,m]=S.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;sw(o,nt[i.dtype],g,x)}if(d&&t.disposeData(u.dataId),s){let x=S.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var jne={kernelName:Ji,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne},qne=!1,Xne=ea(Qi,qne),iw;function Kne(e){iw=e.wasm.cwrap(eo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yne(e){let{inputs:t,attrs:a,backend:n}=e,r=t.x,s=n.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=S.computePool2DInfo(r.shape,i,o,1,l,u),c=p.filterHeight,d=p.filterWidth,h=p.padInfo.top,f=p.padInfo.right,m=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,x=p.dilationWidth,A=p.strideHeight,b=p.strideWidth,w=p.inChannels,C=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(p.outShape,"float32"),N=n.dataIdMap.get(T.dataId).id;return iw(s,r.shape[0],r.shape[1],r.shape[2],c,d,h,f,m,g,y,x,A,b,w,C,N),T}var Zne={kernelName:eo,backendName:"wasm",setupFunc:Kne,kernelFunc:Yne},ow;function Jne(e){ow=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=S.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.makeOutput(p.outShape,r.dtype);return ow(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var ere={kernelName:hu,backendName:"wasm",setupFunc:Jne,kernelFunc:Qne},lw;function tre(e){lw=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function are(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=S.computePool3DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return lw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var nre={kernelName:dp,backendName:"wasm",setupFunc:tre,kernelFunc:are},uw;function rre(e){uw=e.wasm.cwrap(to,null,["number, number, number"])}function sre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ps(i,r,t),f=c;if(h){let b=t.dataIdMap.get(p.dataId).id;b!==o&&(u=p,l=b,f=S.getInnerMostAxes(f.length,u.shape.length))}S.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=S.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=cs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(m,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;uw(l,y,b)}if(h&&t.disposeData(p.dataId),s){let b=S.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var ire={kernelName:to,backendName:"wasm",setupFunc:rre,kernelFunc:sre},dw;function ore(e){dw=e.wasm.cwrap(ao,null,["number","number","number","number"])}function lre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ps(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let f=u.shape.length;S.assertAxesAreInnerMostDims("min",c,f);let[m,g]=S.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;dw(l,nt[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=S.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var ure={kernelName:ao,backendName:"wasm",setupFunc:ore,kernelFunc:lre},dre=!1,pre=ea(no,dre),E1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(E1||(E1={}));var pw;function cre(e){pw=e.wasm.cwrap(ro,null,["number","array","number","number","array","array","number","number"])}function hre(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(f=>f[0]),c=n.map(f=>f[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new 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fw(a.dataIdMap.get(l.dataId).id,u,p,s,i,a.dataIdMap.get(c.dataId).id),o||a.disposeData(l.dataId),c}var Are={kernelName:so,backendName:"wasm",setupFunc:yre,kernelFunc:xre},bre=!0,vre=ea(io,bre),wre=at(mu);function Tg(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var mw;function kre(e){mw=e.wasm.cwrap(lo,"number",["number","number","number","number","number"])}function Ire(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=mw(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Tg(t,c);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Sre={kernelName:lo,backendName:"wasm",setupFunc:kre,kernelFunc:Ire},gw;function Cre(e){gw=e.wasm.cwrap(gu,"number",["number","number","number","number","number","bool"])}function Tre(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=gw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Tg(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var Nre={kernelName:gu,backendName:"wasm",setupFunc:Cre,kernelFunc:Tre},yw;function Rre(e){yw=e.wasm.cwrap(uo,"number",["number","number","number","number","number","number"])}function Ere(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=yw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Tg(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var Mre={kernelName:uo,backendName:"wasm",setupFunc:Rre,kernelFunc:Ere},Fre=!1,_re=ea(oo,Fre,"bool"),xw;function $re(e){xw=e.wasm.cwrap(po,null,["number","number","number","number","number"])}function Dre(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return xw(c,i,o,l,p),u}var Pre={kernelName:po,backendName:"wasm",setupFunc:$re,kernelFunc:Dre};function Ore(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var zre={kernelName:yu,backendName:"wasm",kernelFunc:Ore};function Lre(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return R1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching 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bw={kernelName:co,backendName:"wasm",kernelFunc:Vre,setupFunc:Bre},Ure=!1,Gre=ea(ho,Ure),vw;function Hre(e){vw=e.wasm.cwrap(fo,null,["number","number","number"])}function jre(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=cs({backend:a,inputs:{x:n},attrs:{dtype:"float32"}}),o=a.dataIdMap.get(u.dataId).id);let p=a.makeOutput(n.shape,"float32"),c=a.dataIdMap.get(p.dataId).id;return vw(o,i,c),l.dtype!=="float32"&&a.disposeData(u.dataId),p}var qre={kernelName:fo,backendName:"wasm",setupFunc:Hre,kernelFunc:jre},ww;function Xre(e){ww=e.wasm.cwrap(mo,null,["number","number","number","number"])}function Kre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ps(i,r,t),f=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,f=S.getInnerMostAxes(f.length,u.shape.length))}S.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=S.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;ww(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=S.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Yre={kernelName:mo,backendName:"wasm",setupFunc:Xre,kernelFunc:Kre},Zre=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=ag(n,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Jre={kernelName:Au,backendName:"wasm",kernelFunc:Zre},Qre=!0,ese=ea(Ci,Qre),tse=at(go),ase=at(yo),nse=at(bo),kw;function rse(e){kw=e.wasm.cwrap(Ao,null,["number","number","number","number","number","number","number","number","number","number"])}function sse(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,f=[p,l,u,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=cs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,x=t.makeOutput(f,"float32");if(v.sizeFromShape(r.shape)===0)return x;let A=t.dataIdMap.get(x.dataId).id;return kw(y,p,c,d,h,l,u,s?1:0,i?1:0,A),g!=null&&t.disposeData(g.dataId),x}var ise={kernelName:Ao,backendName:"wasm",setupFunc:rse,kernelFunc:sse},Iw;function ose(e){Iw=e.wasm.cwrap(wu,null,["number","number","number","array","array","boolean"])}function lse(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=cs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Iw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new 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fse(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=cs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Cw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var mse={kernelName:vu,backendName:"wasm",setupFunc:hse,kernelFunc:fse},Tw;function gse(e){Tw=e.wasm.cwrap(vo,null,["number","array","number","array","number","number"])}function yse(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return a0({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new Int32Array(r.shape).buffer);Tw(l,p,i.length,c,r.shape.length,u);let d=Ba({inputs:{x:o},attrs:{shape:r.shape},backend:a});return a.disposeData(o.dataId),d}var xse={kernelName:vo,backendName:"wasm",kernelFunc:yse,setupFunc:gse},Nw;function Ase(e){Nw=e.wasm.cwrap(Uo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function bse(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=n,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(r.dataId).id,p=a.dataIdMap.get(l.dataId).id,[c,d,h,f]=r.shape,[m,g]=S.getImageCenter(o,d,h),y=i===0,x=255,A=typeof i=="number"?[i,i,i,y?0:x]:[...i,x],b=new Uint8Array(new Int32Array(A).buffer);return Nw(u,c,d,h,f,s,m,g,b,A.length,p),l}var vse={kernelName:Uo,backendName:"wasm",kernelFunc:bse,setupFunc:Ase},wse=at(wo),kse=at(ko),Rw;function Ise(e){Rw=e.wasm.cwrap(Io,null,["number","number","number","number","number","number","array","number","number"])}function Sse(e){let{backend:t,inputs:a,attrs:n}=e,{indices:r,updates:s}=a,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=Wh.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(c).buffer),g=t.dataIdMap.get(o.dataId).id;return Rw(h,f,nt[s.dtype],l,u,p,m,d,g),o}var Cse={kernelName:Io,backendName:"wasm",setupFunc:Ise,kernelFunc:Sse},Ew;function Tse(e){Ew=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","bool","number"])}function Nse(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n;if(r.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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Jd;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Jd||(Jd={}));var E8;function ote(e){E8=e.wasm.cwrap(Kr,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function lte(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n,d=a.dataIdMap.get(r.dataId).id,h=a.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let N=a.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);m=N.id}let f=o==null?0:a.dataIdMap.get(o.dataId).id,g=Jd[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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nae={kernelName:su,backendName:"wasm",kernelFunc:aae},B8;function rae(e){B8=e.wasm.cwrap(hi,null,["number","number","boolean","number","number","number"])}function sae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i}=n,o=s.shape.reduce((c,d)=>c*d,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(c){return t.dataIdMap.get(c.dataId).id}return B8(p(r),i,o,p(s),nt[s.dtype],p(u)),u}var iae={kernelName:hi,backendName:"wasm",setupFunc:rae,kernelFunc:sae},oae=!0,lae=Ut(iu,oae);function uae(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.typedArrayFromHeap(n),i=a.typedArrayFromHeap(r),o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var dae={kernelName:ou,backendName:"wasm",kernelFunc:uae};function hs(e){let{inputs:{x:t},attrs:{dtype:a},backend:n}=e,r=n.makeOutput(t.shape,a),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var pae={kernelName:mi,backendName:"wasm",kernelFunc:hs},cae=Qe(fi),V8;function hae(e){V8=e.wasm.cwrap(ss,null,["number","number","number","number"])}function mae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o=a.dataIdMap.get(r.dataId).id,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(l.dataId).id;return V8(o,s,i,u),l}var fae={kernelName:ss,backendName:"wasm",setupFunc:hae,kernelFunc:mae};function U8(e){let{inputs:t,backend:a}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);S.assertParamsConsistent(r,n);let s=S.computeOutShape(t.map(h=>h.shape),n),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return r0({inputs:{x:i[0]},backend:a});let o=a.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(A=>{let b=[-1,v.sizeFromShape(A.shape.slice(n))];return 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Yae(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=S.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,I=h.strideHeight,T=h.strideWidth,N=h.inChannels,M=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=n.makeOutput(h.outShape,"float32"),C=n.dataIdMap.get(E.dataId).id;return ew(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,x,A,P,b,w,I,T,N,M,C),E}var Zae={kernelName:Ci,backendName:"wasm",setupFunc:Kae,kernelFunc:Yae},tw;function Jae(e){tw=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Qae(e){let{inputs:t,backend:a}=e,{x:n}=t,r=v.sizeFromShape(n.shape),s=a.makeOutput([...n.shape,...n.shape],n.dtype);return tw(a.dataIdMap.get(n.dataId).id,nt[n.dtype],r,a.dataIdMap.get(s.dataId).id),s}var ene={kernelName:pu,backendName:"wasm",setupFunc:Jae,kernelFunc:Qae},aw;function tne(e){aw=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ane(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=a.makeOutput(u.outShape,r.dtype);return aw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(p.dataId).id,nt[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var nne={kernelName:Ti,backendName:"wasm",setupFunc:tne,kernelFunc:ane},nw;function rne(e){nw=e.wasm.cwrap(Bl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(s.shape,s.dtype);return nw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var ine={kernelName:Bl,backendName:"wasm",setupFunc:rne,kernelFunc:sne},rw;function one(e){rw=e.wasm.cwrap(Wl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(r.shape,r.dtype);return rw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var une={kernelName:Wl,backendName:"wasm",setupFunc:one,kernelFunc:lne},dne=Qe(Ri),sw;function pne(e){sw=e.wasm.cwrap(cu,null,["number","number","number"])}function cne(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=a.makeOutput(r.shape,"float32"),i=o=>a.dataIdMap.get(o.dataId).id;return sw(i(r),i(n),i(s)),s}var hne={kernelName:cu,backendName:"wasm",setupFunc:pne,kernelFunc:cne},mne=!1,fne=Ut(Mi,mne,"bool"),gne=Qe(Ei),yne=Qe(_i,"float32");function O1(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ba({inputs:{x:r},backend:n,attrs:{shape:o}})}var xne={kernelName:hu,backendName:"wasm",kernelFunc:O1},Ane=Qe(Pi,"float32");function iw(e){let{attrs:{shape:t,value:a,dtype:n},backend:r}=e,s=r.makeOutput(t,n);return r.typedArrayFromHeap(s).fill(a),s}var bne={kernelName:mu,backendName:"wasm",kernelFunc:iw},ow;function vne(e){ow=e.wasm.cwrap($i,null,["number","number","number","number","number","number"])}function wne(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return ow(s,o,l,u,p,i),r}var kne={kernelName:$i,backendName:"wasm",kernelFunc:wne,setupFunc:vne},Ine=Qe(Fi),Sne=!1,Cne=Ut(Di,Sne),lw;function Tne(e){lw=e.wasm.cwrap(Oi,null,["number","number","number","number","number","number","number"])}function Nne(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return lw(p,c,d,h,m,r,g),f}var Rne={kernelName:Oi,backendName:"wasm",setupFunc:Tne,kernelFunc:Nne},uw;function Ene(e){uw=e.wasm.cwrap(Yr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=S.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=Jd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,P=f.padInfo.left,E=f.dilationHeight,C=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,B=f.inChannels,$=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return uw(y,U,G,q,x,w,I,b,T,N,M,P,$,E,C,_,O,B,A,g,Z,m||0,V),H}var _ne={kernelName:Yr,backendName:"wasm",setupFunc:Ene,kernelFunc:Mne},dw;function Pne(e){dw=e.wasm.cwrap(Zr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $ne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=S.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!0),g=Jd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,P=f.padInfo.left,E=f.dilationHeight,C=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,B=f.inChannels,$=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return dw(y,U,G,q,x,w,I,b,T,N,M,P,$,E,C,_,O,B,A,g,Z,m||0,V),H}var Fne={kernelName:Zr,backendName:"wasm",setupFunc:Pne,kernelFunc:$ne},pw;function Dne(e){pw=e.wasm.cwrap(zi,null,["number","number","number","number","number","number","array","number"])}function One(e){let{backend:t,inputs:a}=e,{params:n,indices:r}=a,[s,i,o,l]=Q3.prepareAndValidate(n,r),u=t.makeOutput(s,n.dtype);if(i===0)return u;let p=r.shape,c=p[p.length-1],d=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return pw(d,nt[n.dtype],h,i,c,o,m,f),u}var zne={kernelName:zi,backendName:"wasm",setupFunc:Dne,kernelFunc:One},cw;function Lne(e){cw=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Wne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,indices:s}=a,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let T=0;T=0,()=>`GatherV2: the index value ${N} is not in [0, ${p-1}]`)}let c=S.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=Ba({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Ba({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),f=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,x=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return cw(x,nt[r.dtype],w,y,A,c.batchSize,I,b),t.disposeData(d.dataId),t.disposeData(m.dataId),g.shape=c.outputShape,g}var Bne={kernelName:fu,backendName:"wasm",setupFunc:Lne,kernelFunc:Wne},Vne=!1,Une=Ut(Li,Vne,"bool"),Gne=!1,Hne=Ut(Wi,Gne,"bool"),jne=Qe(Vi,"bool"),qne=Qe(Ui,"bool"),Xne=Qe(Gi,"bool"),hw;function Kne(e){hw=e.wasm.cwrap(Hi,null,["number","number","number","number"])}function Yne(e){let{inputs:{x:t},attrs:{alpha:a},backend:n}=e,r=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;hw(r,nt[t.dtype],a,i)}return s}var Zne={kernelName:Hi,backendName:"wasm",setupFunc:Kne,kernelFunc:Yne},Jne=!1,Qne=Ut(ji,Jne,"bool"),ere=!1,tre=Ut(qi,ere,"bool"),mw;function are(e){mw=e.wasm.cwrap(Xi,null,["number","number","number","number"])}function nre(e){let{attrs:t,backend:a}=e,{start:n,stop:r,num:s}=t,i=Math.floor(s),o=a.makeOutput([i],"float32");return mw(a.dataIdMap.get(o.dataId).id,n,r,i),o}var rre={kernelName:Xi,backendName:"wasm",setupFunc:are,kernelFunc:nre},sre=Qe(Ki),ire=Qe(Yi),ore=!1,lre=Ut(Zi,ore,"bool"),ure=Qe(Ji),dre=!1,pre=Ut(Qi,dre,"bool"),cre=!1,hre=Ut(yA,cre,"bool"),fw;function mre(e){fw=e.wasm.cwrap(eo,null,["number","number","number","number","number","number","number"])}function fre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=a.makeOutput(r.shape,r.dtype);return fw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var gre={kernelName:eo,backendName:"wasm",setupFunc:mre,kernelFunc:fre},gw;function yre(e){gw=e.wasm.cwrap(gu,null,["number","number","number","number","number","number","number","number","number"])}function xre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let c=a.makeOutput(r.shape,r.dtype);return gw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,i.shape[3],o,l,u,p),c}var Are={kernelName:gu,backendName:"wasm",setupFunc:yre,kernelFunc:xre},yw;function bre(e){yw=e.wasm.cwrap(to,null,["number","number","number","number"])}function vre(e){let{backend:t,inputs:a,attrs:n}=e,{reductionIndices:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:c,inputWasTransposed:d}=cs(i,r,t);if(d){let x=t.dataIdMap.get(u.dataId).id;l=u,o=x}let h=l.shape.length;S.assertAxesAreInnerMostDims("max",p,h);let[m,f]=S.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;yw(o,nt[i.dtype],g,x)}if(d&&t.disposeData(u.dataId),s){let x=S.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var wre={kernelName:to,backendName:"wasm",setupFunc:bre,kernelFunc:vre},kre=!1,Ire=Ut(ao,kre),xw;function Sre(e){xw=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cre(e){let{inputs:t,attrs:a,backend:n}=e,r=t.x,s=n.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=S.computePool2DInfo(r.shape,i,o,1,l,u),c=p.filterHeight,d=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,x=p.dilationWidth,A=p.strideHeight,b=p.strideWidth,w=p.inChannels,I=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(p.outShape,"float32"),N=n.dataIdMap.get(T.dataId).id;return xw(s,r.shape[0],r.shape[1],r.shape[2],c,d,h,m,f,g,y,x,A,b,w,I,N),T}var Tre={kernelName:no,backendName:"wasm",setupFunc:Sre,kernelFunc:Cre},Aw;function Nre(e){Aw=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Rre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=S.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.makeOutput(p.outShape,r.dtype);return Aw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var Ere={kernelName:yu,backendName:"wasm",setupFunc:Nre,kernelFunc:Rre},bw;function Mre(e){bw=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _re(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=S.computePool3DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return bw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var Pre={kernelName:gp,backendName:"wasm",setupFunc:Mre,kernelFunc:_re},vw;function $re(e){vw=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fre(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=S.computePool2DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return vw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),c}var Dre={kernelName:fp,backendName:"wasm",setupFunc:$re,kernelFunc:Fre},ww;function Ore(e){ww=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,[1,1],o),c=a.makeOutput(p.outShape,r.dtype),d=a.makeOutput(p.outShape,"int32");return ww(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,a.dataIdMap.get(d.dataId).id,nt[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[c,d]}var Lre={kernelName:xu,backendName:"wasm",setupFunc:Ore,kernelFunc:zre},kw;function Wre(e){kw=e.wasm.cwrap(ro,null,["number, number, number"])}function Bre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=cs(i,r,t),m=c;if(h){let b=t.dataIdMap.get(p.dataId).id;b!==o&&(u=p,l=b,m=S.getInnerMostAxes(m.length,u.shape.length))}S.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=S.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=hs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;kw(l,y,b)}if(h&&t.disposeData(p.dataId),s){let b=S.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var Vre={kernelName:ro,backendName:"wasm",setupFunc:Wre,kernelFunc:Bre},Iw;function Ure(e){Iw=e.wasm.cwrap(so,null,["number","number","number","number"])}function Gre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=cs(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let m=u.shape.length;S.assertAxesAreInnerMostDims("min",c,m);let[f,g]=S.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Iw(l,nt[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=S.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Hre={kernelName:so,backendName:"wasm",setupFunc:Ure,kernelFunc:Gre},jre=!1,qre=Ut(io,jre),z1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(z1||(z1={}));var Sw;function Xre(e){Sw=e.wasm.cwrap(oo,null,["number","array","number","number","array","array","number","number"])}function Kre(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Sw(i,u,t.shape.length,nt[t.dtype],d,h,z1[r],l),o}var Yre={kernelName:oo,backendName:"wasm",kernelFunc:Kre,setupFunc:Xre},Cw;function Zre(e){Cw=e.wasm.cwrap(Lo,null,["number","number","number","number"])}function Tw(e){let{backend:t,inputs:{logits:a},attrs:{dim:n}}=e,r=t.dataIdMap.get(a.dataId).id,s=t.makeOutput(a.shape,a.dtype),i=t.dataIdMap.get(s.dataId).id,o=a.shape[n],l=v.sizeFromShape(a.shape)/o;return v.sizeFromShape(s.shape)===0||Cw(r,i,o,l),s}var Jre={kernelName:Lo,backendName:"wasm",setupFunc:Zre,kernelFunc:Tw},Nw;function Qre(e){Nw=e.wasm.cwrap(uo,null,["number","number","number","number","number","number"])}function ese(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;if(r.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${r.dtype}`);let l=o?r:Tw({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,c=a.makeOutput([u,s],"int32");return Nw(a.dataIdMap.get(l.dataId).id,u,p,s,i,a.dataIdMap.get(c.dataId).id),o||a.disposeData(l.dataId),c}var tse={kernelName:uo,backendName:"wasm",setupFunc:Qre,kernelFunc:ese},ase=Ut(lo,!0),nse=!0,rse=Ut(po,nse),sse=Qe(Au);function Fg(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Rw;function ise(e){Rw=e.wasm.cwrap(ho,"number",["number","number","number","number","number"])}function ose(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Rw(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Fg(t,c);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var lse={kernelName:ho,backendName:"wasm",setupFunc:ise,kernelFunc:ose},Ew;function use(e){Ew=e.wasm.cwrap(bu,"number",["number","number","number","number","number","bool"])}function dse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Ew(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Fg(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var pse={kernelName:bu,backendName:"wasm",setupFunc:use,kernelFunc:dse},Mw;function cse(e){Mw=e.wasm.cwrap(mo,"number",["number","number","number","number","number","number"])}function hse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Mw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Fg(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([m],"float32",f);return[y,x]}var mse={kernelName:mo,backendName:"wasm",setupFunc:cse,kernelFunc:hse},fse=!1,gse=Ut(co,fse,"bool"),_w;function yse(e){_w=e.wasm.cwrap(fo,null,["number","number","number","number","number"])}function xse(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return _w(c,i,o,l,p),u}var Ase={kernelName:fo,backendName:"wasm",setupFunc:yse,kernelFunc:xse};function bse(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var vse={kernelName:vu,backendName:"wasm",kernelFunc:bse};function wse(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return O1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching 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$w={kernelName:go,backendName:"wasm",kernelFunc:Sse,setupFunc:Ise},Cse=!1,Tse=Ut(yo,Cse),Fw;function Nse(e){Fw=e.wasm.cwrap(xo,null,["number","number","number"])}function Rse(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=hs({backend:a,inputs:{x:n},attrs:{dtype:"float32"}}),o=a.dataIdMap.get(u.dataId).id);let p=a.makeOutput(n.shape,"float32"),c=a.dataIdMap.get(p.dataId).id;return Fw(o,i,c),l.dtype!=="float32"&&a.disposeData(u.dataId),p}var Ese={kernelName:xo,backendName:"wasm",setupFunc:Nse,kernelFunc:Rse},Dw;function Mse(e){Dw=e.wasm.cwrap(Ao,null,["number","number","number","number"])}function _se(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=cs(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=S.getInnerMostAxes(m.length,u.shape.length))}S.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=S.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Dw(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=S.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Pse={kernelName:Ao,backendName:"wasm",setupFunc:Mse,kernelFunc:_se},$se=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=dg(n,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Fse={kernelName:ku,backendName:"wasm",kernelFunc:$se},Dse=!0,Ose=Ut(Ni,Dse),zse=Qe(bo),Lse=Qe(vo),Wse=Qe(Io),Ow;function Bse(e){Ow=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","number","number","number","number"])}function Vse(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=hs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,x=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return x;let A=t.dataIdMap.get(x.dataId).id;return Ow(y,p,c,d,h,l,u,s?1:0,i?1:0,A),g!=null&&t.disposeData(g.dataId),x}var Use={kernelName:ko,backendName:"wasm",setupFunc:Bse,kernelFunc:Vse},zw;function Gse(e){zw=e.wasm.cwrap(Cu,null,["number","number","number","array","array","boolean"])}function Hse(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=hs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),zw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var jse={kernelName:Cu,backendName:"wasm",setupFunc:Gse,kernelFunc:Hse},Lw;function qse(e){Lw=e.wasm.cwrap(wo,null,["number","number","number","number","number","number","number","number","number","number"])}function Xse(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return f;let g=t.dataIdMap.get(r.dataId),y;g.dtype!=="float32"&&(y=hs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(y.dataId));let x=g.id,A=t.dataIdMap.get(f.dataId).id;return Lw(x,p,c,d,h,l,u,s?1:0,i?1:0,A),y!=null&&t.disposeData(y.dataId),f}var Kse={kernelName:wo,backendName:"wasm",setupFunc:qse,kernelFunc:Xse},Ww;function Yse(e){Ww=e.wasm.cwrap(Su,null,["number","number","number","array","array","boolean"])}function Zse(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=hs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Ww(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var Jse={kernelName:Su,backendName:"wasm",setupFunc:Yse,kernelFunc:Zse},Bw;function Qse(e){Bw=e.wasm.cwrap(So,null,["number","array","number","array","number","number"])}function eie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return r0({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new Int32Array(r.shape).buffer);Bw(l,p,i.length,c,r.shape.length,u);let d=Ba({inputs:{x:o},attrs:{shape:r.shape},backend:a});return a.disposeData(o.dataId),d}var tie={kernelName:So,backendName:"wasm",kernelFunc:eie,setupFunc:Qse},Vw;function aie(e){Vw=e.wasm.cwrap(Xo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function nie(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=n,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(r.dataId).id,p=a.dataIdMap.get(l.dataId).id,[c,d,h,m]=r.shape,[f,g]=S.getImageCenter(o,d,h),y=i===0,x=255,A=typeof i=="number"?[i,i,i,y?0:x]:[...i,x],b=new Uint8Array(new Int32Array(A).buffer);return Vw(u,c,d,h,m,s,f,g,b,A.length,p),l}var rie={kernelName:Xo,backendName:"wasm",kernelFunc:nie,setupFunc:aie},sie=Qe(Co),iie=Qe(To),Uw;function oie(e){Uw=e.wasm.cwrap(No,null,["number","number","number","number","number","number","array","number","number"])}function lie(e){let{backend:t,inputs:a,attrs:n}=e,{indices:r,updates:s}=a,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=Vh.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(c).buffer),g=t.dataIdMap.get(o.dataId).id;return Uw(h,m,nt[s.dtype],l,u,p,f,d,g),o}var uie={kernelName:No,backendName:"wasm",setupFunc:oie,kernelFunc:lie},Gw;function die(e){Gw=e.wasm.cwrap(Eo,null,["number","number","number","number","number","number","bool","number"])}function pie(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n;if(r.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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t;switch(e.length){case 0:t=` + }`,Goe=(e,t,a,n,r)=>{let s={dtype:n.dtype,shape:n.shape},i=joe(a,s,t),o=e.createShaderModule({code:i,label:t.constructor.name}),l=W().get("WEBGPU_PRINT_SHADER");if(l!==""){l=l.toLowerCase();let u=l.split(",");(l==="all"||u.some(p=>t.shaderKey.toLowerCase().includes(p)))&&(console.group(t.shaderKey),console.debug(i),console.groupEnd())}return r?e.createComputePipelineAsync({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"}):e.createComputePipeline({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})},Ke=(e,t="f32")=>{switch(e){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component ${t} is not supported.`)}};function $t(e){if(e<=1)return"i32";if(e===2)return"vec2";if(e===3)return"vec3";if(e===4)return"vec4";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function 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n=[],r=a.workgroupSize[0]*a.workgroupSize[1]*a.workgroupSize[2];if(a.outputComponent=a.outputComponent?a.outputComponent:1,n.push(` +`}function joe(e,t,a){let n=[],r=a.workgroupSize[0]*a.workgroupSize[1]*a.workgroupSize[2];if(a.outputComponent=a.outputComponent?a.outputComponent:1,n.push(` var localId: vec3; var localIndex: u32; @@ -5028,7 +5036,7 @@ return a / b;`,MZ=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${Xw(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${lk(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r}u + localIndex); `} @@ -5040,23 +5048,23 @@ return a / b;`,MZ=` outShapeStrides : vec2, }; - @group(0) @binding(0) var result: array<${Nl(t.dtype,a.outputComponent)}>; + @group(0) @binding(0) var result: array<${Pl(t.dtype,a.outputComponent)}>; @group(0) @binding(2) var uniforms: Uniform; - `);let h=Px(a);return[Dx,n.join(` -`),V2(t.shape),a.getUserCode(),$x(h,a)].join(` -`)}let s,i,o="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,f)=>{let m=Jt(e[f].shape.length);o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${m}, `,s=e[f].shape.length-1,i=Jt(s),o+=`${h.charAt(0).toLowerCase()+h.slice(1)}ShapeStrides: ${i}, `});let l=Jt(t.shape.length);o+=`outShape : ${l}, `,s=t.shape.length-1,i=Jt(s),o+=` - outShapeStrides: ${i}, `,a.size&&(o+="size : i32, "),a.uniforms&&(o+=a.uniforms),o+="};",o=yoe(o),n.push(o),a.atomic?n.push(` + `);let h=jx(a);return[Hx,n.join(` +`),nh(t.shape),a.getUserCode(),Gx(h,a)].join(` +`)}let s,i,o="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,m)=>{let f=$t(e[m].shape.length);o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${f}, `,s=e[m].shape.length-1,i=$t(s),o+=`${h.charAt(0).toLowerCase()+h.slice(1)}ShapeStrides: ${i}, `});let l=$t(t.shape.length);o+=`outShape : ${l}, `,s=t.shape.length-1,i=$t(s),o+=` + outShapeStrides: ${i}, `,a.size&&(o+="size : i32, "),a.uniforms&&(o+=a.uniforms),o+="};",o=tle(o),n.push(o),a.atomic?n.push(` @group(0) @binding(0) var result: array>; `):n.push(` - @group(0) @binding(0) var result: array<${Nl(t.dtype,a.outputComponent)}>; - `),a.variableNames.forEach((h,f)=>{n.push(` - @group(0) @binding(${1+f}) var ${h}: array<${a.variableComponents?Nl(e[f].dtype,a.variableComponents[f]):Nl(e[f].dtype,a.outputComponent)}>; + @group(0) @binding(0) var result: array<${Pl(t.dtype,a.outputComponent)}>; + `),a.variableNames.forEach((h,m)=>{n.push(` + @group(0) @binding(${1+m}) var ${h}: array<${a.variableComponents?Pl(e[m].dtype,a.variableComponents[m]):Pl(e[m].dtype,a.outputComponent)}>; `)}),o!==""&&n.push(` @group(0) @binding(${1+a.variableNames.length}) var uniforms: Uniforms; - `);let u=foe(t.shape,a.dispatchLayout),p=[Dx,n.join(` -`)+doe,V2(t.shape),u,moe(t.shape.length)];a.atomic||p.push(goe(t.shape,t.dtype,a.outputComponent)),a.variableNames.forEach((h,f)=>{p.push(`${V2(e[f].shape,h)}`)});let c=e.map((h,f)=>hoe(h,t.shape,a.variableComponents?a.variableComponents[f]:a.outputComponent,a.dispatchLayout.x.length===t.shape.length)).join(` -`);p.push(c),p.push(a.getUserCode());let d=Px(a);return p.push($x(d,a)),p.join(` -`)}function uoe(e,t,a,n){let r=e.shaderKey;if(e.isFromPixels)return r;let s=a.map(p=>p.dtype).concat(n.dtype),i=a.map(p=>S.getBroadcastDims(p.shape,n.shape)),o=a.map(p=>v.arraysEqual(p.shape,n.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=Xw(e)?"flatDispatch":"";return r+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+t.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,r}var Dx=` + `);let u=Joe(t.shape,a.dispatchLayout),p=[Hx,n.join(` +`)+Xoe,nh(t.shape),u,Qoe(t.shape.length)];a.atomic||p.push(ele(t.shape,t.dtype,a.outputComponent)),a.variableNames.forEach((h,m)=>{p.push(`${nh(e[m].shape,h)}`)});let c=e.map((h,m)=>Zoe(h,t.shape,a.variableComponents?a.variableComponents[m]:a.outputComponent,a.dispatchLayout.x.length===t.shape.length)).join(` +`);p.push(c),p.push(a.getUserCode());let d=jx(a);return p.push(Gx(d,a)),p.join(` +`)}function qoe(e,t,a){let n=e.shaderKey;if(e.isFromPixels)return n;let r=[],s=[];t.forEach(p=>{r.push(p.shape),s.push(p.dtype)}),r.push(a.shape),s.push(a.dtype);let i=t.map(p=>S.getBroadcastDims(p.shape,a.shape)),o=t.map(p=>v.arraysEqual(p.shape,a.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=lk(e)?"flatDispatch":"";return n+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+r.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,n}var Hx=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -5115,63 +5123,63 @@ return a / b;`,MZ=` let floatToUint: vec4 = bitcast>(val); return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } -`,doe=` +`,Xoe=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function V2(e,t=""){let a=e.length,n=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",r=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(a<=1)return`fn ${n}(index : i32) -> i32 { return index; }`;let s=v.computeStrides(e),i=Jt(a),o=[];for(let u=0;u vec2 { +`;function nh(e,t=""){let a=e.length,n=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",r=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(a<=1)return`fn ${n}(index : i32) -> i32 { return index; }`;let s=v.computeStrides(e),i=$t(a),o=[];for(let u=0;u vec2 { let d0 = index / uniforms.${r}; let d1 = index - d0 * uniforms.${r}; return vec2(d0, d1); - }`;let l;return l="var index2 = index;"+s.map((u,p)=>{let c=`let ${o[p]} = index2 / uniforms.${r}.${Ir(p)}`,d=p===s.length-1?`let ${o[p+1]} = index2 - ${o[p]} * uniforms.${r}.${Ir(p)}`:`index2 = index2 - ${o[p]} * uniforms.${r}.${Ir(p)}`;return`${c}; ${d};`}).join(""),` + }`;let l;return l="var index2 = index;"+s.map((u,p)=>{let c=`let ${o[p]} = index2 / uniforms.${r}.${Sr(p)}`,d=p===s.length-1?`let ${o[p+1]} = index2 - ${o[p]} * uniforms.${r}.${Sr(p)}`:`index2 = index2 - ${o[p]} * uniforms.${r}.${Sr(p)}`;return`${c}; ${d};`}).join(""),` fn ${n}(index : i32) -> ${i} { ${l} return ${i}(${o.join(",")}); } - `}function poe(e,t){let a=e.name,n=e.shape.length,r=Jt(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return` - fn ${s}() -> ${Xe(t)} { - return ${Xe(t)}(${a}[0]); + `}function Koe(e,t){let a=e.name,n=e.shape.length,r=$t(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return` + fn ${s}() -> ${Ke(t)} { + return ${Ke(t)}(${a}[0]); } `;let l=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),` - fn ${s}(${o}) -> ${Xe(t)} { - return ${Xe(t)}(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}), + fn ${s}(${o}) -> ${Ke(t)} { + return ${Ke(t)}(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}), ${l})${t===1?"":` / ${t}`}]); } - `}function coe(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=Jt(l);if(v.arraysEqual(e.shape,t)&&n)return` - fn ${i}Index(globalIndex : i32) -> ${Xe(a)} { - return ${Xe(a)}(${r}[globalIndex]); + `}function Yoe(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=$t(l);if(v.arraysEqual(e.shape,t)&&n)return` + fn ${i}Index(globalIndex : i32) -> ${Ke(a)} { + return ${Ke(a)}(${r}[globalIndex]); } - fn ${i}Coords(coords : ${u}) -> ${Xe(a)} { - return ${Xe(a)}(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}${a===1?"":` / ${a}`}]); + fn ${i}Coords(coords : ${u}) -> ${Ke(a)} { + return ${Ke(a)}(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}${a===1?"":` / ${a}`}]); } `;let p=S.getBroadcastDims(e.shape,t),c=l-o,d="";if(o===0)return` - fn ${i}Index(globalIndex : i32) -> ${Xe(a)}{ + fn ${i}Index(globalIndex : i32) -> ${Ke(a)}{ return get${s}(); } - fn ${i}Coords(coords : ${u}) -> ${Xe(a)}{ + fn ${i}Coords(coords : ${u}) -> ${Ke(a)}{ return get${s}(); } - `;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${Ir(g+c)} = 0;`).join(` -`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=Jt(o),y=e.shape.map((x,A)=>`coords.${Ir(A+c)}`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return` - fn ${i}Index(globalIndex : i32) -> ${Xe(a)} { + `;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${Sr(g+c)} = 0;`).join(` +`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=$t(o),y=e.shape.map((x,A)=>`coords.${Sr(A+c)}`).join(", ");h=`${g}(${y})`}else h="coords";let m=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,f=`${o}D`;return` + fn ${i}Index(globalIndex : i32) -> ${Ke(a)} { var coords = getCoordsFromIndex(globalIndex); ${d} - return ${Xe(a)}(${r}[getIndexFromCoords${m}(${h}, ${f})${a===1?"":` / ${a}`}]); + return ${Ke(a)}(${r}[getIndexFromCoords${f}(${h}, ${m})${a===1?"":` / ${a}`}]); } - fn ${i}Coords(coordsIn : ${u}) -> ${Xe(a)} { + fn ${i}Coords(coordsIn : ${u}) -> ${Ke(a)} { var coords = coordsIn; ${d} - return ${Xe(a)}(${r}[getIndexFromCoords${m}(${h}, ${f})${a===1?"":` / ${a}`}]); + return ${Ke(a)}(${r}[getIndexFromCoords${f}(${h}, ${m})${a===1?"":` / ${a}`}]); } -`}function hoe(e,t,a,n){let r=poe(e,a);return e.shape.length<=t.length&&(r+=coe(e,t,a,n)),r}function foe(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${Jt(s)}{ +`}function Zoe(e,t,a,n){let r=Koe(e,a);return e.shape.length<=t.length&&(r+=Yoe(e,t,a,n)),r}function Joe(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${$t(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } - `;let o="",l=[a,n,r];for(let d=0;d ${p} { + `;let o="",l=[a,n,r];for(let d=0;d ${p} { ${o} -`;return u.length===0?c+=`return ${p}(0); }`:c+=`return ${p}(${u.join(",")}); }`,c}function moe(e){let t="";switch(e){case 0:case 1:t+=` +`;return u.length===0?c+=`return ${p}(0); }`:c+=`return ${p}(${u.join(",")}); }`,c}function Qoe(e){let t="";switch(e){case 0:case 1:t+=` fn getOutputIndexFromCoords(coords : i32) -> i32 { return coords; } @@ -5205,33 +5213,28 @@ return a / b;`,MZ=` coords.u * uniforms.outShapeStrides.u + coords.v; } - `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Xw(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Nl(e,t=1){if(e==="float32")return Xe(t,"f32");if(e==="int32"||e==="bool")return Xe(t,"i32");throw new Error(`type ${e} is not supported.`)}function goe(e,t,a){let n=e.length,r=Nl(t,a),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Xe(a)}) { + `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function lk(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Pl(e,t=1){if(e==="float32")return Ke(t,"f32");if(e==="int32"||e==="bool")return Ke(t,"i32");throw new Error(`type ${e} is not supported.`)}function ele(e,t,a){let n=e.length,r=Pl(t,a),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ke(a)}) { result[flatIndex] = ${r}(value); } - fn setOutputAtIndexI32(flatIndex : i32, value : ${Xe(a,"i32")}) { + fn setOutputAtIndexI32(flatIndex : i32, value : ${Ke(a,"i32")}) { result[flatIndex] = ${r}(value); } - `;if(n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=Jt(n);s+=` - fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Xe(a)}) { + `;if(n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=$t(n);s+=` + fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Ke(a)}) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndex(flatIndex${a===1?"":` / ${a}`}, value); } - fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Xe(a,"i32")}) { + fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Ke(a,"i32")}) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndexI32(flatIndex${a===1?"":` / ${a}`}, value); } - `}return s}function yoe(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function Px(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var Kw={};Ze(Kw,{GPUBytesPerElement:()=>_1,MatMulProgramType:()=>Pn,assertNotComplex:()=>Fg,computeDispatch:()=>ce,computeWorkPerThreadForConv2d:()=>Eg,computeWorkgroupInfoForMatMul:()=>Yw,computeWorkgroupSizeForConv2d:()=>Rg,flatDispatchLayout:()=>be,isWebGPUSupported:()=>Mg,tilesFitEvenlyIntoShape:()=>xoe});var Vs=e=>{let t=1;for(let a=0;aa%e[n]===0)}function ce(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Vs(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Vs(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Vs(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function Yw(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function Rg(e,t,a=!1){if(a)return[8,8,1];let n=Vs(e.x.map(s=>t[s])),r=Vs(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function Eg(e,t,a=!1){if(a)return[4,4,1];let n=Vs(e.x.map(s=>t[s])),r=Vs(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function be(e){return{x:e.map((t,a)=>a)}}function _1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function Mg(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function Fg(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Aoe=W().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),boe=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},r0=class extends Kl{nextDataId(){return r0.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!Mg())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new aoe(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new noe(this.device),this.textureManager=new roe(this.device),this.tensorMap=new Zd(this,It()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,a){if(a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a,complexTensorInfos:n}=t;if(a!=null||t.dtype==="string")return a;if(t.dtype==="complex64"){let f=this.readSync(n.real.dataId),m=this.readSync(n.imag.dataId),g=v.convertBackendValuesAndArrayBuffer(S.mergeRealAndImagArrays(f,m).buffer,"float32");return this.convertAndCacheOnCPU(e,g),g}let r=["opaque","premultiplied"],s=t.resourceInfo,i=s.size;v.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let o=i/4,l=new ArrayBuffer(i),u=256,p=256,c=r.map(f=>new OffscreenCanvas(u,p)),d=new OffscreenCanvas(u,p);this.ensureComputePassEnded(),c.map((f,m)=>{let g=f.getContext("webgpu");return g.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:r[m]}),g.getCurrentTexture()}).map((f,m)=>{let g=u*4,y=(T,N,M)=>{this.ensureCommandEncoderReady(),this.currentCommandEncoder.copyBufferToTexture({buffer:s.buffer,bytesPerRow:g,offset:M},{texture:f},{width:T,height:N}),this.submitQueue();let F=d.getContext("2d",{willReadFrequently:!0});F.clearRect(0,0,T,N),F.drawImage(c[m],0,0);let E=F.getImageData(0,0,T,N).data,I=r[m],_=new Uint8ClampedArray(l,M,T*N*4);for(let O=0;O<_.length;O+=4)if(I==="premultiplied")_[O+3]=E[O+3];else{let B=E[O];_[O]=E[O+2],_[O+1]=E[O+1],_[O+2]=B}},x=Math.floor(o/(u*p)),A=u,b=p,w=0;for(let T=0;T0&&(y(A,b,w),w+=b*(u*4)),A=C%u,A>0&&y(A,1,w)});let h=v.convertBackendValuesAndArrayBuffer(l,t.dtype);return this.convertAndCacheOnCPU(e,h),h}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return a;let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=S.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=v.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,a){let n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=_1(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.sizev.decodeString(n));return $e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=_1(t.dtype)*v.sizeFromShape(t.shape),n;if(t.values){if(n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage(),!0),n.mapState==="unmapped"){let r=this.bufferManager.acquireBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a),this.stagingPendingDisposal.push({size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r})}else{let r=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(r).set(t.values):new Float32Array(r).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n}}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(a===5||a===6)&&(u=16),u>r&&(r=u),t=Math.ceil(t/u)*u,a=l.data.length,n.push(t),t+=l.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((l,u)=>{let p=n[u];l.type==="int32"?new Int32Array(s,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(s,p,l.data.length).set(l.data):new Float32Array(s,p,l.data.length).set(l.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,s,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=boe(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(r).map(m=>m.shape);let f="int32";if(i.map(m=>{s.push({type:f,data:m});let g=v.computeStrides(m);s.push({type:f,data:g})}),e.size){let m=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.outputComponent?m/e.outputComponent:m]})}}let o=t.map((f,m)=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=uoe(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=ioe(this.device,e,o,r,l),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let p=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:p.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,c),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),W().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Aoe){return W().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape){let e={powerPreference:W().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query-inside-passes")&&(a.requiredFeatures=["timestamp-query-inside-passes"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new r0(r,s)},3);var _e;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.ELU_DER=5]="ELU_DER",e[e.EQUAL=6]="EQUAL",e[e.GREATER=7]="GREATER",e[e.GREATER_EQUAL=8]="GREATER_EQUAL",e[e.INT_DIV=9]="INT_DIV",e[e.LESS=10]="LESS",e[e.LESS_EQUAL=11]="LESS_EQUAL",e[e.LOGICAL_AND=12]="LOGICAL_AND",e[e.LOGICAL_OR=13]="LOGICAL_OR",e[e.MAX=14]="MAX",e[e.MIN=15]="MIN",e[e.MOD=16]="MOD",e[e.MUL=17]="MUL",e[e.NOT_EQUAL=18]="NOT_EQUAL",e[e.POW=19]="POW",e[e.PRELU=20]="PRELU",e[e.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",e[e.SUB=22]="SUB"})(_e||(_e={}));var voe=` - resultTemp = select(resultTemp, valueForNaN, isNaN | isnan(a) | isnan(b));`,woe=` - resultTemp = select( - resultTemp, vec4(valueForNaN), - vec4(isNaN) | isnanVec4(a) | isnanVec4(b)); - `,koe="return a + b;",Ioe="var resultTemp = atan2(a, b);",Soe="return areal * breal - aimag * bimag;",Coe="return areal * bimag + aimag * breal;",Toe="return a / b;",Noe="return select(a * (b + 1.0), a, b >= 0.);",Roe="return select(a * (b + vec4(1.0)), a, b >= vec4(0.));",Eoe="return f32(a == b);",Moe="return vec4(a == b);",Foe="return f32(a > b);",_oe="return vec4(a > b);",$oe="return f32(a >= b);",Doe="return vec4(a >= b);",Poe=` + `}return s}function tle(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function jx(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var uk={};Ze(uk,{GPUBytesPerElement:()=>B1,MatMulProgramType:()=>Dn,assertNotComplex:()=>Wg,computeDispatch:()=>pe,computeWorkPerThreadForConv2d:()=>zg,computeWorkgroupInfoForMatMul:()=>dk,computeWorkgroupSizeForConv2d:()=>Og,flatDispatchLayout:()=>xe,isWebGPUSupported:()=>Lg,tilesFitEvenlyIntoShape:()=>ale});var Us=e=>{let t=1;for(let a=0;aa%e[n]===0)}function pe(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Us(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Us(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Us(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function dk(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function Og(e,t,a=!1){if(a)return[8,8,1];let n=Us(e.x.map(s=>t[s])),r=Us(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function zg(e,t,a=!1){if(a)return[4,4,1];let n=Us(e.x.map(s=>t[s])),r=Us(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function xe(e){return{x:e.map((t,a)=>a)}}function B1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function Lg(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function Wg(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Dn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Dn||(Dn={}));var nle=W().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),rle=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},i0=class extends Ql{nextDataId(){return i0.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!Lg())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Woe(t),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Boe(this.device),this.textureManager=new Voe(this.device),this.tensorMap=new tp(this,It()),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);return t?a.refCount=0:a.refCount--,a.refCount>0?!1:(a.complexTensorInfos!=null&&(this.disposeData(a.complexTensorInfos.real.dataId),this.disposeData(a.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,a){if(a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,a)=>{this.pipelineCache[t]=e[a]})}async getBufferData(e){if(W().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let t=e.size,a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a,complexTensorInfos:n}=t;if(a!=null||t.dtype==="string")return a;if(t.dtype==="complex64"){let m=this.readSync(n.real.dataId),f=this.readSync(n.imag.dataId),g=v.convertBackendValuesAndArrayBuffer(S.mergeRealAndImagArrays(m,f).buffer,"float32");return this.convertAndCacheOnCPU(e,g),g}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let r=["opaque","premultiplied"],s=t.resource,i=s.size;v.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let o=i/4,l=new ArrayBuffer(i),u=256,p=256,c=r.map(m=>new OffscreenCanvas(u,p)),d=new OffscreenCanvas(u,p);this.endComputePassEncoder(),c.map((m,f)=>{let g=m.getContext("webgpu");return g.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:r[f]}),g.getCurrentTexture()}).map((m,f)=>{let g=u*4,y=(T,N,M)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:s,bytesPerRow:g,offset:M},{texture:m},{width:T,height:N}),this.submitQueue();let P=d.getContext("2d",{willReadFrequently:!0});P.clearRect(0,0,T,N),P.drawImage(c[f],0,0);let E=P.getImageData(0,0,T,N).data,C=r[f],_=new Uint8ClampedArray(l,M,T*N*4);for(let O=0;O<_.length;O+=4)if(C==="premultiplied")_[O+3]=E[O+3];else{let B=E[O];_[O]=E[O+2],_[O+1]=E[O+1],_[O+2]=B}},x=Math.floor(o/(u*p)),A=u,b=p,w=0;for(let T=0;T0&&(y(A,b,w),w+=b*(u*4)),A=I%u,A>0&&y(A,1,w)});let h=v.convertBackendValuesAndArrayBuffer(l,t.dtype);return this.convertAndCacheOnCPU(e,h),h}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return a;let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=S.mergeRealAndImagArrays(s,i)}else{let r=await this.getBufferData(t.resource);n=v.convertBackendValuesAndArrayBuffer(r,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,a=e.usage,n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=B1(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.sizev.decodeString(n));return $e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId).resource;return t instanceof GPUBuffer?{buffer:t}:t instanceof GPUTexture?t.createView():t}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let a=B1(t.dtype)*v.sizeFromShape(t.shape),n,r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(a,r,!0),n.mapState==="unmapped"){let s=this.bufferManager.acquireBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=s.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(s,0,n,0,a),this.stagingPendingDisposal.push(s)}else{let s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(a,r);t.resource=n}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(a===5||a===6)&&(l=16),l>r&&(r=l),t=Math.ceil(t/l)*l,a=o.data.length,n.push(t),t+=o.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(s,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(s,u,o.data.length).set(o.data):new Float32Array(s,u,o.data.length).set(o.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,s,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=rle(this.device,e);let s=t.map((o,l)=>{if(o.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(o.dataId),{dtype:this.tensorMap.get(o.dataId).dtype,shape:o.shape,name:e.variableNames[l]}});e.shaderKey=qoe(e,s,r);let i=W().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=Goe(this.device,e,s,r,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,r,t,n),r}recordAndSubmit(e,t,a,n){if(e.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let r=[],s=[];if(!e.isFromPixels){r.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),s=a.concat(t).map(c=>c.shape);let p="int32";if(s.map(c=>{r.push({type:p,data:c});let d=v.computeStrides(c);r.push({type:p,data:d})}),e.size){let c=v.sizeFromShape(e.outputShape);r.push({type:p,data:[e.outputComponent?c/e.outputComponent:c]})}}n&&(r=[...r,...n]);let i=[this.tensorToBinding(t),...a.map(p=>this.tensorToBinding(p)),this.makeUniforms(r)];a.forEach(p=>{this.commandQueueOwnedIds.add(p.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let o=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:i.map((p,c)=>({binding:c,resource:p}))}),l=this.activeTimers!=null;this.ensureCommandEncoderReady();let u={};l&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),u.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:"beginning"},{querySet:this.querySet,queryIndex:1,location:"end"}],this.computePassEncoder=this.commandEncoder.beginComputePass(u)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(u)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,o),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(l||W().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass)&&(this.endComputePassEncoder(),l?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),a=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),a}shouldExecuteOnCPU(e,t=nle){return W().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resource==null&&v.sizeFromShape(a.shape){let e={powerPreference:W().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query")&&(a.requiredFeatures=["timestamp-query"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new i0(r,s)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.ELU_DER=5]="ELU_DER",e[e.EQUAL=6]="EQUAL",e[e.GREATER=7]="GREATER",e[e.GREATER_EQUAL=8]="GREATER_EQUAL",e[e.INT_DIV=9]="INT_DIV",e[e.LESS=10]="LESS",e[e.LESS_EQUAL=11]="LESS_EQUAL",e[e.LOGICAL_AND=12]="LOGICAL_AND",e[e.LOGICAL_OR=13]="LOGICAL_OR",e[e.MAX=14]="MAX",e[e.MIN=15]="MIN",e[e.MOD=16]="MOD",e[e.MUL=17]="MUL",e[e.NOT_EQUAL=18]="NOT_EQUAL",e[e.POW=19]="POW",e[e.PRELU=20]="PRELU",e[e.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",e[e.SUB=22]="SUB"})(Pe||(Pe={}));var sle="return a + b;",ile="var resultTemp = atan2(a, b);",ole="return areal * breal - aimag * bimag;",lle="return areal * bimag + aimag * breal;",ule="return a / b;",dle="return select(a * (b + 1.0), a, b >= 0.);",ple="return select(a * (b + vec4(1.0)), a, b >= vec4(0.));",cle="return f32(a == b);",hle="return vec4(a == b);",mle="return f32(a > b);",fle="return vec4(a > b);",gle="return f32(a >= b);",yle="return vec4(a >= b);",xle=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); -`,Ooe=` +`,Ale=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); @@ -5252,14 +5255,14 @@ return a / b;`,MZ=` resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); -`,zoe="return f32(a < b);",Loe="return vec4(a < b);",Woe="return f32(a <= b);",Boe="return vec4(a <= b);",Voe="return f32(a >= 1.0 && b >= 1.0);",Uoe=`return (vec4(a >= vec4(1.0)) * - vec4(b >= vec4(1.0)));`,Goe="return f32(a >= 1.0 || b >= 1.0);",Hoe=`return min(vec4(a >= vec4(1.0)) + - vec4(b >= vec4(1.0)), vec4(1.0));`,joe="var resultTemp = max(a, b);",qoe="var resultTemp = min(a, b);",Xoe=` +`,ble="return f32(a < b);",vle="return vec4(a < b);",wle="return f32(a <= b);",kle="return vec4(a <= b);",Ile="return f32(a >= 1.0 && b >= 1.0);",Sle=`return (vec4(a >= vec4(1.0)) * + vec4(b >= vec4(1.0)));`,Cle="return f32(a >= 1.0 || b >= 1.0);",Tle=`return min(vec4(a >= vec4(1.0)) + + vec4(b >= vec4(1.0)), vec4(1.0));`,Nle="var resultTemp = max(a, b);",Rle="var resultTemp = min(a, b);",Ele=` let isNaN = b == 0.; var resultTemp = a % b; resultTemp = select((resultTemp + b) % b, resultTemp, (a < 0. && b < 0.) || (a >= 0. && b > 0.)); -`,Koe=` +`,Mle=` let isNaN = !vec4(b); var resultTemp = vec4(a % b); if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { @@ -5274,20 +5277,20 @@ return a / b;`,MZ=` if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) { resultTemp[3] = (resultTemp[3] + b[3]) % b[3]; } -`,Yoe="return a * b;",Zoe=` +`,_le="return a * b;",Ple=` var resultTemp = f32(a != b); let valueForNaN = 1.0; -`,Joe=` +`,$le=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; -`,Qoe=` +`,Fle=` let isNaN = a < 0.0 && floor(b) < b; if (b == 0.0) { return 1.0; } var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b), round(abs(b) % 2.0) != 1.0); -`,ele=` +`,Dle=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -5308,38 +5311,48 @@ return a / b;`,MZ=` resultTemp.a = 1.0; } let isNaN = (a < vec4(0.0)) & (floor(b) < b); -`,tle="if (a < 0.0) { return b * a; } return a;",ale=` +`,Ole="if (a < 0.0) { return b * a; } return a;",zle=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,nle="return (a - b) * (a - b);",rle="return a - b;";function _g(e,t){do{let a;switch(e){case _e.ATAN2:a=Ioe;break;case _e.MAX:a=joe;break;case _e.MIN:a=qoe;break;case _e.MOD:a=t?Koe:Xoe;break;case _e.NOT_EQUAL:a=t?Joe:Zoe;break;case _e.POW:a=t?ele:Qoe;break;default:continue}return` +`,Lle="return (a - b) * (a - b);",Wle="return a - b;";function Bg(e,t){let a;do{switch(e){case Pe.ATAN2:a=ile;break;case Pe.MAX:a=Nle;break;case Pe.MIN:a=Rle;break;case Pe.MOD:a=t?Mle:Ele;break;case Pe.NOT_EQUAL:a=t?$le:Ple;break;case Pe.POW:a=t?Dle:Fle;break;default:continue}let n,r,s;return t?(n="isnanVec4",r="vec4",s="vec4"):(n="isnan",r="f32",s="bool"),` + let aIsNaN = ${n}(a); + let aPostLegalization = select(a, ${r}(42), aIsNaN); + let bIsNaN = ${n}(b); + let bPostLegalization = select(b, ${r}(42), bIsNaN); let isNaN = false; let valueForNaN = uniforms.NAN; { + let a = aPostLegalization; + let b = bPostLegalization; ${a} - ${t?woe:voe} - return resultTemp; + return select( + resultTemp, ${r}(valueForNaN), + ${s}(isNaN) | aIsNaN | bIsNaN); } - `}while(!1);switch(e){case _e.ADD:return koe;case _e.COMPLEX_MULTIPLY_IMAG:return Coe;case _e.COMPLEX_MULTIPLY_REAL:return Soe;case _e.DIV:return Toe;case _e.ELU_DER:return t?Roe:Noe;case _e.EQUAL:return t?Moe:Eoe;case _e.GREATER:return t?_oe:Foe;case _e.GREATER_EQUAL:return t?Doe:$oe;case _e.INT_DIV:return t?Ooe:Poe;case _e.LESS:return t?Loe:zoe;case _e.LESS_EQUAL:return t?Boe:Woe;case _e.LOGICAL_AND:return t?Uoe:Voe;case _e.LOGICAL_OR:return t?Hoe:Goe;case _e.MUL:return Yoe;case _e.PRELU:return t?ale:tle;case _e.SQUARED_DIFFERENCE:return nle;case _e.SUB:return rle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var le;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.ROUND=27]="ROUND",e[e.RSQRT=28]="RSQRT",e[e.SELU=29]="SELU",e[e.SIGMOID=30]="SIGMOID",e[e.SIGN=31]="SIGN",e[e.SIN=32]="SIN",e[e.SINH=33]="SINH",e[e.SOFTPLUS=34]="SOFTPLUS",e[e.SQRT=35]="SQRT",e[e.SQUARE=36]="SQUARE",e[e.STEP=37]="STEP",e[e.TAN=38]="TAN",e[e.TANH=39]="TANH",e[e.TO_INT=40]="TO_INT"})(le||(le={}));var sle="return abs(a);",ile=` + `}while(!1);switch(e){case Pe.ADD:return sle;case Pe.COMPLEX_MULTIPLY_IMAG:return lle;case Pe.COMPLEX_MULTIPLY_REAL:return ole;case Pe.DIV:return ule;case Pe.ELU_DER:return t?ple:dle;case Pe.EQUAL:return t?hle:cle;case Pe.GREATER:return t?fle:mle;case Pe.GREATER_EQUAL:return t?yle:gle;case Pe.INT_DIV:return t?Ale:xle;case Pe.LESS:return t?vle:ble;case Pe.LESS_EQUAL:return t?kle:wle;case Pe.LOGICAL_AND:return t?Sle:Ile;case Pe.LOGICAL_OR:return t?Tle:Cle;case Pe.MUL:return _le;case Pe.PRELU:return t?zle:Ole;case Pe.SQUARED_DIFFERENCE:return Lle;case Pe.SUB:return Wle;default:}return` + ${a} + return resultTemp; + `}var le;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.ROUND=27]="ROUND",e[e.RSQRT=28]="RSQRT",e[e.SELU=29]="SELU",e[e.SIGMOID=30]="SIGMOID",e[e.SIGN=31]="SIGN",e[e.SIN=32]="SIN",e[e.SINH=33]="SINH",e[e.SOFTPLUS=34]="SOFTPLUS",e[e.SQRT=35]="SQRT",e[e.SQUARE=36]="SQUARE",e[e.STEP=37]="STEP",e[e.TAN=38]="TAN",e[e.TANH=39]="TANH",e[e.TO_INT=40]="TO_INT"})(le||(le={}));var Ble="return abs(a);",Vle=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); -`,ole=` +`,Ule=` if (a < 1.) { return uniforms.NAN; } return acosh(a); -`,lle=` +`,Gle=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); -`,ule="return asinh(a);",dle=` +`,Hle="return asinh(a);",jle=` if (isnan(a)) { return uniforms.NAN; } return atan(a); -`,ple=` +`,qle=` if (abs(a) > 1.) { return uniforms.NAN; } @@ -5350,10 +5363,10 @@ return a / b;`,MZ=` return -uniforms.INFINITY; } return atanh(a); -`,cle="return ceil(a);",hle="return cos(a);",fle=` +`,Xle="return ceil(a);",Kle="return cos(a);",Yle=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; -`,mle="return exp(a) - 1.0;",gle="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",yle=` +`,Zle="return exp(a) - 1.0;",Jle="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Qle=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -5368,7 +5381,7 @@ return a / b;`,MZ=` resFloat.a = a.a; } return resFloat; -`,xle=` +`,eue=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -5383,25 +5396,25 @@ return a / b;`,MZ=` let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); -`,Ale="return exp(a);",ble="return floor(a);",vle="return f32(!isnan(a) && !isinf(a));",wle="return f32(isinf(a));",kle="return f32(isnan(a));",Ile="return a;",Sle=`if (a < 0.0) { return uniforms.NAN; } - return log(a);`,Cle=` +`,tue="return exp(a);",aue="return floor(a);",nue="return f32(!isnan(a) && !isinf(a));",rue="return f32(isinf(a));",sue="return f32(isnan(a));",iue="return a;",oue=`if (a < 0.0) { return uniforms.NAN; } + return log(a);`,lue=` if (isnan(a)) { return a; } return log(1.0 + a); -`,Tle="return f32(!(a >= 1.0));",Nle="return -a;",Rle="if (a < 0.0) { return uniforms.alpha * a; } return a;",Ele=` +`,uue="return f32(!(a >= 1.0));",due="return -a;",pue="if (a < 0.0) { return uniforms.alpha * a; } return a;",cue=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Mle="return 1.0 / a;",Fle="return select(a, 0.0, a < 0.0);",_le="return clamp(a, 0.0, 6.0);",$le="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",Dle=` +`,hue="return 1.0 / a;",mue="return select(a, 0.0, a < 0.0);",fue="return clamp(a, 0.0, 6.0);",gue="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",yue=` return select(a, vec4(0.0), a < vec4(0.0)); -`,Ple="return round(a);",Ole="return inverseSqrt(a);",zle=` +`,xue="return round(a);",Aue="return inverseSqrt(a);",bue=` if (a >= 0.0) { return ${S.SELU_SCALE} * a; } else { return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0); } -`,Lle="return 1.0 / (1.0 + exp(-1.0 * a));",Wle="return sign(a);",Ble="return sin(a);",Vle=` +`,vue="return 1.0 / (1.0 + exp(-1.0 * a));",wue="return sign(a);",kue="return sin(a);",Iue=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; -`,Ule=` +`,Sue=` let epsilon = 1.1920928955078125e-7; let threshold = log(epsilon) + 2.0; @@ -5416,31 +5429,31 @@ return a / b;`,MZ=` } else { return log(exp_a + 1.0); } -`,Gle="return sqrt(a);",Hle="return a * a;",jle=` +`,Cue="return sqrt(a);",Tue="return a * a;",Nue=` if (isnan(a)) { return a; } return select(uniforms.stepAlpha, 1.0, a > 0.0); -`,qle="return tan(a);",Xle=` +`,Rue="return tan(a);",Eue=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,Kle="return f32(i32((a)));";function Ds(e,t){switch(e){case le.ABS:return sle;case le.ACOS:return ile;case le.ACOSH:return ole;case le.ASIN:return lle;case le.ASINH:return ule;case le.ATAN:return dle;case le.ATANH:return ple;case le.COS:return hle;case le.COSH:return fle;case le.CEIL:return cle;case le.ELU:return t?yle:gle;case le.ERF:return xle;case le.EXP:return Ale;case le.EXPM1:return mle;case le.FLOOR:return ble;case le.IS_FINITE:return vle;case le.IS_INF:return wle;case le.IS_NAN:return kle;case le.LINEAR:return Ile;case le.LOG:return Sle;case le.LOG1P:return Cle;case le.LOGICAL_NOT:return Tle;case le.NEG:return Nle;case le.LEAKYRELU:return t?Ele:Rle;case le.RECIPROCAL:return Mle;case le.RELU:return t?Dle:Fle;case le.RELU6:return t?$le:_le;case le.ROUND:return Ple;case le.RSQRT:return Ole;case le.SELU:return zle;case le.SIGMOID:return Lle;case le.SIGN:return Wle;case le.SIN:return Ble;case le.SINH:return Vle;case le.SOFTPLUS:return Ule;case le.SQRT:return Gle;case le.SQUARE:return Hle;case le.STEP:return jle;case le.TAN:return qle;case le.TANH:return Xle;case le.TO_INT:return Kle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Mr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Ds(le.LINEAR);else if(e==="relu")r=Ds(le.RELU,a);else if(e==="elu")r=Ds(le.ELU,a);else if(e==="relu6")r=Ds(le.RELU6,a);else if(e==="prelu")r=_g(_e.PRELU,a);else if(e==="sigmoid")r=Ds(le.SIGMOID,a);else if(e==="leakyrelu")r=Ds(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Xe(a?4:1),i="";return t?i=` +`,Mue="return f32(i32((a)));";function Ds(e,t){switch(e){case le.ABS:return Ble;case le.ACOS:return Vle;case le.ACOSH:return Ule;case le.ASIN:return Gle;case le.ASINH:return Hle;case le.ATAN:return jle;case le.ATANH:return qle;case le.COS:return Kle;case le.COSH:return Yle;case le.CEIL:return Xle;case le.ELU:return t?Qle:Jle;case le.ERF:return eue;case le.EXP:return tue;case le.EXPM1:return Zle;case le.FLOOR:return aue;case le.IS_FINITE:return nue;case le.IS_INF:return rue;case le.IS_NAN:return sue;case le.LINEAR:return iue;case le.LOG:return oue;case le.LOG1P:return lue;case le.LOGICAL_NOT:return uue;case le.NEG:return due;case le.LEAKYRELU:return t?cue:pue;case le.RECIPROCAL:return hue;case le.RELU:return t?yue:mue;case le.RELU6:return t?gue:fue;case le.ROUND:return xue;case le.RSQRT:return Aue;case le.SELU:return bue;case le.SIGMOID:return vue;case le.SIGN:return wue;case le.SIN:return kue;case le.SINH:return Iue;case le.SOFTPLUS:return Sue;case le.SQRT:return Cue;case le.SQUARE:return Tue;case le.STEP:return Nue;case le.TAN:return Rue;case le.TANH:return Eue;case le.TO_INT:return Mue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function _r(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Ds(le.LINEAR);else if(e==="relu")r=Ds(le.RELU,a);else if(e==="elu")r=Ds(le.ELU,a);else if(e==="relu6")r=Ds(le.RELU6,a);else if(e==="prelu")r=Bg(Pe.PRELU,a);else if(e==="sigmoid")r=Ds(le.SIGMOID,a);else if(e==="leakyrelu")r=Ds(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Ke(a?4:1),i="";return t?i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { let b = getPreluActivationWeightsByOutputCoords(coords); ${r} }`:i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { ${r} - }`,i}function Yo(e,t){return` + }`,i}function tl(e,t){return` ${e?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} - `}function Zw(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` + `}function pk(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` ${e?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,o=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` - fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${Xe(s)} { - var value = ${Xe(s)}(0.0); + fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${Ke(s)} { + var value = ${Ke(s)}(0.0); let col = colIn * ${s}; ${a&&r?i:` ${e?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} @@ -5451,25 +5464,25 @@ return a / b;`,MZ=` return value; } - fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${Xe(s)} { + fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${Ke(s)} { let col = colIn * ${s}; - var value = ${Xe(s)}(0.0); + var value = ${Ke(s)}(0.0); ${o} return value; } - `}function $g(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` - ${Zw(a,n,r,s,i,o)} - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Xe(o)}) { + `}function Vg(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` + ${pk(a,n,r,s,i,o)} + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ke(o)}) { let col = colIn * ${o}; ${r&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${Yo(e,t)} + ${tl(e,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } - `}var Yle=(e,t)=>e?` + `}var _ue=(e,t)=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart / ${t} + inputCol); @@ -5477,7 +5490,7 @@ return a / b;`,MZ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, kStart / ${t} + inputCol); - `,Zle=(e,t,a)=>e?` + `,Pue=(e,t,a)=>e?` let ACached0 = mm_Asub[k * ${t}][localRow]; let ACached1 = mm_Asub[k * ${t} + 1][localRow]; let ACached2 = mm_Asub[k * ${t} + 2][localRow]; @@ -5494,13 +5507,13 @@ return a / b;`,MZ=` acc[i] = fma(BCached1, vec4(ACached.y), acc[i]); acc[i] = fma(BCached2, vec4(ACached.z), acc[i]); ${t===3?"":"acc[i] = fma(BCached3, vec4(ACached.w), acc[i]);"} - }`;function s0(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1],h=e[1];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. + }`;function o0(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1],h=e[1];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. Otherwise, innerElementSize ${c} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),` var mm_Asub : array, ${u/c}>, ${p}>; var mm_Bsub : array, ${l/e[0]}>, ${n}>; - ${de()} { + ${ue()} { let localRow = i32(localId.y); let tileRow = localRow * ${h}; let tileCol = i32(localId.x); @@ -5524,7 +5537,7 @@ return a / b;`,MZ=` for (var innerRow = 0; innerRow < ${h}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${Yle(a,c)} + ${_ue(a,c)} } // Load one tile of B into local memory. @@ -5543,7 +5556,7 @@ return a / b;`,MZ=` let BCached2 = mm_Bsub[k * ${c} + 2][tileCol]; ${c===3?"":`let BCached3 = mm_Bsub[k * ${c} + 3][tileCol];`} - ${Zle(a,c,h)} + ${Pue(a,c,h)} } workgroupBarrier(); @@ -5552,7 +5565,7 @@ return a / b;`,MZ=` for (var innerRow = 0; innerRow < ${h}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var Ox=e=>e?` + }`}var qx=e=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -5560,7 +5573,7 @@ return a / b;`,MZ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,Jle=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function i0(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],f=n/t[1],m=e[1],g=e[0],y=i?` + `,$ue=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function l0(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],m=n/t[1],f=e[1],g=e[0],y=i?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${l}; @@ -5571,7 +5584,7 @@ return a / b;`,MZ=` // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) { - ${Ox(a)} + ${qx(a)} } } // Load one tile of B into local memory. @@ -5591,7 +5604,7 @@ return a / b;`,MZ=` for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; } - for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = @@ -5601,7 +5614,7 @@ return a / b;`,MZ=` } workgroupBarrier(); } - for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { let gRow = globalRowStart + localRow + innerRow * ${t[1]}; for (var innerCol = 0; innerCol < ${g}; innerCol++) { let gCol = globalColStart + localCol + innerCol * ${t[0]}; @@ -5609,16 +5622,16 @@ return a / b;`,MZ=` } } `:` - let tileRow = i32(localId.y) * ${m}; + let tileRow = i32(localId.y) * ${f}; let tileCol = i32(localId.x) * ${g}; - let globalRow = i32(globalId.y) * ${m}; + let globalRow = i32(globalId.y) * ${f}; let globalCol = i32(globalId.x) * ${g}; let globalRowStart = i32(workgroupId.y) * ${l}; let tileRowA = i32(localId.y) * ${d}; let tileColA = i32(localId.x) * ${h}; - let tileRowB = i32(localId.y) * ${f}; + let tileRowB = i32(localId.y) * ${m}; // Loop over shared dimension. for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. @@ -5626,12 +5639,12 @@ return a / b;`,MZ=` for (var innerCol = 0; innerCol < ${h}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${Ox(a)} + ${qx(a)} } } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${f}; innerRow++) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { for (var innerCol = 0; innerCol < ${g}; innerCol++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; @@ -5650,8 +5663,8 @@ return a / b;`,MZ=` BCached[inner] = mm_Bsub[k][tileCol + inner]; } - for (var innerRow = 0; innerRow < ${m}; innerRow++) { - ${Jle(a)} + for (var innerRow = 0; innerRow < ${f}; innerRow++) { + ${$ue(a)} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); @@ -5662,7 +5675,7 @@ return a / b;`,MZ=` workgroupBarrier(); } - for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { for (var innerCol = 0; innerCol < ${g}; innerCol++) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); @@ -5672,24 +5685,24 @@ return a / b;`,MZ=` var mm_Asub : array, ${c}>; var mm_Bsub : array, ${n}>; - ${de()} { + ${ue()} { let batch = ${r?"0":"i32(globalId.z)"}; let batchA = ${r||!o?"batch":"batch % uniforms.aShape[0]"}; let batchB = ${r||!o?"batch":"batch % uniforms.bShape[0]"}; let numTiles = ${r?`${Math.ceil(s/n)}`:`(uniforms.dimInner - 1) / ${n} + 1`}; var kStart = ${r?`i32(globalId.z) * ${s}`:"0"}; - var acc : array, ${m}>; + var acc : array, ${f}>; // Without this initialization strange values show up in acc. - for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = 0.0; } } ${y} } - `}var Qle=e=>e?` + `}var Fue=e=>e?` mm_readA(batchA, colA, globalRow), mm_readA(batchA, colA + 1, globalRow), mm_readA(batchA, colA + 2, globalRow), @@ -5699,10 +5712,10 @@ return a / b;`,MZ=` mm_readA(batchA, globalRow, colA + 1), mm_readA(batchA, globalRow, colA + 2), mm_readA(batchA, globalRow, colA + 3) - `;function eue(e,t=!1){v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`);let a=e[0]*4;return` + `;function Due(e,t=!1){v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`);let a=e[0]*4;return` var mm_Asub : array, ${e[0]}>; - ${de()} { + ${ue()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); @@ -5718,7 +5731,7 @@ return a / b;`,MZ=` for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. let colA = t * ${a} + tileCol * 4; - mm_Asub[tileCol] = vec4(${Qle(t)}); + mm_Asub[tileCol] = vec4(${Fue(t)}); workgroupBarrier(); // Compute acc values for a single thread. @@ -5738,13 +5751,13 @@ return a / b;`,MZ=` mm_write(batch, globalRow, globalCol, acc); } - `}var tue=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=Yw(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` - ${Mr(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${$g(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?s0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?eue(this.workgroupSize,this.transposeA):i0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} - `}};function aue(e){return` + `}var Oue=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=dk(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` + ${_r(this.activation,this.hasPreluActivationWeights,this.isVec4)} + ${Vg(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?o0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Due(this.workgroupSize,this.transposeA):l0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + `}};function zue(e){return` var sumValues : array; - ${de()} { + ${ue()} { let coords = getOutputCoords(); let batch = coords[0]; let batchA = batch % uniforms.aShape[0]; @@ -5775,11 +5788,11 @@ return a / b;`,MZ=` mm_write(batch, row, col, sum); } } - `}var nue=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return` - ${Mr(this.activation,this.hasPreluActivationWeights)} - ${$g(this.addBias,this.activation,this.transposeA,this.transposeB)} - ${aue(this.workgroupSize[0])} - `}};function rue(e){let t=e[1],a=e[0],n=t>a?t:a;return` + `}var Lue=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return` + ${_r(this.activation,this.hasPreluActivationWeights)} + ${Vg(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${zue(this.workgroupSize[0])} + `}};function Wue(e){let t=e[1],a=e[0],n=t>a?t:a;return` var mm_Asub : array, ${t}>; var mm_Bsub : array, ${n}>; @@ -5789,7 +5802,7 @@ return a / b;`,MZ=` // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. - ${de()} { + ${ue()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); @@ -5831,13 +5844,13 @@ return a / b;`,MZ=` mm_write(batch, globalRow, globalCol, acc); } - `}var sue=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return` - ${Mr(this.activation,this.hasPreluActivationWeights)} - ${$g(this.addBias,this.activation,this.transposeA,this.transposeB)} - ${rue(this.workgroupSize)} - `}},iue=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let r=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=r?4:1,r||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=ce(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` - ${Zw(!1,this.transposeB,!1,!1,!1,e)} - fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Xe(e)}) { + `}var Bue=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return` + ${_r(this.activation,this.hasPreluActivationWeights)} + ${Vg(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Wue(this.workgroupSize)} + `}},Vue=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let r=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=r?4:1,r||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=pe(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` + ${pk(!1,this.transposeB,!1,!1,!1,e)} + fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Ke(e)}) { let col = colIn * ${e}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); @@ -5845,34 +5858,34 @@ return a / b;`,MZ=` // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${e}; i = i + 1) { - ${Gu("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} + ${Xu("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} } } } - ${e===4?s0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):i0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} - `}},oue=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` - ${Mr(this.activation,this.hasPreluActivationWeights)} - ${de("index")} { + ${e===4?o0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):l0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + `}},Uue=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` + ${_r(this.activation,this.hasPreluActivationWeights)} + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${Yo(this.addBias,this.activation)} + ${tl(this.addBias,this.activation)} setOutputAtIndex(index, value); } } - `}},lue=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` - ${de("index")} { + `}},Gue=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } - `}};function Mn(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new lue(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var uue={kernelName:du,backendName:"webgpu",kernelFunc:Mn};function we(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var due={kernelName:bu,backendName:"webgpu",kernelFunc:we};function o0({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=Ho.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,f,d]:[x,d,f],C=we({inputs:{x:e},backend:r,attrs:{shape:b}}),T=we({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[C,T],M=Math.max(y,x),F=[C,T],E=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],I,_,O=[M,h,f],B=W().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(B<0){let U=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=M*Math.ceil(h/32)*Math.ceil(f/32);q<=G||h<=8&&q<=G*2?M*h*f<=128?B=Pn.MatMulReduceProgram:M===1&&d>=2e3?B=Pn.MatMulSplitKProgram:B=Pn.MatMulSmallOutputSizeProgram:B=Pn.MatMulPackedProgram}switch(B){case Pn.MatMulReduceProgram:I=new nue(O,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(_=Mn({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),I=new iue(O,d,a,n),s||l){_=r.runWebGPUProgram(I,F,e.dtype,E,_);let G=new oue(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=we({inputs:{x:V},backend:r,attrs:{shape:A}});N.push(V);for(let X of N)r.disposeData(X.dataId);return Z}break}case Pn.MatMulSmallOutputSizeProgram:I=new sue(b,w,O,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();I=new tue(b,O,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${B}.`)}s&&F.push(s),i&&F.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),I.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(I,F,e.dtype,E,_);let $=we({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return $}function pue(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return o0({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var cue={kernelName:Xr,backendName:"webgpu",kernelFunc:pue},zx=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,a),this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` + `}};function Mn(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Gue(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Hue={kernelName:mu,backendName:"webgpu",kernelFunc:Mn};function ke(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var jue={kernelName:Iu,backendName:"webgpu",kernelFunc:ke};function u0({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],m=n?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),x=v.sizeFromShape(g),A=Yo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=ke({inputs:{x:e},backend:r,attrs:{shape:b}}),T=ke({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),P=[I,T],E=[{type:"int32",data:[h]},{type:"int32",data:[m]},{type:"int32",data:[c]}],C,_,O=[M,h,m],B=W().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(B<0){let U=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=M*Math.ceil(h/32)*Math.ceil(m/32);q<=G||h<=8&&q<=G*2?M*h*m<=128?B=Dn.MatMulReduceProgram:M===1&&d>=2e3?B=Dn.MatMulSplitKProgram:B=Dn.MatMulSmallOutputSizeProgram:B=Dn.MatMulPackedProgram}switch(B){case Dn.MatMulReduceProgram:C=new Lue(O,a,n,s,l,i);break;case Dn.MatMulSplitKProgram:{if(_=Mn({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),C=new Vue(O,d,a,n),s||l){_=r.runWebGPUProgram(C,P,e.dtype,E,_);let G=new Uue(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=ke({inputs:{x:V},backend:r,attrs:{shape:A}});N.push(V);for(let X of N)r.disposeData(X.dataId);return Z}break}case Dn.MatMulSmallOutputSizeProgram:C=new Bue(b,w,O,a,n,s,l,i);break;case Dn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();C=new Oue(b,O,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${B}.`)}s&&P.push(s),i&&P.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),C.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(C,P,e.dtype,E,_);let $=ke({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return $}function que(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return u0({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Xue={kernelName:Kr,backendName:"webgpu",kernelFunc:que},Xx=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,a),this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${_g(this.op,!1)} + ${Bg(this.op,!1)} } - ${de("index")} { + ${ue("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); @@ -5881,16 +5894,16 @@ return a / b;`,MZ=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}},xh=class{constructor(e,t,a){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.dispatchLayout=be(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,r=a.length>0&&a[a.length-1]%4===0;n&&r?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(v.isScalarShape(a)||a[a.length-1]===1)||r&&(v.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",a=` + `}},kh=class{constructor(e,t,a){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.dispatchLayout=xe(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,r=a.length>0&&a[a.length-1]%4===0;n&&r?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(v.isScalarShape(a)||a[a.length-1]===1)||r&&(v.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",a=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { - ${_g(this.op,this.outputComponent===4)} + ${Bg(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",r=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${a} var sharedBuf : array; - ${de("index")} { + ${ue("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { @@ -5906,7 +5919,7 @@ return a / b;`,MZ=` } `}else e=` ${a} - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index * ${this.outputComponent}); let a = ${t}(getAByOutputCoords(coords)); @@ -5914,18 +5927,18 @@ return a / b;`,MZ=` setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return e}};function tn(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var hue={kernelName:zi,backendName:"webgpu",kernelFunc:tn};function Zo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=tn({inputs:{x:n},backend:a}),l=tn({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var fue={kernelName:tp,backendName:"webgpu",kernelFunc:Zo},Hu=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` + `;return e}};function tn(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Kue={kernelName:Bi,backendName:"webgpu",kernelFunc:tn};function al(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=tn({inputs:{x:n},backend:a}),l=tn({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Yue={kernelName:ip,backendName:"webgpu",kernelFunc:al},Ku=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${Ds(this.op,!1)} } - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } - `}};function tt({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new Hu(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function ta({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,f;if(e!==_e.MUL)[h,f]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},w=new xh(e,i.shape,o.shape);return l.runWebGPUProgram(w,[A,b],pa(y.dtype,x.dtype))});else{let g=new zx(_e.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new zx(_e.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=Zo({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=n||pa(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(c):c,f=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let p=new xh(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:mue,castImpl:gue,ceilImpl:yue,concatImpl:xue,equalImpl:Aue,expImpl:bue,expm1Impl:vue,floorImpl:wue,floorDivImpl:kue,gatherNdImpl:Iue,gatherV2Impl:Sue,greaterEqualImpl:Cue,greaterImpl:Tue,lessEqualImpl:Nue,lessImpl:Rue,logImpl:Eue,maxImpl:Mue,maximumImpl:Fue,minimumImpl:_ue,multiplyImpl:$ue,negImpl:Due,notEqualImpl:Pue,prodImpl:Oue,rangeImpl:zue,rsqrtImpl:Lue,scatterImpl:Wue,simpleAbsImpl:Bue,sliceImpl:Vue,stridedSliceImpl:Uue,stringNGramsImpl:Gue,subImpl:Hue,tileImpl:jue,topKImpl:que,transposeImpl:Xue,uniqueImpl:J3e}=Kh,Kue=tt({opType:le.ABS,cpuKernelImpl:Bue}),Yue={kernelName:Zl,backendName:"webgpu",kernelFunc:Kue},Zue=tt({opType:le.ACOS}),Jue={kernelName:Qs,backendName:"webgpu",kernelFunc:Zue},Que=tt({opType:le.ACOSH}),ede={kernelName:ei,backendName:"webgpu",kernelFunc:Que},tde=ta({opType:_e.ADD,cpuKernelImpl:mue,supportsComplex:!0}),ade={kernelName:ns,backendName:"webgpu",kernelFunc:tde},nde=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` - ${de("index")} { + `}};function at({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new Ku(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function ta({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,m;if(e!==Pe.MUL)[h,m]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},w=new kh(e,i.shape,o.shape);return l.runWebGPUProgram(w,[A,b],pa(y.dtype,x.dtype))});else{let g=new Xx(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new Xx(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),m=l.runWebGPUProgram(y,x,"float32")}let f=al({inputs:{real:h,imag:m},backend:l});return l.disposeData(h.dataId),l.disposeData(m.dataId),f}let u=n||pa(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(c):c,m=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[f,g]=t(i.shape,o.shape,h,m,u);return l.makeTensorInfo(g,u,f)}let p=new kh(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:Zue,castImpl:Jue,ceilImpl:Que,concatImpl:ede,equalImpl:tde,expImpl:ade,expm1Impl:nde,floorImpl:rde,floorDivImpl:sde,gatherNdImpl:ide,gatherV2Impl:ode,greaterEqualImpl:lde,greaterImpl:ude,lessEqualImpl:dde,lessImpl:pde,logImpl:cde,maxImpl:hde,maximumImpl:mde,minimumImpl:fde,multiplyImpl:gde,negImpl:yde,notEqualImpl:xde,prodImpl:Ade,rangeImpl:bde,rsqrtImpl:vde,scatterImpl:wde,simpleAbsImpl:kde,sliceImpl:Ide,stridedSliceImpl:Sde,stringNGramsImpl:Cde,subImpl:Tde,tileImpl:Nde,topKImpl:Rde,transposeImpl:Ede,uniqueImpl:Dge}=Zh,Mde=at({opType:le.ABS,cpuKernelImpl:kde}),_de={kernelName:tu,backendName:"webgpu",kernelFunc:Mde},Pde=at({opType:le.ACOS}),$de={kernelName:ti,backendName:"webgpu",kernelFunc:Pde},Fde=at({opType:le.ACOSH}),Dde={kernelName:ai,backendName:"webgpu",kernelFunc:Fde},Ode=ta({opType:Pe.ADD,cpuKernelImpl:Zue,supportsComplex:!0}),zde={kernelName:rs,backendName:"webgpu",kernelFunc:Ode},Lde=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` + ${ue("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { @@ -5936,9 +5949,9 @@ return a / b;`,MZ=` } } } - `}};function rde(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return tn({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=n.map(o=>o.shape),i=new nde(s);return a.runWebGPUProgram(i,n,r)}var sde={kernelName:ti,backendName:"webgpu",kernelFunc:rde},ide=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` + `}};function Wde(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return tn({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=n.map(o=>o.shape),i=new Lde(s);return a.runWebGPUProgram(i,n,r)}var Bde={kernelName:ni,backendName:"webgpu",kernelFunc:Wde},Vde=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; - ${de()} { + ${ue()} { var x = i32(workgroupId.x) * ${e} + i32(localId.x); var y = i32(workgroupId.y) * ${e} + i32(localId.y); let width = uniforms.outShape[0]; @@ -5955,18 +5968,18 @@ return a / b;`,MZ=` [localId.y]); } } - `}},ode=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n=32768&&a>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` + `}};function ck(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n=32768&&a>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) @@ -5983,7 +5996,7 @@ return a / b;`,MZ=` let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } - ${de("index")} { + ${ue("index")} { let outputIndex = index / ${a}; let offset = getOffset(outputIndex); var bestValue = ${t}; @@ -6014,7 +6027,7 @@ return a / b;`,MZ=` ${n} } } - `}};function Jo(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=S.getAxesPermutation(l,s),p=e;u!=null&&(p=Bn({inputs:{x:e},attrs:{perm:u},backend:r}),l=S.getInnerMostAxes(l.length,s),i.push(p)),S.assertAxesAreInnerMostDims(n,l,s);let[c,d]=S.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=S.expandShapeToKeepDim(c,o));let f;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let m=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=Mue(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=Oue(p.shape,p.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},x=n==="mean"?"float32":Ip(e.dtype),A=[{type:"int32",data:[m]}],b=new dde(y,n,r.device.limits.maxComputeWorkgroupSizeX),w=r.runWebGPUProgram(b,[p],x,A);i.push(w),f=we({inputs:{x:w},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function pde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Jo(r,i,s,"all",a)}var cde={kernelName:ai,backendName:"webgpu",kernelFunc:pde};function hde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Jo(r,i,s,"any",a)}var fde={kernelName:ni,backendName:"webgpu",kernelFunc:hde},Jw=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=S.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=be(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=ce(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ir(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;rr.disposeData(f.dataId)),m}function jde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return nl(r,i,s,"all",a)}var qde={kernelName:ri,backendName:"webgpu",kernelFunc:jde};function Xde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return nl(r,i,s,"any",a)}var Kde={kernelName:si,backendName:"webgpu",kernelFunc:Xde},hk=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=S.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=xe(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=pe(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Sr(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r u32 { return ((a - 1u) / b + 1u); } @@ -6024,7 +6037,7 @@ return a / b;`,MZ=` var xBestValues : array; `} - ${de("index")} { + ${ue("index")} { let outputIndex = index / ${e}; let reduceLength = ${t()}; @@ -6064,7 +6077,7 @@ return a / b;`,MZ=` } } `:` - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; @@ -6080,8 +6093,8 @@ return a / b;`,MZ=` setOutputAtIndexI32(index, bestIndex); } } - `}};function mde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Bn({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new Jw(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var gde={kernelName:Jl,backendName:"webgpu",kernelFunc:mde};function yde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Bn({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new Jw(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var xde={kernelName:Ql,backendName:"webgpu",kernelFunc:yde},Ade=tt({opType:le.ASIN}),bde={kernelName:ri,backendName:"webgpu",kernelFunc:Ade},vde=tt({opType:le.ASINH}),wde={kernelName:si,backendName:"webgpu",kernelFunc:vde},kde=tt({opType:le.ATAN}),Ide={kernelName:ii,backendName:"webgpu",kernelFunc:kde},Sde=ta({opType:_e.ATAN2}),Cde={kernelName:li,backendName:"webgpu",kernelFunc:Sde},Tde=tt({opType:le.ATANH}),Nde={kernelName:oi,backendName:"webgpu",kernelFunc:Tde},Rde=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` - ${de("index")} { + `}};function Yde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ar({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new hk(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Zde={kernelName:au,backendName:"webgpu",kernelFunc:Yde};function Jde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ar({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new hk(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Qde={kernelName:nu,backendName:"webgpu",kernelFunc:Jde},epe=at({opType:le.ASIN}),tpe={kernelName:ii,backendName:"webgpu",kernelFunc:epe},ape=at({opType:le.ASINH}),npe={kernelName:oi,backendName:"webgpu",kernelFunc:ape},rpe=at({opType:le.ATAN}),spe={kernelName:li,backendName:"webgpu",kernelFunc:rpe},ipe=ta({opType:Pe.ATAN2}),ope={kernelName:di,backendName:"webgpu",kernelFunc:ipe},lpe=at({opType:le.ATANH}),upe={kernelName:ui,backendName:"webgpu",kernelFunc:lpe},dpe=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6095,13 +6108,13 @@ return a / b;`,MZ=` setOutputAtIndex(index, value); } } - `}},Kd=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool2D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},Qd=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool2D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; maxPosition = ${this.flattenPositions?this.includeBatchIndex?"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d":"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d":"wR * uniforms.filterDims.y + wC"}; }`:e="resultValue = max(value, resultValue);";let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),` - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6136,13 +6149,13 @@ return a / b;`,MZ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}},Dg=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool3D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},Ug=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool3D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; maxPosition = ${this.flattenPositions?this.includeBatchIndex?"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch":"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch":"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC"}; }`:e="resultValue = max(value, resultValue);";let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),` - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -6185,9 +6198,9 @@ return a / b;`,MZ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}};function Qw(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return Jo(r,s,i,"max",a)}var Ede={kernelName:Ji,backendName:"webgpu",kernelFunc:Qw};function ek(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Jo(r,i,s,"mean",a)}var Mde={kernelName:to,backendName:"webgpu",kernelFunc:ek};function tk(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return tn({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=we({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=ek({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=Qw({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=we({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Rde(t):(a==="avg"?r=new Kd(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new Kd(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function Fde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=S.computePool2DInfo(r.shape,s,i,u,o,l);return tk(r,p,"avg",a)}var _de={kernelName:ui,backendName:"webgpu",kernelFunc:Fde};function $de(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Dg(c,"avg"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var Dde={kernelName:eu,backendName:"webgpu",kernelFunc:$de},Pde=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` - ${de("index")} { + `}};function mk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return nl(r,s,i,"max",a)}var ppe={kernelName:to,backendName:"webgpu",kernelFunc:mk};function fk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return nl(r,i,s,"mean",a)}var cpe={kernelName:ro,backendName:"webgpu",kernelFunc:fk};function gk(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return tn({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=ke({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=fk({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=mk({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=ke({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new dpe(t):(a==="avg"?r=new Qd(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new Qd(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function hpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=S.computePool2DInfo(r.shape,s,i,u,o,l);return gk(r,p,"avg",a)}var mpe={kernelName:pi,backendName:"webgpu",kernelFunc:hpe};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Ug(c,"avg"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var gpe={kernelName:ru,backendName:"webgpu",kernelFunc:fpe},ype=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6224,9 +6237,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}},Ode=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, - outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` - ${de("index")} { + `}},xpe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -6273,8 +6286,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}};function zde(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=S.computePool3DInfo(i.shape,o,l,1,u,p),d=new Ode(c),h=1/(c.filterDepth*c.filterHeight*c.filterWidth),f=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[h]}];return a.runWebGPUProgram(d,[r],i.dtype,f)}var Lde={kernelName:ep,backendName:"webgpu",kernelFunc:zde};function Wde(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Fg([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=new Pde(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var Bde={kernelName:kh,backendName:"webgpu",kernelFunc:Wde};function Vde(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return o0({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Ude={kernelName:di,backendName:"webgpu",kernelFunc:Vde},Gde=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Jt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Jt(this.rank),t=Hde(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${$1[r]} = uniforms.start.${Ir(r)} + coords.${$1[r]};`),` - ${de("index")} { + `}};function Ape(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=S.computePool3DInfo(i.shape,o,l,1,u,p),d=new xpe(c),h=1/(c.filterDepth*c.filterHeight*c.filterWidth),m=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[h]}];return a.runWebGPUProgram(d,[r],i.dtype,m)}var bpe={kernelName:sp,backendName:"webgpu",kernelFunc:Ape};function vpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Wg([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=new ype(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var wpe={kernelName:rp,backendName:"webgpu",kernelFunc:vpe};function kpe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return u0({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Ipe={kernelName:ci,backendName:"webgpu",kernelFunc:kpe},Spe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${$t(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=$t(this.rank),t=Cpe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${V1[r]} = uniforms.start.${Sr(r)} + coords.${V1[r]};`),` + ${ue("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); @@ -6283,17 +6296,17 @@ return a / b;`,MZ=` setOutputAtIndex(index, getSource(${t})); } } - `}},$1=["x","y","z","w","u","v"];function Hde(e){if(e===1)return"sourceLoc";if(e<=6)return $1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function ju(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=Vue(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new Gde(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var jde={kernelName:Iu,backendName:"webgpu",kernelFunc:ju},qde=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=[],f=we({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Bn({inputs:{x:f},backend:a,attrs:{perm:u}}),g=we({inputs:{x:m},backend:a,attrs:{shape:p}}),y=ju({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},Xde={kernelName:tu,backendName:"webgpu",kernelFunc:qde},Kde=` + `}},V1=["x","y","z","w","u","v"];function Cpe(e){if(e===1)return"sourceLoc";if(e<=6)return V1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Yu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=Ide(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new Spe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Tpe={kernelName:Nu,backendName:"webgpu",kernelFunc:Yu},Npe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=[],m=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),f=ar({inputs:{x:m},backend:a,attrs:{perm:u}}),g=ke({inputs:{x:f},backend:a,attrs:{shape:p}}),y=Yu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},Rpe={kernelName:su,backendName:"webgpu",kernelFunc:Npe},Epe=` fn bincount_write(index: i32, value: f32) { - ${Gu("&result[index]","value","float32")} + ${Xu("&result[index]","value","float32")} } -`,Yde=` +`,Mpe=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } -`,ak=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` - ${this.binaryOutput?Yde:Kde} - ${de("index")} { +`,yk=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` + ${this.binaryOutput?Mpe:Epe} + ${ue("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { @@ -6309,8 +6322,8 @@ return a / b;`,MZ=` } }`} } - `}};function Zde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Mn({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new ak([o],l),h=[{type:"int32",data:[i]}],f=l?[r,s]:[r];return a.runWebGPUProgram(d,f,p,h,c)}var Jde={kernelName:pi,backendName:"webgpu",kernelFunc:Zde},Qde=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` - ${de("index")} { + `}};function _pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Mn({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new yk([o],l),h=[{type:"int32",data:[i]}],m=l?[r,s]:[r];return a.runWebGPUProgram(d,m,p,h,c)}var Ppe={kernelName:hi,backendName:"webgpu",kernelFunc:_pe},$pe=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { var s0 = 1.0; var s1 = 1.0; @@ -6334,8 +6347,8 @@ return a / b;`,MZ=` } } } - `}};function epe(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t;if(a.shouldExecuteOnCPU([n,r])){let p=a.tensorMap.get(n.dataId),c=a.tensorMap.get(r.dataId),d=p.values,h=c.values,f=S.assertAndGetBroadcastShape(Array.from(d),Array.from(h));return a.makeTensorInfo([f.length],"int32",Int32Array.from(f))}let s=v.sizeFromShape(n.shape),i=v.sizeFromShape(r.shape),o=Math.max(s,i),l=new Qde(o),u=[{type:"int32",data:[s]},{type:"int32",data:[i]}];return a.runWebGPUProgram(l,[n,r],"int32",u)}var tpe={kernelName:au,backendName:"webgpu",kernelFunc:epe},nk=ta({opType:_e.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Pue}),ape={kernelName:oo,backendName:"webgpu",kernelFunc:nk};function jp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.real},backend:a})}var npe={kernelName:cp,backendName:"webgpu",kernelFunc:jp};function rpe(e,t){let a=new Hu(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function D1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return tn({inputs:{x:r},backend:a});let i=gn(r.shape),o=D1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=Zo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=jp({inputs:{input:r},backend:a}),o=D1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=tn({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=gue(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return rpe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=nk({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var spe={kernelName:ci,backendName:"webgpu",kernelFunc:D1},ipe=tt({opType:le.CEIL,cpuKernelImpl:yue}),ope={kernelName:hi,backendName:"webgpu",kernelFunc:ipe},lpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` - ${de("index")} { + `}};function Fpe(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t;if(a.shouldExecuteOnCPU([n,r])){let p=a.tensorMap.get(n.dataId),c=a.tensorMap.get(r.dataId),d=p.values,h=c.values,m=S.assertAndGetBroadcastShape(Array.from(d),Array.from(h));return a.makeTensorInfo([m.length],"int32",Int32Array.from(m))}let s=v.sizeFromShape(n.shape),i=v.sizeFromShape(r.shape),o=Math.max(s,i),l=new $pe(o),u=[{type:"int32",data:[s]},{type:"int32",data:[i]}];return a.runWebGPUProgram(l,[n,r],"int32",u)}var Dpe={kernelName:ou,backendName:"webgpu",kernelFunc:Fpe},xk=ta({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:xde}),Ope={kernelName:co,backendName:"webgpu",kernelFunc:xk};function Yp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.real},backend:a})}var zpe={kernelName:yp,backendName:"webgpu",kernelFunc:Yp};function Lpe(e,t){let a=new Ku(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function U1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return tn({inputs:{x:r},backend:a});let i=gn(r.shape),o=U1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=al({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=Yp({inputs:{input:r},backend:a}),o=U1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=tn({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Jue(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return Lpe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=xk({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var Wpe={kernelName:mi,backendName:"webgpu",kernelFunc:U1},Bpe=at({opType:le.CEIL,cpuKernelImpl:Que}),Vpe={kernelName:fi,backendName:"webgpu",kernelFunc:Bpe},Upe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + ${ue("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); var clampedValue = clamp( @@ -6344,8 +6357,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, clampedValue); } } - `}},upe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` - ${de("index")} { + `}},Gpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` + ${ue("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { @@ -6355,8 +6368,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function dpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new lpe(r.shape):o=new upe(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var ppe={kernelName:rs,backendName:"webgpu",kernelFunc:dpe},cpe=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` - ${de("index")} { + `}};function Hpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new Upe(r.shape):o=new Gpe(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var jpe={kernelName:ss,backendName:"webgpu",kernelFunc:Hpe},qpe=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); let im = abs(getImagByOutputIndex(index)); @@ -6367,8 +6380,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } - `}};function Lx(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function hpe(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.tensorMap.get(n.dataId),s=new cpe(n.shape),i=[Lx(n,r.complexTensorInfos.real),Lx(n,r.complexTensorInfos.imag)];return a.runWebGPUProgram(s,i,i[0].dtype)}var fpe={kernelName:ap,backendName:"webgpu",kernelFunc:hpe},mpe=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n`T${a}`),this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;njp({inputs:{input:A},backend:a})),m=e.map(A=>l0({inputs:{input:A},backend:a})),g=Id(f,t,a),y=Id(m,t,a),x=Zo({inputs:{real:g,imag:y},backend:a});return f.forEach(A=>a.disposeData(A.dataId)),m.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(w=>{let C=[-1,v.sizeFromShape(w.shape.slice(t))];return we({inputs:{x:w},backend:a,attrs:{shape:C}})}),m=f.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=S.computeOutShape(f.map(w=>w.shape),1),y=f[0].shape[0]===1,x=xue(m,g,n,y),A=S.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(A,n,x);return f.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;gf.shape),u=new mpe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let f=1;fa.disposeData(f.dataId));let h=we({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function ype(e,t,a){let n=S.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>we({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function rk(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?tn({inputs:{x:l[0]},backend:a}):Id(l,s,a)}var xpe={kernelName:nu,backendName:"webgpu",kernelFunc:rk};function Ape(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?` + `}};function d0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.imag},backend:a})}var Zpe={kernelName:mp,backendName:"webgpu",kernelFunc:d0};function Nd(e,t,a){let n=e[0].dtype;if(n==="complex64"){let m=e.map(A=>Yp({inputs:{input:A},backend:a})),f=e.map(A=>d0({inputs:{input:A},backend:a})),g=Nd(m,t,a),y=Nd(f,t,a),x=al({inputs:{real:g,imag:y},backend:a});return m.forEach(A=>a.disposeData(A.dataId)),f.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let m=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ke({inputs:{x:w},backend:a,attrs:{shape:I}})}),f=m.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=S.computeOutShape(m.map(w=>w.shape),1),y=m[0].shape[0]===1,x=ede(f,g,n,y),A=S.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(A,n,x);return m.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let m=[];for(let g=0;gm.shape),u=new Ype(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let m=1;ma.disposeData(m.dataId));let h=ke({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Jpe(e,t,a){let n=S.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function Ak(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?tn({inputs:{x:l[0]},backend:a}):Nd(l,s,a)}var Qpe={kernelName:lu,backendName:"webgpu",kernelFunc:Ak};function ece(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6397,7 +6410,7 @@ return a / b;`,MZ=` row, col / outWidth, col % outWidth); - `,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",y=e?"col":"row",x=` + `,m=e?"uniforms.xShape[1]":"uniforms.xShape[2]",f=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",y=e?"col":"row",x=` let inChannels = uniforms.wShape[2]; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; let outRow = ${g} / outWidth; @@ -6408,10 +6421,10 @@ return a / b;`,MZ=` let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0]; let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1]; let xCh = ${y} % inChannels; - var resData = ${Xe(o)}(0.0); + var resData = ${Ke(o)}(0.0); // The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. - if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) { + if (xRow >= 0 && xRow < ${m} && xCol >= 0 && xCol < ${f}) { ${d} let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${p(o)} @@ -6423,16 +6436,16 @@ return a / b;`,MZ=` if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${x} } - return ${Xe(o)}(0.0);`:n&&a?` + return ${Ke(o)}(0.0);`:n&&a?` let col = colIn * ${o}; ${x}`:` let col = colIn * ${o}; if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${x} } - return ${Xe(o)}(0.0);`,b=`${c(l)}`,w=Xe(u),C=Xe(e?o:l),T=Xe(e?l:o);return` - ${Mr(s,i,u===4,4)} - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${C} { + return ${Ke(o)}(0.0);`,b=`${c(l)}`,w=Ke(u),I=Ke(e?o:l),T=Ke(e?l:o);return` + ${_r(s,i,u===4,4)} + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${I} { ${e?A:b} } @@ -6447,14 +6460,14 @@ return a / b;`,MZ=` var value = valueIn; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${h} - ${Yo(r,s)} + ${tl(r,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var bpe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Rg(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Eg(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),r&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?s0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):i0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` - ${Ape(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} + }`}var tce=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Og(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=zg(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),r&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?o0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):l0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + ${ece(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} - `}},vpe=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` - ${Mr(this.activation,this.hasPreluActivationWeights,!1,4)} + `}},ace=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` + ${_r(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { @@ -6475,11 +6488,11 @@ return a / b;`,MZ=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${Yo(this.addBias,this.activation)} + ${tl(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } - ${de("index")} { + ${ue("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} @@ -6499,9 +6512,9 @@ return a / b;`,MZ=` } writeResult(batch, outRow, outCol, outChannel, acc); } - `}},wpe=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, - inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",r=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` - ${de("index")} { + `}},nce=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, + inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",r=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` + ${ue("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; @@ -6523,8 +6536,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, value); } } - `}};function Ah(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function kpe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,f;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=we({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),f=we({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=we({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=we({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(f),s!=null){let y=Ah(s.shape,l);y!=null&&(s=we({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Ah(r.shape,l);y!=null&&(r=we({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let m=o0({a:l?h:f,b:l?f:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=we({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});d.push(m);for(let y of d)n.disposeData(y.dataId);return g}function Ipe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,w=m*f,C=A?[a.batchSize,w,b]:[a.batchSize,b,w],T=new wpe(C,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[f]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],M=n.runWebGPUProgram(T,[e],e.dtype,N),F=[];F.push(M);let E=we({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(F.push(E),s!=null){let O=Ah(s.shape,A);O!=null&&(s=we({inputs:{x:s},backend:n,attrs:{shape:O}}),F.push(s))}if(r!=null){let O=Ah(r.shape,A);O!=null&&(r=we({inputs:{x:r},backend:n,attrs:{shape:O}}),F.push(r))}let I=o0({a:A?M:E,b:A?E:M,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=we({inputs:{x:I},backend:n,attrs:{shape:a.outShape}});F.push(I);for(let O of F)n.disposeData(O.dataId);return _}function sk({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=W().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return kpe({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=h>0?h:n.thresholdToIncreaseWorkgroups,m=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(W().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||m<=f)return Ipe({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new vpe(a,l,o,u);else{let C=p?a.outHeight*a.outWidth:a.outChannels,T=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[C]},{type:"int32",data:[T]},{type:"int32",data:[N]});let M=n.adapterInfo.isIntel();g=new bpe(a,C,T,N,l,o,u,M)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=we({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=we({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,x);for(let C of A)n.disposeData(C.dataId);return w}function Spe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return sk({x:r,filter:s,convInfo:d,backend:n})}var Cpe={kernelName:fi,backendName:"webgpu",kernelFunc:Spe},Tpe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1,n=` - ${de()} { + `}};function Ih(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function rce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,m;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=ke({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=ke({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(m),s!=null){let y=Ih(s.shape,l);y!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Ih(r.shape,l);y!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let f=u0({a:l?h:m,b:l?m:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=ke({inputs:{x:f},backend:n,attrs:{shape:a.outShape}});d.push(f);for(let y of d)n.disposeData(y.dataId);return g}function sce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:m,outHeight:f,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,w=f*m,I=A?[a.batchSize,w,b]:[a.batchSize,b,w],T=new nce(I,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[m]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],M=n.runWebGPUProgram(T,[e],e.dtype,N),P=[];P.push(M);let E=ke({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(P.push(E),s!=null){let O=Ih(s.shape,A);O!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:O}}),P.push(s))}if(r!=null){let O=Ih(r.shape,A);O!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:O}}),P.push(r))}let C=u0({a:A?M:E,b:A?E:M,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=ke({inputs:{x:C},backend:n,attrs:{shape:a.outShape}});P.push(C);for(let O of P)n.disposeData(O.dataId);return _}function bk({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=W().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return rce({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),m=h>0?h:n.thresholdToIncreaseWorkgroups,f=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(W().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||f<=m)return sce({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new ace(a,l,o,u);else{let I=p?a.outHeight*a.outWidth:a.outChannels,T=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[I]},{type:"int32",data:[T]},{type:"int32",data:[N]});let M=n.adapterInfo.isIntel();g=new tce(a,I,T,N,l,o,u,M)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,x);for(let I of A)n.disposeData(I.dataId);return w}function ice(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return bk({x:r,filter:s,convInfo:d,backend:n})}var oce={kernelName:gi,backendName:"webgpu",kernelFunc:ice},lce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1,n=` + ${ue()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; @@ -6625,7 +6638,7 @@ return a / b;`,MZ=` `;return this.isVec4?` ${n} `:` - ${de("index")} { + ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6666,8 +6679,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}},Npe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` - ${de("index")} { + `}},uce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` + ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; @@ -6707,9 +6720,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}},Rpe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, - outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` - ${de("index")} { + `}},dce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, + outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` + ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wF = coords.x; @@ -6748,9 +6761,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}},Epe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, - outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` - ${de("index")} { + `}},pce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, + outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` + ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -6802,7 +6815,7 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}};function Mpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Npe(d),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,f)}var Fpe={kernelName:np,backendName:"webgpu",kernelFunc:Mpe};function _pe(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + `}};function cce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new uce(d),m=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,m)}var hce={kernelName:lp,backendName:"webgpu",kernelFunc:cce};function mce(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); @@ -6821,10 +6834,10 @@ return a / b;`,MZ=` let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { - return ${Xe(e)}(0.0); + return ${Ke(e)}(0.0); } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { - return ${Xe(e)}(0.0); + return ${Ke(e)}(0.0); } let coord = vec4( batch, @@ -6833,13 +6846,13 @@ return a / b;`,MZ=` col % uniforms.outBackprop[3]); return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`} } - return ${Xe(e)}(0.0);`;return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Xe(e)} { + return ${Ke(e)}(0.0);`;return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ke(e)} { let col = colIn * ${e}; ${a} } - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Xe(e)} { + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ke(e)} { let col = colIn * ${e}; let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); @@ -6851,10 +6864,10 @@ return a / b;`,MZ=` let coord = vec4(coordX, coordY, col, rowInner); ${t(e)} } - return ${Xe(e)}(0.0); + return ${Ke(e)}(0.0); } - fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Xe(e)}) { + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Ke(e)}) { let col = colIn * ${e}; if (row < uniforms.dimAOuter && (col + ${e-1}) < uniforms.dimBOuter) { var value = valueInput; @@ -6865,11 +6878,11 @@ return a / b;`,MZ=` col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value; } - }`}var $pe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Rg(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Eg(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?s0(this.elementsPerThread,this.workgroupSize):i0(this.elementsPerThread,this.workgroupSize);return` - ${_pe(this.isVec4?4:1)} + }`}var fce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Og(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=zg(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?o0(this.elementsPerThread,this.workgroupSize):l0(this.elementsPerThread,this.workgroupSize);return` + ${mce(this.isVec4?4:1)} ${e} - `}};function Dpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(W().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.dataFormat!=="channelsLast")f=new Tpe(d);else{f=new $pe(d);let m=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(f,[r,s],"float32",h)}var Ppe={kernelName:mi,backendName:"webgpu",kernelFunc:Dpe},Ope=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` - ${de("index")} { + `}};function gce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],m;if(W().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.dataFormat!=="channelsLast")m=new lce(d);else{m=new fce(d);let f=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(m,[r,s],"float32",h)}var yce={kernelName:yi,backendName:"webgpu",kernelFunc:gce},xce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords.x; @@ -6950,8 +6963,8 @@ return a / b;`,MZ=` } setOutputAtIndex(index, dotProd); } - }`}};function zpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeConv3DInfo(r.shape,s.shape,i,l,o),p=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],d=new Ope(u),h=pa(r.dtype,s.dtype);return a.runWebGPUProgram(d,[r,s],h,c)}var Lpe={kernelName:gi,backendName:"webgpu",kernelFunc:zpe};function Wpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=S.computeConv3DInfo(r.shape,l,i,1,o),p=new Rpe(u),c=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return a.runWebGPUProgram(p,[r,s],s.dtype,c)}var Bpe={kernelName:ru,backendName:"webgpu",kernelFunc:Wpe};function Vpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,pad:o,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,i,1,o),p=new Epe(u),c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return a.runWebGPUProgram(p,[r,s],r.dtype,c)}var Upe={kernelName:yi,backendName:"webgpu",kernelFunc:Vpe},Gpe=tt({opType:le.COS}),Hpe={kernelName:xi,backendName:"webgpu",kernelFunc:Gpe},jpe=tt({opType:le.COSH}),qpe={kernelName:Ai,backendName:"webgpu",kernelFunc:jpe},Xpe=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` - ${de("index")} { + }`}};function Ace(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeConv3DInfo(r.shape,s.shape,i,l,o),p=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],d=new xce(u),h=pa(r.dtype,s.dtype);return a.runWebGPUProgram(d,[r,s],h,c)}var bce={kernelName:xi,backendName:"webgpu",kernelFunc:Ace};function vce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=S.computeConv3DInfo(r.shape,l,i,1,o),p=new dce(u),c=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return a.runWebGPUProgram(p,[r,s],s.dtype,c)}var wce={kernelName:uu,backendName:"webgpu",kernelFunc:vce};function kce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,pad:o,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,i,1,o),p=new pce(u),c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return a.runWebGPUProgram(p,[r,s],r.dtype,c)}var Ice={kernelName:Ai,backendName:"webgpu",kernelFunc:kce},Sce=at({opType:le.COS}),Cce={kernelName:bi,backendName:"webgpu",kernelFunc:Sce},Tce=at({opType:le.COSH}),Nce={kernelName:vi,backendName:"webgpu",kernelFunc:Tce},Rce=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${a}); @@ -7006,24 +7019,24 @@ return a / b;`,MZ=` } } } - `}},Kpe=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Xpe(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Ype={kernelName:wi,backendName:"webgpu",kernelFunc:Kpe},Yd;(function(e){e.Prod="*",e.Sum="+"})(Yd||(Yd={}));var Wx=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Yd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${Bx(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` - ${de("index")} { + `}},Ece=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Rce(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Mce={kernelName:Ii,backendName:"webgpu",kernelFunc:Ece},ep;(function(e){e.Prod="*",e.Sum="+"})(ep||(ep={}));var Yx=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===ep.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${Zx(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` + ${ue("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${Vx(e,"coords",this.op)}; + let end = ${Jx(e,"coords",this.op)}; var val = ${a}; let pow2 = i32(pow(2.0, uniforms.index)); if (${r}) { let idx = ${s}; - ${Vx(e,"coords",this.op)} = idx; - val ${this.op}= getX(${Bx(e,"coords",this.op)}); + ${Jx(e,"coords",this.op)} = idx; + val ${this.op}= getX(${Zx(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function Bx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Vx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function ik(e,t,a,n,r,s){let i=t.shape.length,o=S.getAxesPermutation([n],i),l=t;o!=null&&(l=Bn({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=S.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=tn({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Wx(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let d=new Wx(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=Bn({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function Zpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return ik(Yd.Prod,r,a,s,i,o)}var Jpe={kernelName:bi,backendName:"webgpu",kernelFunc:Zpe};function Qpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return ik(Yd.Sum,r,a,s,i,o)}var ece={kernelName:vi,backendName:"webgpu",kernelFunc:Qpe};function tce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Mn({backend:a,attrs:{shape:d,value:0,dtype:p}}),f=new ak(c,u,o),m=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(f,g,p,m,h)}var ace={kernelName:su,backendName:"webgpu",kernelFunc:tce},nce=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` - ${de("index")} { + `}};function Zx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Jx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function vk(e,t,a,n,r,s){let i=t.shape.length,o=S.getAxesPermutation([n],i),l=t;o!=null&&(l=ar({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=S.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=tn({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Yx(e,l.shape,!1,s),m=c,f=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,f),a.disposeData(m.dataId)}if(r){let d=new Yx(e,l.shape,r,s),h=c,m=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,m),a.disposeData(h.dataId)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=ar({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function _ce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return vk(ep.Prod,r,a,s,i,o)}var Pce={kernelName:wi,backendName:"webgpu",kernelFunc:_ce};function $ce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return vk(ep.Sum,r,a,s,i,o)}var Fce={kernelName:ki,backendName:"webgpu",kernelFunc:$ce};function Dce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Mn({backend:a,attrs:{shape:d,value:0,dtype:p}}),m=new yk(c,u,o),f=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(m,g,p,f,h)}var Oce={kernelName:du,backendName:"webgpu",kernelFunc:Dce},zce=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -7042,8 +7055,8 @@ return a / b;`,MZ=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function rce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=[{type:"int32",data:[s]}],g=new nce(f,i);return a.runWebGPUProgram(g,[r],r.dtype,m)}var sce={kernelName:ki,backendName:"webgpu",kernelFunc:rce},ice=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` - ${Mr(this.activation,this.hasPreluActivation,!1,4)} + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Lce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=[{type:"int32",data:[s]}],g=new zce(m,i);return a.runWebGPUProgram(g,[r],r.dtype,f)}var Wce={kernelName:Si,backendName:"webgpu",kernelFunc:Lce},Bce=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + ${_r(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${a}>; var mm_Bsub : array, ${this.filterHeight}>; @@ -7056,7 +7069,7 @@ return a / b;`,MZ=` return value; } - ${de()} { + ${ue()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pads; @@ -7099,13 +7112,13 @@ return a / b;`,MZ=` value = fma(xVal, wVal, value); } } - ${Yo(this.addBias,this.activation)} + ${tl(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}},ok=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` - ${Mr(this.activation,this.hasPreluActivation,!0,4)} + `}},wk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let r=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=xe(r),this.dispatch=pe(this.dispatchLayout,r,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` + ${_r(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { @@ -7114,11 +7127,16 @@ return a / b;`,MZ=` return value; } - ${de()} { - let batch = i32(globalId.z) / uniforms.outShape[1]; - let r = i32(globalId.z) % uniforms.outShape[1]; - let c = i32(globalId.y) * ${this.workPerThread}; - let d1 = i32(globalId.x) * 4; + ${ue("index")} { + let width0 = uniforms.outShape[3] / ${this.outputComponent}; + let d1 = (index % width0) * ${this.outputComponent}; + var index1 = index / width0; + let width1 = uniforms.virtualWidth / ${this.workPerThread}; + let c = (index1 % width1) * ${this.workPerThread}; + index1 = index1 / width1; + let r = index1 % uniforms.outShape[1]; + let batch = index1 / uniforms.outShape[1]; + let xRCCorner = vec2(r, c) * vec2(${t}, ${a}) - uniforms.pads; let xRCorner = xRCCorner.x; @@ -7149,16 +7167,16 @@ return a / b;`,MZ=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${Yo(this.addBias,this.activation)} + ${tl(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}},lk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, - filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` - ${Mr(this.activation,this.hasPreluActivation,!1,4)} + `}},kk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, + filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` + ${_r(this.activation,this.hasPreluActivation,!1,4)} - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -7217,13 +7235,13 @@ return a / b;`,MZ=` } } } - ${Yo(this.addBias,this.activation)} + ${tl(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function oce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=S.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new ice(h.outShape,h.filterHeight,h.filterWidth):m&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new ok(h):(g=new lk(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var lce={kernelName:Ii,backendName:"webgpu",kernelFunc:oce},uce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, - outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` - ${de("index")} { + `}};function Vce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=S.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),m=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],f=h.dataFormat==="channelsLast",g;return!f&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new Bce(h.outShape,h.filterHeight,h.filterWidth):f&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(g=new wk(h),m.push({type:"int32",data:[g.virtualWidth]})):(g=new kk(h),m.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,m)}var Uce={kernelName:Ci,backendName:"webgpu",kernelFunc:Vce},Gce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, + outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; @@ -7257,9 +7275,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}},dce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` - ${de("index")} { + `}},Hce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -7300,16 +7318,16 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}};function pce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=S.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new uce(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],"float32",h)}var cce={kernelName:rp,backendName:"webgpu",kernelFunc:pce};function hce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=S.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new dce(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],r.dtype,h)}var fce={kernelName:sp,backendName:"webgpu",kernelFunc:hce},mce=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` - ${de("index")} { + `}};function jce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=S.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new Gce(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],"float32",h)}var qce={kernelName:up,backendName:"webgpu",kernelFunc:jce};function Xce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=S.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new Hce(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],r.dtype,h)}var Kce={kernelName:dp,backendName:"webgpu",kernelFunc:Xce},Yce=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); setOutputAtIndex(index, value); } } - `}};function gce(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=we({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new mce(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=we({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var yce={kernelName:iu,backendName:"webgpu",kernelFunc:gce},xce=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` - ${de("index")} { + `}};function Zce(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ke({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new Yce(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Jce={kernelName:pu,backendName:"webgpu",kernelFunc:Zce},Qce=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; let coords = getOutputCoords(); @@ -7340,9 +7358,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, curVal); } } - `}};function Ace(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new xce(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var bce={kernelName:Si,backendName:"webgpu",kernelFunc:Ace},vce=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=be(e.outShape),this.dispatch=ce(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 + `}};function ehe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new Qce(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var the={kernelName:Ti,backendName:"webgpu",kernelFunc:ehe},ahe=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(e.outShape),this.dispatch=pe(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${t} type.`);this.type=t,this.shaderKey="dilation2DBackpropInput"}getUserCode(){return` - ${de("index")} { + ${ue("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; @@ -7381,12 +7399,12 @@ return a / b;`,MZ=` let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${Gu("&result[flatIndexIn]","value",this.type)} + ${Xu("&result[flatIndexIn]","value",this.type)} } } - `}},wce=class{constructor(e,t,a){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=be(e.outShape),this.dispatch=ce(this.dispatchLayout,e.outShape,this.workgroupSize),a!=="float32"&&a!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 + `}},nhe=class{constructor(e,t,a){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=xe(e.outShape),this.dispatch=pe(this.dispatchLayout,e.outShape,this.workgroupSize),a!=="float32"&&a!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 types, does not support ${a} type.`);this.type=a,this.shaderKey="dilation2DBackpropFilter"}getUserCode(){return` - ${de("index")} { + ${ue("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; @@ -7424,10 +7442,10 @@ return a / b;`,MZ=` let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${Gu("&result[flatIndexIn]","value",this.type)} + ${Xu("&result[flatIndexIn]","value",this.type)} } } - `}};function kce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=s.dtype,d=new wce(p,s.shape,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],f=Mn({backend:a,attrs:{shape:s.shape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,f)}var Ice={kernelName:Pl,backendName:"webgpu",kernelFunc:kce};function Sce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=r.dtype,d=new vce(p,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],f=Mn({backend:a,attrs:{shape:p.inShape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,f)}var Cce={kernelName:Dl,backendName:"webgpu",kernelFunc:Sce},uk=ta({opType:_e.MUL,cpuKernelImpl:$ue,supportsComplex:!0}),Tce={kernelName:io,backendName:"webgpu",kernelFunc:uk};function dk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Jo(r,s,i,"sum",a)}var Nce={kernelName:_o,backendName:"webgpu",kernelFunc:dk};function Rce(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=S.decodeEinsumEquation(r,s.length);S.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=S.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m=0&&(d=dk({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeData(m.dataId);return d}var Ece={kernelName:ip,backendName:"webgpu",kernelFunc:Rce},Mce=tt({opType:le.ELU}),Fce={kernelName:Ti,backendName:"webgpu",kernelFunc:Mce},_ce=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=new xh(_e.ELU_DER,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],n.dtype)},$ce={kernelName:ou,backendName:"webgpu",kernelFunc:_ce},Dce=ta({opType:_e.EQUAL,dtype:"bool",cpuKernelImpl:Aue}),Pce={kernelName:Ni,backendName:"webgpu",kernelFunc:Dce},Oce=tt({opType:le.ERF}),zce={kernelName:lu,backendName:"webgpu",kernelFunc:Oce},Lce=tt({opType:le.EXP,cpuKernelImpl:bue,dtype:"float32"}),Wce={kernelName:Ri,backendName:"webgpu",kernelFunc:Lce};function P1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),we({inputs:{x:s},backend:n,attrs:{shape:o}})}var Bce={kernelName:uu,backendName:"webgpu",kernelFunc:P1},Vce=tt({opType:le.EXPM1,cpuKernelImpl:vue}),Uce={kernelName:Ei,backendName:"webgpu",kernelFunc:Vce},Ux=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function rhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=s.dtype,d=new nhe(p,s.shape,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Mn({backend:a,attrs:{shape:s.shape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var she={kernelName:Bl,backendName:"webgpu",kernelFunc:rhe};function ihe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=r.dtype,d=new ahe(p,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Mn({backend:a,attrs:{shape:p.inShape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var ohe={kernelName:Wl,backendName:"webgpu",kernelFunc:ihe},Ik=ta({opType:Pe.MUL,cpuKernelImpl:gde,supportsComplex:!0}),lhe={kernelName:po,backendName:"webgpu",kernelFunc:Ik};function Sk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return nl(r,s,i,"sum",a)}var uhe={kernelName:zo,backendName:"webgpu",kernelFunc:Sk};function dhe(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=S.decodeEinsumEquation(r,s.length);S.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=S.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=Sk({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeData(f.dataId);return d}var phe={kernelName:pp,backendName:"webgpu",kernelFunc:dhe},che=at({opType:le.ELU}),hhe={kernelName:Ri,backendName:"webgpu",kernelFunc:che},mhe=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=new kh(Pe.ELU_DER,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],n.dtype)},fhe={kernelName:cu,backendName:"webgpu",kernelFunc:mhe},ghe=ta({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:tde}),yhe={kernelName:Mi,backendName:"webgpu",kernelFunc:ghe},xhe=at({opType:le.ERF}),Ahe={kernelName:Ei,backendName:"webgpu",kernelFunc:xhe},bhe=at({opType:le.EXP,cpuKernelImpl:ade,dtype:"float32"}),vhe={kernelName:_i,backendName:"webgpu",kernelFunc:bhe};function G1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ke({inputs:{x:s},backend:n,attrs:{shape:o}})}var whe={kernelName:hu,backendName:"webgpu",kernelFunc:G1},khe=at({opType:le.EXPM1,cpuKernelImpl:nde}),Ihe={kernelName:Pi,backendName:"webgpu",kernelFunc:khe},Qx=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -7454,14 +7472,14 @@ return a / b;`,MZ=` return result; } - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function pk(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=we({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Ux("real",u),c=new Ux("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,m=[{type:"float32",data:[h]},{type:"float32",data:[f]}],g=a.runWebGPUProgram(p,d,"float32",m);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",m);o.push(y);let x=Zo({inputs:{real:g,imag:y},backend:a});o.push(x);let A=we({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function Gce(e){let{inputs:t,backend:a}=e,{input:n}=t;return pk(n,!1,a)}var Hce={kernelName:op,backendName:"webgpu",kernelFunc:Gce},jce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` - ${de("index")} { + `}};function Ck(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=ke({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Qx("real",u),c=new Qx("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,m=t?u[1]:1,f=[{type:"float32",data:[h]},{type:"float32",data:[m]}],g=a.runWebGPUProgram(p,d,"float32",f);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",f);o.push(y);let x=al({inputs:{real:g,imag:y},backend:a});o.push(x);let A=ke({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function She(e){let{inputs:t,backend:a}=e,{input:n}=t;return Ck(n,!1,a)}var Che={kernelName:cp,backendName:"webgpu",kernelFunc:She},The=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; @@ -7469,9 +7487,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, outputValue); } } - `}},qce={kernelName:Mi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new jce(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Xce=tt({opType:le.FLOOR,cpuKernelImpl:wue}),Kce={kernelName:Fi,backendName:"webgpu",kernelFunc:Xce},Yce=ta({opType:_e.INT_DIV,cpuKernelImpl:kue,dtype:"int32"}),Zce={kernelName:_i,backendName:"webgpu",kernelFunc:Yce},Jce=class{constructor(e,t,a=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}},Nhe={kernelName:$i,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new The(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Rhe=at({opType:le.FLOOR,cpuKernelImpl:rde}),Ehe={kernelName:Fi,backendName:"webgpu",kernelFunc:Rhe},Mhe=ta({opType:Pe.INT_DIV,cpuKernelImpl:sde,dtype:"int32"}),_he={kernelName:Di,backendName:"webgpu",kernelFunc:Mhe},Phe=class{constructor(e,t,a=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; - ${de("index")} { + ${ue("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); @@ -7481,8 +7499,8 @@ return a / b;`,MZ=` } } } - `}},Qce={kernelName:Ed,backendName:"webgpu",kernelFunc:ehe},Il,U2=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function ehe(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=!1,f=i||o;if(u||l||f){let x;if(h)x={width:p,height:c,format:null,usage:null,texture:a.device.importExternalTexture({source:r})};else{if(f){let _=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Il==null||_!==U2)&&(U2=_,Il=document.createElement("canvas").getContext("2d",{willReadFrequently:U2})),Il.canvas.width=p,Il.canvas.height=c,Il.drawImage(r,0,0,p,c),r=Il.canvas}let F=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,E="rgba8unorm",I=a.textureManager.acquireTexture(d[1],d[0],E,F);a.queue.copyExternalImageToTexture({source:r},{texture:I},[d[1],d[0]]),x={width:p,height:c,format:E,usage:F,texture:I}}let A=v.sizeFromShape(d),b=v.computeStrides(d),w=new Jce(d,s,h),C=[{type:"uint32",data:[A]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],T=a.makeTensorInfo([c,p],"int32"),N=a.tensorMap.get(T.dataId);N.resourceInfo=x;let M=a.runWebGPUProgram(w,[T],"int32",C);return a.disposeData(T.dataId),M}let m=r.data,g=m;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let x=m.length,A=0;for(let b=0;b(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}},ahe={kernelName:$i,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new the(n.shape,i.shape,o.shape,c,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,f)}};function nhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.convertConv2DDataFormat(p),g=S.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m);return sk({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var rhe={kernelName:Kr,backendName:"webgpu",kernelFunc:nhe};function she(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=p;f==null&&(f=[1,1]),v.assert(S.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,s.shape,l,f,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.outHeight>4&&m.outWidth>4&&m.strideWidth<=2&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new ok(m,y,d,x):(b=new lk(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var ihe={kernelName:Yr,backendName:"webgpu",kernelFunc:she},ohe=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Jt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` - ${de("index")} { + `}},Ohe={kernelName:Oi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Dhe(n.shape,i.shape,o.shape,c,d),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,m)}};function zhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=S.convertConv2DDataFormat(p),g=S.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,f);return bk({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:h})}var Lhe={kernelName:Yr,backendName:"webgpu",kernelFunc:zhe};function Whe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,m=p;m==null&&(m=[1,1]),v.assert(S.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=S.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.outHeight>4&&f.outWidth>4&&f.strideWidth<=2&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.inChannels%4===0?(b=new wk(f,y,d,x),A.push({type:"int32",data:[b.virtualWidth]})):(b=new kk(f,y,d,x),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var Bhe={kernelName:Zr,backendName:"webgpu",kernelFunc:Whe},Vhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${$t(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; @@ -7508,8 +7526,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function lhe(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=S.prepareAndValidate(n,r),d=we({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=we({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=Iue(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let f=new ohe(i,[u,p]),m=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(f,[h,d],h.dtype,m),y=we({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var uhe={kernelName:Di,backendName:"webgpu",kernelFunc:lhe},dhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=phe(this.aShape);return` - ${de("index")} { + `}};function Uhe(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=S.prepareAndValidate(n,r),d=ke({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=ke({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=ide(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let m=new Vhe(i,[u,p]),f=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(m,[h,d],h.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var Ghe={kernelName:zi,backendName:"webgpu",kernelFunc:Uhe},Hhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=jhe(this.aShape);return` + ${ue("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); @@ -7517,13 +7535,13 @@ return a / b;`,MZ=` setOutputAtIndex(index, inBounds * getA(${e})); } } - `}};function phe(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(T.dataId)),a.makeTensorInfo(u.outputShape,C.dtype,C.values)}let m=new dhe(d.shape,f),g=a.runWebGPUProgram(m,[d,h],d.dtype);c.push(g);let y=we({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var che={kernelName:pu,backendName:"webgpu",kernelFunc:ck},hhe=ta({opType:_e.GREATER,cpuKernelImpl:Tue,dtype:"bool"}),fhe={kernelName:Pi,backendName:"webgpu",kernelFunc:hhe},mhe=ta({opType:_e.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Cue}),ghe={kernelName:Oi,backendName:"webgpu",kernelFunc:mhe};function yhe(e){let{inputs:t,backend:a}=e,{input:n}=t;return pk(n,!0,a)}var xhe={kernelName:lp,backendName:"webgpu",kernelFunc:yhe},Ahe=tt({opType:le.IS_FINITE,dtype:"bool"}),bhe={kernelName:Li,backendName:"webgpu",kernelFunc:Ahe},vhe=tt({opType:le.IS_INF,dtype:"bool"}),whe={kernelName:Wi,backendName:"webgpu",kernelFunc:vhe},khe=tt({opType:le.IS_NAN,dtype:"bool"}),Ihe={kernelName:Bi,backendName:"webgpu",kernelFunc:khe};function She(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Hu(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var Che={kernelName:Vi,backendName:"webgpu",kernelFunc:She},The=ta({opType:_e.LESS,dtype:"bool",cpuKernelImpl:Rue}),Nhe={kernelName:Ui,backendName:"webgpu",kernelFunc:The},Rhe=ta({opType:_e.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Nue}),Ehe={kernelName:Gi,backendName:"webgpu",kernelFunc:Rhe},Mhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` - ${de("index")} { + `}};function jhe(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(T.dataId)),a.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new Hhe(d.shape,m),g=a.runWebGPUProgram(f,[d,h],d.dtype);c.push(g);let y=ke({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var qhe={kernelName:fu,backendName:"webgpu",kernelFunc:Tk},Xhe=ta({opType:Pe.GREATER,cpuKernelImpl:ude,dtype:"bool"}),Khe={kernelName:Li,backendName:"webgpu",kernelFunc:Xhe},Yhe=ta({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:lde}),Zhe={kernelName:Wi,backendName:"webgpu",kernelFunc:Yhe};function Jhe(e){let{inputs:t,backend:a}=e,{input:n}=t;return Ck(n,!0,a)}var Qhe={kernelName:hp,backendName:"webgpu",kernelFunc:Jhe},e0e=at({opType:le.IS_FINITE,dtype:"bool"}),t0e={kernelName:Vi,backendName:"webgpu",kernelFunc:e0e},a0e=at({opType:le.IS_INF,dtype:"bool"}),n0e={kernelName:Ui,backendName:"webgpu",kernelFunc:a0e},r0e=at({opType:le.IS_NAN,dtype:"bool"}),s0e={kernelName:Gi,backendName:"webgpu",kernelFunc:r0e};function i0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Ku(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var o0e={kernelName:Hi,backendName:"webgpu",kernelFunc:i0e},l0e=ta({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:pde}),u0e={kernelName:ji,backendName:"webgpu",kernelFunc:l0e},d0e=ta({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:dde}),p0e={kernelName:qi,backendName:"webgpu",kernelFunc:d0e},c0e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } - `}};function Fhe(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new Mhe(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var _he={kernelName:Hi,backendName:"webgpu",kernelFunc:Fhe},$he=tt({opType:le.LOG,cpuKernelImpl:Eue}),Dhe={kernelName:ji,backendName:"webgpu",kernelFunc:$he},Phe=tt({opType:le.LOG1P}),Ohe={kernelName:qi,backendName:"webgpu",kernelFunc:Phe},zhe=ta({opType:_e.LOGICAL_AND,dtype:"bool"}),Lhe={kernelName:Xi,backendName:"webgpu",kernelFunc:zhe},Whe=tt({opType:le.LOGICAL_NOT}),Bhe={kernelName:Ki,backendName:"webgpu",kernelFunc:Whe},Vhe=ta({opType:_e.LOGICAL_OR}),Uhe={kernelName:Yi,backendName:"webgpu",kernelFunc:Vhe},hk=` + `}};function h0e(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new c0e(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var m0e={kernelName:Xi,backendName:"webgpu",kernelFunc:h0e},f0e=at({opType:le.LOG,cpuKernelImpl:cde}),g0e={kernelName:Ki,backendName:"webgpu",kernelFunc:f0e},y0e=at({opType:le.LOG1P}),x0e={kernelName:Yi,backendName:"webgpu",kernelFunc:y0e},A0e=ta({opType:Pe.LOGICAL_AND,dtype:"bool"}),b0e={kernelName:Zi,backendName:"webgpu",kernelFunc:A0e},v0e=at({opType:le.LOGICAL_NOT}),w0e={kernelName:Ji,backendName:"webgpu",kernelFunc:v0e},k0e=ta({opType:Pe.LOGICAL_OR}),I0e={kernelName:Qi,backendName:"webgpu",kernelFunc:k0e},Nk=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -7533,8 +7551,8 @@ return a / b;`,MZ=` } else { powValue = exp(log(basis) * (-uniforms.beta)); } -`,Ghe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` - ${de("index")} { +`,S0e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -7551,17 +7569,17 @@ return a / b;`,MZ=` sum = sum + z * z; } } - ${hk} + ${Nk} setOutputAtIndex(index, x * powValue); } } - `}},Hhe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,v.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=ce(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` + `}},C0e=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,v.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=pe(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` var lrnSub: array; const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; const maxAllowRadius = ${this.maxAllowRadius}; - ${de()} { + ${ue()} { let localDepth = i32(localId.x); let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; let xDepth = workgroupDepth + localDepth - maxAllowRadius; @@ -7584,12 +7602,12 @@ return a / b;`,MZ=` let z = lrnSub[index + i]; sum = sum + z * z; } - ${hk} + ${Nk} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } - } `}};function jhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new Ghe(r.shape):u=new Hhe(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var qhe={kernelName:Zi,backendName:"webgpu",kernelFunc:jhe},Xhe=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` - ${de("index")} { + } `}};function T0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new S0e(r.shape):u=new C0e(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var N0e={kernelName:eo,backendName:"webgpu",kernelFunc:T0e},R0e=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -7638,9 +7656,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, result); } } - `}};function Khe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new Xhe(r.shape),d=[{type:"int32",data:[o]},{type:"float32",data:[l]},{type:"float32",data:[u]},{type:"float32",data:[p]}];return a.runWebGPUProgram(c,[r,s,i],r.dtype,d)}var Yhe={kernelName:cu,backendName:"webgpu",kernelFunc:Khe},Zhe=ta({opType:_e.MAX,cpuKernelImpl:Fue}),Jhe={kernelName:Qi,backendName:"webgpu",kernelFunc:Zhe};function Qhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=S.computePool2DInfo(r.shape,s,i,u,o,l);return tk(r,p,"max",a)}var e0e={kernelName:eo,backendName:"webgpu",kernelFunc:Qhe};function t0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Dg(c,"max"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var a0e={kernelName:hu,backendName:"webgpu",kernelFunc:t0e},n0e=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` - ${de("index")} { + `}};function E0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new R0e(r.shape),d=[{type:"int32",data:[o]},{type:"float32",data:[l]},{type:"float32",data:[u]},{type:"float32",data:[p]}];return a.runWebGPUProgram(c,[r,s,i],r.dtype,d)}var M0e={kernelName:gu,backendName:"webgpu",kernelFunc:E0e},_0e=ta({opType:Pe.MAX,cpuKernelImpl:mde}),P0e={kernelName:ao,backendName:"webgpu",kernelFunc:_0e};function $0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=S.computePool2DInfo(r.shape,s,i,u,o,l);return gk(r,p,"max",a)}var F0e={kernelName:no,backendName:"webgpu",kernelFunc:$0e};function D0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Ug(c,"max"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var O0e={kernelName:yu,backendName:"webgpu",kernelFunc:D0e},z0e=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -7683,9 +7701,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}},r0e=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, - outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` - ${de("index")} { + `}},L0e=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -7741,8 +7759,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, dotProd); } } - `}};function s0e(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new Dg(d,"max",!0),f=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.front,d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inDepth,d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]}],m=a.runWebGPUProgram(h,[i],"int32",f),g=new r0e(d);f=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterDepth-1-d.padInfo.front,d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outDepth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,m],i.dtype,f);return a.disposeData(m.dataId),y}var i0e={kernelName:dp,backendName:"webgpu",kernelFunc:s0e};function o0e(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Fg([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=new Kd(d,"max",!0),f=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]}],m=a.runWebGPUProgram(h,[o],"int32",f),g=new n0e(d);f=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,m],o.dtype,f);return a.disposeData(m.dataId),y}var l0e={kernelName:Sh,backendName:"webgpu",kernelFunc:o0e};function u0e(e){let{inputs:t,backend:a,attrs:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=n,{x:l}=t;v.assert(l.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${l.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=S.computePool2DInfo(l.shape,r,s,u,i),c=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]}],d=new Kd(p,"max",!1),h=a.runWebGPUProgram(d,[l],l.dtype,c);d=new Kd(p,"max",!0,!0,o);let f=a.runWebGPUProgram(d,[l],"int32",c);return[h,f]}var d0e={kernelName:pp,backendName:"webgpu",kernelFunc:u0e};function p0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Jo(r,s,i,"min",a)}var c0e={kernelName:ao,backendName:"webgpu",kernelFunc:p0e},h0e=ta({opType:_e.MIN,cpuKernelImpl:_ue}),f0e={kernelName:no,backendName:"webgpu",kernelFunc:h0e},m0e=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Jt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${de("index")} { + `}};function W0e(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new Ug(d,"max",!0),m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.front,d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inDepth,d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[i],"int32",m),g=new L0e(d);m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterDepth-1-d.padInfo.front,d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outDepth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],i.dtype,m);return a.disposeData(f.dataId),y}var B0e={kernelName:gp,backendName:"webgpu",kernelFunc:W0e};function V0e(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Wg([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=new Qd(d,"max",!0),m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[o],"int32",m),g=new z0e(d);m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],o.dtype,m);return a.disposeData(f.dataId),y}var U0e={kernelName:fp,backendName:"webgpu",kernelFunc:V0e};function G0e(e){let{inputs:t,backend:a,attrs:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=n,{x:l}=t;v.assert(l.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${l.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=S.computePool2DInfo(l.shape,r,s,u,i),c=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]}],d=new Qd(p,"max",!1),h=a.runWebGPUProgram(d,[l],l.dtype,c);d=new Qd(p,"max",!0,!0,o);let m=a.runWebGPUProgram(d,[l],"int32",c);return[h,m]}var H0e={kernelName:xu,backendName:"webgpu",kernelFunc:G0e};function j0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return nl(r,s,i,"min",a)}var q0e={kernelName:so,backendName:"webgpu",kernelFunc:j0e},X0e=ta({opType:Pe.MIN,cpuKernelImpl:fde}),K0e={kernelName:io,backendName:"webgpu",kernelFunc:X0e},Y0e=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=$t(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + ${ue("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${a}); @@ -7758,7 +7776,7 @@ return a / b;`,MZ=` setOutputAtIndex(index, getX(${o})); } } - `}},g0e={kernelName:ro,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new m0e(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},y0e=ta({opType:_e.MOD}),x0e={kernelName:fu,backendName:"webgpu",kernelFunc:y0e},A0e=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` + `}},Z0e={kernelName:oo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Y0e(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},J0e=ta({opType:Pe.MOD}),Q0e={kernelName:lo,backendName:"webgpu",kernelFunc:J0e},eme=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { @@ -7769,7 +7787,7 @@ return a / b;`,MZ=` return fract((p3.x + p3.y) * p3.z); } - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -7791,12 +7809,12 @@ return a / b;`,MZ=` setOutputAtIndexI32(index, uniforms.numOutcomes - 1); } } - `}},b0e=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` + `}},tme=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` var buf : array; var rowMaxShared : f32; var rowSumShared : f32; const blockSize = ${this.workgroupSize[0]}; - ${de("index")} { + ${ue("index")} { let row = index / blockSize; let tid = i32(localId.x); let cols = uniforms.outShape[1]; @@ -7850,31 +7868,32 @@ return a / b;`,MZ=` setOutputAtCoords(row, col, value); } } - `}};function fk(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=we({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape)/r.shape[s],r.shape[s]]}}),o=new b0e(i.shape),l=a.runWebGPUProgram(o,[i],r.dtype),u=we({inputs:{x:l},backend:a,attrs:{shape:r.shape}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var v0e={kernelName:$o,backendName:"webgpu",kernelFunc:fk};function w0e(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:fk({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new A0e(u,s),d=[{type:"float32",data:[i]},{type:"int32",data:[p]}],h=a.runWebGPUProgram(c,[l],"int32",d);return o||a.disposeData(l.dataId),h}var k0e={kernelName:so,backendName:"webgpu",kernelFunc:w0e};function I0e(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=Due(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new Hu(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var S0e={kernelName:mu,backendName:"webgpu",kernelFunc:I0e};function C0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=Nn.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var T0e={kernelName:lo,backendName:"webgpu",kernelFunc:C0e};function N0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Nn.nonMaxSuppressionV5Impl(p,c,d,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var R0e={kernelName:uo,backendName:"webgpu",kernelFunc:N0e},E0e=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` - ${de("index")} { + `}};function Rk(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape)/r.shape[s],r.shape[s]]}}),o=new tme(i.shape),l=a.runWebGPUProgram(o,[i],r.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r.shape}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var ame={kernelName:Lo,backendName:"webgpu",kernelFunc:Rk};function nme(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:Rk({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new eme(u,s),d=[{type:"float32",data:[i]},{type:"int32",data:[p]}],h=a.runWebGPUProgram(c,[l],"int32",d);return o||a.disposeData(l.dataId),h}var rme={kernelName:uo,backendName:"webgpu",kernelFunc:nme};function sme(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=yde(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new Ku(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var ime={kernelName:Au,backendName:"webgpu",kernelFunc:sme};function ome(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=Rn.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var lme={kernelName:ho,backendName:"webgpu",kernelFunc:ome};function ume(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Rn.nonMaxSuppressionV5Impl(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var dme={kernelName:mo,backendName:"webgpu",kernelFunc:ume},pme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function M0e(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new E0e(u,i),c=we({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let f=[...r.shape,i],m=we({inputs:{x:h},backend:a,attrs:{shape:f}});return a.disposeData(h.dataId),m}var F0e={kernelName:po,backendName:"webgpu",kernelFunc:M0e};function bh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=jp({inputs:{input:n},backend:a}),s=bh({inputs:{x:r},backend:a}),i=l0({inputs:{input:n},backend:a}),o=bh({inputs:{x:i},backend:a}),l=Zo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Mn({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var _0e={kernelName:Mu,backendName:"webgpu",kernelFunc:bh};function mk(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=jp({inputs:{input:n},backend:a}),s=mk({inputs:{x:r},backend:a}),i=l0({inputs:{input:n},backend:a}),o=bh({inputs:{x:i},backend:a}),l=Zo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Mn({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var $0e={kernelName:yu,backendName:"webgpu",kernelFunc:mk};function D0e(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return P1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=P1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=rk({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var P0e={kernelName:xu,backendName:"webgpu",kernelFunc:D0e},O0e=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Jt(e),a=this.xShape.map((u,p)=>`uniforms.pad${p}[0]`).join(","),n=this.xShape.map((u,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${de("index")} { + `}};function cme(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new pme(u,i),c=ke({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let m=[...r.shape,i],f=ke({inputs:{x:h},backend:a,attrs:{shape:m}});return a.disposeData(h.dataId),f}var hme={kernelName:fo,backendName:"webgpu",kernelFunc:cme};function Sh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Yp({inputs:{input:n},backend:a}),s=Sh({inputs:{x:r},backend:a}),i=d0({inputs:{input:n},backend:a}),o=Sh({inputs:{x:i},backend:a}),l=al({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Mn({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var mme={kernelName:$u,backendName:"webgpu",kernelFunc:Sh};function Ek(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Yp({inputs:{input:n},backend:a}),s=Ek({inputs:{x:r},backend:a}),i=d0({inputs:{input:n},backend:a}),o=Sh({inputs:{x:i},backend:a}),l=al({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Mn({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var fme={kernelName:vu,backendName:"webgpu",kernelFunc:Ek};function gme(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return G1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=G1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=Ak({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var yme={kernelName:wu,backendName:"webgpu",kernelFunc:gme};function Mk(e,t=!1){let a=e.length,n=$t(a),r=e.map((c,d)=>`uniforms.pad${d}[0]`).join(","),s=e.map((c,d)=>`uniforms.pad${d}[0] + uniforms.xShape${a>1?`[${d}]`:""}`).join(","),i=a>1?`${n}(${r})`:`${r}`,o=a>1?`${n}(${s})`:`${s}`,l=a>1?"any(paddedCoords < start)":"paddedCoords < start",u=a>1?"any(paddedCoords >= end)":"paddedCoords >= end",p=a>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a):"coords";return` + let start = ${i}; + let end = ${o}; + if (${l} || ${u}) { + setOutputAtIndex(index, ${t?0:"uniforms.constantValue"}); + } else { + let coords = paddedCoords - start; + setOutputAtIndex(index, getX(${p})); + } + `}var xme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { - let start = ${r}; - let end = ${s}; - let outC = getCoordsFromIndex(index); - - if (${i} || ${o}) { - setOutputAtIndex(index, uniforms.constantValue); - } else { - let coords = outC - start; - setOutputAtIndex(index, getX(${l})); - } + let paddedCoords = getCoordsFromIndex(index); + ${Mk(this.xShape)} } } - `}},gk=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return tn({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Mn({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new O0e(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},z0e={kernelName:co,backendName:"webgpu",kernelFunc:gk},L0e=ta({opType:_e.POW}),W0e={kernelName:ho,backendName:"webgpu",kernelFunc:L0e};function B0e(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new xh(_e.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var V0e={kernelName:fo,backendName:"webgpu",kernelFunc:B0e};function U0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Jo(r,s,i,"prod",a)}var G0e={kernelName:mo,backendName:"webgpu",kernelFunc:U0e},H0e=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=zue(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},j0e={kernelName:Au,backendName:"webgpu",kernelFunc:H0e},q0e=ta({opType:_e.DIV}),X0e={kernelName:Ci,backendName:"webgpu",kernelFunc:q0e},K0e=tt({opType:le.RECIPROCAL}),Y0e={kernelName:go,backendName:"webgpu",kernelFunc:K0e},Z0e=tt({opType:le.RELU}),J0e={kernelName:yo,backendName:"webgpu",kernelFunc:Z0e},Q0e=tt({opType:le.RELU6}),efe={kernelName:bo,backendName:"webgpu",kernelFunc:Q0e},tfe=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` - ${de("index")} { + `}},Ame=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return tn({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Mn({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new xme(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},bme={kernelName:go,backendName:"webgpu",kernelFunc:Ame},vme=ta({opType:Pe.POW}),wme={kernelName:yo,backendName:"webgpu",kernelFunc:vme};function kme(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new kh(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var Ime={kernelName:xo,backendName:"webgpu",kernelFunc:kme};function Sme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return nl(r,s,i,"prod",a)}var Cme={kernelName:Ao,backendName:"webgpu",kernelFunc:Sme},Tme=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=bde(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},Nme={kernelName:ku,backendName:"webgpu",kernelFunc:Tme},Rme=ta({opType:Pe.DIV}),Eme={kernelName:Ni,backendName:"webgpu",kernelFunc:Rme},Mme=at({opType:le.RECIPROCAL}),_me={kernelName:bo,backendName:"webgpu",kernelFunc:Mme},Pme=at({opType:le.RELU}),$me={kernelName:vo,backendName:"webgpu",kernelFunc:Pme},Fme=at({opType:le.RELU6}),Dme={kernelName:Io,backendName:"webgpu",kernelFunc:Fme},Ome=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -7916,9 +7935,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, newValue); } } - `}};function afe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new tfe(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var nfe={kernelName:Ao,backendName:"webgpu",kernelFunc:afe},rfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, - invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return` - ${de("index")} { + `}};function zme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new Ome(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var Lme={kernelName:ko,backendName:"webgpu",kernelFunc:zme},Wme=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, + invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -7991,8 +8010,8 @@ return a / b;`,MZ=` setOutputAtIndex(index, accumulator); } } - `}};function sfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],f=c[1]/d[1],m=1/h,g=1/f,y=Math.ceil(m)*2+2,x=Math.ceil(g)*2+2,A=new rfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[h]},{type:"float32",data:[f]},{type:"float32",data:[m]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var ife={kernelName:wu,backendName:"webgpu",kernelFunc:sfe},ofe=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` - ${de("index")} { + `}};function Bme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new Wme(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[h]},{type:"float32",data:[m]},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var Vme={kernelName:Cu,backendName:"webgpu",kernelFunc:Bme},Ume=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -8022,9 +8041,9 @@ return a / b;`,MZ=` setOutputAtIndex(index, newValue); } } - `}};function lfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new ofe(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var ufe={kernelName:xo,backendName:"webgpu",kernelFunc:lfe},dfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, - winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return` - ${de("index")} { + `}};function Gme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new Ume(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var Hme={kernelName:wo,backendName:"webgpu",kernelFunc:Gme},jme=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, + winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -8082,7 +8101,7 @@ return a / b;`,MZ=` setOutputAtIndex(index, accumulator); } } - `}};function pfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],f=c[1]/d[1],m=1/h,g=1/f,y=Math.ceil(m)*2+2,x=Math.ceil(g)*2+2,A=new dfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[m]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var cfe={kernelName:vu,backendName:"webgpu",kernelFunc:pfe},hfe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function qme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new jme(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var Xme={kernelName:Su,backendName:"webgpu",kernelFunc:qme},Kme=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -8104,7 +8123,7 @@ return a / b;`,MZ=` return reverseCoords; } - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); @@ -8112,9 +8131,9 @@ return a / b;`,MZ=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function ffe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return tn({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=we({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new hfe(l),f=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let m=we({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeData(f.dataId),m}var mfe={kernelName:vo,backendName:"webgpu",kernelFunc:ffe},gfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function Yme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return tn({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new Kme(l),m=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let f=ke({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),f}var Zme={kernelName:So,backendName:"webgpu",kernelFunc:Yme},Jme=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * @@ -8133,7 +8152,7 @@ return a / b;`,MZ=` setOutputAtIndex(index, outputValue); } } - `}},yfe={kernelName:Uo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new gfe(n.shape,s),[u,p]=S.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},xfe=tt({opType:le.ROUND}),Afe={kernelName:wo,backendName:"webgpu",kernelFunc:xfe},bfe=tt({opType:le.RSQRT,cpuKernelImpl:Lue}),vfe={kernelName:ko,backendName:"webgpu",kernelFunc:bfe},Nd=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=be(e),this.dispatch=ce(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=Jt(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` + `}},Qme={kernelName:Xo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new Jme(n.shape,s),[u,p]=S.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},efe=at({opType:le.ROUND}),tfe={kernelName:Co,backendName:"webgpu",kernelFunc:efe},afe=at({opType:le.RSQRT,cpuKernelImpl:vde}),nfe={kernelName:To,backendName:"webgpu",kernelFunc:afe},_d=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=xe(e),this.dispatch=pe(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=$t(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -8150,7 +8169,7 @@ return a / b;`,MZ=` } `);let s=`getUpdates(${Array.from({length:this.updatesRank},(i,o)=>`coords[${o}]`).join(", ")})`;return` ${r} - ${de("index")} { + ${ue("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; @@ -8159,12 +8178,12 @@ return a / b;`,MZ=` flattenedIndex = flattenedIndex + indexInside * ${a}; } let updateValue = - ${Nl(this.type)}(${s}); + ${Pl(this.type)}(${s}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices?Gu("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices?Xu("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function wfe(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=we({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=we({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=Mn({backend:a,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Nd(f.shape,o,h.shape.length,f.shape.length,p,d,m),b=a.runWebGPUProgram(A,[f,h],m,x,g),w=we({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),w}var kfe={kernelName:Io,backendName:"webgpu",kernelFunc:wfe},Ife=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` + }`}};function rfe(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ke({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=m.dtype,g=Mn({backend:a,attrs:{shape:d,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new _d(m.shape,o,h.shape.length,m.shape.length,p,d,f),b=a.runWebGPUProgram(A,[m,h],f,x,g),w=ke({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(b.dataId),w}var sfe={kernelName:No,backendName:"webgpu",kernelFunc:rfe},ife=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -8179,15 +8198,15 @@ return a / b;`,MZ=` return right; } - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function Sfe(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new Ife([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var Cfe={kernelName:Co,backendName:"webgpu",kernelFunc:Sfe},Tfe=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let y=1+s.length;ya.disposeData(y.dataId)),g},Ufe={kernelName:Cu,backendName:"webgpu",kernelFunc:Vfe},Gfe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n{this.uniforms+=` pad${l} : vec2,`}),this.shaderKey=`spaceToBatchND_${r}`}getUserCode(){let e=$t(this.outputShape.length),t=ck(this.newDim);return` + ${nh(this.paddedXShape,"PaddedX")} + ${ue("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape); + let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); + ${Mk(this.xShape,!0)} + } + } + `}},Sfe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let 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Yfe={kernelName:Tu,backendName:"webgpu",kernelFunc:Kfe},Zfe=tt({opType:le.SQRT}),Jfe={kernelName:Fo,backendName:"webgpu",kernelFunc:Zfe},Qfe={kernelName:gp,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new Hu(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},eme=ta({opType:_e.SQUARED_DIFFERENCE}),tme={kernelName:Po,backendName:"webgpu",kernelFunc:eme};function ame({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new Hu(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var nme={kernelName:is,backendName:"webgpu",kernelFunc:ame},rme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Jt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` - ${de("index")} { + `}};function Nfe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=$e(r.shape,r.dtype,l),p=Nde(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Tfe(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var Rfe={kernelName:is,backendName:"webgpu",kernelFunc:Gg};function Efe(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=S.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),M=a.bufferSync(s),P=v.decodeString(a.readSync(i.dataId)[0]),E=wde(N,M,o,d,p,u,l,c,P,h);return a.makeTensorInfo(o,E.dtype,E.values)}let m=[d/p,p],f=ke({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?ke({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):tn({inputs:{x:s},backend:a}),y=g.dtype,x=a.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=ke({inputs:{x:i},backend:a,attrs:{shape:Array(m.length).fill(1)}}),b=Gg({inputs:{x:A},backend:a,attrs:{reps:m}}),w=v.sizeFromShape([u,p]),I=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[w]}];switch(u){case 0:break;case 1:{let N=new _d([u,p],l,f.shape.length,g.shape.length,c,m,y,h);a.runWebGPUProgram(N,[g,f],y,I,b)}break;default:{let N=new _d([u,p],l,f.shape.length,x.shape.length,c,m,y,h);a.runWebGPUProgram(N,[x,f],y,I,b)}{let N=new _d([u,p],l,f.shape.length,g.shape.length,c,m,y);a.runWebGPUProgram(N,[g,f],y,I,b)}}let T=ke({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(f.dataId),a.disposeData(g.dataId),a.disposeData(A.dataId),a.disposeData(x.dataId),a.disposeData(b.dataId),T}var Mfe={kernelName:Wo,backendName:"webgpu",kernelFunc:Efe};function _fe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=S.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=Yu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var Pfe={kernelName:Eu,backendName:"webgpu",kernelFunc:_fe},$fe=at({opType:le.SQRT}),Ffe={kernelName:Oo,backendName:"webgpu",kernelFunc:$fe},Dfe={kernelName:vp,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new Ku(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},Ofe=ta({opType:Pe.SQUARED_DIFFERENCE}),zfe={kernelName:Bo,backendName:"webgpu",kernelFunc:Ofe};function Lfe({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new Ku(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var Wfe={kernelName:os,backendName:"webgpu",kernelFunc:Lfe},Bfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=$t(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } - `}};function sme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(m)w=we({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Nt.computeOutShape(x,A,b),T=ju({inputs:{x:r},backend:a,attrs:{begin:x,size:C}});w=we({inputs:{x:T},backend:a,attrs:{shape:f}}),a.disposeData(T.dataId)}else if(a.shouldExecuteOnCPU([r])){let C=a.readSync(r.dataId),T=$e(r.shape,r.dtype,C),N=Uue(h,T,b,x);w=a.makeTensorInfo(f,r.dtype,N.values)}else{let C=new rme(h),T=[{type:"int32",data:x},{type:"int32",data:b}],N=a.runWebGPUProgram(C,[r],r.dtype,T);w=we({inputs:{x:N},backend:a,attrs:{shape:f}}),a.disposeData(N.dataId)}return w}var ime={kernelName:Oo,backendName:"webgpu",kernelFunc:sme};function ome(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=Gue(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var lme={kernelName:Ru,backendName:"webgpu",kernelFunc:ome},ume=ta({opType:_e.SUB,cpuKernelImpl:Hue,supportsComplex:!0}),dme={kernelName:zo,backendName:"webgpu",kernelFunc:ume},pme=tt({opType:le.TAN}),cme={kernelName:Lo,backendName:"webgpu",kernelFunc:pme},hme=tt({opType:le.TANH}),fme={kernelName:Wo,backendName:"webgpu",kernelFunc:hme};function mme(e){let{inputs:t,backend:a,attrs:n}=e,{tensor:r,indices:s,updates:i}=t,{}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(i,s,r.shape),d=[c/u,u];if(c===0)return a.makeTensorInfo(r.shape,s.dtype);let h=[],f=we({inputs:{x:s},backend:a,attrs:{shape:[l,o]}});h.push(f);let m=we({inputs:{x:i},backend:a,attrs:{shape:[l,u]}});h.push(m);let g=we({inputs:{x:r},backend:a,attrs:{shape:d}});h.push(g);let y=Pg({inputs:{x:g},backend:a,attrs:{reps:Array(d.length).fill(1)}}),x=new Nd([l,u],o,f.shape.length,m.shape.length,p,d,r.dtype,!1),A=v.sizeFromShape([l,u]),b=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[A]}],w=a.runWebGPUProgram(x,[m,f],g.dtype,b,y);h.push(w);let C=we({inputs:{x:w},backend:a,attrs:{shape:r.shape}});return h.forEach(T=>a.disposeData(T.dataId)),C}var gme={kernelName:So,backendName:"webgpu",kernelFunc:mme},yme=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, + `}};function Vfe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=ke({inputs:{x:r},backend:a,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Nt.computeOutShape(x,A,b),T=Yu({inputs:{x:r},backend:a,attrs:{begin:x,size:I}});w=ke({inputs:{x:T},backend:a,attrs:{shape:m}}),a.disposeData(T.dataId)}else if(a.shouldExecuteOnCPU([r])){let I=a.readSync(r.dataId),T=$e(r.shape,r.dtype,I),N=Sde(h,T,b,x);w=a.makeTensorInfo(m,r.dtype,N.values)}else{let I=new Bfe(h),T=[{type:"int32",data:x},{type:"int32",data:b}],N=a.runWebGPUProgram(I,[r],r.dtype,T);w=ke({inputs:{x:N},backend:a,attrs:{shape:m}}),a.disposeData(N.dataId)}return w}var Ufe={kernelName:Vo,backendName:"webgpu",kernelFunc:Vfe};function Gfe(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[m,f]=Cde(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([m.length],"string",m),a.makeTensorInfo(c.shape,"int32",f)]}var Hfe={kernelName:_u,backendName:"webgpu",kernelFunc:Gfe},jfe=ta({opType:Pe.SUB,cpuKernelImpl:Tde,supportsComplex:!0}),qfe={kernelName:Uo,backendName:"webgpu",kernelFunc:jfe},Xfe=at({opType:le.TAN}),Kfe={kernelName:Go,backendName:"webgpu",kernelFunc:Xfe},Yfe=at({opType:le.TANH}),Zfe={kernelName:Ho,backendName:"webgpu",kernelFunc:Yfe};function Jfe(e){let{inputs:t,backend:a,attrs:n}=e,{tensor:r,indices:s,updates:i}=t,{}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(i,s,r.shape),d=[c/u,u];if(c===0)return a.makeTensorInfo(r.shape,s.dtype);let h=[],m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,o]}});h.push(m);let f=ke({inputs:{x:i},backend:a,attrs:{shape:[l,u]}});h.push(f);let g=ke({inputs:{x:r},backend:a,attrs:{shape:d}});h.push(g);let y=Gg({inputs:{x:g},backend:a,attrs:{reps:Array(d.length).fill(1)}}),x=new _d([l,u],o,m.shape.length,f.shape.length,p,d,r.dtype,!1),A=v.sizeFromShape([l,u]),b=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[A]}],w=a.runWebGPUProgram(x,[f,m],g.dtype,b,y);h.push(w);let I=ke({inputs:{x:w},backend:a,attrs:{shape:r.shape}});return h.forEach(T=>a.disposeData(T.dataId)),I}var Qfe={kernelName:Ro,backendName:"webgpu",kernelFunc:Jfe},e2e=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -8280,8 +8309,8 @@ return a / b;`,MZ=` } } } - `}},xme=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=be(this.outputShape),this.dispatch=ce(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` - ${de("index")} { + `}},t2e=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` + ${ue("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -8339,7 +8368,7 @@ return a / b;`,MZ=` } } } - `}};function Sl(e,t){t!==null&&e.disposeData(t.dataId)}function Gx(e){let t=1;for(;th===null?[p,p]:[p,h],m=(b,w,C)=>{let T=f(),N=new 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y=Tk({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});El(a,p);let x=o.slice(0,-1);x.push(s),g=h,h=ke({inputs:{x:h},attrs:{shape:x},backend:a}),El(a,g);let A=y;return y=ke({inputs:{x:y},attrs:{shape:x},backend:a}),El(a,A),[y,h]}var n2e={kernelName:jo,backendName:"webgpu",kernelFunc:a2e},r2e=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=xe(this.outputShape),this.dispatch=pe(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { @@ -8403,7 +8432,7 @@ return a / b;`,MZ=` return outputValue; } - ${de("index")} { + ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; @@ -8455,9 +8484,9 @@ return a / 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xk=` + `}};function d2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,segmentIds:s}=t,{numSegments:i}=n,o=r.shape.length,l=[],u=0,p=S.getAxesPermutation([u],o),c=r;p!=null&&(c=ar({inputs:{x:r},backend:a,attrs:{perm:p}}),l.push(c),u=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(c.shape,u,i),h=v.sizeFromShape([c.shape[u]]),m=ke({inputs:{x:c},backend:a,attrs:{shape:[-1,h]}});l.push(m);let f=r.dtype,g=[m.shape[0],i],y=Mn({backend:a,attrs:{shape:g,value:0,dtype:f}}),x=new u2e(m.shape,g,f),A=[{type:"int32",data:[i]},{type:"int32",data:[v.sizeFromShape(m.shape)]}],b=a.runWebGPUProgram(x,[m,s],f,A,y),w=ke({inputs:{x:b},backend:a,attrs:{shape:d}});l.push(b);let I=w;if(p!=null){l.push(w);let T=S.getUndoAxesPermutation(p);I=ar({inputs:{x:I},backend:a,attrs:{perm:T}})}return l.forEach(T=>a.disposeData(T.dataId)),I}var 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t.debug&&a==="config"&&n.length>0&&K("invalid configuration",n),n}function Et(...e){let t=a=>a&&typeof a=="object";return e.reduce((a,n)=>(Object.keys(n||{}).forEach(r=>{let s=a[r],i=n[r];Array.isArray(s)&&Array.isArray(i)?a[r]=s.concat(...i):t(s)&&t(i)?a[r]=Et(s,i):a[r]=i}),a),{})}var 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Pk=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -8482,7 +8511,7 @@ return a / b;`,MZ=` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var Ak=` +`;var $k=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8494,7 +8523,7 @@ return a / b;`,MZ=` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,bk=` +`,Fk=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8506,7 +8535,7 @@ return a / b;`,MZ=` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,vk=` +`,Dk=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -8519,7 +8548,7 @@ return a / b;`,MZ=` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,wk=` +`,Ok=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8542,7 +8571,7 @@ return a / 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g0=class{constructor(){he(this,"config");he(this,"element");he(this,"stream");he(this,"devices",[]);he(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(a=>a.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});he(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let i=document.getElementById(t.element);if(i&&i instanceof HTMLVideoElement)this.element=i;else return this.config.debug&&K("webcam","cannot get dom element",t.element),`webcam error: cannot get dom element: ${t.element}`}else if(t.element instanceof 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a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Jg(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Jg(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function s3e(e,t){if(rt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;ae.mesh[r]);Zg(t,n,rt)}a3e(e,t)}}function i3e(e,t){if(rt.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let a=0;a0&&(i3e(r,n),s3e(r,n),n3e(r,n),r3e(r,n))}}function A0(e,t,a){var s,i;let n=Et(Dt,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round";for(let o=0;o0)){let l=n.bodyLabels.slice();l=ut(l,"[id]",t[o].id.toFixed(0)),l=ut(l,"[score]",100*t[o].score),vn(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=ut(u,"[label]",l.part),u=ut(u,"[score]",100*l.score),vn(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)qk(r,u,n)}}}function b0(e,t,a){var s,i;let n=Et(Dt,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=ut(l,"[id]",o.id.toFixed(0)),l=ut(l,"[label]",o.label),l=ut(l,"[score]",100*o.score),vn(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=il(l[2],n),ur(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=ut(p,"[label]",l),vn(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function v0(e,t,a){var s;let n=Et(Dt,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=ut(o,"[id]",i.id.toFixed(0)),o=ut(o,"[label]",i.label),o=ut(o,"[score]",100*i.score),vn(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function w0(e,t,a){var r;let n=Et(Dt,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=bn(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=ut(c,"[where]",l[0]),c=ut(c,"[who]",p),c=ut(c,"[what]",u[1]),vn(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var gs={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -8586,7 +8615,7 @@ return a / b;`,MZ=` live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: 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vn,il=224,Dk,N1e=5,k0=[8,16,32,32,32];function R1e(){let e=[],t=0;for(;ta.x)),y:jt(e.map(a=>a.y))}}async function Pk(e){if(ne.initial&&(vn=null),!vn&&e.body.detector&&e.body.detector.modelPath){vn=await Fe(e.body.detector.modelPath);let t=vn!=null&&vn.executor?Object.values(vn.modelSignature.inputs):void 0;il=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&vn&&K("cached model:",vn.modelUrl);return R1e(),vn}var $k=[5,5];function E1e(e,t){return Pe(()=>{let a=Ca(e,12,1),n=Oe(a[0]),r=Oe(a[1]),s=Oe(a[2]),i=Oe(a[3]);n=ke(ve(n,il),t.x),r=ke(ve(r,il),t.y),s=te(ve(s,il),$k[0]),i=te(ve(i,il),$k[1]);let o=ye(n,ve(s,2)),l=ye(r,ve(i,2)),u=ke(o,s),p=ke(l,i);return ca([o,l,u,p],1)})}async function M1e(e,t,a,n){var u,p;let r=[],s={};s.boxes=E1e(e,Dk),s.scores=Wa(t),s.nms=await fe.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await 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a=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function I0(e,t){let a=[e[2]*t,e[3]*t];return[e[0]-(a[0]-e[2])/2,e[1]-(a[1]-e[3])/2,a[0],a[1]]}var Ga,Qg=256,Jg=Number.MAX_SAFE_INTEGER,F1e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},C0=[],ys=[[0,0],[0,0],[0,0],[0,0]],Lk=0,Wk=e=>1-1/(1+Math.exp(e)),Vk=e=>Pk(e);async function Uk(e){if(ne.initial&&(Ga=null),Ga)e.debug&&K("cached model:",Ga.modelUrl);else{Ga=await Fe(e.body.modelPath);let t=Ga!=null&&Ga.executor?Object.values(Ga.modelSignature.inputs):void 0;Qg=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Ga}function Bk(e,t,a){var s,i;let n={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((i=e==null?void 0:e.shape)!=null&&i[2]))return e;let r;if(a&&(n.cropped=fe.cropAndResize(e,[a],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ys=[[0,0],o,l,[0,0]],n.pad=ir(n.cropped||e,ys),n.resize=fe.resizeBilinear(n.pad,[t,t]),r=ve(n.resize,ze.tf255)}else e.shape[1]!==t?(n.resize=fe.resizeBilinear(n.cropped||e,[t,t]),r=ve(n.resize,ze.tf255)):r=ve(n.cropped||e,ze.tf255);return Object.keys(n).forEach(o=>J(n[o])),r}function _1e(e,t,a){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+ys[2][0]+ys[2][1])/t[0]-ys[2][0]),Math.trunc(n.position[1]*(t[1]+ys[1][0]+ys[1][1])/t[1]-ys[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(a){let n=a[2]-a[0],r=a[3]-a[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+a[1],s.positionRaw[1]/n+a[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function $1e(e){let t=e.find(o=>o.part==="leftPalm"),a=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((a.position[2]||0)+(n.position[2]||0))/2;let r=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");r.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function D1e(e,t,a){if(!(Ga!=null&&Ga.executor))return null;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=Ga==null?void 0:Ga.execute(e,F1e.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>J(n[f]));let o=[],l=5;for(let f=0;ff.position),c=gs(p,[a[0],a[1]]),d={};for(let[f,m]of Object.entries(Zg)){let g=[];for(let 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ry=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],sy={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mt,Xk=0,Fa={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},iy=Number.MAX_SAFE_INTEGER;async function Kk(e){return ne.initial&&(Mt=null),Mt?e.debug&&K("cached model:",Mt.modelUrl):Mt=await Fe(e.body.modelPath),Mt}async function O1e(e,t){let[a,n]=e.shape,r=Q(e,[n*a]),s=ga(r,0),i=(await s.data())[0];if(i>t){let o=rr(r,0),l=_u(o,a),u=(await l.data())[0],p=ve(o,a),c=(await p.data())[0];return J([r,s,o,l,p]),[u,c,i]}return J([r,s]),[0,0,i]}async function oy(e,t){if(!(Mt!=null&&Mt.executor)||!(Mt!=null&&Mt.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-Xk,n=iy<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(Fa.keypoints).length>0?(iy++,[Fa]):(iy=0,new Promise(async r=>{let s=Pe(()=>{var f,m;let c=fe.resizeBilinear(e,[((f=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:f[2])||0,((m=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:m[1])||0],!1),d=te(c,ze.tf2);return ye(d,ze.tf1)}),i;if(t.body.enabled&&(i=Mt==null?void 0:Mt.execute(s)),Xk=ae(),J(s),i){Fa.keypoints.length=0;let c=Oe(i);J(i);let d=Ra(c,2);J(c);for(let h=0;h(t.body.minConfidence||0)&&Fa.keypoints.push({score:Math.round(100*g)/100,part:ry[h],positionRaw:[f/Mt.inputs[0].shape[2],m/Mt.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/Mt.inputs[0].shape[2]),Math.round(e.shape[1]*m/Mt.inputs[0].shape[1])]})}d.forEach(h=>J(h))}Fa.score=Fa.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let 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Ku=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],N0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],R0=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],E0=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],Qk=(e,t,a)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(i=>[(i[0]+a[0])*t[0],(i[1]+a[1])*t[1]]);return{startPoint:n,endPoint:r,landmarks:s,confidence:e.confidence}},ly=(e,t,a)=>{let n=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r],i=fe.cropAndResize(t,[s],[0],a),o=ve(i,ze.tf255);return J(i),o},M0=(e,t)=>{let a=N0(e),n=Ku(e),r=[t*n[0]/2,t*n[1]/2];return{startPoint:[a[0]-r[0],a[1]-r[1]],endPoint:[a[0]+r[0],a[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:n}},F0=e=>{let t=N0(e),a=Ku(e),n=Math.max(...a)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(a[0]),Math.round(a[1])]}},e9=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...a)],endPoint:[Math.max(...t),Math.max(...a)],landmarks:e}},uy=[[1,0,0],[0,1,0],[0,0,1]],z1e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),L1e=(e,t)=>z1e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var Zk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],ll=(e,t)=>{let a=0;for(let n=0;n{let a=[];for(let n=0;n{let 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u=N0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=fe.rotateWithOffset(a,s,0,[p[0],p[1]]);i=t9(-s,u),o=ly(t,c,[n,n]),J(c)}else o=ly(t,a,[n,n]);else o=ly(t,a,[n,n]);return[s,i,o]}var U1e=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},s9=(e,t)=>{let a=U1e(e),n=Ku(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var i9=6,G1e=1.4,Vn,_0=null,xs=0,Yu=null,o9=()=>xs;async function l9(e){var t;return ne.initial&&(Vn=null),Vn?e.debug&&K("cached model:",Vn.modelUrl):Vn=await Fe((t=e.face.detector)==null?void 0:t.modelPath),xs=Vn.executor&&Vn.inputs[0].shape?Vn.inputs[0].shape[2]:256,Yu=Ue(xs,"int32"),_0=Zn(a9(xs)),Vn}function H1e(e){if(!_0||!Yu)return gn([0,0]);let t={};t.boxStarts=De(e,[0,1],[-1,2]),t.centers=ke(t.boxStarts,_0),t.boxSizes=De(e,[0,3],[-1,2]),t.boxSizesNormalized=ve(t.boxSizes,Yu),t.centersNormalized=ve(t.centers,Yu),t.halfBoxSize=ve(t.boxSizesNormalized,ze.tf2),t.starts=ye(t.centersNormalized,t.halfBoxSize),t.ends=ke(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,Yu),t.endNormalized=te(t.ends,Yu);let a=Fu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function u9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=fe.resizeBilinear(e,[xs,xs]),a.div=ve(a.resized,ze.tf127),a.normalized=ye(a.div,ze.tf05);let n=Vn==null?void 0:Vn.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((f,m)=>f.size-m.size);a.concat384=lt([h[0],h[2]],2),a.concat512=lt([h[1],h[3]],2),a.concat=lt([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(n)?a.batch=Oe(n[0]):a.batch=Oe(n);J(n),a.boxes=H1e(a.batch),a.logits=De(a.batch,[0,0],[-1,1]),a.sigmoid=Wa(a.logits),a.scores=Oe(a.sigmoid),a.nms=await fe.nonMaxSuppressionAsync(a.boxes,a.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let r=await a.nms.array(),s=[],i=await a.scores.data();for(let h=0;h(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m={};m.bbox=De(a.boxes,[r[h],0],[1,-1]),m.slice=De(a.batch,[r[h],i9-1],[1,-1]),m.squeeze=Oe(m.slice),m.landmarks=Q(m.squeeze,[i9,-1]);let g=await m.bbox.data(),y={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await m.landmarks.array(),confidence:f};m.anchor=De(_0,[r[h],0],[1,2]);let x=await m.anchor.data(),A=Qk(y,[(e.shape[2]||0)/xs,(e.shape[1]||0)/xs],x),b=M0(A,t.face.scale||G1e),w=F0(b);w.size[0]>(((c=t.face.detector)==null?void 0:c.minSize)||0)&&w.size[1]>(((d=t.face.detector)==null?void 0:d.minSize)||0)&&s.push(w),Object.keys(m).forEach(C=>J(m[C]))}}return Object.keys(a).forEach(h=>J(a[h])),s}var nn,As=0,j1e=2.3,py=_n.leftEyeLower0,cy=_n.rightEyeLower0,Zu={leftBounds:[py[0],py[py.length-1]],rightBounds:[cy[0],cy[cy.length-1]]},Ju={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function f9(e){var t,a;return ne.initial&&(nn=null),nn?e.debug&&K("cached model:",nn.modelUrl):nn=await Fe((t=e.face.iris)==null?void 0:t.modelPath),As=nn!=null&&nn.executor&&((a=nn.inputs)!=null&&a[0].shape)?nn.inputs[0].shape[2]:0,As===-1&&(As=64),nn}function $0(e,t,a,n){for(let r=0;r{let t=e[Zu.leftBounds[0]][2],a=e[Zu.rightBounds[0]][2];return t-a},p9=(e,t,a,n,r,s=!1)=>{let i=F0(M0(e9([e[a],e[n]]),j1e)),o=Ku(i),l=fe.cropAndResize(t,[[i.startPoint[1]/r,i.startPoint[0]/r,i.endPoint[1]/r,i.endPoint[0]/r]],[0],[As,As]);if(s&&ne.kernels.includes("flipleftright")){let u=fe.flipLeftRight(l);J(l),l=u}return{box:i,boxSize:o,crop:l}},c9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s{let n=e[_n[`${a}EyeUpper0`][Ju.upperCenter]][2],r=e[_n[`${a}EyeLower0`][Ju.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function m9(e,t,a){if(!(nn!=null&&nn.executor))return e;let{box:n,boxSize:r,crop:s}=p9(e,t,Zu.leftBounds[0],Zu.leftBounds[1],a,!0),{box:i,boxSize:o,crop:l}=p9(e,t,Zu.rightBounds[0],Zu.rightBounds[1],a,!0),u=lt([s,l]);J(s),J(l);let p=nn.execute(u);J(u);let c=await p.data();J(p);let d=c.slice(0,Ju.numCoordinates*3),{rawCoords:h,iris:f}=c9(d,n,r,!0),m=c.slice(Ju.numCoordinates*3),{rawCoords:g,iris:y}=c9(m,i,o,!1),x=q1e(e);Math.abs(x)<30?($0(e,h,"left",null),$0(e,g,"right",null)):x<1?$0(e,h,"left",["EyeUpper0","EyeLower0"]):$0(e,g,"right",["EyeUpper0","EyeLower0"]);let A=h9(e,f,"left"),b=h9(e,y,"right");return e.concat(A).concat(b)}async function y9(e,t){var s,i,o,l,u,p,c,d,h,f;let a={lips:await((i=(s=t.filter(m=>m.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(m=>m.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(m=>m.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(m=>m.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((f=(h=t.filter(m=>m.size===142))==null?void 0:h[1])==null?void 0:f.data())};for(let m of Object.values(a))if(!m)return e;let n=rl.reduce((m,g)=>m+=e[g][2],0)/rl.length;for(let m=0;mm+=e[g][2],0)/sl.length;for(let m=0;mae()-dr.timestamp,n=dr.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||dr.boxes.length===0?(dr.boxes=await u9(e,t),dr.timestamp=ae(),dr.skipped=0):dr.skipped++;let r=[],s=[],i=0,o=tc;for(let x=0;x[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/o]);for(let T of Object.keys(al))C.annotations[T]=[C.mesh[al[T]]]}else if(!Ct)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(C.tensor),r;let T=Ct.execute(C.tensor),M=await T.find(F=>F.shape[F.shape.length-1]===1).data();if(C.faceScore=Math.round(100*M[0])/100,C.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(A.confidence=C.faceScore,t.face.mesh.keepInvalid){C.box=R0(A,e),C.boxRaw=E0(A,e),C.score=C.boxScore,C.mesh=A.landmarks,C.meshRaw=C.mesh.map(F=>[F[0]/(e.shape[2]||1),F[1]/(e.shape[1]||1),(F[2]||0)/o]);for(let F of Object.keys(al))C.annotations[F]=[C.mesh[al[F]]]}}else{let F=T.find(O=>O.shape[O.shape.length-1]===1404),E=Q(F,[-1,3]),I=await E.array();J(E),(m=t.face.attention)!=null&&m.enabled?I=await y9(I,T):(g=t.face.iris)!=null&&g.enabled&&(I=await m9(I,C.tensor,tc)),C.mesh=n9(I,A,b,w,tc),C.meshRaw=C.mesh.map(O=>[O[0]/(e.shape[2]||0),O[1]/(e.shape[1]||0),(O[2]||0)/o]);for(let O of Object.keys(_n))C.annotations[O]=_n[O].map(B=>C.mesh[B]);C.score=C.faceScore;let _={...s9(C.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};C.box=R0(_,e),C.boxRaw=E0(_,e),s.push(_)}J(T)}C.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(C):J(C.tensor)}return dr.boxes=s,r}async function A9(e){var t,a,n,r,s,i;return ne.initial&&(Ct=null),(t=e.face.attention)!=null&&t.enabled&&(Ct!=null&&Ct.signature)&&Object.keys(((a=Ct==null?void 0:Ct.signature)==null?void 0:a.outputs)||{}).length<6&&(Ct=null),Ct?e.debug&&K("cached model:",Ct.modelUrl):(n=e.face.attention)!=null&&n.enabled?Ct=await Fe(e.face.attention.modelPath):Ct=await Fe((r=e.face.mesh)==null?void 0:r.modelPath),tc=Ct.executor&&((s=Ct==null?void 0:Ct.inputs)!=null&&s[0].shape)?(i=Ct==null?void 0:Ct.inputs)==null?void 0:i[0].shape[2]:256,Ct}var b9=nl,v9=Qp;var my=[],ra,D0=[],w9=0,k9=0,fy=Number.MAX_SAFE_INTEGER,gy=!1;async function I9(e){var t,a,n;return ne.initial&&(ra=null),ra?e.debug&&K("cached model:",ra.modelUrl):(ra=await Fe((t=e.face.emotion)==null?void 0:t.modelPath),gy=((n=(a=ra==null?void 0:ra.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,gy?my=["angry","disgust","fear","happy","neutral","sad","surprise"]:my=["angry","disgust","fear","happy","sad","surprise","neutral"]),ra}async function yy(e,t,a,n){var i,o;if(!ra)return[];let r=fy<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-k9;return t.skipAllowed&&s&&r&&w9===n&&D0[a]&&D0[a].length>0?(fy++,D0[a]):(fy=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},f=ra!=null&&ra.inputs[0].shape?ra.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=fe.cropAndResize(e,y,[0],[f,f])}else h.resize=fe.resizeBilinear(e,[f,f],!1);gy?(h.mul=te(h.resize,255),h.normalize=ye(h.mul,[103.939,116.779,123.68]),h.emotion=ra==null?void 0:ra.execute(h.normalize)):(h.channels=te(h.resize,ze.rgb),h.grayscale=ot(h.channels,3,!0),h.grayscaleSub=ye(h.grayscale,ze.tf05),h.grayscaleMul=te(h.grayscaleSub,ze.tf2),h.emotion=ra==null?void 0:ra.execute(h.grayscaleMul)),k9=ae();let m=await h.emotion.data();for(let g=0;g(t.face.emotion.minConfidence||0)&&u.push({score:Math.min(.99,Math.trunc(100*m[g])/100),emotion:my[g]});u.sort((g,y)=>y.score-g.score),Object.keys(h).forEach(g=>J(h[g]))}D0[a]=u,w9=n,l(u)}))}var sa,bs=[],C9=0,T9=0,xy=Number.MAX_SAFE_INTEGER;async function N9(e){var t;return ne.initial&&(sa=null),sa?e.debug&&K("cached model:",sa.modelUrl):sa=await Fe((t=e.face.description)==null?void 0:t.modelPath),sa}function K1e(e,t){var s,i;let a=e.image||e.tensor||e;if(!(sa!=null&&sa.inputs[0].shape))return a;let n;if(((s=t.face.description)==null?void 0:s.crop)>0){let o=(i=t.face.description)==null?void 0:i.crop,l=[[o,o,1-o,1-o]];n=fe.cropAndResize(a,l,[0],[sa.inputs[0].shape[2],sa.inputs[0].shape[1]])}else n=fe.resizeBilinear(a,[sa.inputs[0].shape[2],sa.inputs[0].shape[1]],!1);let r=te(n,ze.tf255);return J(n),r}async function Ay(e,t,a,n){var o,l,u,p;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(sa!=null&&sa.executor))return r;let s=xy<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-C9;return t.skipAllowed&&s&&i&&T9===n&&((u=bs==null?void 0:bs[a])==null?void 0:u.age)>0&&((p=bs==null?void 0:bs[a])==null?void 0:p.genderScore)>0?(xy++,bs[a]):(xy=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=K1e(e,t),f=sa==null?void 0:sa.execute(h);C9=ae(),J(h);let g=await f.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=rr(f.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let w=await f.find(N=>N.shape[1]===100).data();r.age=Math.round(w[A-1]>w[A+1]?10*A-100*w[A-1]:10*A+100*w[A+1])/10,(Number.isNaN(g[0])||Number.isNaN(w[0]))&&K("faceres error:",{model:sa,result:f});let C=f.find(N=>N.shape[1]===1024),T=C?await C.data():[];r.descriptor=Array.from(T),f.forEach(N=>J(N))}bs[a]=r,T9=n,c(r)}))}var Qu=.1,by=.5;function Y1e(e,t,a){let n=!1,r=a.length-1;for(let s=0;st!=a[r].y>t&&e<(a[r].x-a[s].x)*(t-a[s].y)/(a[r].y-a[s].y)+a[s].x&&(n=!n);return n}async function E9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,a=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let i of _n.silhouette)r.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Qu&&Qu>0&&(r=r.map(i=>({x:i.x>.5?i.x+Qu:i.x-Qu,y:i.y>.5?i.y+Qu:i.y-Qu})));for(let i=0;iae()-F9,s=vy<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&M9===n&&P0[a]?(vy++,P0[a]):(vy=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[2]:0,ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[1]:0],!1),p=ia==null?void 0:ia.execute(u),c=(await p.data())[0];P0[a]=Math.round(100*c)/100,M9=n,F9=ae(),J([u,p]),l(P0[a])}))}var oa,O0=[],ky=Number.MAX_SAFE_INTEGER,D9=0,P9=0;async function O9(e){var t;return ne.initial&&(oa=null),oa?e.debug&&K("cached model:",oa.modelUrl):oa=await Fe((t=e.face.liveness)==null?void 0:t.modelPath),oa}async function Iy(e,t,a,n){var i,o;if(!(oa!=null&&oa.executor))return 0;let r=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>ae()-P9,s=ky<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&D9===n&&O0[a]?(ky++,O0[a]):(ky=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[2]:0,oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[1]:0],!1),p=oa==null?void 0:oa.execute(u),c=(await p.data())[0];O0[a]=Math.round(100*c)/100,D9=n,P9=ae(),J([u,p]),l(O0[a])}))}var $n,Sy=[],J1e=["white","black","asian","indian","other"],Q1e=[15,23,28,35.5,45.5,55.5,65],L9=0,W9=0,Cy=Number.MAX_SAFE_INTEGER;async function B9(e){var t;return ne.initial&&($n=null),$n?e.debug&&K("cached model:",$n.modelUrl):$n=await Fe((t=e.face.gear)==null?void 0:t.modelPath),$n}async function Ty(e,t,a,n){var i,o;if(!$n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Cy<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-W9;return t.skipAllowed&&s&&r&&L9===n&&Sy[a]?(Cy++,Sy[a]):(Cy=0,new Promise(async l=>{var y,x,A,b;if(!($n!=null&&$n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let w=(x=t.face.gear)==null?void 0:x.crop;p=[[w,w,1-w,1-w]]}u.resize=fe.cropAndResize(e,p,[0],[$n.inputs[0].shape[2],$n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=$n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let w=0;w(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[w])/100,race:J1e[w]});c.race.sort((w,C)=>C.score-w.score);let m=Array.from(await u.age.data()).map((w,C)=>[Q1e[C],w]).sort((w,C)=>C[1]-w[1]),g=m[0][0];for(let w=1;wJ(u[w])),Sy[a]=c,L9=n,W9=ae(),l(c)}))}var _a,z0=[],U9=0,G9=0,Ny=Number.MAX_SAFE_INTEGER;async function H9(e){return ne.initial&&(_a=null),_a?e.debug&&K("cached model:",_a.modelUrl):_a=await Fe(e.face.ssrnet.modelPathAge),_a}async function Ry(e,t,a,n){var i,o,l,u;if(!_a)return{age:0};let r=Ny<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-G9;return t.skipAllowed&&r&&s&&U9===n&&((l=z0[a])!=null&&l.age)&&((u=z0[a])==null?void 0:u.age)>0?(Ny++,z0[a]):(Ny=0,new Promise(async p=>{var h,f,m;if(!(_a!=null&&_a.inputs)||!_a.inputs[0]||!_a.inputs[0].shape)return;let c={};if(((h=t.face.ssrnet)==null?void 0:h.crop)>0){let g=(f=t.face.ssrnet)==null?void 0:f.crop,y=[[g,g,1-g,1-g]];c.resize=fe.cropAndResize(e,y,[0],[_a.inputs[0].shape[2],_a.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[_a.inputs[0].shape[2],_a.inputs[0].shape[1]],!1);c.enhance=te(c.resize,ze.tf255);let d={age:0};if((m=t.face.ssrnet)!=null&&m.enabled&&(c.age=_a.execute(c.enhance)),c.age){let g=await c.age.data();d.age=Math.trunc(10*g[0])/10}Object.keys(c).forEach(g=>J(c[g])),z0[a]=d,U9=n,G9=ae(),p(d)}))}var Aa,L0=[],q9=0,X9=0,Ey=Number.MAX_SAFE_INTEGER,My=[.2989,.587,.114];async function K9(e){var t;return ne.initial&&(Aa=null),Aa?e.debug&&K("cached model:",Aa.modelUrl):Aa=await Fe((t=e.face.ssrnet)==null?void 0:t.modelPathGender),Aa}async function Fy(e,t,a,n){var i,o,l,u;if(!Aa)return{gender:"unknown",genderScore:0};let r=Ey<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-X9;return t.skipAllowed&&r&&s&&q9===n&&((l=L0[a])!=null&&l.gender)&&((u=L0[a])==null?void 0:u.genderScore)>0?(Ey++,L0[a]):(Ey=0,new Promise(async p=>{var f,m,g;if(!(Aa!=null&&Aa.inputs[0].shape))return;let c={};if(((f=t.face.ssrnet)==null?void 0:f.crop)>0){let y=(m=t.face.ssrnet)==null?void 0:m.crop,x=[[y,y,1-y,1-y]];c.resize=fe.cropAndResize(e,x,[0],[Aa.inputs[0].shape[2],Aa.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[Aa.inputs[0].shape[2],Aa.inputs[0].shape[1]],!1);c.enhance=Pe(()=>{var x,A;let y;if(((A=(x=Aa==null?void 0:Aa.inputs)==null?void 0:x[0].shape)==null?void 0:A[3])===1){let[b,w,C]=Ca(c.resize,3,3),T=te(b,My[0]),N=te(w,My[1]),M=te(C,My[2]),F=Eh([T,N,M]);y=te(ye(F,ze.tf05),2)}else y=te(ye(c.resize,ze.tf05),2);return y});let 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$a.all)a[$a.getName(n)]={curl:ws.getName(t.curls[n]),direction:Ft.getName(t.directions[n])};return a}function gI(e){let t=[];if(!e||e.length===0)return t;let a=mI(e);for(let n of uI){let r=n.matchAgainst(a.curls,a.directions);r>=s3e&&t.push({name:n.name,confidence:r})}return t}var yI=e=>{if(!e)return[];let t=[];for(let a=0;al.part==="leftWrist"),r=e[a].keypoints.find(l=>l.part==="rightWrist"),s=e[a].keypoints.find(l=>l.part==="nose");s&&n&&r&&n.position[1]l.part==="leftShoulder"),o=e[a].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:a,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},xI=e=>{if(!e)return[];let t=[];for(let a=0;a450){let n=(e[a].mesh[33][2]||0)-(e[a].mesh[263][2]||0),r=e[a].mesh[33][0]-e[a].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:a,gesture:"facing center"}):t.push({face:a,gesture:`facing 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y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],x=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},bI=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function V0(e,t=1.5){let a=ac(e),n=B0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function U0(e){let t=ac(e),a=B0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function d3e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function SI(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return d3e(a)}var vI=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Cs(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Zn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=jt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=jt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=De(t,[0,0],[-1,2]),a.boxSizes=De(t,[0,2],[-1,2]),a.div=ve(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=ke(a.div,this.anchorsTensor),a.halfBoxSizes=ve(a.boxSizes,this.doubleInputSizeTensor),a.sub=ye(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=te(a.sub,this.inputSizeTensor),a.add=ke(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=te(a.add,this.inputSizeTensor);let n=Fu([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=ve(n.reshape,this.inputSizeTensor),n.landmarks=ke(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=fe.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=ve(n.resize,ze.tf127),n.image=ye(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=De(n.predictions,[0,0],[-1,1]),n.sigmoid=Wa(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=De(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await fe.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=De(n.norm,[l,0],[1,-1]),u.slice=De(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},m=II(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var f3e=5,RI=1.65,EI=[0,5,9,13,17,1,2],m3e=0,g3e=2,MI=0,H0=class{constructor(t,a){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>By([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return V0(U0(r),f3e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=V0(U0(a),RI);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Wy(n,[0,0]),u=o.map(h=>[...By(h,l),h[2]]),p=CI(r),c=[...ac(a),1],d=[Cs(c,p[0]),Cs(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-MI,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=Q(A,[-1,3]),C=await w.array();J(A),J(w);let T=this.transformRawCoords(C,m,p,f),N=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...N,confidence:b};let M={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(M)}else this.storedBoxes[l]=null;J(A)}else{let p=V0(U0(u),RI),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var FI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},cl,hl,_I;async function Vy(e,t){let a=await _I.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=W0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function $I(e){var a,n;ne.initial&&(cl=null,hl=null),!cl||!hl?[cl,hl]=await Promise.all([e.hand.enabled?Fe((a=e.hand.detector)==null?void 0:a.modelPath):null,e.hand.landmarks?Fe((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&K("cached model:",cl.modelUrl),e.debug&&K("cached model:",hl.modelUrl));let t=cl?new G0(cl):void 0;return t&&hl&&(_I=new H0(t,hl)),[cl,hl]}var Ot=[null,null],x3e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ts=[[0,0],[0,0]],A3e=["hand","fist","pinch","point","face","tip","pinchtip"],PI=4,OI=1.6,b3e=512,v3e=1.4,j0=Number.MAX_SAFE_INTEGER,Uy=0,$r=[0,0],Pt={boxes:[],hands:[]},zI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function LI(e){var t;if(ne.initial&&(Ot[0]=null),Ot[0])e.debug&&K("cached model:",Ot[0].modelUrl);else{m0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ot[0]=await Fe((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ot[0].executor?Object.values(Ot[0].modelSignature.inputs):void 0;Ts[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ts[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[0]}async function WI(e){var t;if(ne.initial&&(Ot[1]=null),Ot[1])e.debug&&K("cached model:",Ot[1].modelUrl);else{Ot[1]=await Fe((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ot[1].executor?Object.values(Ot[1].modelSignature.inputs):void 0;Ts[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ts[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[1]}async function w3e(e,t){let a=[];if(!e||!Ot[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,b3e),i=Math.round(s*r/8)*8;n.resize=fe.resizeBilinear(e,[s,i]),n.cast=Ke(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ot[0].executeAsync(n.cast,x3e),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Ra(n.scores,1);J(o[PI]),o.splice(PI,1),n.filtered=ca(o,1),J(o),n.max=ga(n.filtered,1),n.argmax=rr(n.filtered,1);let l=0;n.nms=await fe.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=De(n.boxes,d,1),f=await h.data();J(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=I0(m,v3e),y=[Math.trunc(m[0]*$r[0]),Math.trunc(m[1]*$r[1]),Math.trunc(m[2]*$r[0]),Math.trunc(m[3]*$r[1])],x=p[d],A=A3e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Gy(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ot[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=fe.cropAndResize(e,[s],[0],[Ts[1][0],Ts[1][1]],"bilinear"),r.div=ve(r.crop,ze.tf255),[r.score,r.keypoints]=Ot[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Ts[1][1],c[1]/Ts[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[$r[0]*(c[0]+t.boxRaw[0]),$r[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=W0(n.keypoints);for(let c of Object.keys(zI))n.annotations[c]=zI[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function Hy(e,t){var r,s;if(!((r=Ot[0])!=null&&r.executor)||!((s=Ot[1])!=null&&s.executor)||!Ot[0].inputs[0].shape||!Ot[1].inputs[0].shape)return[];$r=[e.shape[2]||0,e.shape[1]||0],j0++;let a=(t.hand.skipTime||0)>ae()-Uy,n=j0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Pt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-Uy,l=j0<3*(t.hand.skipFrames||0);t.skipAllowed&&Pt.hands.length===t.hand.maxDetected?Pt.hands=await Promise.all(Pt.boxes.map(p=>Gy(e,p,t))):t.skipAllowed&&o&&l&&Pt.hands.length>0?Pt.hands=await Promise.all(Pt.boxes.map(p=>Gy(e,p,t))):(Pt.boxes=await w3e(e,t),Uy=ae(),Pt.hands=await Promise.all(Pt.boxes.map(p=>Gy(e,p,t))),j0=0);let u=[...Pt.boxes];if(Pt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Pt.hands[p].fingerScore&&Pt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=I0(c.box,OI),h=I0(c.boxRaw,OI);Pt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var nc={};yr(nc,{connected:()=>X0,horizontal:()=>jy,kpt:()=>q0,relative:()=>Xy,vertical:()=>qy});var q0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],jy=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],qy=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Xy=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],X0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var xe=pr(),Ky=0;function VI(e,t){var i,o,l,u,p,c,d,h,f,m,g,y,x,A,b,w,C,T,N,M,F,E,I,_,O,B;let a=ae();if(!e)return pr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(xe.canvas=e.canvas),e.error&&(xe.error=e.error),!xe.body||e.body.length!==xe.body.length)xe.body=JSON.parse(JSON.stringify(e.body));else for(let $=0;$((r-1)*xe.body[$].box[X]+Z)/r),G=e.body[$].boxRaw.map((Z,X)=>((r-1)*xe.body[$].boxRaw[X]+Z)/r),q=e.body[$].keypoints.map((Z,X)=>{var re,ee,me,ie,Ae,Ce,Re,Le,qe;return{score:Z.score,part:Z.part,position:[xe.body[$].keypoints[X]?((r-1)*(xe.body[$].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],xe.body[$].keypoints[X]?((r-1)*(xe.body[$].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],xe.body[$].keypoints[X]?((r-1)*(xe.body[$].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[xe.body[$].keypoints[X]?((r-1)*(xe.body[$].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],xe.body[$].keypoints[X]?((r-1)*(xe.body[$].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],xe.body[$].keypoints[X]?((r-1)*(xe.body[$].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[xe.body[$].keypoints[X]?((r-1)*(((re=xe.body[$].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(me=Z.distance)==null?void 0:me[0],xe.body[$].keypoints[X]?((r-1)*(((ie=xe.body[$].keypoints[X].distance)==null?void 0:ie[1])||0)+(((Ae=Z.distance)==null?void 0:Ae[1])||0))/r:(Ce=Z.distance)==null?void 0:Ce[1],xe.body[$].keypoints[X]?((r-1)*(((Re=xe.body[$].keypoints[X].distance)==null?void 0:Re[2])||0)+(((Le=Z.distance)==null?void 0:Le[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},V={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?V=T0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?V=w0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(V=nc);for(let[Z,X]of Object.entries(V.connected)){let re=[];for(let ee=0;eeAe.part===X[ee]),ie=q.find(Ae=>Ae.part===X[ee+1]);me&&ie&&re.push([me.position,ie.position])}H[Z]=re}xe.body[$]={...e.body[$],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!xe.hand||e.hand.length!==xe.hand.length)xe.hand=JSON.parse(JSON.stringify(e.hand));else for(let 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a={};a.resize=fe.resizeBilinear(e,[ba.inputs[0].shape?ba.inputs[0].shape[1]:0,ba.inputs[0].shape?ba.inputs[0].shape[2]:0],!1),a.norm=ve(a.resize,ze.tf255),a.res=ba.execute(a.norm),a.squeeze=Oe(a.res,[0]),[a.bgRaw,a.fgRaw]=Ra(a.squeeze,2),a.fg=Oh(a.fgRaw),a.mul=te(a.fg,ze.tf255),a.expand=Ht(a.mul,2),a.output=fe.resizeBilinear(a.expand,[e.shape[1]||0,e.shape[2]||0]);let n;switch(t.segmentation.mode||"default"){case"default":a.input=Oe(e),a.concat=lt([a.input,a.output],-1),n=Ke(a.concat,"int32");break;case"alpha":n=Ke(a.output,"int32");break;default:n=Ve(0)}return Object.keys(a).forEach(s=>J(a[s])),n}var K0={};yr(K0,{distance:()=>Zy,find:()=>S3e,similarity:()=>I3e});function Zy(e,t,a={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let n=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-a)/(n-a);return Math.max(Math.min(s,1),0)};function I3e(e,t,a={order:2,multiplier:25,min:.2,max:.8}){let n=Zy(e,t,a);return 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aS(e){let t=e.reduce(({maxX:a,maxY:n,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(a,i),maxY:Math.max(n,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function nS(e,[t,a],[n,r]){let s=t/n,i=a/r,o=(u,p)=>({id:p,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/n,u.box[2]/r,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:c,part:d,position:h})=>({score:c,part:d,position:[Math.trunc(h.x*i),Math.trunc(h.y*s)],positionRaw:[h.x/n,h.y/n]})),annotations:{}});return e.map((u,p)=>o(u,p))}var ef=class{constructor(t,a){he(this,"priorityQueue");he(this,"numberOfElements");he(this,"getElementValue");this.priorityQueue=new 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fy(e,t){if(!(Mt!=null&&Mt.executor)||!(Mt!=null&&Mt.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-u9,n=my<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(_a.keypoints).length>0?(my++,[_a]):(my=0,new Promise(async r=>{let s=De(()=>{var m,f;let c=me.resizeBilinear(e,[((m=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:m[2])||0,((f=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:f[1])||0],!1),d=te(c,ze.tf2);return ye(d,ze.tf1)}),i;if(t.body.enabled&&(i=Mt==null?void 0:Mt.execute(s)),u9=ae(),J(s),i){_a.keypoints.length=0;let c=Oe(i);J(i);let d=Ra(c,2);J(c);for(let h=0;h(t.body.minConfidence||0)&&_a.keypoints.push({score:Math.round(100*g)/100,part:cy[h],positionRaw:[m/Mt.inputs[0].shape[2],f/Mt.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mt.inputs[0].shape[2]),Math.round(e.shape[1]*f/Mt.inputs[0].shape[1])]})}d.forEach(h=>J(h))}_a.score=_a.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let 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u=E0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=me.rotateWithOffset(a,s,0,[p[0],p[1]]);i=g9(-s,u),o=gy(t,c,[n,n]),J(c)}else o=gy(t,a,[n,n]);else o=gy(t,a,[n,n]);return[s,i,o]}var C3e=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},b9=(e,t)=>{let a=C3e(e),n=Qu(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var v9=6,T3e=1.4,Bn,F0=null,As=0,ed=null,w9=()=>As;async function k9(e){var t;return ne.initial&&(Bn=null),Bn?e.debug&&K("cached model:",Bn.modelUrl):Bn=await _e((t=e.face.detector)==null?void 0:t.modelPath),As=Bn.executor&&Bn.inputs[0].shape?Bn.inputs[0].shape[2]:256,ed=Ue(As,"int32"),F0=Yn(y9(As)),Bn}function N3e(e){if(!F0||!ed)return gn([0,0]);let t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=we(t.boxStarts,F0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=ve(t.boxSizes,ed),t.centersNormalized=ve(t.centers,ed),t.halfBoxSize=ve(t.boxSizesNormalized,ze.tf2),t.starts=ye(t.centersNormalized,t.halfBoxSize),t.ends=we(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,ed),t.endNormalized=te(t.ends,ed);let a=Fu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function I9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=me.resizeBilinear(e,[As,As]),a.div=ve(a.resized,ze.tf127),a.normalized=ye(a.div,ze.tf05);let n=Bn==null?void 0:Bn.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((m,f)=>m.size-f.size);a.concat384=lt([h[0],h[2]],2),a.concat512=lt([h[1],h[3]],2),a.concat=lt([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(n)?a.batch=Oe(n[0]):a.batch=Oe(n);J(n),a.boxes=N3e(a.batch),a.logits=Fe(a.batch,[0,0],[-1,1]),a.sigmoid=Wa(a.logits),a.scores=Oe(a.sigmoid),a.nms=await me.nonMaxSuppressionAsync(a.boxes,a.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let r=await a.nms.array(),s=[],i=await a.scores.data();for(let h=0;h(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let f={};f.bbox=Fe(a.boxes,[r[h],0],[1,-1]),f.slice=Fe(a.batch,[r[h],v9-1],[1,-1]),f.squeeze=Oe(f.slice),f.landmarks=Q(f.squeeze,[v9,-1]);let g=await f.bbox.data(),y={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await f.landmarks.array(),confidence:m};f.anchor=Fe(F0,[r[h],0],[1,2]);let x=await f.anchor.data(),A=m9(y,[(e.shape[2]||0)/As,(e.shape[1]||0)/As],x),b=P0(A,t.face.scale||T3e),w=$0(b);w.size[0]>(((c=t.face.detector)==null?void 0:c.minSize)||0)&&w.size[1]>(((d=t.face.detector)==null?void 0:d.minSize)||0)&&s.push(w),Object.keys(f).forEach(I=>J(f[I]))}}return Object.keys(a).forEach(h=>J(a[h])),s}var nn,bs=0,R3e=2.3,Ay=Pn.leftEyeLower0,by=Pn.rightEyeLower0,td={leftBounds:[Ay[0],Ay[Ay.length-1]],rightBounds:[by[0],by[by.length-1]]},ad={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function R9(e){var t,a;return ne.initial&&(nn=null),nn?e.debug&&K("cached model:",nn.modelUrl):nn=await _e((t=e.face.iris)==null?void 0:t.modelPath),bs=nn!=null&&nn.executor&&((a=nn.inputs)!=null&&a[0].shape)?nn.inputs[0].shape[2]:0,bs===-1&&(bs=64),nn}function D0(e,t,a,n){for(let r=0;r{let t=e[td.leftBounds[0]][2],a=e[td.rightBounds[0]][2];return t-a},C9=(e,t,a,n,r,s=!1)=>{let i=$0(P0(f9([e[a],e[n]]),R3e)),o=Qu(i),l=me.cropAndResize(t,[[i.startPoint[1]/r,i.startPoint[0]/r,i.endPoint[1]/r,i.endPoint[0]/r]],[0],[bs,bs]);if(s&&ne.kernels.includes("flipleftright")){let u=me.flipLeftRight(l);J(l),l=u}return{box:i,boxSize:o,crop:l}},T9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s{let n=e[Pn[`${a}EyeUpper0`][ad.upperCenter]][2],r=e[Pn[`${a}EyeLower0`][ad.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function E9(e,t,a){if(!(nn!=null&&nn.executor))return e;let{box:n,boxSize:r,crop:s}=C9(e,t,td.leftBounds[0],td.leftBounds[1],a,!0),{box:i,boxSize:o,crop:l}=C9(e,t,td.rightBounds[0],td.rightBounds[1],a,!0),u=lt([s,l]);J(s),J(l);let p=nn.execute(u);J(u);let c=await p.data();J(p);let d=c.slice(0,ad.numCoordinates*3),{rawCoords:h,iris:m}=T9(d,n,r,!0),f=c.slice(ad.numCoordinates*3),{rawCoords:g,iris:y}=T9(f,i,o,!1),x=E3e(e);Math.abs(x)<30?(D0(e,h,"left",null),D0(e,g,"right",null)):x<1?D0(e,h,"left",["EyeUpper0","EyeLower0"]):D0(e,g,"right",["EyeUpper0","EyeLower0"]);let A=N9(e,m,"left"),b=N9(e,y,"right");return e.concat(A).concat(b)}async function _9(e,t){var s,i,o,l,u,p,c,d,h,m;let a={lips:await((i=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(f=>f.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(f=>f.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(f=>f.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((m=(h=t.filter(f=>f.size===142))==null?void 0:h[1])==null?void 0:m.data())};for(let f of Object.values(a))if(!f)return e;let n=ul.reduce((f,g)=>f+=e[g][2],0)/ul.length;for(let f=0;ff+=e[g][2],0)/dl.length;for(let f=0;fae()-pr.timestamp,n=pr.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||pr.boxes.length===0?(pr.boxes=await I9(e,t),pr.timestamp=ae(),pr.skipped=0):pr.skipped++;let r=[],s=[],i=0,o=sc;for(let x=0;x[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/o]);for(let T of Object.keys(ol))I.annotations[T]=[I.mesh[ol[T]]]}else if(!Ct)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(I.tensor),r;let T=Ct.execute(I.tensor),M=await T.find(P=>P.shape[P.shape.length-1]===1).data();if(I.faceScore=Math.round(100*M[0])/100,I.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(A.confidence=I.faceScore,t.face.mesh.keepInvalid){I.box=M0(A,e),I.boxRaw=_0(A,e),I.size=A.size,I.score=I.boxScore,I.mesh=A.landmarks,I.meshRaw=I.mesh.map(P=>[P[0]/(e.shape[2]||1),P[1]/(e.shape[1]||1),(P[2]||0)/o]);for(let P of Object.keys(ol))I.annotations[P]=[I.mesh[ol[P]]]}}else{let P=T.find(O=>O.shape[O.shape.length-1]===1404),E=Q(P,[-1,3]),C=await E.array();J(E),(f=t.face.attention)!=null&&f.enabled?C=await _9(C,T):(g=t.face.iris)!=null&&g.enabled&&(C=await E9(C,I.tensor,sc)),I.mesh=x9(C,A,b,w,sc),I.meshRaw=I.mesh.map(O=>[O[0]/(e.shape[2]||0),O[1]/(e.shape[1]||0),(O[2]||0)/o]);for(let O of Object.keys(Pn))I.annotations[O]=Pn[O].map(B=>I.mesh[B]);I.score=I.faceScore;let _={...b9(I.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};I.box=M0(_,e),I.boxRaw=_0(_,e),I.size=_.size,s.push(_)}J(T)}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(I):J(I.tensor)}return pr.boxes=s,r}async function $9(e){var t,a,n,r,s,i;return ne.initial&&(Ct=null),(t=e.face.attention)!=null&&t.enabled&&(Ct!=null&&Ct.signature)&&Object.keys(((a=Ct==null?void 0:Ct.signature)==null?void 0:a.outputs)||{}).length<6&&(Ct=null),Ct?e.debug&&K("cached model:",Ct.modelUrl):(n=e.face.attention)!=null&&n.enabled?Ct=await _e(e.face.attention.modelPath):Ct=await _e((r=e.face.mesh)==null?void 0:r.modelPath),sc=Ct.executor&&((s=Ct==null?void 0:Ct.inputs)!=null&&s[0].shape)?(i=Ct==null?void 0:Ct.inputs)==null?void 0:i[0].shape[2]:256,Ct}var F9=ll,D9=nc;var ky=[],ra,O0=[],O9=0,z9=0,wy=Number.MAX_SAFE_INTEGER,Iy=!1;async function L9(e){var t,a,n;return ne.initial&&(ra=null),ra?e.debug&&K("cached model:",ra.modelUrl):(ra=await _e((t=e.face.emotion)==null?void 0:t.modelPath),Iy=((n=(a=ra==null?void 0:ra.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,Iy?ky=["angry","disgust","fear","happy","neutral","sad","surprise"]:ky=["angry","disgust","fear","happy","sad","surprise","neutral"]),ra}async function Sy(e,t,a,n){var i,o;if(!ra)return[];let r=wy<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-z9;return t.skipAllowed&&s&&r&&O9===n&&O0[a]&&O0[a].length>0?(wy++,O0[a]):(wy=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},m=ra!=null&&ra.inputs[0].shape?ra.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=me.cropAndResize(e,y,[0],[m,m])}else h.resize=me.resizeBilinear(e,[m,m],!1);Iy?(h.mul=te(h.resize,255),h.normalize=ye(h.mul,[103.939,116.779,123.68]),h.emotion=ra==null?void 0:ra.execute(h.normalize)):(h.channels=te(h.resize,ze.rgb),h.grayscale=ot(h.channels,3,!0),h.grayscaleSub=ye(h.grayscale,ze.tf05),h.grayscaleMul=te(h.grayscaleSub,ze.tf2),h.emotion=ra==null?void 0:ra.execute(h.grayscaleMul)),z9=ae();let f=await h.emotion.data();for(let g=0;g(t.face.emotion.minConfidence||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[g])/100),emotion:ky[g]});u.sort((g,y)=>y.score-g.score),Object.keys(h).forEach(g=>J(h[g]))}O0[a]=u,O9=n,l(u)}))}var sa,vs=[],B9=0,V9=0,Cy=Number.MAX_SAFE_INTEGER;async function U9(e){var t;return ne.initial&&(sa=null),sa?e.debug&&K("cached model:",sa.modelUrl):sa=await _e((t=e.face.description)==null?void 0:t.modelPath),sa}function _3e(e,t){var s,i;let a=e.image||e.tensor||e;if(!(sa!=null&&sa.inputs[0].shape))return a;let n;if(((s=t.face.description)==null?void 0:s.crop)>0){let o=(i=t.face.description)==null?void 0:i.crop,l=[[o,o,1-o,1-o]];n=me.cropAndResize(a,l,[0],[sa.inputs[0].shape[2],sa.inputs[0].shape[1]])}else n=me.resizeBilinear(a,[sa.inputs[0].shape[2],sa.inputs[0].shape[1]],!1);let r=te(n,ze.tf255);return J(n),r}async function Ty(e,t,a,n){var o,l,u,p;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(sa!=null&&sa.executor))return r;let s=Cy<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-B9;return t.skipAllowed&&s&&i&&V9===n&&((u=vs==null?void 0:vs[a])==null?void 0:u.age)>0&&((p=vs==null?void 0:vs[a])==null?void 0:p.genderScore)>0?(Cy++,vs[a]):(Cy=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=_3e(e,t),m=sa==null?void 0:sa.execute(h);B9=ae(),J(h);let g=await m.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=sr(m.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let w=await m.find(N=>N.shape[1]===100).data();r.age=Math.round(w[A-1]>w[A+1]?10*A-100*w[A-1]:10*A+100*w[A+1])/10,(Number.isNaN(g[0])||Number.isNaN(w[0]))&&K("faceres error:",{model:sa,result:m});let I=m.find(N=>N.shape[1]===1024),T=I?await I.data():[];r.descriptor=Array.from(T),m.forEach(N=>J(N))}vs[a]=r,V9=n,c(r)}))}var nd=.1,Ny=.5;function P3e(e,t,a){let n=!1,r=a.length-1;for(let s=0;st!=a[r].y>t&&e<(a[r].x-a[s].x)*(t-a[s].y)/(a[r].y-a[s].y)+a[s].x&&(n=!n);return n}async function H9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,a=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let i of Pn.silhouette)r.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});nd&&nd>0&&(r=r.map(i=>({x:i.x>.5?i.x+nd:i.x-nd,y:i.y>.5?i.y+nd:i.y-nd})));for(let i=0;iae()-q9,s=Ry<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&j9===n&&z0[a]?(Ry++,z0[a]):(Ry=0,new Promise(async l=>{let u=me.resizeBilinear(e,[ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[2]:0,ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[1]:0],!1),p=ia==null?void 0:ia.execute(u),c=(await p.data())[0];z0[a]=Math.round(100*c)/100,j9=n,q9=ae(),J([u,p]),l(z0[a])}))}var oa,L0=[],My=Number.MAX_SAFE_INTEGER,Y9=0,Z9=0;async function J9(e){var t;return ne.initial&&(oa=null),oa?e.debug&&K("cached model:",oa.modelUrl):oa=await _e((t=e.face.liveness)==null?void 0:t.modelPath),oa}async function _y(e,t,a,n){var i,o;if(!(oa!=null&&oa.executor))return 0;let r=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>ae()-Z9,s=My<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&Y9===n&&L0[a]?(My++,L0[a]):(My=0,new Promise(async l=>{let u=me.resizeBilinear(e,[oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[2]:0,oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[1]:0],!1),p=oa==null?void 0:oa.execute(u),c=(await p.data())[0];L0[a]=Math.round(100*c)/100,Y9=n,Z9=ae(),J([u,p]),l(L0[a])}))}var $n,Py=[],F3e=["white","black","asian","indian","other"],D3e=[15,23,28,35.5,45.5,55.5,65],eI=0,tI=0,$y=Number.MAX_SAFE_INTEGER;async function aI(e){var t;return ne.initial&&($n=null),$n?e.debug&&K("cached model:",$n.modelUrl):$n=await _e((t=e.face.gear)==null?void 0:t.modelPath),$n}async function Fy(e,t,a,n){var i,o;if(!$n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=$y<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-tI;return t.skipAllowed&&s&&r&&eI===n&&Py[a]?($y++,Py[a]):($y=0,new Promise(async l=>{var y,x,A,b;if(!($n!=null&&$n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let w=(x=t.face.gear)==null?void 0:x.crop;p=[[w,w,1-w,1-w]]}u.resize=me.cropAndResize(e,p,[0],[$n.inputs[0].shape[2],$n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=$n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let w=0;w(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[w])/100,race:F3e[w]});c.race.sort((w,I)=>I.score-w.score);let f=Array.from(await u.age.data()).map((w,I)=>[D3e[I],w]).sort((w,I)=>I[1]-w[1]),g=f[0][0];for(let w=1;wJ(u[w])),Py[a]=c,eI=n,tI=ae(),l(c)}))}var Pa,W0=[],rI=0,sI=0,Dy=Number.MAX_SAFE_INTEGER;async function iI(e){return ne.initial&&(Pa=null),Pa?e.debug&&K("cached model:",Pa.modelUrl):Pa=await 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Aa,B0=[],lI=0,uI=0,zy=Number.MAX_SAFE_INTEGER,Ly=[.2989,.587,.114];async function dI(e){var t;return ne.initial&&(Aa=null),Aa?e.debug&&K("cached model:",Aa.modelUrl):Aa=await _e((t=e.face.ssrnet)==null?void 0:t.modelPathGender),Aa}async function Wy(e,t,a,n){var i,o,l,u;if(!Aa)return{gender:"unknown",genderScore:0};let r=zy<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-uI;return t.skipAllowed&&r&&s&&lI===n&&((l=B0[a])!=null&&l.gender)&&((u=B0[a])==null?void 0:u.genderScore)>0?(zy++,B0[a]):(zy=0,new Promise(async p=>{var m,f,g;if(!(Aa!=null&&Aa.inputs[0].shape))return;let c={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let y=(f=t.face.ssrnet)==null?void 0:f.crop,x=[[y,y,1-y,1-y]];c.resize=me.cropAndResize(e,x,[0],[Aa.inputs[0].shape[2],Aa.inputs[0].shape[1]])}else c.resize=me.resizeBilinear(e,[Aa.inputs[0].shape[2],Aa.inputs[0].shape[1]],!1);c.enhance=De(()=>{var x,A;let y;if(((A=(x=Aa==null?void 0:Aa.inputs)==null?void 0:x[0].shape)==null?void 0:A[3])===1){let[b,w,I]=Ca(c.resize,3,3),T=te(b,Ly[0]),N=te(w,Ly[1]),M=te(I,Ly[2]),P=_h([T,N,M]);y=te(ye(P,ze.tf05),2)}else y=te(ye(c.resize,ze.tf05),2);return y});let d={gender:"unknown",genderScore:0};(g=t.face.ssrnet)!=null&&g.enabled&&(c.gender=Aa.execute(c.enhance));let h=await c.gender.data();d.gender=h[0]>h[1]?"female":"male",d.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(y=>J(c[y])),B0[a]=d,lI=n,uI=ae(),p(d)}))}var rn,By=[],cI=0,hI=0,mI=Number.MAX_SAFE_INTEGER;async function fI(e){var t;return ne.initial&&(rn=null),rn?e.debug&&K("cached model:",rn.modelUrl):rn=await _e((t=e.face.mobilefacenet)==null?void 0:t.modelPath),rn}async function Vy(e,t,a,n){var i,o;if(!(rn!=null&&rn.executor))return[];let r=mI<(((i=t.face.mobilefacenet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.mobilefacenet)==null?void 0:o.skipTime)||0)>ae()-hI;return t.skipAllowed&&s&&r&&cI===n&&By[a]?(mI++,By[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.mobilefacenet)!=null&&p.enabled&&(rn!=null&&rn.inputs[0].shape)){let c={};c.crop=me.resizeBilinear(e,[rn.inputs[0].shape[2],rn.inputs[0].shape[1]],!1),c.data=rn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>J(c[h]))}By[a]=u,cI=n,hI=ae(),l(u)})}var sn,Uy=[],yI=0,xI=0,AI=Number.MAX_SAFE_INTEGER;async function bI(e){return ne.initial&&(sn=null),sn?e.debug&&K("cached model:",sn.modelUrl):sn=await _e(e.face.insightface.modelPath),sn}async function Gy(e,t,a,n){var i,o;if(!(sn!=null&&sn.executor))return[];let r=AI<(((i=t.face.insightface)==null?void 0:i.skipFrames)||0),s=(((o=t.face.insightface)==null?void 0:o.skipTime)||0)>ae()-xI;return t.skipAllowed&&s&&r&&yI===n&&Uy[a]?(AI++,Uy[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.insightface)!=null&&p.enabled&&(sn!=null&&sn.inputs[0].shape)){let c={};c.crop=me.resizeBilinear(e,[sn.inputs[0].shape[2],sn.inputs[0].shape[1]],!1),c.data=sn.execute(c.crop);let d=await 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y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],x=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},FI=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function G0(e,t=1.5){let a=ic(e),n=U0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function H0(e){let t=ic(e),a=U0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function q3e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function WI(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return q3e(a)}var DI=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ts(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Yn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Xt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Xt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=Fe(t,[0,0],[-1,2]),a.boxSizes=Fe(t,[0,2],[-1,2]),a.div=ve(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=we(a.div,this.anchorsTensor),a.halfBoxSizes=ve(a.boxSizes,this.doubleInputSizeTensor),a.sub=ye(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=te(a.sub,this.inputSizeTensor),a.add=we(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=te(a.add,this.inputSizeTensor);let n=Fu([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=ve(n.reshape,this.inputSizeTensor),n.landmarks=we(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=me.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=ve(n.resize,ze.tf127),n.image=ye(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=Wa(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await me.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),m={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},f=LI(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(f),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var Z3e=5,GI=1.65,HI=[0,5,9,13,17,1,2],J3e=0,Q3e=2,jI=0,q0=class{constructor(t,a){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>Ky([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return G0(H0(r),Z3e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=G0(H0(a),GI);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Xy(n,[0,0]),u=o.map(h=>[...Ky(h,l),h[2]]),p=BI(r),c=[...ic(a),1],d=[Ts(c,p[0]),Ts(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-jI,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i?this.skipped++:(r=await this.handDetector.predict(t,a),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=Q(A,[-1,3]),I=await w.array();J(A),J(w);let T=this.transformRawCoords(I,f,p,m),N=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...N,confidence:b};let M={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(M)}else this.storedBoxes[l]=null;J(A)}else{let p=G0(H0(u),GI),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var qI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},yl,xl,Yy;function tge(){let e=yl?new j0(yl):void 0;e&&xl&&(Yy=new q0(e,xl))}async function Zy(e,t){Yy||tge();let a=await Yy.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=V0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function XI(e){var t;return ne.initial&&(yl=null),yl?e.debug&&K("cached model:",yl.modelUrl):yl=await _e((t=e.hand.detector)==null?void 0:t.modelPath),yl}async function KI(e){var t;return ne.initial&&(xl=null),xl?e.debug&&K("cached model:",xl.modelUrl):xl=await _e((t=e.hand.skeleton)==null?void 0:t.modelPath),xl}var zt=[null,null],age=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ns=[[0,0],[0,0]],nge=["hand","fist","pinch","point","face","tip","pinchtip"],ZI=4,JI=1.6,rge=512,sge=1.4,X0=Number.MAX_SAFE_INTEGER,Jy=0,Fr=[0,0],Ot={boxes:[],hands:[]},QI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function eS(e){var t;if(ne.initial&&(zt[0]=null),zt[0])e.debug&&K("cached model:",zt[0].modelUrl);else{y0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),zt[0]=await _e((t=e.hand.detector)==null?void 0:t.modelPath);let a=zt[0].executor?Object.values(zt[0].modelSignature.inputs):void 0;Ns[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ns[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return zt[0]}async function tS(e){var t;if(ne.initial&&(zt[1]=null),zt[1])e.debug&&K("cached model:",zt[1].modelUrl);else{zt[1]=await _e((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=zt[1].executor?Object.values(zt[1].modelSignature.inputs):void 0;Ns[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ns[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return zt[1]}async function ige(e,t){let a=[];if(!e||!zt[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,rge),i=Math.round(s*r/8)*8;n.resize=me.resizeBilinear(e,[s,i]),n.cast=je(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await zt[0].executeAsync(n.cast,age),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Ra(n.scores,1);J(o[ZI]),o.splice(ZI,1),n.filtered=ca(o,1),J(o),n.max=ga(n.filtered,1),n.argmax=sr(n.filtered,1);let l=0;n.nms=await me.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),m=await h.data();J(h);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=C0(f,sge),y=[Math.trunc(f[0]*Fr[0]),Math.trunc(f[1]*Fr[1]),Math.trunc(f[2]*Fr[0]),Math.trunc(f[3]*Fr[1])],x=p[d],A=nge[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Qy(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&zt[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=me.cropAndResize(e,[s],[0],[Ns[1][0],Ns[1][1]],"bilinear"),r.div=ve(r.crop,ze.tf255),[r.score,r.keypoints]=zt[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Ns[1][1],c[1]/Ns[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[Fr[0]*(c[0]+t.boxRaw[0]),Fr[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=V0(n.keypoints);for(let c of Object.keys(QI))n.annotations[c]=QI[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function e5(e,t){var r,s;if(!((r=zt[0])!=null&&r.executor)||!((s=zt[1])!=null&&s.executor)||!zt[0].inputs[0].shape||!zt[1].inputs[0].shape)return[];Fr=[e.shape[2]||0,e.shape[1]||0],X0++;let a=(t.hand.skipTime||0)>ae()-Jy,n=X0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Ot.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-Jy,l=X0<3*(t.hand.skipFrames||0);t.skipAllowed&&Ot.hands.length===t.hand.maxDetected?Ot.hands=await Promise.all(Ot.boxes.map(p=>Qy(e,p,t))):t.skipAllowed&&o&&l&&Ot.hands.length>0?Ot.hands=await Promise.all(Ot.boxes.map(p=>Qy(e,p,t))):(Ot.boxes=await ige(e,t),Jy=ae(),Ot.hands=await Promise.all(Ot.boxes.map(p=>Qy(e,p,t))),X0=0);let u=[...Ot.boxes];if(Ot.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Ot.hands[p].fingerScore&&Ot.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=C0(c.box,JI),h=C0(c.boxRaw,JI);Ot.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var oc={};xr(oc,{connected:()=>Y0,horizontal:()=>t5,kpt:()=>K0,relative:()=>n5,vertical:()=>a5});var K0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],t5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],n5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Y0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ae=cr(),r5=0;function nS(e,t){var i,o,l,u,p,c,d,h,m,f,g,y,x,A,b,w,I,T,N,M,P,E,C,_,O,B;let a=ae();if(!e)return cr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ae.canvas=e.canvas),e.error&&(Ae.error=e.error),!Ae.body||e.body.length!==Ae.body.length)Ae.body=JSON.parse(JSON.stringify(e.body));else for(let $=0;$((r-1)*Ae.body[$].box[X]+Z)/r),G=e.body[$].boxRaw.map((Z,X)=>((r-1)*Ae.body[$].boxRaw[X]+Z)/r),q=e.body[$].keypoints.map((Z,X)=>{var re,ee,fe,ie,be,Ce,Re,Le,Xe;return{score:Z.score,part:Z.part,position:[Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[Ae.body[$].keypoints[X]?((r-1)*(((re=Ae.body[$].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(fe=Z.distance)==null?void 0:fe[0],Ae.body[$].keypoints[X]?((r-1)*(((ie=Ae.body[$].keypoints[X].distance)==null?void 0:ie[1])||0)+(((be=Z.distance)==null?void 0:be[1])||0))/r:(Ce=Z.distance)==null?void 0:Ce[1],Ae.body[$].keypoints[X]?((r-1)*(((Re=Ae.body[$].keypoints[X].distance)==null?void 0:Re[2])||0)+(((Le=Z.distance)==null?void 0:Le[2])||0))/r:(Xe=Z.distance)==null?void 0:Xe[2]]}}),H={},V={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?V=R0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?V=I0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(V=oc);for(let[Z,X]of Object.entries(V.connected)){let re=[];for(let ee=0;eebe.part===X[ee]),ie=q.find(be=>be.part===X[ee+1]);fe&&ie&&re.push([fe.position,ie.position])}H[Z]=re}Ae.body[$]={...e.body[$],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.hand||e.hand.length!==Ae.hand.length)Ae.hand=JSON.parse(JSON.stringify(e.hand));else for(let $=0;$((r-1)*Ae.hand[$].box[Z]+V)/r),G=e.hand[$].boxRaw.map((V,Z)=>((r-1)*Ae.hand[$].boxRaw[Z]+V)/r);Ae.hand[$].keypoints.length!==e.hand[$].keypoints.length&&(Ae.hand[$].keypoints=e.hand[$].keypoints);let q=e.hand[$].keypoints&&e.hand[$].keypoints.length>0?e.hand[$].keypoints.map((V,Z)=>V.map((X,re)=>((r-1)*(Ae.hand[$].keypoints[Z][re]||1)+(X||0))/r)):[],H={};if(Object.keys(Ae.hand[$].annotations).length!==Object.keys(e.hand[$].annotations).length)Ae.hand[$].annotations=e.hand[$].annotations,H=Ae.hand[$].annotations;else if(e.hand[$].annotations)for(let V of Object.keys(e.hand[$].annotations))H[V]=(c=(p=(u=e.hand[$])==null?void 0:u.annotations)==null?void 0:p[V])!=null&&c[0]?e.hand[$].annotations[V].map((Z,X)=>Z.map((re,ee)=>((r-1)*Ae.hand[$].annotations[V][X][ee]+re)/r)):null;Ae.hand[$]={...e.hand[$],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.face||e.face.length!==Ae.face.length)Ae.face=JSON.parse(JSON.stringify(e.face));else for(let $=0;$((r-1)*Ae.face[$].box[V]+H)/r),G=e.face[$].boxRaw.map((H,V)=>((r-1)*Ae.face[$].boxRaw[V]+H)/r),q=e.face[$].annotations;if(Object.keys(Ae.face[$].annotations).length!==Object.keys(e.face[$].annotations).length)Ae.face[$].annotations=e.face[$].annotations,q=Ae.face[$].annotations;else if(e.face[$].annotations)for(let H of Object.keys(e.face[$].annotations))q[H]=(m=(h=(d=e.face[$])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&m[0]?e.face[$].annotations[H].map((V,Z)=>V.map((X,re)=>((r-1)*Ae.face[$].annotations[H][Z][re]+X)/r)):null;if(e.face[$].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[$].rotation)==null?void 0:f.matrix,H.angle={roll:((r-1)*(((y=(g=Ae.face[$].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[$].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((w=(b=Ae.face[$].rotation)==null?void 0:b.angle)==null?void 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state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return VI(t,this.config)}async warmup(t){let a=ae(),n=await gS(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var 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Sk(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Oy(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await Oy(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Et(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?o5(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?ey(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?oy(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?e5(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await o5(o.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await ey(o.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("efficientpose")?u=this.config.body.enabled?await oy(o.tensor,d):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await e5(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Et(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((M=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&M.includes("handdetect")?p=this.config.hand.enabled?Vy(o.tensor,h):[]:(E=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&E.includes("handtrack")&&(p=this.config.hand.enabled?Hy(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await Vy(o.tensor,h):[]:(B=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&B.includes("handtrack")&&(p=this.config.hand.enabled?await Hy(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(($=this.config.object.modelPath)!=null&&$.includes("nanodet")?c=this.config.object.enabled?a5(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?ny(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await a5(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await ny(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ae(),f=[...xI(l),...yI(u),...bI(p),...AI(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let m=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:f,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return mS(l,u,p,f,m)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Xa(this,Ns)[t.id]||(this.config.debug&&K("video start",t.id),Xa(this,Ns)[t.id]=!0),!t.paused&&Xa(this,Ns)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),Xa(this,Ns)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),Xa(this,Ns)[t.id]=!1)}};rd=new WeakMap,oc=new WeakMap,lc=new WeakMap,sf=new WeakMap,Ns=new WeakMap;return tC(Y3e);})(); +2Q==`;async function Nge(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),a,n;switch(e.config.warmup){case"face":a=await t(sm);break;case"body":case"full":a=await t(im);break;default:a=null}if(a){let r=await createImageBitmap(a);n=await e.detect(r,e.config),r.close()}return n}async function Rge(e){return new Promise(t=>{let a;switch(e.config.warmup){case"face":a="data:image/jpeg;base64,"+sm;break;case"full":case"body":a="data:image/jpeg;base64,"+im;break;default:a=""}let n;if(typeof Image!="undefined")n=new Image;else if(ne.Image)n=new ne.Image;else{t(void 0);return}n.onload=async()=>{let r=_n(n.naturalWidth,n.naturalHeight);if(!r)K("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(r,!0),o=i.tensor?await e.detect(i.tensor,e.config):void 0;t(o)}},a?n.src=a:t(void 0)})}async function Ege(e){let t=r=>Buffer.from(r,"base64"),a;e.config.warmup==="face"?a=t(sm):a=t(im);let n;if("node"in qe&&ha()==="tensorflow"){let r=(void 0).decodeJpeg(a),s=qt(r,0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&K("Warmup tfjs-node not loaded");return n}async function Mge(e){let t;return typeof createImageBitmap=="function"?t=await Nge(e):typeof Image!="undefined"||ne.Canvas!==void 0?t=await Rge(e):t=await Ege(e),t}async function _ge(e){var o,l,u,p;if(!W().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=ha(),a=rr();if(t!=="webgl"&&t!=="humangl"||!(a!=null&&a.checkCompileCompletion))return;W().set("ENGINE_COMPILE_ONLY",!0);let n=It().state.numTensors,r=[];for(let[c,d]of Object.entries(e.models.models)){if(!d)continue;let h=d!=null&&d.modelSignature&&((l=(o=d==null?void 0:d.inputs)==null?void 0:o[0])!=null&&l.shape)?[...d.inputs[0].shape]:[1,64,64,3],m=d!=null&&d.modelSignature&&((p=(u=d==null?void 0:d.inputs)==null?void 0:u[0])!=null&&p.dtype)?d.inputs[0].dtype:"float32";for(let g=0;gJ(y)):J(g)}catch(g){e.config.debug&&K("compile fail model:",c)}J(f)}let s=await a.checkCompileCompletionAsync();a.getUniformLocations(),e.config.debug&&K("compile pass:",{models:r,kernels:s.length}),W().set("ENGINE_COMPILE_ONLY",!1);let i=It().state.numTensors;i-n>0&&K("tensor leak:",i-n)}async function MS(e,t){await ac(e,!1);let a=ae();return e.state="warmup",t&&(e.config=Et(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?cr():new Promise(async n=>{await e.models.load(),await _ge(e);let r=await Mge(e),s=ae();e.config.debug&&K("warmup",e.config.warmup,Math.round(s-a),"ms"),e.emit("warmup"),n(r)})}var ld,pc,cc,om,Rs,w5=class{constructor(t){he(this,"version");he(this,"config");he(this,"result");he(this,"state");he(this,"process");he(this,"tf");he(this,"env",ne);he(this,"draw",k0);he(this,"match",Z0);he(this,"models");he(this,"events");he(this,"faceTriangulation");he(this,"faceUVMap");he(this,"performance");jn(this,ld,void 0);jn(this,pc,void 0);jn(this,cc,void 0);he(this,"analyze",(...t)=>{if(!Xa(this,pc))return;let a=this.tf.engine().state.numTensors,n=Xa(this,ld);Ar(this,ld,a);let r=a-n;r!==0&&K(...t,r)});jn(this,om,t=>{if(!Xa(this,cc))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof mt))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});he(this,"webcam",new g0);he(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});jn(this,Rs,{});let a=(Zp.tfjs||eg).replace(/-(.*)/,"");rl.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,rl.modelBasePath=ne.browser?"../models/":"file://models/",this.version=Yg,Object.defineProperty(this,"version",{value:Yg}),this.config=JSON.parse(JSON.stringify(rl)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Et(this.config,t)),Vk(this.config),this.tf=qe,this.state="idle",Ar(this,ld,0),Ar(this,pc,!1),Ar(this,cc,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new dc(this),ny(),this.result=cr(),this.process={tensor:null,canvas:null},this.faceTriangulation=F9,this.faceUVMap=D9,rm(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(rl)),this.config.backend=t,Xg(),ne.initial=!0}validate(t){let a=Hg(rl,t||this.config);return a.length===0&&(this.config=Et(this.config,t)),a}now(){return ae()}image(t,a=!1){return m0(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Et(this.config,a)),!this.config.segmentation.enabled)return null;let n=await m0(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await CS(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await rS(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await NS(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return Bk(this.config,t,a)}async init(){await ac(this,!0),await this.tf.ready(),Xg()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Et(this.config,t)),this.env.initial&&(await ac(this,!1)||K("error: backend check failed"),await Pp(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return nS(t,this.config)}async warmup(t){let a=ae(),n=await MS(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,w,I,T,N,M,P,E,C,_,O,B,$,U,G,q,H;this.state="config";let r;this.config=Et(this.config,a),this.state="check";let s=Xa(this,om).call(this,t);s&&(K(s,t),this.emit("error"),n(cr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await m0(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(cr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await Wk(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Hy(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await Hy(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Et(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?g5(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?ly(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?fy(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?u5(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await g5(o.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await ly(o.tensor,d):[]:(I=this.config.body.modelPath)!=null&&I.includes("efficientpose")?u=this.config.body.enabled?await fy(o.tensor,d):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await u5(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Et(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((M=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&M.includes("handdetect")?p=this.config.hand.enabled?Zy(o.tensor,h):[]:(E=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&E.includes("handtrack")&&(p=this.config.hand.enabled?e5(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await Zy(o.tensor,h):[]:(B=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&B.includes("handtrack")&&(p=this.config.hand.enabled?await e5(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(($=this.config.object.modelPath)!=null&&$.includes("nanodet")?c=this.config.object.enabled?p5(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?py(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await p5(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await py(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ae(),m=[...PI(l),..._I(u),...FI(p),...$I(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:m,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return ES(l,u,p,m,f)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Xa(this,Rs)[t.id]||(this.config.debug&&K("video start",t.id),Xa(this,Rs)[t.id]=!0),!t.paused&&Xa(this,Rs)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),Xa(this,Rs)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),Xa(this,Rs)[t.id]=!1)}};ld=new WeakMap,pc=new WeakMap,cc=new WeakMap,om=new WeakMap,Rs=new WeakMap;return gC($ge);})(); diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js index 37bb48f2..60e8d70d 100644 --- a/dist/human.node-gpu.js +++ b/dist/human.node-gpu.js @@ -4,7 +4,7 @@ author: ' */ -"use strict";var Wo=Object.create;var w2=Object.defineProperty;var Do=Object.getOwnPropertyDescriptor;var Fo=Object.getOwnPropertyNames;var Bo=Object.getPrototypeOf,Ho=Object.prototype.hasOwnProperty;var Go=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Vo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ze=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},j1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof 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p=new Float32Array(g),b=1/l.width,k=1/l.height,P=u(B1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,b,k),y())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,b=g/7/l.height,k=u(F1);k&&(i.uniform2f(k.uniform.px,0,b),y(x.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),y())},pixelate:g=>{let p=g/l.width,b=g/l.height,k=u(D1);k&&(i.uniform2f(k.uniform.size,p,b),y())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),b=T[g];s.push({func:b,args:p})},this.reset=function(){s=[]},this.get=function(){return 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$t(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function q2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function U2(e,t,n=!0){var f,d,m;if(!e)return t.debug&&h("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let y=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new 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s=o,A=r;if(s>X2&&(s=X2,A=Math.trunc(s*r/o)),A>X2&&(A=X2,s=Math.trunc(A*o/r)),(((f=t.filter)==null?void 0:f.width)||0)>0?s=t.filter.width:(((d=t.filter)==null?void 0:d.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!R0||R0.width!==s||R0.height!==A)&&(R0=Ae(s,A));let a=R0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,R0.width,R0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,R0.width,R0.height),(!M0||R0.width!==M0.width||R0.height!==M0.height)&&(M0=Ae(R0.width,R0.height)),t.filter.enabled&&R.webgl.supported?(e0||(e0=R.browser?new 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y.data();t.filter.brightness=u[0]>1?1-u[0]/255:1-u[0],I.dispose(y)}return{tensor:i,canvas:t.filter.return?M0:null}}async function G1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function V1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||h("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||h("input tensors must 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n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};z2=new WeakMap,S2=new WeakMap,j2=new WeakMap;var R=new E2;var K2=class{constructor(){w(this,"config");w(this,"element");w(this,"stream");w(this,"devices",[]);w(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});w(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else{this.config.debug&&h("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof 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d=0;d(t.body.minConfidence||0)&&B0.keypoints.push({score:Math.round(100*u)/100,part:u5[d],positionRaw:[m/i0.inputs[0].shape[2],y/i0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/i0.inputs[0].shape[2]),Math.round(e.shape[1]*y/i0.inputs[0].shape[1])]})}f.forEach(d=>J.dispose(d))}B0.score=B0.keypoints.reduce((i,f)=>f.score>i?f.score:i,0);let a=B0.keypoints.map(i=>i.position[0]),l=B0.keypoints.map(i=>i.position[1]);B0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=B0.keypoints.map(i=>i.positionRaw[0]),x=B0.keypoints.map(i=>i.positionRaw[1]);B0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,f]of Object.entries(h5)){let d=[];for(let m=0;mT.part===f[m]),u=B0.keypoints.find(T=>T.part===f[m+1]);y&&u&&y.score>(t.body.minConfidence||0)&&u.score>(t.body.minConfidence||0)&&d.push([y.position,u.position])}B0.annotations[i]=d}r([B0])}))}var l0=G(H());var De=G(H());var O=G(H());var Me=G(H());var p2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],lt=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],ct=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],dt=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],p3=(e,t,n)=>{let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(A=>[(A[0]+n[0])*t[0],(A[1]+n[1])*t[1]]);return{startPoint:o,endPoint:r,landmarks:s,confidence:e.confidence}},T5=(e,t,n)=>{let 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c=lt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=h3(-s,c),a=T5(t,i,[o,o]),Me.dispose(i)}else a=T5(t,n,[o,o]);else a=T5(t,n,[o,o]);return[s,A,a]}var dA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},v3=(e,t)=>{let n=dA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var R3=6,xA=1.4,xe,ft=null,Ce=0,u2=null,M3=()=>Ce;async function P3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await L((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=O.scalar(Ce,"int32"),ft=O.tensor2d(b3(Ce)),xe}function yA(e){if(!ft||!u2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,ft),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function k3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=yA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],R3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[R3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=p3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||xA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,fA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function j3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},E3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(u3([e[n],e[o]]),fA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},z3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function I3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=E3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=E3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=z3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=z3(y,A,a,!1),g=mA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=S3(e,m,"left"),b=S3(e,T,"right");return e.concat(p).concat(b)}async function L3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await k3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(t0=>t0.shape[t0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await L3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await I3(A0,P.tensor,O2)),P.mesh=g3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(ae))P.annotations[t0]=ae[t0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let n0={...v3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(n0,e),P.boxRaw=dt(n0,e),s.push(n0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function C3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var W3=$e,D3=N2;var J0=G(H());var E5=[],P0,pt=[],F3=0,B3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function H3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-B3;return t.skipAllowed&&s&&r&&F3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),B3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,F3=o,l(c)}))}var ie=G(H());var k0,Fe=[],V3=0,Z3=0,j5=Number.MAX_SAFE_INTEGER;async function X3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function uA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let 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function rn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-on;return t.skipAllowed&&s&&r&&nn===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await 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d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Rt[n]=f,dn=o,xn=v(),x(f)}))}var Mt=G(H());var Q0,q5=[],mn=0,pn=0,un=Number.MAX_SAFE_INTEGER;async function hn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function U5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=un<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-pn;return t.skipAllowed&&s&&r&&mn===o&&q5[n]?(un++,q5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=Mt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await 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this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Yn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Qn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,_n;async function t1(e,t){let n=await _n.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var n,o;R.initial&&(l2=null,c2=null),!l2||!c2?[l2,c2]=await Promise.all([e.hand.enabled?L((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?L((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached model:",l2.modelUrl),e.debug&&h("cached model:",c2.modelUrl));let t=l2?new St(l2):void 0;return t&&c2&&(_n=new jt(t,c2)),[l2,c2]}var Q=G(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],to=4,no=1.6,GA=512,VA=1.4,It=Number.MAX_SAFE_INTEGER,n1=0,we=[0,0],m0={boxes:[],hands:[]},oo={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function ro(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function so(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[to]),a.splice(to,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,VA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=HA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function o1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(oo))o.annotations[i]=oo[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function r1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-n1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-n1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):(m0.boxes=await ZA(e,t),n1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,no),d=st(i.boxRaw,no);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>s1,kpt:()=>Nt,relative:()=>a1,vertical:()=>A1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],s1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],A1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],a1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),i1=0;function ao(e,t){var A,a,l,c,x,i,f,d,m,y,u,T,g,p,b,k,P,N,V,_,Z,$,A0,n0,t0,j0;let n=v();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[X]+B)/r),C0=e.body[M].boxRaw.map((B,X)=>((r-1)*z.body[M].boxRaw[X]+B)/r),x0=e.body[M].keypoints.map((B,X)=>{var U0,y0,Ee,k2,x2,w1,E1,z1,S1;return{score:B.score,part:B.part,position:[z.body[M].keypoints[X]?((r-1)*(z.body[M].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],z.body[M].keypoints[X]?((r-1)*(z.body[M].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],z.body[M].keypoints[X]?((r-1)*(z.body[M].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[z.body[M].keypoints[X]?((r-1)*(z.body[M].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],z.body[M].keypoints[X]?((r-1)*(z.body[M].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],z.body[M].keypoints[X]?((r-1)*(z.body[M].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[z.body[M].keypoints[X]?((r-1)*(((U0=z.body[M].keypoints[X].distance)==null?void 0:U0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(Ee=B.distance)==null?void 0:Ee[0],z.body[M].keypoints[X]?((r-1)*(((k2=z.body[M].keypoints[X].distance)==null?void 0:k2[1])||0)+(((x2=B.distance)==null?void 0:x2[1])||0))/r:(w1=B.distance)==null?void 0:w1[1],z.body[M].keypoints[X]?((r-1)*(((E1=z.body[M].keypoints[X].distance)==null?void 0:E1[2])||0)+(((z1=B.distance)==null?void 0:z1[2])||0))/r:(S1=B.distance)==null?void 0:S1[2]]}}),K={},E={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?E=it:(a=t.body.modelPath)!=null&&a.includes("blazepose")?E=ot:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(E=W2);for(let[B,X]of Object.entries(E.connected)){let U0=[];for(let y0=0;y0x2.part===X[y0]),k2=x0.find(x2=>x2.part===X[y0+1]);Ee&&k2&&U0.push([Ee.position,k2.position])}K[B]=U0}z.body[M]={...e.body[M],box:T0,boxRaw:C0,keypoints:x0,annotations:K}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let M=0;M((r-1)*z.hand[M].box[B]+E)/r),C0=e.hand[M].boxRaw.map((E,B)=>((r-1)*z.hand[M].boxRaw[B]+E)/r);z.hand[M].keypoints.length!==e.hand[M].keypoints.length&&(z.hand[M].keypoints=e.hand[M].keypoints);let x0=e.hand[M].keypoints&&e.hand[M].keypoints.length>0?e.hand[M].keypoints.map((E,B)=>E.map((X,U0)=>((r-1)*(z.hand[M].keypoints[B][U0]||1)+(X||0))/r)):[],K={};if(Object.keys(z.hand[M].annotations).length!==Object.keys(e.hand[M].annotations).length)z.hand[M].annotations=e.hand[M].annotations,K=z.hand[M].annotations;else if(e.hand[M].annotations)for(let E of Object.keys(e.hand[M].annotations))K[E]=(i=(x=(c=e.hand[M])==null?void 0:c.annotations)==null?void 0:x[E])!=null&&i[0]?e.hand[M].annotations[E].map((B,X)=>B.map((U0,y0)=>((r-1)*z.hand[M].annotations[E][X][y0]+U0)/r)):null;z.hand[M]={...e.hand[M],box:T0,boxRaw:C0,keypoints:x0,annotations:K}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((X,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+X)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return i1=R.perfadd?i1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:i1}),z}var s0=G(H());var L0;async function l1(e){return!L0||R.initial?L0=await L(e.segmentation.modelPath):e.debug&&h("cached model:",L0.modelUrl),L0}async function io(e,t){var r;if(L0||(L0=await l1(t)),!(L0!=null&&L0.executor)||!((r=L0==null?void 0:L0.inputs)!=null&&r[0].shape))return null;let 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Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Gt(this.instance,o,n);r&&t.push(r)}return t}};function Oo(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var Vt=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var a5=0;function Ys(e,t,n){let o=a0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sc5,kpt:()=>l5});var l5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],c5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=G(H());var se,n2=224,$1,Qs=5,rt=[8,16,32,32,32];function _s(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function e3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await L(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&h("cached model:",se.modelUrl);return _s(),se}var _1=[5,5];function $s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),_1[0]),A=D.mul(D.div(A,n2),_1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function eA(e,t,n,o){var c,x;let r=[],s={};s.boxes=$s(e,$1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function t3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await eA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Le(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function n3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,x5=256,d5=Number.MAX_SAFE_INTEGER,tA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Oe=[[0,0],[0,0],[0,0],[0,0]],o3=0,r3=e=>1-1/(1+Math.exp(e)),A3=e=>e3(e);async function a3(e){if(R.initial&&(Z0=null),Z0)e.debug&&h("cached model:",Z0.modelUrl);else{Z0=await L(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function s3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=X0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Oe=[[0,0],a,l,[0,0]],o.pad=X0.pad(o.cropped||e,Oe),o.resize=X0.image.resizeBilinear(o.pad,[t,t]),r=X0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=X0.image.resizeBilinear(o.cropped||e,[t,t]),r=X0.div(o.resize,C.tf255)):r=X0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>X0.dispose(o[a])),r}function nA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Oe[2][0]+Oe[2][1])/t[0]-Oe[2][0]),Math.trunc(o.position[1]*(t[1]+Oe[1][0]+Oe[1][1])/t[1]-Oe[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function oA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function rA(e,t,n){if(!(Z0!=null&&Z0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=Z0==null?void 0:Z0.execute(e,tA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>X0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Le(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(c5)){let u=[];for(let T=0;Tb.part===y[T]),p=c.find(b=>b.part===y[T+1]);g&&p&&u.push([g.position,p.position])}f[m]=u}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function y5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-o3,r=d5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&at!==null)d5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=s3(e,224);l=await t3(c,t,n),X0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function p5(e,t){if(!(q0!=null&&q0.executor))return[];let n=(t.object.skipTime||0)>v()-l3,o=m5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&f5.length>0?(m5++,f5):(m5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=F0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?q0==null?void 0:q0.execute(A,["tower_0/detections"]):null;l3=v(),F0.dispose(A);let l=await sA(a,s,t);f5=l,r(l)}))}var J=G(H());var it={};ze(it,{connected:()=>h5,kpt:()=>u5});var u5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],h5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,x3=0,B0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},b5=Number.MAX_SAFE_INTEGER;async function y3(e){return R.initial&&(i0=null),i0?e.debug&&h("cached model:",i0.modelUrl):i0=await L(e.body.modelPath),i0}async function AA(e,t){let[n,o]=e.shape,r=J.reshape(e,[o*n]),s=J.max(r,0),A=(await s.data())[0];if(A>t){let a=J.argMax(r,0),l=J.mod(a,n),c=(await l.data())[0],x=J.div(a,n),i=(await x.data())[0];return J.dispose([r,s,a,l,x]),[c,i,A]}return J.dispose([r,s]),[0,0,A]}async function g5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>v()-x3,o=b5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(B0.keypoints).length>0?(b5++,[B0]):(b5=0,new Promise(async r=>{let s=J.tidy(()=>{var m,y;let i=J.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=J.mul(i,C.tf2);return J.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),x3=v(),J.dispose(s),A){B0.keypoints.length=0;let i=J.squeeze(A);J.dispose(A);let f=J.unstack(i,2);J.dispose(i);for(let 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n=[],o=e.length;for(let r=0;r{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=m3(t[0],t[1]),A=p3(s,r),a=m3(-t[0],-t[1]);return p3(A,a)},cA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},dA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function g3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?b3(n,[0,0]):v5,c=a?A.map(d=>[...dA(d,l),d[2]]):A,x=a?cA(o):v5,i=lt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function v3(e,t,n,o){let r=t.landmarks.length>=r5.count?r5.symmetryLine:_e.symmetryLine,s=0,A=v5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=iA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=lt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=b3(-s,c),a=T5(t,i,[o,o]),Me.dispose(i)}else a=T5(t,n,[o,o]);else a=T5(t,n,[o,o]);return[s,A,a]}var xA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},R3=(e,t)=>{let n=xA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var M3=6,yA=1.4,xe,ft=null,Ce=0,u2=null,P3=()=>Ce;async function k3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await L((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=O.scalar(Ce,"int32"),ft=O.tensor2d(g3(Ce)),xe}function fA(e){if(!ft||!u2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,ft),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function w3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=fA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],M3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[M3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=u3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||yA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},z3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(h3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,O2)),P.mesh=T3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(t0,e),P.boxRaw=dt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=N2;var J0=G(H());var E5=[],P0,pt=[],B3=0,H3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,X3=0,j5=Number.MAX_SAFE_INTEGER;async function q3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await 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d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),vt[n]=f,an=o,ln=v(),x(f)}))}var v0=G(H());var N0,Rt=[],xn=0,yn=0,V5=Number.MAX_SAFE_INTEGER,Z5=[.2989,.587,.114];async function fn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=V5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-yn;return t.skipAllowed&&r&&s&&xn===o&&((l=Rt[n])!=null&&l.gender)&&((c=Rt[n])==null?void 0:c.genderScore)>0?(V5++,Rt[n]):(V5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,Z5[0]),V=v0.mul(k,Z5[1]),_=v0.mul(P,Z5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Rt[n]=f,xn=o,yn=v(),x(f)}))}var Mt=G(H());var Q0,q5=[],pn=0,un=0,hn=Number.MAX_SAFE_INTEGER;async function bn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function U5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=hn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-un;return t.skipAllowed&&s&&r&&pn===o&&q5[n]?(hn++,q5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=Mt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await 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t=(i,f)=>Math.atan2(i[1]-f[1],i[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},kn=(e,t)=>{let n=y=>{let u=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=u,y[1]/=u,y[2]/=u,y},o=(y,u)=>{let T=y[0]-u[0],g=y[1]-u[1],p=y[2]-u[2];return[T,g,p]},r=(y,u)=>{let T=y[1]*u[2]-y[2]*u[1],g=y[2]*u[0]-y[0]*u[2],p=y[0]*u[1]-y[1]*u[0];return[T,g,p]},s=y=>{let[u,T,g,p,b,k,P,N,V]=y,_,Z,$;return 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this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function HA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||HA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function eo(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached 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0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,XA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-o1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,oo),d=st(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Nt,relative:()=>i1,vertical:()=>a1});var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((X,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+X)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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n={};n.resize=s0.image.resizeBilinear(e,[L0.inputs[0].shape?L0.inputs[0].shape[1]:0,L0.inputs[0].shape?L0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=L0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Ot={};ze(Ot,{distance:()=>d1,find:()=>KA,similarity:()=>YA});function d1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?k3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?Q3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?tn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?q3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?G3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?I3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?W3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?sn(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?cn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?fn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?bn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Mn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?a3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?A3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((u=this.instance.config.body.modelPath)!=null&&u.includes("efficientpose"))?y3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?uo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?Eo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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Object.entries(n))j0!=null&&j0.then&&j0.then(M=>this.models[n0]=M);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Gt(this.instance,o,n);r&&t.push(r)}return t}};function Co(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var Vt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -837,4 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA 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s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function fa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Vt):n=t(Zt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let u=0;ud0.dispose(T)):d0.dispose(u)}catch(u){e.config.debug&&h("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&h("tensor leak:",A-o)}async function Co(e,t){await I2(e,!1);let n=v();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=v();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,G2,V2,Xt,Ye,k1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",nt);w(this,"match",Ot);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2,void 0);ue(this,G2,void 0);ue(this,V2,void 0);w(this,"analyze",(...t)=>{if(!Y0(this,G2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&h(...t,r)});ue(this,Xt,t=>{if(!Y0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new K2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),Z1(this.config),this.tf=de,this.state="idle",Re(this,P2,0),Re(this,G2,!1),Re(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),i5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=W3,this.faceUVMap=D3,Gt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return v()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await io(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await No(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return V1(this.config,t,n)}async init(){await I2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await I2(this,!1)||h("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return ao(t,this.config)}async warmup(t){let n=v(),o=await Co(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var u,T,g,p,b,k,P,N,V,_,Z,$,A0,n0,t0,j0,M,T0,C0,x0,K;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=Y0(this,Xt).call(this,t);s&&(h(s,t),this.emit("error"),o(Te(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await G1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?g1(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,f):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?y1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await g1(a.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await g5(a.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await y1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&_.includes("handdetect")?x=this.config.hand.enabled?t1(a.tensor,d):[]:($=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?r1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await t1(a.tensor,d):[]:(j0=(t0=this.config.hand.detector)==null?void 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R.Image;else{t(void 0);return}o.onload=async()=>{let r=Ae(o.naturalWidth,o.naturalHeight);if(!r)h("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function pa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Vt):n=t(Zt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function ua(e){let t;return typeof createImageBitmap=="function"?t=await fa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ma(e):t=await pa(e),t}async function ha(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let u=0;ud0.dispose(T)):d0.dispose(u)}catch(u){e.config.debug&&h("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&h("tensor leak:",A-o)}async function Wo(e,t){await I2(e,!1);let n=v();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await ha(e);let r=await ua(e),s=v();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,G2,V2,Xt,Ye,w1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",nt);w(this,"match",Ot);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2,void 0);ue(this,G2,void 0);ue(this,V2,void 0);w(this,"analyze",(...t)=>{if(!Y0(this,G2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&h(...t,r)});ue(this,Xt,t=>{if(!Y0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new K2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),X1(this.config),this.tf=de,this.state="idle",Re(this,P2,0),Re(this,G2,!1),Re(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),i5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=D3,this.faceUVMap=F3,Gt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return v()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await lo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await 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n=v(),o=await Wo(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var u,T,g,p,b,k,P,N,V,_,Z,$,A0,t0,n0,j0,M,T0,C0,x0,K;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=Y0(this,Xt).call(this,t);s&&(h(s,t),this.emit("error"),o(Te(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await V1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?T1(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,f):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?f1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await T1(a.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await g5(a.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await f1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&_.includes("handdetect")?x=this.config.hand.enabled?n1(a.tensor,d):[]:($=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?s1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(t0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&t0.includes("handdetect")?x=this.config.hand.enabled?await n1(a.tensor,d):[]:(j0=(n0=this.config.hand.detector)==null?void 0:n0.modelPath)!=null&&j0.includes("handtrack")&&(x=this.config.hand.enabled?await s1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?i=this.config.object.enabled?p1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?p5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(C0=this.config.object.modelPath)!=null&&C0.includes("nanodet")?i=this.config.object.enabled?await p1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await p5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=v(),m=[...Wn(l),...Cn(c),...Fn(x),...Dn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-A):Math.trunc(v()-A);let y=((K=this.process.tensor)==null?void 0:K.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Co(l,c,x,m,y)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&h("video start",t.id),Y0(this,Ye)[t.id]=!0),!t.paused&&Y0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),Y0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),Y0(this,Ye)[t.id]=!1)}};P2=new WeakMap,G2=new WeakMap,V2=new WeakMap,Xt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js index 8f3f55a8..059ccb28 100644 --- a/dist/human.node-wasm.js +++ b/dist/human.node-wasm.js @@ -4,7 +4,7 @@ author: ' */ -"use strict";var Do=Object.create;var w2=Object.defineProperty;var Fo=Object.getOwnPropertyDescriptor;var Bo=Object.getOwnPropertyNames;var Ho=Object.getPrototypeOf,Go=Object.prototype.hasOwnProperty;var Vo=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Zo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ze=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},I1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Bo(t))!Go.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=Fo(t,r))||o.enumerable});return e};var G=(e,t,n)=>(n=e!=null?Do(Ho(e)):{},I1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),qo=e=>I1(w2({},"__esModule",{value:!0}),e);var w=(e,t,n)=>(Vo(e,typeof t!="symbol"?t+"":t,n),n),N1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var Y0=(e,t,n)=>(N1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same 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n=[],o=e.length;for(let r=0;r{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=m3(t[0],t[1]),A=p3(s,r),a=m3(-t[0],-t[1]);return p3(A,a)},lA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},cA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function g3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?b3(n,[0,0]):R5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):R5,i=dt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function v3(e,t,n,o){let r=t.landmarks.length>=s5.count?s5.symmetryLine:_e.symmetryLine,s=0,A=R5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=dt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=b3(-s,c),a=v5(t,i,[o,o]),Me.dispose(i)}else a=v5(t,n,[o,o]);else a=v5(t,n,[o,o]);return[s,A,a]}var dA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},R3=(e,t)=>{let n=dA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var M3=6,xA=1.4,xe,pt=null,Ce=0,u2=null,P3=()=>Ce;async function k3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(Ce,"int32"),pt=L.tensor2d(g3(Ce)),xe}function yA(e){if(!pt||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,pt),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function w3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=yA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],M3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[M3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=L.slice(pt,[r[d],0],[1,2]);let g=await y.anchor.data(),p=u3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=ft(p,t.face.scale||xA),k=mt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>L.dispose(y[P]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var ye=G(H());var K0,We=0,fA=2.3,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},z3=(e,t,n,o,r,s=!1)=>{let A=mt(ft(h3([e[n],e[o]]),fA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=mA(e);Math.abs(g)<30?(ut(e,d,"left",null),ut(e,u,"right",null)):g<1?ut(e,d,"left",["EyeUpper0","EyeLower0"]):ut(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function L3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(p,e),P.boxRaw=yt(p,e),P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(t0=>t0.shape[t0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await L3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,W2)),P.mesh=T3(A0,p,b,k,W2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(ae))P.annotations[t0]=ae[t0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let n0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=xt(n0,e),P.boxRaw=yt(n0,e),s.push(n0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),W2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=L2;var J0=G(H());var z5=[],P0,ht=[],B3=0,H3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await O((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function j5(e,t,n,o){var A,a;if(!P0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&ht[n]&&ht[n].length>0?(E5++,ht[n]):(E5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);S5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:z5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}ht[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,q3=0,I5=Number.MAX_SAFE_INTEGER;async function X3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await O((t=e.face.description)==null?void 0:t.modelPath),k0}function uA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function N5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=I5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&q3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(I5++,Fe[n]):(I5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=uA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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x.data())[0];bt[n]=Math.round(100*i)/100,K3=o,J3=v(),gt.dispose([c,x]),l(bt[n])}))}var vt=G(H());var E0,Tt=[],W5=Number.MAX_SAFE_INTEGER,$3=0,en=0;async function tn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await O((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function D5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-en,s=W5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&$3===o&&Tt[n]?(W5++,Tt[n]):(W5=0,new Promise(async l=>{let c=vt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];Tt[n]=Math.round(100*i)/100,$3=o,en=v(),vt.dispose([c,x]),l(Tt[n])}))}var Rt=G(H());var le,F5=[],gA=["white","black","asian","indian","other"],TA=[15,23,28,35.5,45.5,55.5,65],on=0,rn=0,B5=Number.MAX_SAFE_INTEGER;async function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await O((t=e.face.gear)==null?void 0:t.modelPath),le}async function H5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=B5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&F5[n]?(B5++,F5[n]):(B5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Rt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await 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d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),Mt[n]=f,an=o,ln=v(),x(f)}))}var v0=G(H());var N0,Pt=[],xn=0,yn=0,Z5=Number.MAX_SAFE_INTEGER,q5=[.2989,.587,.114];async function fn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await O((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=Z5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-yn;return t.skipAllowed&&r&&s&&xn===o&&((l=Pt[n])!=null&&l.gender)&&((c=Pt[n])==null?void 0:c.genderScore)>0?(Z5++,Pt[n]):(Z5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,q5[0]),V=v0.mul(k,q5[1]),_=v0.mul(P,q5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Pt[n]=f,xn=o,yn=v(),x(f)}))}var kt=G(H());var Q0,U5=[],pn=0,un=0,hn=Number.MAX_SAFE_INTEGER;async function bn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=hn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-un;return t.skipAllowed&&s&&r&&pn===o&&U5[n]?(hn++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=kt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>kt.dispose(i[d]))}U5[n]=c,pn=o,un=v(),l(c)})}var wt=G(H());var _0,K5=[],Tn=0,vn=0,Rn=Number.MAX_SAFE_INTEGER;async function Mn(e){return R.initial&&(_0=null),_0?e.debug&&h("cached model:",_0.modelUrl):_0=await O(e.face.insightface.modelPath),_0}async function J5(e,t,n,o){var A,a;if(!(_0!=null&&_0.executor))return[];let r=Rn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-vn;return t.skipAllowed&&s&&r&&Tn===o&&K5[n]?(Rn++,K5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(_0!=null&&_0.inputs[0].shape)){let i={};i.crop=wt.image.resizeBilinear(e,[_0.inputs[0].shape[2],_0.inputs[0].shape[1]],!1),i.data=_0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>wt.dispose(i[d]))}K5[n]=c,Tn=o,vn=v(),l(c)})}var vA=e=>{let t=(i,f)=>Math.atan2(i[1]-f[1],i[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},kn=(e,t)=>{let n=y=>{let u=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=u,y[1]/=u,y[2]/=u,y},o=(y,u)=>{let T=y[0]-u[0],g=y[1]-u[1],p=y[2]-u[2];return[T,g,p]},r=(y,u)=>{let T=y[1]*u[2]-y[2]*u[1],g=y[2]*u[0]-y[0]*u[2],p=y[0]*u[1]-y[1]*u[0];return[T,g,p]},s=y=>{let[u,T,g,p,b,k,P,N,V]=y,_,Z,$;return 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m,y,u,T,g,p,b,k,P,N,V,_,Z,$,A0,n0,t0,j0,M,T0,C0,x0,K;let n=v(),o,r,s,A,a,l,c,x,i,f=[];e.state="run:face";let d=await C3(t,e.config);if(e.performance.face=R.perfadd?(e.performance.face||0)+Math.trunc(v()-n):Math.trunc(v()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let E=0;E200?kn(d[E],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?A=(y=e.config.face.emotion)!=null&&y.enabled?j5(d[E].tensor||l0.tensor([]),e.config,E,d.length):[]:(e.state="run:emotion",n=v(),A=(u=e.config.face.emotion)!=null&&u.enabled?await j5(d[E].tensor||l0.tensor([]),e.config,E,d.length):[],e.performance.emotion=R.perfadd?(e.performance.emotion||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?c=(T=e.config.face.antispoof)!=null&&T.enabled?C5(d[E].tensor||l0.tensor([]),e.config,E,d.length):0:(e.state="run:antispoof",n=v(),c=(g=e.config.face.antispoof)!=null&&g.enabled?await 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InsightFace:"),e.config.async?l=(n0=e.config.face.insightface)!=null&&n0.enabled?J5(d[E].tensor||l0.tensor([]),e.config,E,d.length):null:(e.state="run:mobilefacenet",n=v(),l=(t0=e.config.face.insightface)!=null&&t0.enabled?await J5(d[E].tensor||l0.tensor([]),e.config,E,d.length):null,e.performance.mobilefacenet=Math.trunc(v()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?i=N5(d[E].tensor||l0.tensor([]),e.config,E,d.length):(e.state="run:description",n=v(),i=await N5(d[E].tensor||l0.tensor([]),e.config,E,d.length),e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Description:"),e.config.async&&([o,s,A,a,l,i,r,c,x]=await Promise.all([o,s,A,a,l,i,r,c,x])),e.analyze("Finish Face:"),(j0=e.config.face.ssrnet)!=null&&j0.enabled&&o&&s&&(i={...i,age:o.age,gender:s.gender,genderScore:s.genderScore}),(M=e.config.face.gear)!=null&&M.enabled&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),(T0=e.config.face.mobilefacenet)!=null&&T0.enabled&&a&&(i.descriptor=a),(C0=e.config.face.insightface)!=null&&C0.enabled&&l&&(i.descriptor=l);let q=(x0=e.config.face.iris)!=null&&x0.enabled?wn(d[E],t.shape[2]):0,U0=(K=e.config.face.detector)!=null&&K.return?l0.squeeze(d[E].tensor):null;l0.dispose(d[E].tensor),d[E].tensor&&delete d[E].tensor;let y0={...d[E],id:E};i.age&&(y0.age=i.age),i.gender&&(y0.gender=i.gender),i.genderScore&&(y0.genderScore=i.genderScore),i.descriptor&&(y0.embedding=i.descriptor),i.race&&(y0.race=i.race),A&&(y0.emotion=A),c&&(y0.real=c),x&&(y0.live=x),q>0&&(y0.distance=q),B&&(y0.rotation=B),U0&&(y0.tensor=U0),f.push(y0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of 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0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>t1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return St(jt(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=St(jt(n),Kn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=e1(o,[0,0]),c=a.map(d=>[...t1(d,l),d[2]]),x=Xn(r),i=[...D2(n),1],f=[qe(i,x[0]),qe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Qn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=St(jt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,$n;async function n1(e,t){let n=await $n.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Et(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function eo(e){var n,o;R.initial&&(l2=null,c2=null),!l2||!c2?[l2,c2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached model:",l2.modelUrl),e.debug&&h("cached model:",c2.modelUrl));let t=l2?new It(l2):void 0;return t&&c2&&($n=new Nt(t,c2)),[l2,c2]}var Q=G(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],no=4,oo=1.6,GA=512,VA=1.4,Ot=Number.MAX_SAFE_INTEGER,o1=0,we=[0,0],m0={boxes:[],hands:[]},ro={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function so(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{_2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=at(y,VA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=HA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],Ot++;let n=(t.hand.skipTime||0)>v()-o1,o=Ot<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=Ot<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await ZA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),Ot=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=at(i.box,oo),d=at(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};ze(F2,{connected:()=>Ct,horizontal:()=>A1,kpt:()=>Lt,relative:()=>i1,vertical:()=>a1});var Lt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),l1=0;function io(e,t){var A,a,l,c,x,i,f,d,m,y,u,T,g,p,b,k,P,N,V,_,Z,$,A0,n0,t0,j0;let n=v();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+B)/r),C0=e.body[M].boxRaw.map((B,q)=>((r-1)*z.body[M].boxRaw[q]+B)/r),x0=e.body[M].keypoints.map((B,q)=>{var U0,y0,Ee,k2,x2,E1,z1,S1,j1;return{score:B.score,part:B.part,position:[z.body[M].keypoints[q]?((r-1)*(z.body[M].keypoints[q].position[0]||0)+(B.position[0]||0))/r:B.position[0],z.body[M].keypoints[q]?((r-1)*(z.body[M].keypoints[q].position[1]||0)+(B.position[1]||0))/r:B.position[1],z.body[M].keypoints[q]?((r-1)*(z.body[M].keypoints[q].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[z.body[M].keypoints[q]?((r-1)*(z.body[M].keypoints[q].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],z.body[M].keypoints[q]?((r-1)*(z.body[M].keypoints[q].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],z.body[M].keypoints[q]?((r-1)*(z.body[M].keypoints[q].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[z.body[M].keypoints[q]?((r-1)*(((U0=z.body[M].keypoints[q].distance)==null?void 0:U0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(Ee=B.distance)==null?void 0:Ee[0],z.body[M].keypoints[q]?((r-1)*(((k2=z.body[M].keypoints[q].distance)==null?void 0:k2[1])||0)+(((x2=B.distance)==null?void 0:x2[1])||0))/r:(E1=B.distance)==null?void 0:E1[1],z.body[M].keypoints[q]?((r-1)*(((z1=z.body[M].keypoints[q].distance)==null?void 0:z1[2])||0)+(((S1=B.distance)==null?void 0:S1[2])||0))/r:(j1=B.distance)==null?void 0:j1[2]]}}),K={},E={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?E=ct:(a=t.body.modelPath)!=null&&a.includes("blazepose")?E=st:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(E=F2);for(let[B,q]of Object.entries(E.connected)){let U0=[];for(let y0=0;y0x2.part===q[y0]),k2=x0.find(x2=>x2.part===q[y0+1]);Ee&&k2&&U0.push([Ee.position,k2.position])}K[B]=U0}z.body[M]={...e.body[M],box:T0,boxRaw:C0,keypoints:x0,annotations:K}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let M=0;M((r-1)*z.hand[M].box[B]+E)/r),C0=e.hand[M].boxRaw.map((E,B)=>((r-1)*z.hand[M].boxRaw[B]+E)/r);z.hand[M].keypoints.length!==e.hand[M].keypoints.length&&(z.hand[M].keypoints=e.hand[M].keypoints);let x0=e.hand[M].keypoints&&e.hand[M].keypoints.length>0?e.hand[M].keypoints.map((E,B)=>E.map((q,U0)=>((r-1)*(z.hand[M].keypoints[B][U0]||1)+(q||0))/r)):[],K={};if(Object.keys(z.hand[M].annotations).length!==Object.keys(e.hand[M].annotations).length)z.hand[M].annotations=e.hand[M].annotations,K=z.hand[M].annotations;else if(e.hand[M].annotations)for(let E of Object.keys(e.hand[M].annotations))K[E]=(i=(x=(c=e.hand[M])==null?void 0:c.annotations)==null?void 0:x[E])!=null&&i[0]?e.hand[M].annotations[E].map((B,q)=>B.map((U0,y0)=>((r-1)*z.hand[M].annotations[E][q][y0]+U0)/r)):null;z.hand[M]={...e.hand[M],box:T0,boxRaw:C0,keypoints:x0,annotations:K}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((q,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+q)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 0:k.yaw)||0)+(((N=(P=e.face[M].rotation)==null?void 0:P.angle)==null?void 0:N.yaw)||0))/r,pitch:((r-1)*(((_=(V=z.face[M].rotation)==null?void 0:V.angle)==null?void 0:_.pitch)||0)+((($=(Z=e.face[M].rotation)==null?void 0:Z.angle)==null?void 0:$.pitch)||0))/r},K.gaze={bearing:((r-1)*(((A0=z.face[M].rotation)==null?void 0:A0.gaze.bearing)||0)+(((n0=e.face[M].rotation)==null?void 0:n0.gaze.bearing)||0))/r,strength:((r-1)*(((t0=z.face[M].rotation)==null?void 0:t0.gaze.strength)||0)+(((j0=e.face[M].rotation)==null?void 0:j0.gaze.strength)||0))/r},z.face[M]={...e.face[M],rotation:K,box:T0,boxRaw:C0,annotations:x0}}else z.face[M]={...e.face[M],box:T0,boxRaw:C0,annotations:x0}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let M=0;M((r-1)*z.object[M].box[K]+x0)/r),C0=e.object[M].boxRaw.map((x0,K)=>((r-1)*z.object[M].boxRaw[K]+x0)/r);z.object[M]={...e.object[M],box:T0,boxRaw:C0}}if(e.persons){let M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return l1=R.perfadd?l1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:l1}),z}var s0=G(H());var O0;async function c1(e){return!O0||R.initial?O0=await O(e.segmentation.modelPath):e.debug&&h("cached model:",O0.modelUrl),O0}async function lo(e,t){var r;if(O0||(O0=await c1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Wt={};ze(Wt,{distance:()=>d1,find:()=>UA,similarity:()=>XA});function d1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function XA(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=d1(e,t,n);return xo(o,n.order||2,n.min||0,n.max||1)}function UA(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let A=0;AV2,validateModel:()=>Zt});var B2=G(H());var Ue=G(H());var yo=.005,ee={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function x1(e){for(let t of A1){let n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),s=e.keypoints.findIndex(c=>c&&c.part===n[0]),A=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[s]||!e.keypoints[A])continue;let 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Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Zt(this.instance,o,n);r&&t.push(r)}return t}};function Co(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var qt=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var i5=0;function Ys(e,t,n){let o=a0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sd5,kpt:()=>c5});var c5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],d5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=G(H());var se,n2=224,e3,Qs=5,At=[8,16,32,32,32];function _s(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function t3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await O(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&h("cached model:",se.modelUrl);return _s(),se}var $1=[5,5];function $s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),$1[0]),A=D.mul(D.div(A,n2),$1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function eA(e,t,n,o){var c,x;let r=[],s={};s.boxes=$s(e,e3),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function n3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await eA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Oe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function o3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function at(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,y5=256,x5=Number.MAX_SAFE_INTEGER,tA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},lt=[],Le=[[0,0],[0,0],[0,0],[0,0]],r3=0,s3=e=>1-1/(1+Math.exp(e)),a3=e=>t3(e);async function i3(e){if(R.initial&&(Z0=null),Z0)e.debug&&h("cached model:",Z0.modelUrl);else{Z0=await O(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;y5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function A3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=q0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Le=[[0,0],a,l,[0,0]],o.pad=q0.pad(o.cropped||e,Le),o.resize=q0.image.resizeBilinear(o.pad,[t,t]),r=q0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=q0.image.resizeBilinear(o.cropped||e,[t,t]),r=q0.div(o.resize,C.tf255)):r=q0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>q0.dispose(o[a])),r}function nA(e,t,n){for(let o of 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o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>q0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Oe(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(d5)){let u=[];for(let T=0;Tb.part===y[T]),p=c.find(b=>b.part===y[T+1]);g&&p&&u.push([g.position,p.position])}f[m]=u}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function f5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-r3,r=x5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&<!==null)x5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=A3(e,224);l=await n3(c,t,n),q0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function u5(e,t){if(!(X0!=null&&X0.executor))return[];let n=(t.object.skipTime||0)>v()-c3,o=p5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&m5.length>0?(p5++,m5):(p5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=F0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?X0==null?void 0:X0.execute(A,["tower_0/detections"]):null;c3=v(),F0.dispose(A);let l=await sA(a,s,t);m5=l,r(l)}))}var J=G(H());var ct={};ze(ct,{connected:()=>b5,kpt:()=>h5});var h5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],b5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,y3=0,B0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},g5=Number.MAX_SAFE_INTEGER;async function f3(e){return R.initial&&(i0=null),i0?e.debug&&h("cached model:",i0.modelUrl):i0=await O(e.body.modelPath),i0}async function AA(e,t){let[n,o]=e.shape,r=J.reshape(e,[o*n]),s=J.max(r,0),A=(await s.data())[0];if(A>t){let a=J.argMax(r,0),l=J.mod(a,n),c=(await l.data())[0],x=J.div(a,n),i=(await x.data())[0];return J.dispose([r,s,a,l,x]),[c,i,A]}return J.dispose([r,s]),[0,0,A]}async function T5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>v()-y3,o=g5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(B0.keypoints).length>0?(g5++,[B0]):(g5=0,new Promise(async r=>{let s=J.tidy(()=>{var m,y;let i=J.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=J.mul(i,C.tf2);return J.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),y3=v(),J.dispose(s),A){B0.keypoints.length=0;let i=J.squeeze(A);J.dispose(A);let f=J.unstack(i,2);J.dispose(i);for(let 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o=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],A=Me.image.cropAndResize(t,[s],[0],n),a=Me.div(A,C.tf255);return Me.dispose(A),a},ft=(e,t)=>{let n=dt(e),o=p2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},mt=e=>{let t=dt(e),n=p2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},b3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},R5=[[1,0,0],[0,1,0],[0,0,1]],aA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),iA=(e,t)=>aA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var p3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],r2=(e,t)=>{let n=0;for(let o=0;o{let n=[];for(let o=0;o{let n=[],o=e.length;for(let r=0;r{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=p3(t[0],t[1]),A=u3(s,r),a=p3(-t[0],-t[1]);return u3(A,a)},cA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},dA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function T3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?g3(n,[0,0]):R5,c=a?A.map(d=>[...dA(d,l),d[2]]):A,x=a?cA(o):R5,i=dt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function R3(e,t,n,o){let r=t.landmarks.length>=s5.count?s5.symmetryLine:_e.symmetryLine,s=0,A=R5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=iA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=dt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=g3(-s,c),a=v5(t,i,[o,o]),Me.dispose(i)}else a=v5(t,n,[o,o]);else a=v5(t,n,[o,o]);return[s,A,a]}var xA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},M3=(e,t)=>{let n=xA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var P3=6,yA=1.4,xe,pt=null,Ce=0,u2=null,k3=()=>Ce;async function w3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(Ce,"int32"),pt=L.tensor2d(T3(Ce)),xe}function fA(e){if(!pt||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,pt),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function E3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=fA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],P3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[P3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=L.slice(pt,[r[d],0],[1,2]);let g=await y.anchor.data(),p=h3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=ft(p,t.face.scale||yA),k=mt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>L.dispose(y[P]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function N3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},S3=(e,t,n,o,r,s=!1)=>{let A=mt(ft(b3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},j3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function O3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=S3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=S3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=j3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=j3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(ut(e,d,"left",null),ut(e,u,"right",null)):g<1?ut(e,d,"left",["EyeUpper0","EyeLower0"]):ut(e,u,"right",["EyeUpper0","EyeLower0"]);let p=I3(e,m,"left"),b=I3(e,T,"right");return e.concat(p).concat(b)}async function C3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await E3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(p,e),P.boxRaw=yt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await C3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await O3(A0,P.tensor,W2)),P.mesh=v3(A0,p,b,k,W2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...M3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=xt(t0,e),P.boxRaw=yt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function D3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),W2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var F3=$e,B3=L2;var J0=G(H());var z5=[],P0,ht=[],H3=0,G3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function V3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await O((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function j5(e,t,n,o){var A,a;if(!P0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-G3;return t.skipAllowed&&s&&r&&H3===o&&ht[n]&&ht[n].length>0?(E5++,ht[n]):(E5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);S5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),G3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:z5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}ht[n]=c,H3=o,l(c)}))}var ie=G(H());var k0,Fe=[],q3=0,X3=0,I5=Number.MAX_SAFE_INTEGER;async function U3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await O((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function N5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=I5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-q3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(I5++,Fe[n]):(I5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);q3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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x.data())[0];bt[n]=Math.round(100*i)/100,J3=o,Q3=v(),gt.dispose([c,x]),l(bt[n])}))}var vt=G(H());var E0,Tt=[],W5=Number.MAX_SAFE_INTEGER,en=0,tn=0;async function nn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await O((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function D5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-tn,s=W5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&en===o&&Tt[n]?(W5++,Tt[n]):(W5=0,new Promise(async l=>{let c=vt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];Tt[n]=Math.round(100*i)/100,en=o,tn=v(),vt.dispose([c,x]),l(Tt[n])}))}var Rt=G(H());var le,F5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],rn=0,sn=0,B5=Number.MAX_SAFE_INTEGER;async function An(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await O((t=e.face.gear)==null?void 0:t.modelPath),le}async function H5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=B5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-sn;return t.skipAllowed&&s&&r&&rn===o&&F5[n]?(B5++,F5[n]):(B5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Rt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let k=0;k(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[k])/100,race:TA[k]});i.race.sort((k,P)=>P.score-k.score);let y=Array.from(await c.age.data()).map((k,P)=>[vA[P],k]).sort((k,P)=>P[1]-k[1]),u=y[0][0];for(let k=1;kRt.dispose(c[k])),F5[n]=i,rn=o,sn=v(),l(i)}))}var s2=G(H());var H0,Mt=[],ln=0,cn=0,G5=Number.MAX_SAFE_INTEGER;async function dn(e){return R.initial&&(H0=null),H0?e.debug&&h("cached model:",H0.modelUrl):H0=await O(e.face.ssrnet.modelPathAge),H0}async function V5(e,t,n,o){var A,a,l,c;if(!H0)return{age:0};let r=G5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-cn;return t.skipAllowed&&r&&s&&ln===o&&((l=Mt[n])!=null&&l.age)&&((c=Mt[n])==null?void 0:c.age)>0?(G5++,Mt[n]):(G5=0,new Promise(async x=>{var d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),Mt[n]=f,ln=o,cn=v(),x(f)}))}var v0=G(H());var N0,Pt=[],yn=0,fn=0,Z5=Number.MAX_SAFE_INTEGER,q5=[.2989,.587,.114];async function mn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await O((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=Z5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-fn;return t.skipAllowed&&r&&s&&yn===o&&((l=Pt[n])!=null&&l.gender)&&((c=Pt[n])==null?void 0:c.genderScore)>0?(Z5++,Pt[n]):(Z5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,q5[0]),V=v0.mul(k,q5[1]),_=v0.mul(P,q5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Pt[n]=f,yn=o,fn=v(),x(f)}))}var kt=G(H());var Q0,U5=[],un=0,hn=0,bn=Number.MAX_SAFE_INTEGER;async function gn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=bn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-hn;return t.skipAllowed&&s&&r&&un===o&&U5[n]?(bn++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=kt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>kt.dispose(i[d]))}U5[n]=c,un=o,hn=v(),l(c)})}var wt=G(H());var _0,K5=[],vn=0,Rn=0,Mn=Number.MAX_SAFE_INTEGER;async function Pn(e){return R.initial&&(_0=null),_0?e.debug&&h("cached model:",_0.modelUrl):_0=await O(e.face.insightface.modelPath),_0}async function J5(e,t,n,o){var A,a;if(!(_0!=null&&_0.executor))return[];let r=Mn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-Rn;return t.skipAllowed&&s&&r&&vn===o&&K5[n]?(Mn++,K5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(_0!=null&&_0.inputs[0].shape)){let i={};i.crop=wt.image.resizeBilinear(e,[_0.inputs[0].shape[2],_0.inputs[0].shape[1]],!1),i.data=_0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>wt.dispose(i[d]))}K5[n]=c,vn=o,Rn=v(),l(c)})}var RA=e=>{let t=(i,f)=>Math.atan2(i[1]-f[1],i[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},wn=(e,t)=>{let n=y=>{let u=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=u,y[1]/=u,y[2]/=u,y},o=(y,u)=>{let T=y[0]-u[0],g=y[1]-u[1],p=y[2]-u[2];return[T,g,p]},r=(y,u)=>{let T=y[1]*u[2]-y[2]*u[1],g=y[2]*u[0]-y[0]*u[2],p=y[0]*u[1]-y[1]*u[0];return[T,g,p]},s=y=>{let[u,T,g,p,b,k,P,N,V]=y,_,Z,$;return 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this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=St(jt(c),Jn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var $n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,n1;function HA(){let e=l2?new It(l2):void 0;e&&c2&&(n1=new Nt(e,c2))}async function o1(e,t){n1||HA();let n=await n1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Et(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function eo(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await O((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function to(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached model:",c2.modelUrl):c2=await O((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var Q=G(H());var p0=[null,null],GA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],VA=["hand","fist","pinch","point","face","tip","pinchtip"],oo=4,ro=1.6,ZA=512,qA=1.4,Ot=Number.MAX_SAFE_INTEGER,r1=0,we=[0,0],m0={boxes:[],hands:[]},so={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Ao(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{_2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function XA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[oo]),a.splice(oo,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=at(y,qA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function s1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(so))o.annotations[i]=so[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function A1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],Ot++;let n=(t.hand.skipTime||0)>v()-r1,o=Ot<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-r1,l=Ot<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))):(m0.boxes=await XA(e,t),r1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))),Ot=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=at(i.box,ro),d=at(i.boxRaw,ro);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};ze(F2,{connected:()=>Ct,horizontal:()=>a1,kpt:()=>Lt,relative:()=>l1,vertical:()=>i1});var Lt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],a1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],i1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],l1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),c1=0;function lo(e,t){var A,a,l,c,x,i,f,d,m,y,u,T,g,p,b,k,P,N,V,_,Z,$,A0,t0,n0,j0;let n=v();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+B)/r),C0=e.body[M].boxRaw.map((B,q)=>((r-1)*z.body[M].boxRaw[q]+B)/r),x0=e.body[M].keypoints.map((B,q)=>{var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((q,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+q)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return c1=R.perfadd?c1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:c1}),z}var s0=G(H());var O0;async function d1(e){return!O0||R.initial?O0=await O(e.segmentation.modelPath):e.debug&&h("cached model:",O0.modelUrl),O0}async function co(e,t){var r;if(O0||(O0=await d1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Wt={};ze(Wt,{distance:()=>x1,find:()=>KA,similarity:()=>YA});function x1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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l=[s[51+1],s[51+0],s[51+3]-s[51+1],s[51+2]-s[51+0]],c=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))],x={};for(let[f,d]of Object.entries(Ct)){let m=[];for(let y=0;yg.part===d[y]),T=a.find(g=>g.part===d[y+1]);u&&T&&u.score>(t.body.minConfidence||0)&&T.score>(t.body.minConfidence||0)&&m.push([u.position,T.position])}x[f]=m}let i={id:r,score:A,box:c,boxRaw:l,keypoints:[...a],annotations:x};y1(i),o.push(i)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function m1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(d2.boxes.length=0),f1++;let n=(t.body.skipTime||0)>v()-d2.last,o=f1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?d2.bodies:new Promise(async s=>{let A={};f1=0,A.input=po(e,Dt),A.res=b0==null?void 0:b0.execute(A.input),d2.last=v();let a=await 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G2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ea=G2.length,H2=G2.reduce((e,t,n)=>(e[t]=n,e),{}),ta=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],q7=ta.map(([e,t])=>[H2[e],H2[t]]),Ro=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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a=T=>({y:s.get(T.y,T.x,e),x:s.get(T.y,T.x,s.shape[2]/2+e)}),l=(T,g,p)=>({y:g1(Math.round(T.y/M2),0,g-1),x:g1(Math.round(T.x/M2),0,p-1)}),[c,x]=o.shape,i=l(t.position,c,x),f=a(i),m=T1(t.position,f);for(let T=0;T[H2[f],H2[d]]),A=s.map(([,f])=>f),a=s.map(([f])=>f),l=t.shape[2],c=A.length,x=new Array(l),i=b1(e.part,M2,n);x[e.part.id]={score:e.score,part:G2[e.part.id],position:i};for(let f=c-1;f>=0;--f){let d=A[f],m=a[f];x[d]&&!x[m]&&(x[m]=wo(f,x[d],m,t,n,r))}for(let f=0;ft){a=!1;break}if(!a)break}return a}function aa(e,t){let[n,o,r]=t.shape,s=new Gt(n*o*r,({score:A})=>A);for(let A=0;A{var A;let s=(A=r[o])==null?void 0:A.position;return s?ko(n,t,s.y,s.x)<=ra:!1})}function ia(e,t){return t.reduce((o,{position:r,score:s},A)=>(Eo(e,r,A)||(o+=s),o),0)/t.length}function la(e,t,n,o,r,s){let A=[],a=aa(s,t);for(;A.lengthd.score>s);let i=ia(A,x),f=Mo(x);i>s&&A.push({keypoints:x,box:f,score:Math.round(100*i)/100})}return A}async function v1(e,t){if(!(te!=null&&te.executor))return[];let 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?w3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?_3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?nn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?U3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?V3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?N3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?D3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?An(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?dn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?mn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?gn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Pn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?i3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?a3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((u=this.instance.config.body.modelPath)!=null&&u.includes("efficientpose"))?f3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?ho(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?zo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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Object.entries(n))j0!=null&&j0.then&&j0.then(M=>this.models[n0]=M);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Zt(this.instance,o,n);r&&t.push(r)}return t}};function Wo(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var qt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -837,4 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA 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s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function fa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(qt):n=t(Xt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let u=0;ud0.dispose(T)):d0.dispose(u)}catch(u){e.config.debug&&h("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&h("tensor leak:",A-o)}async function Wo(e,t){await O2(e,!1);let n=v();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=v();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,Z2,q2,Ut,Ye,w1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",rt);w(this,"match",Wt);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2,void 0);ue(this,Z2,void 0);ue(this,q2,void 0);w(this,"analyze",(...t)=>{if(!Y0(this,Z2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&h(...t,r)});ue(this,Ut,t=>{if(!Y0(this,q2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new Q2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=n5,Object.defineProperty(this,"version",{value:n5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),q1(this.config),this.tf=de,this.state="idle",Re(this,P2,0),Re(this,Z2,!1),Re(this,q2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new V2(this),l5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=D3,this.faceUVMap=F3,Zt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,e5(),R.initial=!0}validate(t){let n=Qt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return v()}image(t,n=!1){return K2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await K2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await lo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Oo(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return Z1(this.config,t,n)}async init(){await O2(this,!0),await this.tf.ready(),e5()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await O2(this,!1)||h("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return io(t,this.config)}async warmup(t){let n=v(),o=await Wo(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var u,T,g,p,b,k,P,N,V,_,Z,$,A0,n0,t0,j0,M,T0,C0,x0,K;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=Y0(this,Ut).call(this,t);s&&(h(s,t),this.emit("error"),o(Te(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await K2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await V1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Q5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await Q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?T1(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?f5(a.tensor,f):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?T5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?f1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await T1(a.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await f5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await 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R.Image;else{t(void 0);return}o.onload=async()=>{let r=Ae(o.naturalWidth,o.naturalHeight);if(!r)h("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function pa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(qt):n=t(Xt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function ua(e){let t;return typeof createImageBitmap=="function"?t=await fa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ma(e):t=await pa(e),t}async function ha(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let u=0;ud0.dispose(T)):d0.dispose(u)}catch(u){e.config.debug&&h("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&h("tensor leak:",A-o)}async function Do(e,t){await O2(e,!1);let n=v();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await ha(e);let r=await ua(e),s=v();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,Z2,q2,Ut,Ye,E1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",rt);w(this,"match",Wt);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2,void 0);ue(this,Z2,void 0);ue(this,q2,void 0);w(this,"analyze",(...t)=>{if(!Y0(this,Z2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&h(...t,r)});ue(this,Ut,t=>{if(!Y0(this,q2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new Q2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=n5,Object.defineProperty(this,"version",{value:n5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),X1(this.config),this.tf=de,this.state="idle",Re(this,P2,0),Re(this,Z2,!1),Re(this,q2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new V2(this),l5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=F3,this.faceUVMap=B3,Zt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,e5(),R.initial=!0}validate(t){let n=Qt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return v()}image(t,n=!1){return K2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await K2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await No(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await co(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Lo(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return q1(this.config,t,n)}async init(){await O2(this,!0),await this.tf.ready(),e5()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await O2(this,!1)||h("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return lo(t,this.config)}async warmup(t){let 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Q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?v1(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?f5(a.tensor,f):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?T5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?m1(a.tensor,f):[]),this.performance.body&&delete 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this.performance.object):(r=v(),(C0=this.config.object.modelPath)!=null&&C0.includes("nanodet")?i=this.config.object.enabled?await u1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await u5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=v(),m=[...Dn(l),...Wn(c),...Bn(x),...Fn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-A):Math.trunc(v()-A);let y=((K=this.process.tensor)==null?void 0:K.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Wo(l,c,x,m,y)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&h("video start",t.id),Y0(this,Ye)[t.id]=!0),!t.paused&&Y0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),Y0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),Y0(this,Ye)[t.id]=!1)}};P2=new WeakMap,Z2=new WeakMap,q2=new WeakMap,Ut=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/human.node.js b/dist/human.node.js index a7cb18af..f8f7d499 100644 --- a/dist/human.node.js +++ b/dist/human.node.js @@ -4,7 +4,7 @@ author: ' */ 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c=lt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=h3(-s,c),a=T5(t,i,[o,o]),Me.dispose(i)}else a=T5(t,n,[o,o]);else a=T5(t,n,[o,o]);return[s,A,a]}var dA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},v3=(e,t)=>{let n=dA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var R3=6,xA=1.4,xe,ft=null,Ce=0,u2=null,M3=()=>Ce;async function P3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await L((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=O.scalar(Ce,"int32"),ft=O.tensor2d(b3(Ce)),xe}function yA(e){if(!ft||!u2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,ft),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function k3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=yA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],R3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[R3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=p3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||xA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,fA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function j3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},E3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(u3([e[n],e[o]]),fA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},z3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function I3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=E3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=E3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=z3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=z3(y,A,a,!1),g=mA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=S3(e,m,"left"),b=S3(e,T,"right");return e.concat(p).concat(b)}async function L3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await k3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(t0=>t0.shape[t0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await L3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await I3(A0,P.tensor,O2)),P.mesh=g3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(ae))P.annotations[t0]=ae[t0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let n0={...v3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(n0,e),P.boxRaw=dt(n0,e),s.push(n0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function C3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var W3=$e,D3=N2;var J0=G(H());var E5=[],P0,pt=[],F3=0,B3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function H3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-B3;return t.skipAllowed&&s&&r&&F3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),B3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,F3=o,l(c)}))}var ie=G(H());var k0,Fe=[],V3=0,Z3=0,j5=Number.MAX_SAFE_INTEGER;async function X3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function uA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-V3;return t.skipAllowed&&s&&A&&Z3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=uA(e,t),m=k0==null?void 0:k0.execute(d);V3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Yn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=Xn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Jn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Yn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return 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a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var n,o;R.initial&&(l2=null,c2=null),!l2||!c2?[l2,c2]=await Promise.all([e.hand.enabled?L((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?L((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached 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a=Q.unstack(o.scores,1);Q.dispose(a[to]),a.splice(to,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,VA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=HA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function o1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((X,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+X)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return i1=R.perfadd?i1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:i1}),z}var s0=G(H());var L0;async function l1(e){return!L0||R.initial?L0=await L(e.segmentation.modelPath):e.debug&&h("cached model:",L0.modelUrl),L0}async function io(e,t){var r;if(L0||(L0=await l1(t)),!(L0!=null&&L0.executor)||!((r=L0==null?void 0:L0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[L0.inputs[0].shape?L0.inputs[0].shape[1]:0,L0.inputs[0].shape?L0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=L0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Ot={};ze(Ot,{distance:()=>c1,find:()=>UA,similarity:()=>qA});function c1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function qA(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=c1(e,t,n);return co(o,n.order||2,n.min||0,n.max||1)}function UA(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let A=0;AH2,validateModel:()=>Gt});var D2=G(H());var Ue=G(H());var xo=.005,ee={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function d1(e){for(let t of s1){let n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),s=e.keypoints.findIndex(c=>c&&c.part===n[0]),A=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[s]||!e.keypoints[A])continue;let 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l=[s[51+1],s[51+0],s[51+3]-s[51+1],s[51+2]-s[51+0]],c=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))],x={};for(let[f,d]of Object.entries(Lt)){let m=[];for(let y=0;yg.part===d[y]),T=a.find(g=>g.part===d[y+1]);u&&T&&u.score>(t.body.minConfidence||0)&&T.score>(t.body.minConfidence||0)&&m.push([u.position,T.position])}x[f]=m}let i={id:r,score:A,box:c,boxRaw:l,keypoints:[...a],annotations:x};d1(i),o.push(i)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function y1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(d2.boxes.length=0),x1++;let n=(t.body.skipTime||0)>v()-d2.last,o=x1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?d2.bodies:new Promise(async s=>{let A={};x1=0,A.input=fo(e,Ct),A.res=b0==null?void 0:b0.execute(A.input),d2.last=v();let a=await 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Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Gt(this.instance,o,n);r&&t.push(r)}return t}};function Oo(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var Vt=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var a5=0;function Ys(e,t,n){let o=a0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sc5,kpt:()=>l5});var l5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],c5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=G(H());var se,n2=224,$1,Qs=5,rt=[8,16,32,32,32];function _s(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function e3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await L(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&h("cached model:",se.modelUrl);return _s(),se}var _1=[5,5];function $s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),_1[0]),A=D.mul(D.div(A,n2),_1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function eA(e,t,n,o){var c,x;let r=[],s={};s.boxes=$s(e,$1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function t3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await eA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Le(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function n3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,x5=256,d5=Number.MAX_SAFE_INTEGER,tA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Oe=[[0,0],[0,0],[0,0],[0,0]],o3=0,r3=e=>1-1/(1+Math.exp(e)),A3=e=>e3(e);async function a3(e){if(R.initial&&(Z0=null),Z0)e.debug&&h("cached model:",Z0.modelUrl);else{Z0=await L(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function s3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=X0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Oe=[[0,0],a,l,[0,0]],o.pad=X0.pad(o.cropped||e,Oe),o.resize=X0.image.resizeBilinear(o.pad,[t,t]),r=X0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=X0.image.resizeBilinear(o.cropped||e,[t,t]),r=X0.div(o.resize,C.tf255)):r=X0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>X0.dispose(o[a])),r}function nA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Oe[2][0]+Oe[2][1])/t[0]-Oe[2][0]),Math.trunc(o.position[1]*(t[1]+Oe[1][0]+Oe[1][1])/t[1]-Oe[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function oA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function rA(e,t,n){if(!(Z0!=null&&Z0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=Z0==null?void 0:Z0.execute(e,tA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>X0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Le(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(c5)){let u=[];for(let T=0;Tb.part===y[T]),p=c.find(b=>b.part===y[T+1]);g&&p&&u.push([g.position,p.position])}f[m]=u}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function y5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-o3,r=d5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&at!==null)d5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=s3(e,224);l=await t3(c,t,n),X0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function p5(e,t){if(!(q0!=null&&q0.executor))return[];let n=(t.object.skipTime||0)>v()-l3,o=m5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&f5.length>0?(m5++,f5):(m5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=F0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?q0==null?void 0:q0.execute(A,["tower_0/detections"]):null;l3=v(),F0.dispose(A);let l=await sA(a,s,t);f5=l,r(l)}))}var J=G(H());var it={};ze(it,{connected:()=>h5,kpt:()=>u5});var u5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],h5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,x3=0,B0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},b5=Number.MAX_SAFE_INTEGER;async function y3(e){return R.initial&&(i0=null),i0?e.debug&&h("cached model:",i0.modelUrl):i0=await L(e.body.modelPath),i0}async function AA(e,t){let[n,o]=e.shape,r=J.reshape(e,[o*n]),s=J.max(r,0),A=(await s.data())[0];if(A>t){let a=J.argMax(r,0),l=J.mod(a,n),c=(await l.data())[0],x=J.div(a,n),i=(await x.data())[0];return J.dispose([r,s,a,l,x]),[c,i,A]}return J.dispose([r,s]),[0,0,A]}async function g5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>v()-x3,o=b5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(B0.keypoints).length>0?(b5++,[B0]):(b5=0,new Promise(async r=>{let s=J.tidy(()=>{var m,y;let i=J.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=J.mul(i,C.tf2);return J.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),x3=v(),J.dispose(s),A){B0.keypoints.length=0;let i=J.squeeze(A);J.dispose(A);let f=J.unstack(i,2);J.dispose(i);for(let 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t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,ft),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function w3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=fA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],M3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[M3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=u3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||yA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},z3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(h3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,O2)),P.mesh=T3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(t0,e),P.boxRaw=dt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=N2;var J0=G(H());var E5=[],P0,pt=[],B3=0,H3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,X3=0,j5=Number.MAX_SAFE_INTEGER;async function q3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await 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0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-yn;return t.skipAllowed&&r&&s&&xn===o&&((l=Rt[n])!=null&&l.gender)&&((c=Rt[n])==null?void 0:c.genderScore)>0?(V5++,Rt[n]):(V5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,Z5[0]),V=v0.mul(k,Z5[1]),_=v0.mul(P,Z5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Rt[n]=f,xn=o,yn=v(),x(f)}))}var Mt=G(H());var Q0,q5=[],pn=0,un=0,hn=Number.MAX_SAFE_INTEGER;async function bn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function U5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=hn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-un;return t.skipAllowed&&s&&r&&pn===o&&q5[n]?(hn++,q5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=Mt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Mt.dispose(i[d]))}q5[n]=c,pn=o,un=v(),l(c)})}var Pt=G(H());var _0,Y5=[],Tn=0,vn=0,Rn=Number.MAX_SAFE_INTEGER;async function Mn(e){return R.initial&&(_0=null),_0?e.debug&&h("cached model:",_0.modelUrl):_0=await L(e.face.insightface.modelPath),_0}async function K5(e,t,n,o){var A,a;if(!(_0!=null&&_0.executor))return[];let r=Rn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-vn;return t.skipAllowed&&s&&r&&Tn===o&&Y5[n]?(Rn++,Y5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(_0!=null&&_0.inputs[0].shape)){let i={};i.crop=Pt.image.resizeBilinear(e,[_0.inputs[0].shape[2],_0.inputs[0].shape[1]],!1),i.data=_0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Pt.dispose(i[d]))}Y5[n]=c,Tn=o,vn=v(),l(c)})}var RA=e=>{let t=(i,f)=>Math.atan2(i[1]-f[1],i[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},kn=(e,t)=>{let n=y=>{let u=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=u,y[1]/=u,y[2]/=u,y},o=(y,u)=>{let T=y[0]-u[0],g=y[1]-u[1],p=y[2]-u[2];return[T,g,p]},r=(y,u)=>{let T=y[1]*u[2]-y[2]*u[1],g=y[2]*u[0]-y[0]*u[2],p=y[0]*u[1]-y[1]*u[0];return[T,g,p]},s=y=>{let[u,T,g,p,b,k,P,N,V]=y,_,Z,$;return 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InsightFace:"),e.config.async?l=(t0=e.config.face.insightface)!=null&&t0.enabled?K5(d[E].tensor||l0.tensor([]),e.config,E,d.length):null:(e.state="run:mobilefacenet",n=v(),l=(n0=e.config.face.insightface)!=null&&n0.enabled?await K5(d[E].tensor||l0.tensor([]),e.config,E,d.length):null,e.performance.mobilefacenet=Math.trunc(v()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?i=I5(d[E].tensor||l0.tensor([]),e.config,E,d.length):(e.state="run:description",n=v(),i=await I5(d[E].tensor||l0.tensor([]),e.config,E,d.length),e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Description:"),e.config.async&&([o,s,A,a,l,i,r,c,x]=await Promise.all([o,s,A,a,l,i,r,c,x])),e.analyze("Finish Face:"),(j0=e.config.face.ssrnet)!=null&&j0.enabled&&o&&s&&(i={...i,age:o.age,gender:s.gender,genderScore:s.genderScore}),(M=e.config.face.gear)!=null&&M.enabled&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),(T0=e.config.face.mobilefacenet)!=null&&T0.enabled&&a&&(i.descriptor=a),(C0=e.config.face.insightface)!=null&&C0.enabled&&l&&(i.descriptor=l);let X=(x0=e.config.face.iris)!=null&&x0.enabled?wn(d[E],t.shape[2]):0,U0=(K=e.config.face.detector)!=null&&K.return?l0.squeeze(d[E].tensor):null;l0.dispose(d[E].tensor),d[E].tensor&&delete d[E].tensor;let y0={...d[E],id:E};i.age&&(y0.age=i.age),i.gender&&(y0.gender=i.gender),i.genderScore&&(y0.genderScore=i.genderScore),i.descriptor&&(y0.embedding=i.descriptor),i.race&&(y0.race=i.race),A&&(y0.emotion=A),c&&(y0.real=c),x&&(y0.live=x),X>0&&(y0.distance=X),B&&(y0.rotation=B),U0&&(y0.tensor=U0),f.push(y0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),f};var G0={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>G0.nameMapping[e],getPoints:e=>G0.pointsMapping[e]},He={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>He.nameMapping[e]},c0={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>c0.nameMapping[e]},Be=class{constructor(t){w(this,"name");w(this,"curls");w(this,"directions");w(this,"weights");w(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,o){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,o])}direction(t,n,o){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,o])}weight(t,n){this.weights[t]=n;let o=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/o)}matchAgainst(t,n){let o=0;for(let r in t){let s=t[r],A=this.curls[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}for(let r in n){let s=n[r],A=this.directions[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}return o/10}};var{thumb:fe,index:Pe,middle:ke,ring:A2,pinky:a2}=G0,{none:me,half:PA,full:pe}=He,{verticalUp:T2,verticalDown:Qi,horizontalLeft:Q5,horizontalRight:kA,diagonalUpRight:wA,diagonalUpLeft:v2,diagonalDownRight:_i,diagonalDownLeft:$i}=c0,Ge=new Be("thumbs 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T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(d=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Fn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Zn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(u=>W.dispose(c[u]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=G(H());var WA=5,Kn=1.65,Jn=[0,5,9,13,17,1,2],DA=0,FA=2,Qn=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),WA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Kn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=qn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Qn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function HA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||HA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function eo(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached 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0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,XA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-o1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,oo),d=st(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Nt,relative:()=>i1,vertical:()=>a1});var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((X,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+X)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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n={};n.resize=s0.image.resizeBilinear(e,[L0.inputs[0].shape?L0.inputs[0].shape[1]:0,L0.inputs[0].shape?L0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=L0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Ot={};ze(Ot,{distance:()=>d1,find:()=>KA,similarity:()=>YA});function d1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?k3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?Q3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?tn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?q3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?G3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?I3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?W3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?sn(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?cn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?fn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?bn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Mn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?a3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?A3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((u=this.instance.config.body.modelPath)!=null&&u.includes("efficientpose"))?y3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?uo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?Eo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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Object.entries(n))j0!=null&&j0.then&&j0.then(M=>this.models[n0]=M);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Gt(this.instance,o,n);r&&t.push(r)}return t}};function Co(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var Vt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -837,4 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA 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s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function fa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Vt):n=t(Zt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let u=0;ud0.dispose(T)):d0.dispose(u)}catch(u){e.config.debug&&h("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&h("tensor leak:",A-o)}async function Co(e,t){await I2(e,!1);let n=v();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=v();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,G2,V2,Xt,Ye,k1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",nt);w(this,"match",Ot);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2,void 0);ue(this,G2,void 0);ue(this,V2,void 0);w(this,"analyze",(...t)=>{if(!Y0(this,G2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&h(...t,r)});ue(this,Xt,t=>{if(!Y0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new K2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),Z1(this.config),this.tf=de,this.state="idle",Re(this,P2,0),Re(this,G2,!1),Re(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),i5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=W3,this.faceUVMap=D3,Gt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return v()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await io(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await No(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return V1(this.config,t,n)}async init(){await I2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await I2(this,!1)||h("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return ao(t,this.config)}async warmup(t){let n=v(),o=await Co(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var u,T,g,p,b,k,P,N,V,_,Z,$,A0,n0,t0,j0,M,T0,C0,x0,K;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=Y0(this,Xt).call(this,t);s&&(h(s,t),this.emit("error"),o(Te(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await G1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?g1(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,f):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?y1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await g1(a.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await g5(a.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await y1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&_.includes("handdetect")?x=this.config.hand.enabled?t1(a.tensor,d):[]:($=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?r1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await t1(a.tensor,d):[]:(j0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&j0.includes("handtrack")&&(x=this.config.hand.enabled?await r1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?i=this.config.object.enabled?m1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?p5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(C0=this.config.object.modelPath)!=null&&C0.includes("nanodet")?i=this.config.object.enabled?await m1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await p5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=v(),m=[...Cn(l),...On(c),...Dn(x),...Wn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-A):Math.trunc(v()-A);let y=((K=this.process.tensor)==null?void 0:K.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Oo(l,c,x,m,y)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&h("video 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R.Image;else{t(void 0);return}o.onload=async()=>{let r=Ae(o.naturalWidth,o.naturalHeight);if(!r)h("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function pa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Vt):n=t(Zt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function ua(e){let t;return typeof createImageBitmap=="function"?t=await fa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ma(e):t=await pa(e),t}async function ha(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let u=0;ud0.dispose(T)):d0.dispose(u)}catch(u){e.config.debug&&h("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&h("tensor leak:",A-o)}async function Wo(e,t){await I2(e,!1);let n=v();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await ha(e);let r=await ua(e),s=v();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,G2,V2,Xt,Ye,w1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",nt);w(this,"match",Ot);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2,void 0);ue(this,G2,void 0);ue(this,V2,void 0);w(this,"analyze",(...t)=>{if(!Y0(this,G2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&h(...t,r)});ue(this,Xt,t=>{if(!Y0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new K2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),X1(this.config),this.tf=de,this.state="idle",Re(this,P2,0),Re(this,G2,!1),Re(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),i5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=D3,this.faceUVMap=F3,Gt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return v()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await lo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Lo(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return Z1(this.config,t,n)}async init(){await I2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await I2(this,!1)||h("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return io(t,this.config)}async warmup(t){let n=v(),o=await Wo(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var u,T,g,p,b,k,P,N,V,_,Z,$,A0,t0,n0,j0,M,T0,C0,x0,K;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=Y0(this,Xt).call(this,t);s&&(h(s,t),this.emit("error"),o(Te(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await V1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?T1(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,f):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?f1(a.tensor,f):[]),this.performance.body&&delete 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0:Z.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?s1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(t0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&t0.includes("handdetect")?x=this.config.hand.enabled?await n1(a.tensor,d):[]:(j0=(n0=this.config.hand.detector)==null?void 0:n0.modelPath)!=null&&j0.includes("handtrack")&&(x=this.config.hand.enabled?await s1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?i=this.config.object.enabled?p1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?p5(a.tensor,this.config):[]),this.performance.object&&delete 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0:K.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Co(l,c,x,m,y)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&h("video start",t.id),Y0(this,Ye)[t.id]=!0),!t.paused&&Y0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),Y0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),Y0(this,Ye)[t.id]=!1)}};P2=new WeakMap,G2=new WeakMap,V2=new WeakMap,Xt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js index b1b818ba..55ff5c0d 100644 --- a/dist/tfjs.esm.js +++ b/dist/tfjs.esm.js @@ -4,57 +4,57 @@ author: ' */ -var 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Yu(r,e,t){if(t==null||t==="linear")return r;if(t==="relu")return se(r,Kd(e));throw new Error(`Cannot compute gradient for fused activation ${t}.`)}function Qu(r,e){let t=e,o=gd(r.shape,e.shape);return o.length>0&&(t=ot(t,o)),W(t,r.shape)}function Zu(r,e,t,o){if(e==="linear")return r;if(e==="relu")return lu(r);if(e==="elu")return yd(r);if(e==="relu6")return Wd(r);if(e==="prelu")return Pd(r,t);if(e==="leakyrelu")return Id(r,o);if(e==="sigmoid")return Na(r);throw new Error(`Unknown fused activation ${e}.`)}var Ju=(r,e)=>!(r>0)||e==="linear";function P6({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:p="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(p=p||"linear",Ju(T.state.gradientDepth,p)===!1){$(n==="NHWC",()=>`Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let _=iu(r,e,t,o,n,s,a);return 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T.tidy(()=>{$(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=bd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Wu(d),h=Xe(s,[u,u],[1,1]),g=co(Uu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=bt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=fc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=bt([Xe(s,[0,0],[u,o]),D],0)}let E=fc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),E));else{let D=Te(R,Ze(Ze(R,i),E));n=bt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var xN=N({qr_:mj});var Et;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Et||(Et={}));function dj(r,e,t=Et.SUM_BY_NONZERO_WEIGHTS){let 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a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(Za,a);return{outputIndices:i[0],outputShape:i[1]}}var TN=N({sparseReshape_:kj});function Nj(r,e,t){let o=v(r,"data","sparseSegmentMean"),n=v(e,"indices","sparseSegmentMean","int32"),s=v(t,"segmentIds","sparseSegmentMean","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(Wi,a)}var yN=N({sparseSegmentMean_:ej});function tj(r,e,t){let o=v(r,"data","sparseSegmentSum"),n=v(e,"indices","sparseSegmentSum","int32"),s=v(t,"segmentIds","sparseSegmentSum","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape + ${s.shape}`);let 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this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return vw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Yu=class extends kr{static get className(){return"Adadelta"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=be(se(p,this.rho),se(Jt(i),1-this.rho)),l=se(Ke(Rr(be(u,this.epsilon)),Rr(be(p,this.epsilon))),i),m=be(se(u,this.rho),se(Jt(l),1-this.rho));p.assign(c),u.assign(m);let d=be(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var Qu=class extends kr{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Sa(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=be(i,Jt(a));i.assign(p);let u=be(se(Ke(a,Rr(be(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var Zu=class extends kr{static get className(){return"Adam"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=be(se(c,this.beta1),se(u,1-this.beta1)),d=be(se(l,this.beta2),se(Jt(u),1-this.beta2)),f=Ke(m,o),h=Ke(d,n);c.assign(m),l.assign(d);let g=be(se(Ke(f,be(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ri(this.beta1,this.iterations_+1)),this.accBeta2.assign(ri(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var Ju=class extends kr{static get className(){return"Adamax"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Ke(-this.learningRate,be(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=be(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Zt(u),h=Ed(d,f);c.assign(m),l.assign(h);let g=be(se(Ke(n,o),Ke(m,be(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var ii=class extends kr{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=be(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Er(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var ep=class extends ii{static get className(){return"Momentum"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=be(se(this.m,a),i);this.useNesterov?p=be(se(this.c,be(i,se(u,this.m))),s):p=be(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var tp=class extends kr{static get className(){return"RMSProp"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=be(se(p,this.decay),se(Jt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=be(se(l,this.decay),se(i,1-this.decay)),d=Ke(se(i,this.learningRate),Rr(Te(c,be(Jt(m),this.epsilon)))),f=be(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=be(se(p,this.decay),se(Jt(i),1-this.decay)),m=be(se(u,this.momentum),Ke(se(i,this.learningRate),Rr(be(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var dj=[Yu,Qu,Zu,Ju,ep,tp,ii];function kN(){for(let r of dj)Lw(r)}var 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p=v(r,"data","stringNGrams","string");if(p.dtype!=="string")throw new Error("Data must be of datatype string");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(xa,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var EN=N({stringNGrams_:_j});function $j(r,e,t=!0){let o=v(r,"input","stringSplit","string"),n=v(e,"delimiter","stringSplit","string");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(Xi,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var RN=N({stringSplit_:$j});function Ej(r,e){let 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e(t)}},Ra=class{constructor(){this.classNameMap={}}static getMap(){return Ra.instance==null&&(Ra.instance=new Ra),Ra.instance}static register(e){Ra.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function qw(r){$(r.className!=null,()=>"Class being registered does not have the static className property defined."),$(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),$(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Ra.register(r)}var Nr=class extends $l{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Aw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Nr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var ep=class extends Nr{static get className(){return"Adadelta"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(er(i),1-this.rho)),l=se(je(Dr(Ce(u,this.epsilon)),Dr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(er(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var tp=class extends Nr{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ta(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,er(a));i.assign(p);let u=Ce(se(je(a,Dr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var rp=class extends Nr{static get className(){return"Adam"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(er(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Dr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(si(this.beta1,this.iterations_+1)),this.accBeta2.assign(si(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var op=class extends Nr{static get className(){return"Adamax"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Jt(u),h=Dd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var ci=class extends Nr{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Rr(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var np=class extends ci{static get className(){return"Momentum"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var sp=class extends Nr{static get className(){return"RMSProp"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(er(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Dr(Te(c,Ce(er(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(er(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Dr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var Bj=[ep,tp,rp,op,np,sp,ci];function PN(){for(let r of Bj)qw(r)}var 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ST=(r,e,t,o=Ze)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var IT=(r,e,t,o=Ze)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vT=(r,e,t,o=Ze)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,e,t),I("pattern",r,e,t),I("rewrite",r,e,t),I("replaceGlobal",r,e,t))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kT=(r,e,t,o=Ze)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let n=I("axis",r,e,t);return[o.squeeze(I("x",r,e,t),n)]}case"Reshape":return[o.reshape(I("x",r,e,t),I("shape",r,e,t))];case"EnsureShape":return[o.ensureShape(I("x",r,e,t),I("shape",r,e,t))];case"MirrorPad":return[o.mirrorPad(I("x",r,e,t),I("padding",r,e,t),I("mode",r,e,t))];case"PadV2":case"Pad":return[o.pad(I("x",r,e,t),I("padding",r,e,t),I("constantValue",r,e,t))];case"SpaceToBatchND":{let n=I("blockShape",r,e,t),s=I("paddings",r,e,t);return[o.spaceToBatchND(I("x",r,e,t),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,e,t),s=I("crops",r,e,t);return[o.batchToSpaceND(I("x",r,e,t),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,e,t),s=I("dataFormat",r,e,t).toUpperCase();return[o.depthToSpace(I("x",r,e,t),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,e,t),I("shape",r,e,t))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,e,t),I("s1",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function bS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>eT(a,i,p));case"basic_math":return n(()=>tT(a,i,p));case"control":return iT(a,i,p);case"convolution":return n(()=>pT(a,i,p));case"creation":return n(()=>cT(a,i,p));case"dynamic":return lT(a,i,p);case"evaluation":return n(()=>mT(a,i,p));case"image":return n(()=>hT(a,i,p));case"graph":return n(()=>dT(a,i,p));case"logical":return n(()=>gT(a,i,p));case"matrices":return n(()=>xT(a,i,p));case"normalization":return n(()=>yT(a,i,p));case"ragged":return n(()=>bT(a,i,p));case"reduction":return n(()=>CT(a,i,p));case"slice_join":return n(()=>wT(a,i,p));case"sparse":return n(()=>ST(a,i,p));case"spectral":return n(()=>IT(a,i,p));case"string":return n(()=>vT(a,i,p));case"transformation":return n(()=>kT(a,i,p));case"hash_table":return fT(a,i,p,o);case"custom":let u=sf(a.op);if(u&&u.customExecutor)return u.customExecutor(new yf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Pl=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function CS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((cu(m)||L5(m)||B5(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function NT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g=="string"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=A5(f,p);return F5(h,p),h}function A5(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var hc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function F5(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i=="string"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i=="string"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new hc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new hc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new hc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new hc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function TT(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>cu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new op(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let o=CS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=NT(this.graph,o),p=TT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Er(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=P().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,w]=Nr(x,m),S=[];S[w]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=bS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(cu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(cu(p))continue;let u=Qw(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return cu(p)||s.has(p.name)}if(!(cu(e)||a==null))for(let p of a){if(i(p))continue;let u=Qw(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=P().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Pl(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=CS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),E=[];E[_]=e[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=u.filter(S=>!cu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(w.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Ds(l.node.name,o)),n[l.node.name]==null){let d=bS(l.node,n,o,this._resourceManager);m||([m]=Ds(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ds(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Sf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var z5="?tfjs-format=file",V5="model.json",Ol=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},o=pi){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new Sf}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new op(Fl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(e.modelInitializer);this.initializer=new op(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof pt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof pt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ot(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function W5(r,e={},t=pi){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");e==null&&(e={}),e.fromTFHub&&typeof r=="string"&&(r=G5(r));let o=new Ol(r,e,t);return await o.load(),o}function U5(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let e;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error("modelJSON must be the first element of the array");if(!n||!(n instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in o))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in o))throw new Error("Model JSON is missing 'weightsManifest'");let s=pi.getWeightSpecs(o.weightsManifest),a=pi.getModelArtifactsForJSONSync(o,s,n);e=pi.fromMemorySync(a)}else if("load"in r)e=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)e=pi.fromMemorySync(r);else throw new Error("Unknown model format");let t=new Ol(e);return t.load(),t}function G5(r){return r.endsWith("/")||(r=r+"/"),`${r}${V5}${z5}`}var H5="4.5.0";function Y(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var K5=Wt.whereImpl,lu=class extends ro{nextDataId(){return lu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Lo(this,ur())}write(e,t,o){this.firstUse&&(this.firstUse=!1,P().get("IS_NODE")&&C.warn(` + ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=Cf(s,t),i=o===0?0:r.size/o,p=De(()=>{let c=[];r=W(r,[1,o,i]);for(let l=0;l{switch(r.op){case"If":case"StatelessIf":{let o=I("thenBranch",r,e,t),n=I("elseBranch",r,e,t),s=I("cond",r,e,t),a=I("args",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case"While":case"StatelessWhile":{let o=I("body",r,e,t),n=I("cond",r,e,t),s=I("args",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let l=u.map(d=>d.id);c.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);p=await m[0].data(),m.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()})}return u}case"LoopCond":{let o=I("pred",r,e,t);return[Ms(o)]}case"Switch":{let o=I("pred",r,e,t),n=I("data",r,e,t);return n.kept||(n=Ms(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case"Merge":{let o=r.inputNames.find(n=>Bt(n,e,t)!==void 0);if(o){let n=Bt(o,e,t);return[Ms(n)]}return}case"Enter":{let o=I("frameName",r,e,t),n=I("tensor",r,e,t);return t.enterFrame(o),[Ms(n)]}case"Exit":{let o=I("tensor",r,e,t);return t.exitFrame(),[Ms(o)]}case"NextIteration":{let o=I("tensor",r,e,t);return t.nextIteration(),[Ms(o)]}case"TensorArrayV3":{let o=I("size",r,e,t),n=I("dtype",r,e,t),s=I("elementShape",r,e,t),a=I("dynamicSize",r,e,t),i=I("clearAfterRead",r,e,t),p=I("identicalElementShapes",r,e,t),u=I("name",r,e,t),c=new wf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,ke(1)]}case"TensorArrayWriteV3":{let o=I("tensorArrayId",r,e,t),n=I("index",r,e,t),s=I("tensor",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case"TensorArrayReadV3":{let o=I("tensorArrayId",r,e,t),n=I("index",r,e,t);return[t.getTensorArray(o.id).read(n)]}case"TensorArrayGatherV3":{let o=I("tensorArrayId",r,e,t),n=I("indices",r,e,t),s=I("dtype",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case"TensorArrayScatterV3":{let o=I("tensorArrayId",r,e,t),n=I("indices",r,e,t),s=I("tensor",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case"TensorArrayConcatV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id),s=I("dtype",r,e,t);return[n.concat(s)]}case"TensorArraySplitV3":{let o=I("tensorArrayId",r,e,t),n=I("tensor",r,e,t),s=I("lengths",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return[ke(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("tensor",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=I("indices",r,e,t),n=I("tensor",r,e,t),s=I("elementShape",r,e,t),a=I("numElements",r,e,t),i=xT(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=I("elementShape",r,e,t),n=I("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=I(s,r,e,t),i=r.op==="TensorListReserve"?-1:a,p=gT(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case"TensorListGather":{let o=I("tensorListId",r,e,t),n=I("indices",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=I("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=hT(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id),s=I("dtype",r,e,t),a=I("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=I("tensorListId",r,e,t),n=I("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("lengths",r,e,t),a=yT(o,s,n);return t.addTensorList(a),[a.idTensor]}case"TensorListLength":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id);return[ke(n.size(),"int32")]}case"TensorListResize":{let o=I("tensorListId",r,e,t),n=I("size",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function CT(r,e,t){let[o,n]=I("fusedOps",r,e,t),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,e,t);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(p)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,e,t),l=Al(r,e,t),m=I("dataFormat",r,e,t).toUpperCase(),d=I("dilations",r,e,t),[f,h]=I("args",r,e,t);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var wT=(r,e,t,o=Je)=>{switch(r.op){case"Conv1D":{let n=I("stride",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilation",r,e,t);return[o.conv1d(I("x",r,e,t),I("filter",r,e,t),n,s,a,i)]}case"Conv2D":{let n=I("strides",r,e,t),s=Al(r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv2d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=CT(r,e,t);return[o.fused.conv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=CT(r,e,t);return[o.fused.depthwiseConv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=I("outputShape",r,e,t),s=I("strides",r,e,t),a=Al(r,e,t);return[o.conv2dTranspose(I("x",r,e,t),I("filter",r,e,t),n,[s[1],s[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=I("strides",r,e,t),s=Al(r,e,t),a=I("dilations",r,e,t),i=I("dataFormat",r,e,t).toUpperCase();return[o.depthwiseConv2d(I("input",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case"Conv3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv3d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case"AvgPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPoolWithArgmax":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t),i=I("includeBatchInIndex",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case"AvgPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"MaxPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"Dilation2D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dilations",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I("x",r,e,t),I("filter",r,e,t),[i,p],s,[u,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ST=(r,e,t,o=Je)=>{switch(r.op){case"Fill":{let n=I("shape",r,e,t),s=I("dtype",r,e,t),a=I("value",r,e,t);return[o.fill(n,a,s)]}case"LinSpace":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("num",r,e,t);return[o.linspace(n,s,a)]}case"Multinomial":{let n=I("logits",r,e,t),s=I("numSamples",r,e,t),a=I("seed",r,e,t);return[o.multinomial(n,s,a)]}case"OneHot":{let n=I("indices",r,e,t),s=I("depth",r,e,t),a=I("onValue",r,e,t),i=I("offValue",r,e,t),p=I("dtype",r,e,t);return[o.oneHot(n,s,a,i,p)]}case"Ones":return[o.ones(I("shape",r,e,t),I("dtype",r,e,t))];case"OnesLike":return[o.onesLike(I("x",r,e,t))];case"RandomStandardNormal":return[o.randomStandardNormal(I("shape",r,e,t),I("dtype",r,e,t),I("seed",r,e,t))];case"RandomUniform":return[o.randomUniform(I("shape",r,e,t),I("minval",r,e,t),I("maxval",r,e,t),I("dtype",r,e,t))];case"RandomUniformInt":return[o.randomUniformInt(I("shape",r,e,t),I("minval",r,e,t),I("maxval",r,e,t),I("seed",r,e,t))];case"Range":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("step",r,e,t);return[o.range(n,s,a,I("dtype",r,e,t))]}case"TruncatedNormal":{let n=I("shape",r,e,t),s=I("mean",r,e,t),a=I("stdDev",r,e,t),i=I("seed",r,e,t);return[o.truncatedNormal(n,s,a,I("dtype",r,e,t),i)]}case"Zeros":return[o.zeros(I("shape",r,e,t),I("dtype",r,e,t))];case"ZerosLike":return[o.zerosLike(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function TS(r,e,t){let o=I("boxes",r,e,t),n=I("scores",r,e,t),s=I("maxOutputSize",r,e,t),a=I("iouThreshold",r,e,t),i=I("scoreThreshold",r,e,t),p=I("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var IT=async(r,e,t,o,n=Je)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=TS(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=TS(r,e,t),c=I("padToMaxOutputSize",r,e,t),l=await 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performance."),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=mo(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,e,t),a=I("valueDType",r,e,t),i=new Sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let 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n=I("images",r,e,t),s=I("transforms",r,e,t),a=I("outputShape",r,e,t),i=I("fillValue",r,e,t),p=I("interpolation",r,e,t),u=I("fillMode",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _T=(r,e,t,o=Je)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,e,t),I("b",r,e,t))];case"NotEqual":return[o.notEqual(I("a",r,e,t),I("b",r,e,t))];case"Greater":return[o.greater(I("a",r,e,t),I("b",r,e,t))];case"GreaterEqual":return[o.greaterEqual(I("a",r,e,t),I("b",r,e,t))];case"Less":return[o.less(I("a",r,e,t),I("b",r,e,t))];case"LessEqual":return[o.lessEqual(I("a",r,e,t),I("b",r,e,t))];case"LogicalAnd":return[o.logicalAnd(I("a",r,e,t),I("b",r,e,t))];case"LogicalNot":return[o.logicalNot(I("a",r,e,t))];case"LogicalOr":return[o.logicalOr(I("a",r,e,t),I("b",r,e,t))];case"Select":case"SelectV2":return[o.where(I("condition",r,e,t),I("a",r,e,t),I("b",r,e,t))];case"BitwiseAnd":return[o.bitwiseAnd(I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $T=(r,e,t,o=Je)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,e,t),I("b",r,e,t),I("transposeA",r,e,t),I("transposeB",r,e,t))];case"Einsum":return[o.einsum(I("equation",r,e,t),...I("tensors",r,e,t))];case"Transpose":return[o.transpose(I("x",r,e,t),I("perm",r,e,t))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,e,t),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,e,t),u=I("leakyreluAlpha",r,e,t);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,e,t);return[o.fused.matMul({a:I("a",r,e,t),b:I("b",r,e,t),transposeA:I("transposeA",r,e,t),transposeB:I("transposeB",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case"MatrixBandPart":return[o.linalg.bandPart(I("a",r,e,t),I("numLower",r,e,t),I("numUpper",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ET=(r,e,t,o=Je)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,e,t),I("axis",r,e,t),I("keepDims",r,e,t))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"LRN":return[o.localResponseNormalization(I("x",r,e,t),I("radius",r,e,t),I("bias",r,e,t),I("alpha",r,e,t),I("beta",r,e,t))];case"Softmax":return[o.softmax(I("x",r,e,t))];case"LogSoftmax":return[o.logSoftmax(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RT=(r,e,t,o=Je)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,e,t),I("paramsDenseValues",r,e,t),I("indices",r,e,t),I("outputRaggedRank",r,e,t));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,e,t),I("limits",r,e,t),I("splits",r,e,t));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,e,t),I("values",r,e,t),I("defaultValue",r,e,t),I("rowPartitionTensors",r,e,t),I("rowPartitionTypes",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DT=(r,e,t,o=Je)=>{switch(r.op){case"Max":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.max(I("x",r,e,t),i,p)]}case"Mean":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.mean(I("x",r,e,t),i,p)]}case"Min":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.min(I("x",r,e,t),i,p)]}case"Sum":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.sum(I("x",r,e,t),i,p)]}case"All":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.all(I("x",r,e,t),i,p)]}case"Any":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.any(I("x",r,e,t),i,p)]}case"ArgMax":{let 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FT=(r,e,t,o=Je)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PT=(r,e,t,o=Je)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OT=(r,e,t,o=Je)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,e,t),I("pattern",r,e,t),I("rewrite",r,e,t),I("replaceGlobal",r,e,t))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,e,t,o=Je)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let 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s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>mT(a,i,p));case"basic_math":return n(()=>dT(a,i,p));case"control":return bT(a,i,p);case"convolution":return n(()=>wT(a,i,p));case"creation":return n(()=>ST(a,i,p));case"dynamic":return IT(a,i,p);case"evaluation":return n(()=>vT(a,i,p));case"image":return n(()=>TT(a,i,p));case"graph":return n(()=>kT(a,i,p));case"logical":return n(()=>_T(a,i,p));case"matrices":return n(()=>$T(a,i,p));case"normalization":return n(()=>ET(a,i,p));case"ragged":return n(()=>RT(a,i,p));case"reduction":return n(()=>DT(a,i,p));case"slice_join":return n(()=>AT(a,i,p));case"sparse":return n(()=>FT(a,i,p));case"spectral":return n(()=>PT(a,i,p));case"string":return n(()=>OT(a,i,p));case"transformation":return n(()=>MT(a,i,p));case"hash_table":return NT(a,i,p,o);case"custom":let u=af(a.op);if(u&&u.customExecutor)return u.customExecutor(new bf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Pl=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function $S(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Tr(m)[0]));o=o||[];let c=new Set(o.map(m=>Tr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||l8(m)||m8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function LT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Tr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g=="string"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=a8(f,p);return i8(h,p),h}function a8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function i8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i=="string"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i=="string"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function BT(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new ip(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let o=$S(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=LT(this.graph,o),p=BT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Rr(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Tr(m)[0]]),s=t.map(m=>Tr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Tr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=_S(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=aS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=aS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Pl(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Tr(S)[0]]),i=o.map(S=>Tr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=$S(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Tr(S),E=[];E[_]=e[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Os(l.node.name,o)),n[l.node.name]==null){let d=_S(l.node,n,o,this._resourceManager);m||([m]=Os(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Os(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Tr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Tr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Tr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var If=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var d8="?tfjs-format=file",f8="model.json",Ol=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},o=mi){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new If}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ip(Fl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(e.modelInitializer);this.initializer=new ip(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof ut?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof ut)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ot(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function h8(r,e={},t=mi){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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c=C.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,S=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,E=S-1-c.padInfo.front,R=_-1-c.padInfo.left,D=k-1-c.padInfo.top,F=me(s.shape,"float32"),O=1/(f*h*g),M=t.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=c.outHeight||Math.floor(ie)!==ie))for(let le=0;le<_;le+=w){let ye=(J+le)/d;if(ye<0||ye>=c.outWidth||Math.floor(ye)!==ye)continue;let _e=M.get(L,ee,ie,ye,B);re+=_e}}}F.set(re*O,L,z,U,j,B)}return t.makeTensorInfo(F.shape,F.dtype,F.values)}var O_={kernelName:Ni,backendName:"cpu",kernelFunc:V8};function W8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Y([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=C.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,"float32"),_=1/(d*f),E=t.data.get(n.dataId).values,R=me(n.shape,"float32",E);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let H=0;H=c.outWidth||Math.floor(X)!==X)continue;let J=R.get(D,j,X,F);z+=J}}k.set(z*_,D,O,M,F)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var M_={kernelName:Gp,backendName:"cpu",kernelFunc:W8};function U8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Y([n,i,p,s,a],"batchNorm");let{varianceEpsilon:u}=o;u==null&&(u=.001);let c=t.data.get(n.dataId).values,l=t.data.get(i.dataId).values,m=t.data.get(p.dataId).values,d=s?t.data.get(s.dataId).values:new Float32Array([1]),f=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=f.length,x=d.length,b=m.length,w=l.length,S=0,k=0,_=0,E=0;for(let R=0;R=g&&(S=0),k>=w&&(k=0),_>=x&&(_=0),E>=b&&(E=0);return t.makeTensorInfo(n.shape,n.dtype,h)}var L_={kernelName:wn,backendName:"cpu",kernelFunc:U8};function G8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;Y([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=Ve({inputs:{x:n},backend:t,attrs:{shape:p}}),f=St({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Ve({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Eo({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var B_={kernelName:js,backendName:"cpu",kernelFunc:G8};function H8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=yc(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var z_={kernelName:Zo,backendName:"cpu",kernelFunc:H8};function K8(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var V_={kernelName:Xs,backendName:"cpu",kernelFunc:K8};var q8=Ie(go,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return lr({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(S=>To({inputs:{input:S},backend:t})),g=p.map(S=>$a({inputs:{input:S},backend:t})),x=mu({inputs:h,backend:t,attrs:{axis:s}}),b=mu({inputs:g,backend:t,attrs:{axis:s}}),w=Kt({inputs:{real:x,imag:b},backend:t});return h.forEach(S=>t.disposeIntermediateTensorInfo(S)),g.forEach(S=>t.disposeIntermediateTensorInfo(S)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),w}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Ve({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=C.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=np(c,i,e[0].dtype,l),d=C.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var H_={kernelName:Ys,backendName:"cpu",kernelFunc:mu};function tI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Y([n,s],"conv2d");let l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=w?k[1]:k[2],D=w?k[2]:1,F=w?1:k[1],O=S.strides[0],M=w?S.strides[1]:S.strides[2],L=w?S.strides[2]:1,B=w?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let H=0;H=m.inHeight)continue;let le=oe*_[0],ye=X+ie*R;for(let _e=0;_e=m.inWidth)continue;let lt=le+Pe*_[1],We=ye+st*D,mt=lt;for(let it=0;it=u.inDepth)continue;let H=U*D[0],X=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=H+ee*D[1],le=X+oe*R[2];for(let ye=0;ye=u.inWidth)continue;let st=ie+Fe*D[2],lt=le+Pe*u.inChannels,We=st;for(let mt=0;mtMath.cos(r)),Z_={kernelName:nn,backendName:"cpu",kernelFunc:eY};var tY=Ie(sn,r=>Math.cosh(r)),J_={kernelName:sn,backendName:"cpu",kernelFunc:tY};function rY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,w=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-F)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let H=0;H1?F*(m-1)+re*z:.5*(F+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?F*(m-1)+H*z:.5*(F+M)*(m-1);if(X<0||X>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=C.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,w=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,D=_.values;for(let F=0;F=d.inHeight)continue;let H=U*l[0],X=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=H+ee*l[1],le=X+oe*d.inChannels,ye=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:F}=C.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),O=y.sizeFromShape(F),M=F.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,H,J],M,y.computeStrides(F));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),F,o.dtype),shape:F,dtype:o.dtype}}};var c$={kernelName:Ai,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=C.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Ai}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let F=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieX&&(X=le,J=ne,re=oe)}}}O[J][re][H]+=F[L][B][U][H]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var l$={kernelName:Di,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=C.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Di}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let F=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieX&&(X=le,J=ee,re=ie)}}}O[L][J][re][H]+=F[L][B][U][H]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function li(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Y(n,"sum");let i;n.dtype==="bool"?i=_o({inputs:{x:n},backend:t,attrs:{dtype:"int32"}}):i=lr({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=C.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=St({inputs:{x:i},backend:t,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,p)),C.assertAxesAreInnerMostDims("sum",l,m.shape.length);let[d,f]=C.computeOutAndReduceShapes(m.shape,l),h=C.upcastType(m.dtype,"int32"),g=gc(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,w=t.data.get(m.dataId).values;for(let S=0;S=0&&(m=li({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var d$={kernelName:Fi,backendName:"cpu",kernelFunc:cY};function lY(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;Y([o,n],"eluGrad");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=0?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,"float32",s)}var f$={kernelName:Va,backendName:"cpu",kernelFunc:lY};var mY=C.ERF_P,dY=C.ERF_A1,fY=C.ERF_A2,hY=C.ERF_A3,gY=C.ERF_A4,xY=C.ERF_A5,yY=Ie(Wa,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+mY*t);return e*(1-((((xY*o+gY)*o+hY)*o+fY)*o+dY)*o*Math.exp(-t*t))}),h$={kernelName:Wa,backendName:"cpu",kernelFunc:yY};function vc(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Ve({inputs:{x:n},backend:t,attrs:{shape:i}})}var g$={kernelName:Js,backendName:"cpu",kernelFunc:vc};var bY=ze((r,e)=>r/e),zl=je(dn,bY),Vl={kernelName:dn,backendName:"cpu",kernelFunc:zl};function Lf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=C.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Ve({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Ve({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),w=kf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,w.dtype,w.values)}var I$={kernelName:ta,backendName:"cpu",kernelFunc:_Y};function $Y(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Ve({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=Lf(i,!0,t),u=Ve({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var v$={kernelName:Oi,backendName:"cpu",kernelFunc:$Y};var EY=Ie(kn,r=>Number.isFinite(r)?1:0,"bool"),k$={kernelName:kn,backendName:"cpu",kernelFunc:EY};var RY=Ie(Nn,r=>Math.abs(r)===1/0?1:0,"bool"),N$={kernelName:Nn,backendName:"cpu",kernelFunc:RY};var DY=Ie(Tn,r=>Number.isNaN(r)?1:0,"bool"),T$={kernelName:Tn,backendName:"cpu",kernelFunc:DY};function AY(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=Nf(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var _$={kernelName:Rn,backendName:"cpu",kernelFunc:AY};var FY=Ie(An,r=>Math.log1p(r)),$$={kernelName:An,backendName:"cpu",kernelFunc:FY};var PY=ze((r,e)=>r&&e),OY=je(Fn,PY,null,"bool"),E$={kernelName:Fn,backendName:"cpu",kernelFunc:OY};var MY=Ie(Pn,r=>r?0:1,"bool"),R$={kernelName:Pn,backendName:"cpu",kernelFunc:MY};var LY=ze((r,e)=>r||e),BY=je(On,LY,null,"bool"),D$={kernelName:On,backendName:"cpu",kernelFunc:BY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;Y(n,"LRN");let u=n.shape[3],c=u-1,l=t.data.get(n.dataId).values,m=y.sizeFromShape(n.shape),d=new Float32Array(m);function f(h){let g=h%u,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let S=l[x];w+=S*S}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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i=s.reduce((x,b)=>x*b),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=Ve({inputs:{x:n},backend:t,attrs:{shape:p}}),f=It({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Ve({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ao({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var Q_={kernelName:Zs,backendName:"cpu",kernelFunc:xY};function yY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=bc(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var Z_={kernelName:Jo,backendName:"cpu",kernelFunc:yY};function bY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var J_={kernelName:Js,backendName:"cpu",kernelFunc:bY};var CY=Ie(bo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return lr({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(S=>Eo({inputs:{input:S},backend:t})),g=p.map(S=>Fa({inputs:{input:S},backend:t})),x=gu({inputs:h,backend:t,attrs:{axis:s}}),b=gu({inputs:g,backend:t,attrs:{axis:s}}),C=Kt({inputs:{real:x,imag:b},backend:t});return h.forEach(S=>t.disposeIntermediateTensorInfo(S)),g.forEach(S=>t.disposeIntermediateTensorInfo(S)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Ve({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=w.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=up(c,i,e[0].dtype,l),d=w.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var o$={kernelName:ea,backendName:"cpu",kernelFunc:gu};function cI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let lt=le+Pe*_[1],Ue=be+st*D,mt=lt;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],lt=le+Pe*u.inChannels,Ue=st;for(let mt=0;mtMath.cos(r)),c$={kernelName:sn,backendName:"cpu",kernelFunc:TY};var _Y=Ie(an,r=>Math.cosh(r)),l$={kernelName:an,backendName:"cpu",kernelFunc:_Y};function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var S$={kernelName:Mi,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var I$={kernelName:Oi,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Oi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function MY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=t.data.get(n.dataId).values,g=n.dtype==="float32"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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Se=A();Se.registerFlag("HAS_WEBGL",()=>Se.getNumber("WEBGL_VERSION")>0);Se.registerFlag("WEBGL_VERSION",()=>jf(2)?2:jf(1)?1:0);Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Se.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Se.get("WEBGL_VERSION")===2);Se.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Se.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Se.registerFlag("WEBGL_PACK",()=>Se.getBool("HAS_WEBGL"));Se.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_CLIP",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_REDUCE",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_LAZILY_UNPACK",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_CONV_IM2COL",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>AI(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>FI(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Se.getNumber("WEBGL_VERSION");return r===0?0:PI(r)});Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!ru.isMobile());Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>OI(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Se.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>MI(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_FENCE_API_ENABLED",()=>LI(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Se.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>ru.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Se.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Se.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Se.registerFlag("WEBGL_EXP_CONV",()=>!1);Se.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Se.getBool("IS_TEST"));Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Se.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Se.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function vt(){let r,e,t,o,n,s,a,i,p,u;return A().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",o="in",n="texture",s="outputColor",a="out vec4 outputColor;",i=A().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -100,15 +100,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:p,defineRound:u}}function Os(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function hp(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / outShapeStrides[${s}]`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${a}; ${i};`}).join("")}function y7(r,e){let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function tR(r,e,t="index"){let o=r.map((s,a)=>a),n=y7(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join("")}function $c(r){let e=y.computeStrides(r).map(t=>t.toString());return` + `),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:p,defineRound:u}}function zs(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function bp(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / outShapeStrides[${s}]`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${a}; ${i};`}).join("")}function KQ(r,e){let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function fR(r,e,t="index"){let o=r.map((s,a)=>a),n=KQ(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join("")}function Ec(r){let e=y.computeStrides(r).map(t=>t.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z; } -`}function Ec(){return` +`}function Rc(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var jf=` +`}var Xf=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -147,22 +147,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`;var{getBroadcastDims:rR}=C;function oR(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Xf(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:o.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:o.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}o.push(`uniform ivec2 ${d.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:o.push("uniform int outShape;");break;case 2:o.push("uniform ivec2 outShape;"),o.push("uniform int outShapeStrides;");break;case 3:o.push("uniform ivec3 outShape;"),o.push("uniform ivec2 outShapeStrides;");break;case 4:o.push("uniform ivec4 outShape;"),o.push("uniform ivec3 outShapeStrides;");break;default:break}o.push("uniform ivec2 outTexShape;")}t.customUniforms&&t.customUniforms.forEach(d=>{o.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let n=o.join(` -`),s=r.map(d=>b7(d,e,t.packedInputs,t.enableShapeUniforms)).join(` -`),a=e.texShape,i=It(),p=S7(i),u,c,l=k7(i);return e.isPacked?(u=C7(e.logicalShape,a,t.enableShapeUniforms),c=v7(i)):(u=w7(e.logicalShape,a,t.enableShapeUniforms),c=I7(i)),t.packedInputs&&(l+=$7),[l,p,c,n,u,s,t.userCode].join(` -`)}function Dc(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return V7(r,e);case 1:return U7(r,e);case 2:return H7(r,e);case 3:return q7(r,e);case 4:return X7(r,e);case 5:return Y7(r);case 6:return Q7(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function nR(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return z7(r);case 1:return W7(r,e);case 2:return G7(r,e);case 3:return K7(r,e);default:return j7(r,e)}}function b7(r,e,t=!1,o){let n="";t?n+=nR(r,o):n+=Dc(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=Z7(r,e):n+=J7(r,e)),n}function C7(r,e,t){switch(r.length){case 0:return sR();case 1:return E7(r,e,t);case 2:return L7(r,e,t);case 3:return D7(r,e,t);default:return F7(r,e,t)}}function w7(r,e,t){switch(r.length){case 0:return sR();case 1:return R7(r,e,t);case 2:return B7(r,e,t);case 3:return A7(r,e,t);case 4:return P7(r,e,t);case 5:return O7(r,e);case 6:return M7(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function S7(r){return` +`;var{getBroadcastDims:hR}=w;function gR(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Yf(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:o.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:o.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}o.push(`uniform ivec2 ${d.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:o.push("uniform int outShape;");break;case 2:o.push("uniform ivec2 outShape;"),o.push("uniform int outShapeStrides;");break;case 3:o.push("uniform ivec3 outShape;"),o.push("uniform ivec2 outShapeStrides;");break;case 4:o.push("uniform ivec4 outShape;"),o.push("uniform ivec3 outShapeStrides;");break;default:break}o.push("uniform ivec2 outTexShape;")}t.customUniforms&&t.customUniforms.forEach(d=>{o.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let n=o.join(` +`),s=r.map(d=>qQ(d,e,t.packedInputs,t.enableShapeUniforms)).join(` +`),a=e.texShape,i=vt(),p=YQ(i),u,c,l=JQ(i);return e.isPacked?(u=jQ(e.logicalShape,a,t.enableShapeUniforms),c=ZQ(i)):(u=XQ(e.logicalShape,a,t.enableShapeUniforms),c=QQ(i)),t.packedInputs&&(l+=oZ),[l,p,c,n,u,s,t.userCode].join(` +`)}function Ac(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return hZ(r,e);case 1:return xZ(r,e);case 2:return bZ(r,e);case 3:return wZ(r,e);case 4:return IZ(r,e);case 5:return vZ(r);case 6:return kZ(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function xR(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return fZ(r);case 1:return gZ(r,e);case 2:return yZ(r,e);case 3:return CZ(r,e);default:return SZ(r,e)}}function qQ(r,e,t=!1,o){let n="";t?n+=xR(r,o):n+=Ac(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=NZ(r,e):n+=TZ(r,e)),n}function jQ(r,e,t){switch(r.length){case 0:return yR();case 1:return nZ(r,e,t);case 2:return mZ(r,e,t);case 3:return aZ(r,e,t);default:return uZ(r,e,t)}}function XQ(r,e,t){switch(r.length){case 0:return yR();case 1:return sZ(r,e,t);case 2:return dZ(r,e,t);case 3:return iZ(r,e,t);case 4:return pZ(r,e,t);case 5:return cZ(r,e);case 6:return lZ(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function YQ(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } - `}function I7(r){return` + `}function QQ(r){return` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } - `}function v7(r){return` + `}function ZQ(r){return` void setOutput(vec4 val) { ${r.output} = val; } - `}function k7(r){return`${r.version} + `}function JQ(r){return`${r.version} precision highp float; precision highp int; precision highp sampler2D; @@ -217,10 +217,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${N7} - ${T7} - ${_7} - `}var N7=` + ${eZ} + ${tZ} + ${rZ} + `}var eZ=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -232,7 +232,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,T7=` +`,tZ=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -240,7 +240,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,_7=` +`,rZ=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -249,7 +249,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,$7=` +`,oZ=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -260,11 +260,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function sR(){return` +`;function yR(){return` int getOutputCoords() { return 0; } - `}function E7(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?` + `}function nZ(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -293,7 +293,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${o[0]}, ${o[1]})); return 2 * (resTexRC.x * ${o[1]} + resTexRC.y); } - `}function R7(r,e,t){return e[0]===1?t?` + `}function sZ(r,e,t){return e[0]===1?t?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -321,7 +321,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${e[0]}, ${e[1]})); return resTexRC.x * ${e[1]} + resTexRC.y; } - `}function D7(r,e,t){if(t)return` + `}function aZ(r,e,t){if(t)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -352,15 +352,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function A7(r,e,t){if(t)return` + `}function iZ(r,e,t){if(t)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${hp(["r","c","d"],r)} + ${bp(["r","c","d"],r)} return ivec3(r, c, d); } -`;let o=Os(["r","c","d"],r);return` +`;let o=zs(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -368,7 +368,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${o} return ivec3(r, c, d); } - `}function F7(r,e,t){if(t)return` + `}function uZ(r,e,t){if(t)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -409,15 +409,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${r.length}(${p}); } - `}function P7(r,e,t){if(t)return` + `}function pZ(r,e,t){if(t)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${hp(["r","c","d","d2"],r)} + ${bp(["r","c","d","d2"],r)} return ivec4(r, c, d, d2); } - `;let o=Os(["r","c","d","d2"],r);return` + `;let o=zs(["r","c","d","d2"],r);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -425,7 +425,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${o} return ivec4(r, c, d, d2); } - `}function O7(r,e){let t=Os(["r","c","d","d2","d3"],r);return` + `}function cZ(r,e){let t=zs(["r","c","d","d2","d3"],r);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function M7(r,e){let t=Os(["r","c","d","d2","d3","d4"],r);return` + `}function lZ(r,e){let t=zs(["r","c","d","d2","d3","d4"],r);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -448,7 +448,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function L7(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return t?` + `}function mZ(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return t?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -481,7 +481,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function B7(r,e,t){return y.arraysEqual(r,e)?t?` + `}function dZ(r,e,t){return y.arraysEqual(r,e)?t?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -535,15 +535,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${r[1]}; return ivec2(r, c); } - `}function gp(r){return`offset${r}`}function z7(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=It();return` + `}function Cp(r){return`offset${r}`}function fZ(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=vt();return` vec4 ${t}() { return ${o.texture2D}(${e}, halfCR); } - `}function V7(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${o}() {return ${t};}`;let[n,s]=r.shapeInfo.texShape;if(n===1&&s===1)return` + `}function hZ(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${o}() {return ${t};}`;let[n,s]=r.shapeInfo.texShape;if(n===1&&s===1)return` float ${o}() { return sampleTexture(${t}, halfCR); } - `;let a=gp(t);if(e)return` + `;let a=Cp(t);if(e)return` float ${o}() { vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${a}); return sampleTexture(${t}, uv); @@ -553,7 +553,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${i}, ${p}, ${a}); return sampleTexture(${t}, uv); } - `}function W7(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=It();if(e)return` + `}function gZ(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=vt();if(e)return` vec4 ${o}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -566,15 +566,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a[0]}, ${a[1]}, index); return ${s.texture2D}(${t}, uv); } - `}function U7(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return` + `}function xZ(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return` float ${o}(int index) { - ${Ac(r)} + ${Fc(r)} } `;let n=r.shapeInfo.texShape,s=n[0],a=n[1];if(a===1&&s===1)return` float ${o}(int index) { return sampleTexture(${t}, halfCR); } - `;let i=gp(t);return a===1?e?` + `;let i=Cp(t);return a===1?e?` float ${o}(int index) { vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t}TexShape[0])); return sampleTexture(${t}, uv); @@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${a}, index + ${i}); return sampleTexture(${t}, uv); } - `}function G7(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=It();if(s!=null&&y.arraysEqual(t,s))return e?` + `}function yZ(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=vt();if(s!=null&&y.arraysEqual(t,s))return e?` vec4 ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); @@ -628,7 +628,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col); return ${p.texture2D}(${o}, uv); } - `}function H7(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(t,s)){if(e)return` + `}function bZ(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(t,s)){if(e)return` float ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); 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${this.enableShapeUniforms?hp(["r","c","d"],e):Os(["r","c","d"],e)} + ${this.enableShapeUniforms?bp(["r","c","d"],e):zs(["r","c","d"],e)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}};var Qf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=du.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` + `}};var Zf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=vt();this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?hp(["r","c","d"],e):Os(["r","c","d"],e)} + ${this.enableShapeUniforms?bp(["r","c","d"],e):zs(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1016,26 +1016,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}};var Zf=class{constructor(e){this.variableNames=["A"],this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=` - ${jf} + `}};var Jf=class{constructor(e){this.variableNames=["A"],this.outTexUsage=mr.DOWNLOAD;let t=vt();this.outputShape=e,this.userCode=` + ${Xf} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}};var Jf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=` - ${jf} + `}};var eh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=vt();this.outputShape=e,this.userCode=` + ${Xf} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}};var rZ={R:0,G:1,B:2,A:3},Xl=class{constructor(e,t=!1,o="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)");let a="";for(let 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rv={};Ke(rv,{bindVertexProgramAttributeStreams:()=>jI,createBufferFromOutputTexture:()=>QI,createFloat16MatrixTexture:()=>GI,createFloat16PackedMatrixTexture:()=>qI,createFloat32MatrixTexture:()=>UI,createIndexBuffer:()=>WI,createPackedMatrixTexture:()=>KI,createUnsignedBytesMatrixTexture:()=>HI,createVertexBuffer:()=>VI,createVertexShader:()=>zI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>JI,downloadFloat32MatrixFromBuffer:()=>ZI,downloadMatrixFromPackedOutputTexture:()=>tv,downloadPackedMatrixFromBuffer:()=>ev,getInternalFormatForFloat16MatrixTexture:()=>oh,getInternalFormatForFloat16PackedMatrixTexture:()=>ah,getInternalFormatForFloat32MatrixTexture:()=>rh,getInternalFormatForPackedMatrixTexture:()=>sh,getInternalFormatForUnsignedBytesMatrixTexture:()=>nh,uploadDenseMatrixToTexture:()=>XI,uploadPixelDataToTexture:()=>YI});function zI(r){let e=vt(),t=`${e.version} precision highp float; ${e.attribute} vec3 clipSpacePos; ${e.attribute} vec2 uv; @@ -1107,7 +1107,7 @@ vec2 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implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),A().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Nc(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Nc(this.gl,a);else if(A().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Nc(this.gl,n);else 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t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),jI(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ql(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?$I(this.gl,e,t):EI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),RI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Pa(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ql(this.gl,this.program),Tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=RZ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;"setTimeoutCustom"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),jl(this.gl,e,this.framebuffer),this.debug&&Tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(jl(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tc(this.gl)):qf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;jl(n,e,this.framebuffer),this.debug&&Tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function RZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:ov(r,e)}function fD(r,e){if(r===1)return"rc";let t="";for(let o=0;o= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}};var Oc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2===1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}};var Mc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length);let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2===1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` ${s} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1146,8 +1146,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${n>0?"}":""} `}this.userCode=` - ${nZ(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?Ec():$c(e)} + ${DZ(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?Rc():Ec(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1162,12 +1162,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function nZ(r,e){return` + `}};function DZ(r,e){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${e?tR(["r","c","d"],"inputShape"):Os(["r","c","d"],r)} + ${e?fR(["r","c","d"],"inputShape"):zs(["r","c","d"],r)} return ivec3(r, c, d); } - `}var ch=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=rD(t,o),s=oD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=tD(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=rD(o,n),a=oD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=tD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=P().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function sZ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function tD(r,e,t,o,n){let s=aZ(e,o),a;if(n){let[p,u]=Ea(r[0],r[1]);a=p*u}else{let[p,u]=fp(r[0],r[1]);a=p*u}let i=sZ(t,s);return a*i}function aZ(r,e){switch(r){case er.PACKED_2X2_FLOAT32:return nh(e);case er.PACKED_2X2_FLOAT16:return sh(e);case er.UNPACKED_FLOAT32:return th(e);case er.UNPACKED_FLOAT16:return rh(e);case er.PACKED_4X1_UNSIGNED_BYTE:return oh(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function iZ(r){return P().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function rD(r,e){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return iZ(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function oD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var tr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` + `}var lh=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=gD(t,o),s=xD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=hD(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===tr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===tr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===tr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===tr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===tr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=gD(o,n),a=xD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=hD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function AZ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function hD(r,e,t,o,n){let s=FZ(e,o),a;if(n){let[p,u]=Pa(r[0],r[1]);a=p*u}else{let[p,u]=yp(r[0],r[1]);a=p*u}let i=AZ(t,s);return a*i}function FZ(r,e){switch(r){case tr.PACKED_2X2_FLOAT32:return sh(e);case tr.PACKED_2X2_FLOAT16:return ah(e);case tr.UNPACKED_FLOAT32:return rh(e);case tr.UNPACKED_FLOAT16:return oh(e);case tr.PACKED_4X1_UNSIGNED_BYTE:return nh(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function PZ(r){return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?tr.PACKED_2X2_FLOAT32:tr.UNPACKED_FLOAT32:r?tr.PACKED_2X2_FLOAT16:tr.UNPACKED_FLOAT16}function gD(r,e){if(r===mr.UPLOAD)return tr.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return PZ(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return tr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function xD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1178,11 +1178,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Ut="if (isnan(x)) return x;",nD="return x;",XI="return abs(x);";var sD="return (x >= 0.0) ? x : (exp(x) - 1.0);",aD=Ut+` + `}},Ut="if (isnan(x)) return x;",yD="return x;",nv="return abs(x);";var bD="return (x >= 0.0) ? x : (exp(x) - 1.0);",CD=Ut+` return (x < 0.0) ? 0.0 : x; -`,iD=Ut+` +`,wD=Ut+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Ra="return x;",uD="return 1.0 / (1.0 + exp(-1.0 * x));";var cD="return x;",lD=` +`,Oa="return x;",SD="return 1.0 / (1.0 + exp(-1.0 * x));";var vD="return x;",kD=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1191,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,mD=` +`,ND=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1201,7 +1201,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,dD=` +`,TD=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1211,7 +1211,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,fD="return 1.0 / (1.0 + exp(-1.0 * x));",Ar=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` +`,_D="return 1.0 / (1.0 + exp(-1.0 * x));",Fr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1222,17 +1222,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var lh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.length,o=Rt("rc",t),n=Re(t),s=eD(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` + `}};var mh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=pt(this.outputShape.length);let t=e.length,o=Rt("rc",t),n=Re(t),s=fD(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${i})); } - `}};var pZ=Wt.whereImpl,cZ=1e-7,lZ=1e-4,mh={};function mZ(r){return r in mh||(mh[r]={}),mh[r]}var dZ=P().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),fZ=600;function hZ(){return P().global.screen==null?1024:P().global.screen.height*P().global.screen.width*window.devicePixelRatio*fZ/1024/1024}var hu=class extends ro{nextDataId(){return hu.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!P().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof xp)t=e;else{let o=Gr(P().getNumber("WEBGL_VERSION"),e);t=new xp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Gr(P().getNumber("WEBGL_VERSION"));t=new xp(o),this.binaryCache=mZ(P().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new ch(this.gpgpu),this.numMBBeforeWarning=hZ(),this.texData=new Lo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=Tc(t),c=new Xl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((P().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||P().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(P().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Ar(i,Ra):m=new tr(i,Ra);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=C.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Ra):f=new tr(n,Ra);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(P().getBool("DEBUG")&&!P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&P().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&P().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Hl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=C.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Ar(s,Ra):d=new tr(s,Ra);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=dZ){return P().getBool("WEBGL_CPU_FORWARD")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new lh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ph(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[di(e.shape),...fi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[di(t),...fi(t)],a=new Oc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Tc(s),p;n?p=new Qf(i):p=new Yf(i);let u=!0,c=[t!=null?t:Hl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===du.DENSE){let x=a!=null?a:Hl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=P().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!fu(b.shape,x.shape)){let w=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),w.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=pR(e,c,l),d=this.getAndSaveBinary(m,()=>iR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),P().get("ENGINE_COMPILE_ONLY")||uR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=P().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!P().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(P().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!P().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=P().getBool("DEBUG");P().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(P().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?cZ:lZ}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=II(o,p),t.texShape=l),s!=null){let m=Tc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ea(l[0],l[1])),p?d=new eh(m,g):d=new Xl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),w=this.texData.get(b.dataId);g?w.usage=mr.PIXELS:w.usage=mr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,P().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=gZ(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Kw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Gf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=EI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||"RGBA";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};hu.nextDataId=0;function gZ(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew hu,2);var cst={forceHalfFloat:hD};var Mc=` + `}};var MZ=Wt.whereImpl,LZ=1e-7,BZ=1e-4,dh={};function zZ(r){return r in dh||(dh[r]={}),dh[r]}var VZ=A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),WZ=600;function UZ(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*WZ/1024/1024}var bu=class extends so{nextDataId(){return bu.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof wp)t=e;else{let o=Kr(A().getNumber("WEBGL_VERSION"),e);t=new wp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber("WEBGL_VERSION"));t=new wp(o),this.binaryCache=zZ(A().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new lh(this.gpgpu),this.numMBBeforeWarning=UZ(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=_c(t),c=new Xl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||A().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Fr(i,Oa):m=new rr(i,Oa);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Fr(n,Oa):f=new rr(n,Oa);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(A().getBool("DEBUG")&&!A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&A().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&A().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Hl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Fr(s,Oa):d=new rr(s,Oa);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=VZ){return A().getBool("WEBGL_CPU_FORWARD")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new mh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ch(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[gi(e.shape),...xi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[gi(t),...xi(t)],a=new Mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=_c(s),p;n?p=new Zf(i):p=new Qf(i);let u=!0,c=[t!=null?t:Hl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===xu.DENSE){let x=a!=null?a:Hl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!yu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=SR(e,c,l),d=this.getAndSaveBinary(m,()=>CR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get("ENGINE_COMPILE_ONLY")||wR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=A().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=A().getBool("DEBUG");A().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?LZ:BZ}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=DI(o,p),t.texShape=l),s!=null){let m=_c(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Pa(l[0],l[1])),p?d=new th(m,g):d=new Xl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,A().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=GZ(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await tS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Hf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=BI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||"RGBA";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};bu.nextDataId=0;function GZ(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew bu,2);var tat={forceHalfFloat:$D};var Lc=` if (isnan(a)) return a; if (isnan(b)) return b; -`;var lo=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` +`;var Pr=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1242,12 +1242,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}};var Kr=` + `}};var Xr=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`;var Ro=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` +`;var jr=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=pt(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var gD={kernelName:xo,backendName:"webgl",kernelFunc:Dt};function Fr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var xD={kernelName:Ti,backendName:"webgl",kernelFunc:Fr};var YI="return (a < 0.) ? b * a : a;",QI=` + `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var ED={kernelName:Co,backendName:"webgl",kernelFunc:Dt};function Or(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var RD={kernelName:Ri,backendName:"webgl",kernelFunc:Or};var sv="return (a < 0.) ? b * a : a;",av=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function yZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=P().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ro(QI,n.shape,a.shape):new lo(YI,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],"float32");return t.disposeIntermediateTensorInfo(a),p}var yD={kernelName:_n,backendName:"webgl",kernelFunc:yZ};var ZI="return (a < 0.) ? b * a : a;",JI=` +`;function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(av,n.shape,a.shape):new Pr(sv,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],"float32");return t.disposeIntermediateTensorInfo(a),p}var DD={kernelName:En,backendName:"webgl",kernelFunc:KZ};var iv="return (a < 0.) ? b * a : a;",uv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function bZ(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=P().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ro(JI,o.shape,n.shape):new lo(ZI,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],"float32")}var bD={kernelName:Jn,backendName:"webgl",kernelFunc:bZ};var Do="if (isnan(x)) return x;";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=P().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[S,k]=w,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new lo(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Fr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?C.fromUint8ToStringArray(f):f,x=p.dtype==="string"?C.fromUint8ToStringArray(h):h,[b,w]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(w,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=P().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,d;return m?d=new Ro(e,p.shape,u.shape,t):d=new lo(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function hi(r,e=!1){if(r==="linear")return e?cD:nD;if(r==="relu")return e?mD:aD;if(r==="elu")return e?lD:sD;if(r==="relu6")return e?dD:iD;if(r==="prelu")return e?JI:ZI;if(r==="leakyrelu")return e?QI:YI;if(r==="sigmoid")return e?fD:uD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Lc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { +`;function qZ(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(uv,o.shape,n.shape):new Pr(iv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],"float32")}var AD={kernelName:rs,backendName:"webgl",kernelFunc:qZ};var Fo="if (isnan(x)) return x;";function xe({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Fr(a.shape,e):c=new rr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?w.fromUint8ToStringArray(f):f,x=p.dtype==="string"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,d;return m?d=new jr(e,p.shape,u.shape,t):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function yi(r,e=!1){if(r==="linear")return e?vD:yD;if(r==="relu")return e?ND:CD;if(r==="elu")return e?kD:bD;if(r==="relu6")return e?TD:wD;if(r==="prelu")return e?uv:iv;if(r==="leakyrelu")return e?av:sv;if(r==="sigmoid")return e?_D:SD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Bc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=pt(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { @@ -1305,14 +1305,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i} }`:g=`vec4 activation(vec4 x) { ${i} - }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",S="rc.x";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!fu(n.shape,p)&&!(c.texture!==null&&fu(c.shape,p))?SD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ID={kernelName:ia,backendName:"webgl",kernelFunc:te};var Jl=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%o>0&&(c=` + `}};var FD="return a * b;";function Zl(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=w.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),u=new Ql(pv.REAL,o.shape,n.shape),c=new Ql(pv.IMAG,o.shape,n.shape),l=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:n.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),[u,c]=GR(o.shape,n.shape,i.values,p.values,s),l=t.makeTensorInfo(c,s),m=t.texData.get(l.dataId);return m.values=u,l}let a;return A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new jr(FD,o.shape,n.shape):a=new Pr(FD,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var PD={kernelName:Xn,backendName:"webgl",kernelFunc:Zl};function OD(r,e,t){let o=[gi(r.shape),...xi(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[gi(e),...xi(e)],a=new Mc(s,o),i=!0,p=[o],u=t.runWebGLProgram(a,[n],r.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}function te(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),p=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(p);y.assert(i===u,()=>`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!yu(n.shape,p)&&!(c.texture!==null&&yu(c.shape,p))?OD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var MD={kernelName:ma,backendName:"webgl",kernelFunc:te};var Jl=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%o>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } @@ -1402,7 +1402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}};var dh=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",p="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",p="min"):t==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,l=o%4,m=` + `}};var fh=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",p="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",p="min"):t==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,l=o%4,m=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${u}); } - `}};function wZ(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=C.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function qr(r,e,t,o){let n=wZ(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=jI("rc",this.rank),a=new Array(this.rank);for(let c=0;c6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=ov("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],F=Math.max(x,b),O=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,B=p==="leakyrelu",z=p!=null?hi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>tv&&U===!1){let X=E,J=R;t&&(X=bt({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(X)),o&&(J=bt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=X;re&&(ee=te({inputs:{x:X},backend:n,attrs:{shape:[F,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[F,1,O]}}),D.push(ie));let le=Zl({inputs:{a:ee,b:ie},backend:n});j=bp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let X=dt(r.dtype,e.dtype),J=new Lc(k,_,[F,d,f],t,o,M,z,L,B),re=[E,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,X)}let H=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let X of D)n.disposeIntermediateTensorInfo(X);return H}function IZ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Cp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var TD={kernelName:bo,backendName:"webgl",kernelFunc:IZ};var _D="return abs(x);";function vZ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=ih(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return P().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,_D):n=new tr(o.shape,_D),t.runWebGLProgram(n,[o],o.dtype)}var $D={kernelName:Gs,backendName:"webgl",kernelFunc:vZ};var kZ=Ut+` + `}};function Cu(r,e,t){let o=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gh(r.shape,e):new hh(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function LD(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,p=w.getAxesPermutation(i,s),u=p!=null,c=r;u&&(c=Cu(r,p,o),i=w.getInnerMostAxes(i.length,s)),w.assertAxesAreInnerMostDims("sum",i,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,i),d=l;t&&(d=w.expandShapeToKeepDim(l,a));let f=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/f,x=te({inputs:{x:c},attrs:{shape:[g,f]},backend:o}),b=ti(r.dtype),C=Yr(x,b,"sum",o),S=te({inputs:{x:C},attrs:{shape:d},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(C),u&&o.disposeIntermediateTensorInfo(c),S}function Ip(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return LD(n,s,a,t)}var BD={kernelName:Ss,backendName:"webgl",kernelFunc:Ip};function Ct(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,p=new Array(i);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,B=p==="leakyrelu",z=p!=null?yi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>cv&&U===!1){let Y=E,J=R;t&&(Y=Ct({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=Ct({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[P,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[P,1,O]}}),D.push(ie));let le=Zl({inputs:{a:ee,b:ie},backend:n});j=Ip({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,e.dtype),J=new Bc(k,_,[P,d,f],t,o,M,z,L,B),re=[E,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function QZ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return vp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var VD={kernelName:So,backendName:"webgl",kernelFunc:QZ};var WD="return abs(x);";function ZZ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=uh(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Fr(o.shape,WD):n=new rr(o.shape,WD),t.runWebGLProgram(n,[o],o.dtype)}var UD={kernelName:js,backendName:"webgl",kernelFunc:ZZ};var JZ=Ut+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,NZ=ge({opSnippet:kZ}),ED={kernelName:zo,backendName:"webgl",kernelFunc:NZ};var TZ=Ut+` +`,e9=xe({opSnippet:JZ}),GD={kernelName:Vo,backendName:"webgl",kernelFunc:e9};var t9=Ut+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,backendName:"webgl",kernelFunc:_Z};var DD="return a + b;",$Z=nt({opSnippet:DD,packedOpSnippet:DD,supportsComplex:!0,cpuKernelImpl:cR}),AD={kernelName:no,backendName:"webgl",kernelFunc:$Z};var gh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,r9=xe({opSnippet:t9}),HD={kernelName:Wo,backendName:"webgl",kernelFunc:r9};var KD="return a + b;",o9=nt({opSnippet:KD,packedOpSnippet:KD,supportsComplex:!0,cpuKernelImpl:IR}),qD={kernelName:io,backendName:"webgl",kernelFunc:o9};var xh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} @@ -1531,7 +1531,7 @@ return log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,back float result = ${n}; setOutput(result); } - `}};var xh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` + `}};var yh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,back vec4 result = ${n}; setOutput(result); } - `}};function yh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>P().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=yh({inputs:o.slice(0,p),backend:t}),c=yh({inputs:o.slice(p),backend:t});return yh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=P().getBool("WEBGL_PACK")?new xh(o[0].shape,s):new gh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var FD={kernelName:Wo,backendName:"webgl",kernelFunc:yh};function EZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=C.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("all",u,i);let[m,d]=C.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=qr(h,h.dtype,"all",t),x;if(a){let b=C.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var PD={kernelName:Uo,backendName:"webgl",kernelFunc:EZ};function RZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=C.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("any",u,i);let[m,d]=C.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=qr(h,h.dtype,"any",t),x;if(a){let b=C.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var OD={kernelName:Go,backendName:"webgl",kernelFunc:RZ};var bh=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function bh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=bh({inputs:o.slice(0,p),backend:t}),c=bh({inputs:o.slice(p),backend:t});return bh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new yh(o[0].shape,s):new xh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var jD={kernelName:Uo,backendName:"webgl",kernelFunc:bh};function n9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("all",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"all",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var XD={kernelName:Go,backendName:"webgl",kernelFunc:n9};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("any",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"any",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var YD={kernelName:Ho,backendName:"webgl",kernelFunc:s9};var Ch=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1559,7 +1559,7 @@ return log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,back } setOutput(float(bestIndex)); } - `}};var Ch=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=Re(p),c=Rt("coords",p),l,m;if(a===1){m=p+1;let R=Re(m);l=` + `}};var wh=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=Re(p),c=Rt("coords",p),l,m;if(a===1){m=p+1;let R=Re(m);l=` ${R} sourceLocR = ${R}(${c.join()}, 0); ++${c[p-1]}; ${R} sourceLocG = ${R}(${c.join()}, 0); @@ -1575,15 +1575,15 @@ return log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,back ${u} sourceLocA = coords; --${c[p-1]}; ${u} sourceLocB = coords; - --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(R=>"int "+R),g=Rt("sourceLocR",m-1).concat("inIdx.r"),x=Rt("sourceLocG",m-1).concat("inIdx.g"),b=Rt("sourceLocB",m-1).concat("inIdx.b"),w=Rt("sourceLocA",m-1).concat("inIdx.a"),S=o==="max"?"greaterThan":"lessThan",k=n?"":` + --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(R=>"int "+R),g=Rt("sourceLocR",m-1).concat("inIdx.r"),x=Rt("sourceLocG",m-1).concat("inIdx.g"),b=Rt("sourceLocB",m-1).concat("inIdx.b"),C=Rt("sourceLocA",m-1).concat("inIdx.a"),S=o==="max"?"greaterThan":"lessThan",k=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), - getBestIndicesAChannel(${w.join()})));`,_=`vec4( + getBestIndicesAChannel(${C.join()})));`,_=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,E=n?"":` + hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,E=n?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,_Z=ge({opSnippet:TZ}),RD={kernelName:Vo,back } setOutput(bestIndex); } - `}};function MD(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=C.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new bh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=MD(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function LD(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=C.computeOptimalWindowSize(s),i=new Ch(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===e.shape.length){let c=LD(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function wh(r,e,t,o){let n=[t];if(C.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!P().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=C.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=MD(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return LD(r,e,o)}function DZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=wh(t,p,a[0],"max");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var BD={kernelName:Hs,backendName:"webgl",kernelFunc:DZ};function AZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=wh(t,p,a[0],"min");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var zD={kernelName:Ks,backendName:"webgl",kernelFunc:AZ};var FZ=Ut+` + `}};function QD(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=w.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new Ch(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=QD(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function ZD(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=w.computeOptimalWindowSize(s),i=new wh(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===e.shape.length){let c=ZD(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Sh(r,e,t,o){let n=[t];if(w.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=QD(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return ZD(r,e,o)}function a9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=Sh(t,p,a[0],"max");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var JD={kernelName:Xs,backendName:"webgl",kernelFunc:a9};function i9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=Sh(t,p,a[0],"min");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var eA={kernelName:Ys,backendName:"webgl",kernelFunc:i9};var u9=Ut+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,PZ=ge({opSnippet:FZ}),VD={kernelName:Ho,backendName:"webgl",kernelFunc:PZ};var OZ=Ut+"return log(x + sqrt(x * x + 1.0));",MZ=ge({opSnippet:OZ}),WD={kernelName:Ko,backendName:"webgl",kernelFunc:MZ};var LZ=Ut+` +`,p9=xe({opSnippet:u9}),tA={kernelName:Ko,backendName:"webgl",kernelFunc:p9};var c9=Ut+"return log(x + sqrt(x * x + 1.0));",l9=xe({opSnippet:c9}),rA={kernelName:qo,backendName:"webgl",kernelFunc:l9};var m9=Ut+` return atan(x); -`,BZ=ge({opSnippet:LZ}),UD={kernelName:qo,backendName:"webgl",kernelFunc:BZ};var zZ=Mc+` +`,d9=xe({opSnippet:m9}),oA={kernelName:jo,backendName:"webgl",kernelFunc:d9};var f9=Lc+` return atan(a, b); -`,VZ=` +`,h9=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+Kr+` + `+Xr+` return result; -`,WZ=nt({opSnippet:zZ,packedOpSnippet:VZ}),GD={kernelName:Xo,backendName:"webgl",kernelFunc:WZ};var UZ=Ut+` +`,g9=nt({opSnippet:f9,packedOpSnippet:h9}),nA={kernelName:Yo,backendName:"webgl",kernelFunc:g9};var x9=Ut+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelName:jo,backendName:"webgl",kernelFunc:GZ};var Ms=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,y9=xe({opSnippet:x9}),sA={kernelName:Xo,backendName:"webgl",kernelFunc:y9};var Vs=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); @@ -1690,11 +1690,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let w="max",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(S="avgValue / max(count, 1.0)");let k=Math.floor(a/4)*4,_=a%4,E=` + `;return}let C="max",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(S="avgValue / max(count, 1.0)");let k=Math.floor(a/4)*4,_=a%4,E=` if (${h}) { avgValue += dot(values, ones); } else { - minMaxValue = ${w}(values, minMaxValue); + minMaxValue = ${C}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); @@ -1780,7 +1780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(${S}); } - `}},xu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",S="0.0";if(w||(S="-1.0 / 1e-20"),o){let F=">=";this.userCode=` + `}},wu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t==="avg",S="0.0";if(C||(S="-1.0 / 1e-20"),o){let P=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); @@ -1831,7 +1831,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${F} currMinMaxValue) { + if (value ${P} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} + @@ -1843,7 +1843,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam setOutput(float(minMaxPosition)); } `;return}let k="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / max(count, 1.0)");let E=Math.floor(a/4)*4,R=a%4,D=` - if (${w}) { + if (${C}) { avgValue += dot(values, ones); } else { minMaxValue = ${k}(values, minMaxValue); @@ -1943,7 +1943,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(${_}); } - `}};function HZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Ps(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Ms(c,"avg",!1);return t.runWebGLProgram(l,[n],"float32")}var KD={kernelName:Yo,backendName:"webgl",kernelFunc:HZ};function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new xu(l,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var qD={kernelName:qs,backendName:"webgl",kernelFunc:KZ};var Sh=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` + `}};function b9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Bs(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Vs(c,"avg",!1);return t.runWebGLProgram(l,[n],"float32")}var aA={kernelName:Qo,backendName:"webgl",kernelFunc:b9};function C9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new wu(l,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var iA={kernelName:Qs,backendName:"webgl",kernelFunc:C9};var Ih=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${m}); @@ -1985,7 +1985,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}},Ih=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=` + `}},vh=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=` const ivec3 pads = ivec3(${f}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}};function qZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,l,u,c),d=new Ih(m);return t.runWebGLProgram(d,[n],a.dtype)}var jD={kernelName:Ni,backendName:"webgl",kernelFunc:qZ};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Ps([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=C.computePool2DInfo(a.shape,i,p,1,u),l=new Sh(c);return t.runWebGLProgram(l,[n],a.dtype)}var XD={kernelName:Gp,backendName:"webgl",kernelFunc:jZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Cp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var YD={kernelName:Qo,backendName:"webgl",kernelFunc:XZ};var vh=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function w9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new vh(m);return t.runWebGLProgram(d,[n],a.dtype)}var uA={kernelName:Ei,backendName:"webgl",kernelFunc:w9};function S9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Bs([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new Ih(c);return t.runWebGLProgram(l,[n],a.dtype)}var pA={kernelName:$i,backendName:"webgl",kernelFunc:S9};function I9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return vp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var cA={kernelName:Zo,backendName:"webgl",kernelFunc:I9};var kh=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2051,7 +2051,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}};var kh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};var Nh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${p}; @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam setOutput((x - mean) * inv + offset); } - `}};var YZ=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=P().getBool("WEBGL_PACK_NORMALIZATION")?new kh(o.shape,n.shape,s.shape,c,l,p):new vh(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},QD={kernelName:wn,backendName:"webgl",kernelFunc:YZ};var Nh=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=QZ(this.rank),n,s=e.map((a,i)=>`sourceLoc.${rv[i]} = start[${i}] + coords.${rv[i]};`);n=` + `}};var v9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool("WEBGL_PACK_NORMALIZATION")?new Nh(o.shape,n.shape,s.shape,c,l,p):new kh(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},lA={kernelName:In,backendName:"webgl",kernelFunc:v9};var Th=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=k9(this.rank),n,s=e.map((a,i)=>`sourceLoc.${lv[i]} = start[${i}] + coords.${lv[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${n} setOutput(getSource(${o})); } - `}},rv=["x","y","z","w","u","v"];function QZ(r){if(r===1)return"sourceLoc";if(r<=6)return rv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Th=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=Re(this.rank),o=Rt("coords",this.rank),n=Rt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` + `}},lv=["x","y","z","w","u","v"];function k9(r){if(r===1)return"sourceLoc";if(r<=6)return lv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var _h=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=Re(this.rank),o=Rt("coords",this.rank),n=Rt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` result.x = ${a}; if (++${o[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,7 +2103,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${p} setOutput(result); } - `}};function ZZ(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ct.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Ls(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.texData.get(n.dataId),m=VR(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=P().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Th(p):new Nh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),ZZ(n,i,p,t)}var ZD={kernelName:pa,backendName:"webgl",kernelFunc:Ls};var JZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ls({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},JD={kernelName:js,backendName:"webgl",kernelFunc:JZ};function e9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ah(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var eA={kernelName:Zo,backendName:"webgl",kernelFunc:e9};function t9(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var tA={kernelName:Xs,backendName:"webgl",kernelFunc:t9};var r9="return float(a != b);",ov=nt({opSnippet:r9,cpuKernelImpl:DR,dtype:"bool"}),rA={kernelName:qn,backendName:"webgl",kernelFunc:ov};function gi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var oA={kernelName:zi,backendName:"webgl",kernelFunc:gi};var o9="return float(int(x));";function nA(r,e){let t=new tr(r.shape,o9),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function nv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Dt({inputs:{x:n},backend:t});let a=Wr(n.shape),i=nv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=Fr({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype==="complex64"){let a=gi({inputs:{input:n},backend:t}),i=nv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=mR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return nA(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=ov({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var sA={kernelName:ho,backendName:"webgl",kernelFunc:nv};var aA="return ceil(x);",n9=ge({opSnippet:aA,packedOpSnippet:aA,cpuKernelImpl:dR}),iA={kernelName:Jo,backendName:"webgl",kernelFunc:n9};var _h=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function N9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ct.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Ws(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.texData.get(n.dataId),m=tD(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _h(p):new Th(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),N9(n,i,p,t)}var mA={kernelName:fa,backendName:"webgl",kernelFunc:Ws};var T9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Ct({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ws({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},dA={kernelName:Zs,backendName:"webgl",kernelFunc:T9};function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ih(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var fA={kernelName:Jo,backendName:"webgl",kernelFunc:_9};var $9=` + int r = int(a.r) & int(b.r); + int g = int(a.g) & int(b.g); + int rb = int(a.b) & int(b.b); + int ra = int(a.a) & int(b.a); + return vec4(r, g, rb, ra); +`,E9=` + return float(int(a.r) & int(b.r)); +`;function R9(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS"),a=A().getNumber("WEBGL_VERSION");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[c,l]=kR(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(l,o.dtype),d=t.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr($9,o.shape,n.shape,!1):i=new Pr(E9,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var hA={kernelName:Ha,backendName:"webgl",kernelFunc:R9};function D9(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var gA={kernelName:Js,backendName:"webgl",kernelFunc:D9};var A9="return float(a != b);",mv=nt({opSnippet:A9,cpuKernelImpl:KR,dtype:"bool"}),xA={kernelName:Yn,backendName:"webgl",kernelFunc:mv};function bi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var yA={kernelName:Gi,backendName:"webgl",kernelFunc:bi};var F9="return float(int(x));";function bA(r,e){let t=new rr(r.shape,F9),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function dv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Dt({inputs:{x:n},backend:t});let a=Gr(n.shape),i=dv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=Or({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype==="complex64"){let a=bi({inputs:{input:n},backend:t}),i=dv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=NR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return bA(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=mv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var CA={kernelName:yo,backendName:"webgl",kernelFunc:dv};var wA="return ceil(x);",P9=xe({opSnippet:wA,packedOpSnippet:wA,cpuKernelImpl:TR}),SA={kernelName:en,backendName:"webgl",kernelFunc:P9};var $h=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2114,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}};var $h=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};var Eh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2125,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;P().getBool("WEBGL_PACK_CLIP")?i=new $h(n.shape):i=new _h(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var uA={kernelName:go,backendName:"webgl",kernelFunc:s9};var Eh=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function O9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool("WEBGL_PACK_CLIP")?i=new Eh(n.shape):i=new $h(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var IA={kernelName:bo,backendName:"webgl",kernelFunc:O9};var Rh=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2138,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function pA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function a9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Eh(o.shape),a=[pA(o,n.complexTensorInfos.real),pA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var cA={kernelName:_i,backendName:"webgl",kernelFunc:a9};var Rh=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) { return getChannel( - getT${h}(${Dh(i,u,g)}), - vec2(${Dh(c,u,g)})); + getT${h}(${Ah(i,u,g)}), + vec2(${Ah(c,u,g)})); }`}let d=p.length,f=p[p.length-1];m+=` return getChannel( - getT${d}(${Dh(i,u,f)}), - vec2(${Dh(c,u,f)}));`,this.userCode=` + getT${d}(${Ah(i,u,f)}), + vec2(${Ah(c,u,f)}));`,this.userCode=` float getValue(${i.map(h=>"int "+h)}) { ${m} } @@ -2184,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(result); } - `}};function Dh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function wp(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var lA={kernelName:Mi,backendName:"webgl",kernelFunc:wp};function Bc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let d=r.map(b=>gi({inputs:{input:b},backend:t})),f=r.map(b=>wp({inputs:{input:b},backend:t})),h=Bc(d,e,t),g=Bc(f,e,t),x=Fr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=C.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=fR(f,h,o,g),b=C.computeOutShape(r.map(S=>S.shape),e),w=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),w}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=P().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,Ra):new Ar(r[0].shape,Ra);return t.runWebGLProgram(d,r,o)}let i=P().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=i9(s,e,t),c=new Rh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function i9(r,e,t){let o=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function sv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):Bc(p,s,t)}var mA={kernelName:Ys,backendName:"webgl",kernelFunc:sv};var zc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,S="",k="";o&&(n?S=`float activation(float a) { + `}};function Ah(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function kp(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var NA={kernelName:Vi,backendName:"webgl",kernelFunc:kp};function zc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let d=r.map(b=>bi({inputs:{input:b},backend:t})),f=r.map(b=>kp({inputs:{input:b},backend:t})),h=zc(d,e,t),g=zc(f,e,t),x=Or({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=_R(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let d=a?new rr(r[0].shape,Oa):new Fr(r[0].shape,Oa);return t.runWebGLProgram(d,r,o)}let i=A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=L9(s,e,t),c=new Dh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function L9(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function fv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):zc(p,s,t)}var TA={kernelName:ea,backendName:"webgl",kernelFunc:fv};var Vc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,C=g?3:1,S="",k="";o&&(n?S=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?S=`float activation(float a) { @@ -2203,7 +2211,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d2 = coords[${w}]; + int d2 = coords[${C}]; ivec2 xRCCorner = ivec2(coords[${x}], coords[${b}]) * strides - pads; @@ -2318,7 +2326,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${k} setOutput(result); } - `}},Fh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=` + `}},Ph=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${a}, ${i}); const ivec3 pads = ivec3(${t}, ${o}, ${n}); @@ -2406,7 +2414,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}};var Vc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=` + `}};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Mh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let S=Oh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Oh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>tv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(fu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let R=Cp({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=Cp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Lh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d==="channelsLast",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,w=!1,S=[];if(s!=null){let H=Oh(s.shape,f);H!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:H}}),S.push(s))}if(n!=null){let H=Oh(n.shape,f);H!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:H}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Ph(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],"float32",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let F=n!=null,O=s!=null,M=i==="leakyrelu",L=i?hi(i,!0):null,B=new Lc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,w,F,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let H=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));z.push(H),S.push(H)}let U=o.runWebGLProgram(B,z,"float32"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let H of S)o.disposeIntermediateTensorInfo(H);return j}function u9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))d=Mh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l==="channelsLast"&&P().getBool("WEBGL_EXP_CONV")){let h=new Vc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],"float32",g)}else if(P().getBool("WEBGL_CONV_IM2COL"))d=Lh({x:n,filter:s,convInfo:m,backend:t});else{let h=new zc(m);d=t.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var dA={kernelName:en,backendName:"webgl",kernelFunc:u9};var Bh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` + `}};function Mh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let S=Mh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Mh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>cv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(yu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let R=vp({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=vp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Bh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d==="channelsLast",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Mh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Mh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Oh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],"float32",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i==="leakyrelu",L=i?yi(i,!0):null,B=new Bc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,"float32"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function B9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))d=Lh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let h=new Wc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],"float32",g)}else if(A().getBool("WEBGL_CONV_IM2COL"))d=Bh({x:n,filter:s,convInfo:m,backend:t});else{let h=new Vc(m);d=t.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var _A={kernelName:tn,backendName:"webgl",kernelFunc:B9};var zh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2686,7 +2694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}},zh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` + `}},Vh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { @@ -2739,7 +2747,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}},Vh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},Wh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2781,7 +2789,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}},Wh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=` + `}},Uh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${p}, ${u}, ${c}); void main() { @@ -2838,7 +2846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}};function p9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Bh(m);return t.runWebGLProgram(d,[n,s],"float32")}var fA={kernelName:$i,backendName:"webgl",kernelFunc:p9};var Uh=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=` + `}};function z9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new zh(m);return t.runWebGLProgram(d,[n,s],"float32")}var $A={kernelName:Ai,backendName:"webgl",kernelFunc:z9};var Gh=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=pt(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${n}, ${s}); void main() { @@ -2912,19 +2920,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(result); } - `}};function c9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(P().getBool("WEBGL_PACK")&&l==="channelsLast"){let d=[[m.strideHeight,m.strideWidth]],f=new Uh(m);return t.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new zh(m);return t.runWebGLProgram(d,[n,s],"float32")}}var hA={kernelName:tn,backendName:"webgl",kernelFunc:c9};function l9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Fh(u);return t.runWebGLProgram(c,[n,s],"float32")}var gA={kernelName:rn,backendName:"webgl",kernelFunc:l9};function m9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=C.computeConv3DInfo(n.shape,p,a,1,i),c=new Vh(u);return t.runWebGLProgram(c,[n,s],"float32")}var xA={kernelName:za,backendName:"webgl",kernelFunc:m9};function d9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=C.computeConv3DInfo(p,s.shape,i,1,a),c=new Wh(u);return t.runWebGLProgram(c,[n,s],"float32")}var yA={kernelName:on,backendName:"webgl",kernelFunc:d9};var f9=Do+` + `}};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool("WEBGL_PACK")&&l==="channelsLast"){let d=[[m.strideHeight,m.strideWidth]],f=new Gh(m);return t.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new Vh(m);return t.runWebGLProgram(d,[n,s],"float32")}}var EA={kernelName:rn,backendName:"webgl",kernelFunc:V9};function W9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ph(u);return t.runWebGLProgram(c,[n,s],"float32")}var RA={kernelName:on,backendName:"webgl",kernelFunc:W9};function U9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Wh(u);return t.runWebGLProgram(c,[n,s],"float32")}var DA={kernelName:Ka,backendName:"webgl",kernelFunc:U9};function G9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Uh(u);return t.runWebGLProgram(c,[n,s],"float32")}var AA={kernelName:nn,backendName:"webgl",kernelFunc:G9};var H9=Fo+` return cos(x); -`,h9=` +`,K9=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Kr} + ${Xr} return result; -`,g9=ge({opSnippet:f9,packedOpSnippet:h9}),bA={kernelName:nn,backendName:"webgl",kernelFunc:g9};var x9=` +`,q9=xe({opSnippet:H9,packedOpSnippet:K9}),FA={kernelName:sn,backendName:"webgl",kernelFunc:q9};var j9=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,y9=ge({opSnippet:x9}),CA={kernelName:sn,backendName:"webgl",kernelFunc:y9};var Gh=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[w,S,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` +`,X9=xe({opSnippet:j9}),PA={kernelName:an,backendName:"webgl",kernelFunc:X9};var Hh=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); - const float width_ratio = float(${w}); + const float width_ratio = float(${C}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2983,20 +2991,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam setOutput(newValue); } } - `}};var b9=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Gh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],"float32")},wA={kernelName:pn,backendName:"webgl",kernelFunc:b9};var Sp;(function(r){r.Prod="*",r.Sum="+"})(Sp||(Sp={}));var em=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Sp.Prod?"1.0":"0.0",i=o?a:`getX(${SA(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",c="";o?(u=n?`end != ${p-1}`:"end != 0",c=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=` + `}};var Y9=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Hh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],"float32")},OA={kernelName:cn,backendName:"webgl",kernelFunc:Y9};var Np;(function(r){r.Prod="*",r.Sum="+"})(Np||(Np={}));var em=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Np.Prod?"1.0":"0.0",i=o?a:`getX(${MA(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",c="";o?(u=n?`end != ${p-1}`:"end != 0",c=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${Re(s)} coords = getOutputCoords(); - int end = ${IA(s,"coords",this.op)}; + int end = ${LA(s,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; - ${IA(s,"coords",this.op)} = idx; - val ${this.op}= getX(${SA(s,"coords",this.op)}); + ${LA(s,"coords",this.op)} = idx; + val ${this.op}= getX(${MA(s,"coords",this.op)}); } setOutput(val); } - `}};function SA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function IA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Hh(r,e,t,o,n,s){let a=e.shape.length,i=C.getAxesPermutation([o],a),p=e;i!=null&&(p=bt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new em(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new em(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=C.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function C9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Hh(Sp.Prod,n,t,s,a,i)}var vA={kernelName:an,backendName:"webgl",kernelFunc:C9};function w9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Hh(Sp.Sum,n,t,s,a,i)}var kA={kernelName:un,backendName:"webgl",kernelFunc:w9};function S9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ah(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=lR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var NA={kernelName:Qs,backendName:"webgl",kernelFunc:S9};var Kh=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=` + `}};function MA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Kh(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=Ct({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new em(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new em(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=Ct({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function Q9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Kh(Np.Prod,n,t,s,a,i)}var BA={kernelName:un,backendName:"webgl",kernelFunc:Q9};function Z9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Kh(Np.Sum,n,t,s,a,i)}var zA={kernelName:pn,backendName:"webgl",kernelFunc:Z9};function J9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ih(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=vR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var VA={kernelName:ta,backendName:"webgl",kernelFunc:J9};var qh=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3015,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function I9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new Kh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var TA={kernelName:cn,backendName:"webgl",kernelFunc:I9};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u="",c="";o&&(n?u=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function eJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new qh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var WA={kernelName:ln,backendName:"webgl",kernelFunc:eJ};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u="",c="";o&&(n?u=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?u=`float activation(float a) { @@ -3068,7 +3076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${c} setOutput(result); } - `}};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=` + `}};var Gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); @@ -3150,10 +3158,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } `:d+=` xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); - `):w===1?d+=` + `):C===1?d+=` xC${b+1} = xTexelC${b}; `:d+=` - xCOffset = xC + ${w}; + xCOffset = xC + ${C}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); @@ -3257,7 +3265,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${h} setOutput(result); } - `}};function v9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p,dimRoundingMode:u}=o,c=p;c==null&&(c=[1,1]),y.assert(C.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=C.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;P().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Uc(l):m=new Wc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],"float32",d)}var _A={kernelName:ln,backendName:"webgl",kernelFunc:v9};var qh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=` + `}};function tJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p,dimRoundingMode:u}=o,c=p;c==null&&(c=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Gc(l):m=new Uc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],"float32",d)}var UA={kernelName:mn,backendName:"webgl",kernelFunc:tJ};var jh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3292,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}},jh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=` + `}},Xh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${i}); void main() { @@ -3337,13 +3345,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}};function k9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=C.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new qh(l);return t.runWebGLProgram(m,[n,s],"float32")}var $A={kernelName:Ei,backendName:"webgl",kernelFunc:k9};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=C.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new jh(l);return t.runWebGLProgram(m,[n,s],"float32")}var EA={kernelName:Ri,backendName:"webgl",kernelFunc:N9};var Xh=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function rJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new jh(l);return t.runWebGLProgram(m,[n,s],"float32")}var GA={kernelName:Fi,backendName:"webgl",kernelFunc:rJ};function oJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Xh(l);return t.runWebGLProgram(m,[n,s],"float32")}var HA={kernelName:Pi,backendName:"webgl",kernelFunc:oJ};var Yh=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function T9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Xh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var RA={kernelName:Zs,backendName:"webgl",kernelFunc:T9};var Yh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=` + `}};function nJ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Yh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var KA={kernelName:ra,backendName:"webgl",kernelFunc:nJ};var Qh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${m}); const float neg_infinity = -3.4e38; @@ -3381,7 +3389,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam float result = curVal; setOutput(result); } - `}};function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Yh(u);c=t.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var DA={kernelName:mn,backendName:"webgl",kernelFunc:_9};function $9(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=C.decodeEinsumEquation(n,s.length);C.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=C.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=bp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var AA={kernelName:Fi,backendName:"webgl",kernelFunc:$9};var E9="return (x >= 0.0) ? x : (exp(x) - 1.0);",R9=` + `}};function sJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Qh(u);c=t.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var qA={kernelName:dn,backendName:"webgl",kernelFunc:sJ};function aJ(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ip({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var jA={kernelName:Li,backendName:"webgl",kernelFunc:aJ};var iJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",uJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3390,29 +3398,29 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,D9=ge({opSnippet:E9,packedOpSnippet:R9}),FA={kernelName:fn,backendName:"webgl",kernelFunc:D9};var A9="return (b >= 0.0) ? a : a * (b + 1.0);",F9=` +`,pJ=xe({opSnippet:iJ,packedOpSnippet:uJ}),XA={kernelName:hn,backendName:"webgl",kernelFunc:pJ};var cJ="return (b >= 0.0) ? a : a * (b + 1.0);",lJ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,P9=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=P().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ro(F9,o.shape,n.shape):new lo(A9,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},PA={kernelName:Va,backendName:"webgl",kernelFunc:P9};var O9=` +`,mJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(lJ,o.shape,n.shape):new Pr(cJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},YA={kernelName:qa,backendName:"webgl",kernelFunc:mJ};var dJ=` return vec4(equal(a, b)); -`,M9="return float(a == b);",L9=nt({opSnippet:M9,packedOpSnippet:O9,dtype:"bool",cpuKernelImpl:hR}),OA={kernelName:hn,backendName:"webgl",kernelFunc:L9};var B9=` +`,fJ="return float(a == b);",hJ=nt({opSnippet:fJ,packedOpSnippet:dJ,dtype:"bool",cpuKernelImpl:$R}),QA={kernelName:xn,backendName:"webgl",kernelFunc:hJ};var gJ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - float p = ${C.ERF_P}; - float a1 = ${C.ERF_A1}; - float a2 = ${C.ERF_A2}; - float a3 = ${C.ERF_A3}; - float a4 = ${C.ERF_A4}; - float a5 = ${C.ERF_A5}; + float p = ${w.ERF_P}; + float a1 = ${w.ERF_A1}; + float a2 = ${w.ERF_A2}; + float a3 = ${w.ERF_A3}; + float a4 = ${w.ERF_A4}; + float a5 = ${w.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,z9=ge({opSnippet:B9}),MA={kernelName:Wa,backendName:"webgl",kernelFunc:z9};var V9=Do+` +`,xJ=xe({opSnippet:gJ}),ZA={kernelName:gn,backendName:"webgl",kernelFunc:xJ};var yJ=Fo+` return exp(x); -`,W9=` +`,bJ=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3421,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,av=ge({opSnippet:V9,packedOpSnippet:W9,cpuKernelImpl:gR,dtype:"float32"}),LA={kernelName:gn,backendName:"webgl",kernelFunc:av};function Qh(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var BA={kernelName:Js,backendName:"webgl",kernelFunc:Qh};var zA="return exp(x) - 1.0;",U9=ge({opSnippet:zA,packedOpSnippet:zA,cpuKernelImpl:xR}),VA={kernelName:xn,backendName:"webgl",kernelFunc:U9};var tm=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,hv=xe({opSnippet:yJ,packedOpSnippet:bJ,cpuKernelImpl:ER,dtype:"float32"}),JA={kernelName:yn,backendName:"webgl",kernelFunc:hv};function Zh(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var eF={kernelName:oa,backendName:"webgl",kernelFunc:Zh};var tF="return exp(x) - 1.0;",CJ=xe({opSnippet:tF,packedOpSnippet:tF,cpuKernelImpl:RR}),rF={kernelName:bn,backendName:"webgl",kernelFunc:CJ};var tm=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3454,12 +3462,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function Zh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new tm("real",p,e),c=new tm("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Fr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function G9(r){let{inputs:e,backend:t}=r,{input:o}=e;return Zh(o,!1,t)}var WA={kernelName:Pi,backendName:"webgl",kernelFunc:G9};var Jh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function Jh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new tm("real",p,e),c=new tm("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function wJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Jh(o,!1,t)}var oF={kernelName:Bi,backendName:"webgl",kernelFunc:wJ};var eg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function xi(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Jh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var UA={kernelName:ea,backendName:"webgl",kernelFunc:xi};var eg=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Ci(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new eg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var nF={kernelName:na,backendName:"webgl",kernelFunc:Ci};var tg=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3473,7 +3481,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(outputValue); } - `}};var GA={kernelName:yn,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new eg(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var HA="return floor(x);",H9=ge({opSnippet:HA,packedOpSnippet:HA,cpuKernelImpl:yR}),KA={kernelName:bn,backendName:"webgl",kernelFunc:H9};var K9=` + `}};var sF={kernelName:Cn,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new tg(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var aF="return floor(x);",SJ=xe({opSnippet:aF,packedOpSnippet:aF,cpuKernelImpl:DR}),iF={kernelName:wn,backendName:"webgl",kernelFunc:SJ};var IJ=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3483,7 +3491,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } else { return NAN; } -`,q9=` +`,vJ=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3504,7 +3512,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,j9=nt({opSnippet:K9,packedOpSnippet:q9,dtype:"int32"}),qA={kernelName:Cn,backendName:"webgl",kernelFunc:j9};var tg=class{constructor(e){this.variableNames=["A"];let t=It(),[o,n]=e;this.outputShape=e,this.userCode=` +`,kJ=nt({opSnippet:IJ,packedOpSnippet:vJ,dtype:"int32"}),uF={kernelName:Sn,backendName:"webgl",kernelFunc:kJ};var rg=class{constructor(e){this.variableNames=["A"];let t=vt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3526,7 +3534,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}};var rg=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=It(),[o,n]=e;this.outputShape=e,this.userCode=` + `}};var og=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=vt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,DJ=nt({opSnippet:EJ,packedOpSnippet:RJ,dtype:"bool"}),mF={kernelName:On,backendName:"webgl",kernelFunc:DJ};var sg=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` +`,pee=nt({opSnippet:iee,packedOpSnippet:uee,dtype:"bool"}),TF={kernelName:Ln,backendName:"webgl",kernelFunc:pee};var ag=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3630,7 +3638,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam float val = x * ${p}; setOutput(val); } - `}};var ag=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` + `}};var ig=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3692,7 +3700,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam vec4 result = xAtOutputCoords * ${p}; setOutput(result); } - `}};var AJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=P().getBool("WEBGL_PACK_NORMALIZATION")?new ag(n.shape,s,a,i,p):new sg(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},dF={kernelName:Mn,backendName:"webgl",kernelFunc:AJ};var ig=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=` + `}};var cee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new ig(n.shape,s,a,i,p):new ag(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},_F={kernelName:Bn,backendName:"webgl",kernelFunc:cee};var ug=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3747,16 +3755,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(result); } - `}};var FJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new ig(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},fF={kernelName:Ua,backendName:"webgl",kernelFunc:FJ};function hF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=qr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function pv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=C.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let w=t.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new ug(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},$F={kernelName:ja,backendName:"webgl",kernelFunc:lee};function EF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function yv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Ms(c,"max",!1);return t.runWebGLProgram(l,[n],n.dtype)}var yF={kernelName:zn,backendName:"webgl",kernelFunc:LJ};function BJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new xu(l,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var bF={kernelName:ra,backendName:"webgl",kernelFunc:BJ};var ug=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=` +`,fee=nt({opSnippet:mee,packedOpSnippet:dee,cpuKernelImpl:WR}),DF={kernelName:Vn,backendName:"webgl",kernelFunc:fee};function hee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Bs(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Vs(c,"max",!1);return t.runWebGLProgram(l,[n],n.dtype)}var AF={kernelName:Wn,backendName:"webgl",kernelFunc:hee};function gee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var FF={kernelName:aa,backendName:"webgl",kernelFunc:gee};var pg=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { @@ -3802,7 +3810,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}},pg=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=` + `}},cg=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=` const ivec3 pads = ivec3(${l}, ${m}, ${d}); void main() { @@ -3866,16 +3874,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam } setOutput(dotProd); } - `}};function zJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,l,u,c),d=new xu(m,"max",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new pg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var CF={kernelName:Li,backendName:"webgl",kernelFunc:zJ};function VJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Ps([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=C.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Ms(m,"max",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new ug(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var wF={kernelName:Hp,backendName:"webgl",kernelFunc:VJ};function SF(r,e,t,o){let n=new Ms(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Ms(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var IF={kernelName:Bi,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(o.shape,n,s,u,a),[l,m]=SF(o,i,c,p);return[l,m]}};function vF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=qr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var kF={kernelName:Vn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=C.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;R{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(o.shape,n,s,u,a),[l,m]=MF(o,i,c,p);return[l,m]}};function BF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var zF={kernelName:Un,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rc[0]+e[l]+c[1]);let n=e.length,s=Re(n),a=t.map(c=>c[0]).join(","),i=t.map((c,l)=>c[0]+e[l]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=` +`,See=nt({opSnippet:Cee,packedOpSnippet:wee,cpuKernelImpl:UR}),WF={kernelName:Hn,backendName:"webgl",kernelFunc:See};var lg=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,l)=>c[0]+e[l]+c[1]);let n=e.length,s=Re(n),a=t.map(c=>c[0]).join(","),i=t.map((c,l)=>c[0]+e[l]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=` int start = ${a}; int end = ${i}; @@ -3904,7 +3912,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${s} coords = outC - start; setOutput(getX(${p})); } - `}};var lg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=Re(n),a=t.map(f=>f[0]).join(","),i=t.map((f,h)=>f[0]+e[h]).join(","),p=Rt("rc",n),u=Rt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` + `}};var mg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=Re(n),a=t.map(f=>f[0]).join(","),i=t.map((f,h)=>f[0]+e[h]).join(","),p=Rt("rc",n),u=Rt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; @@ -3960,13 +3968,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam ${d} setOutput(result); } - `}};var KJ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=P().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lg(o.shape,n,s):new cg(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},_F={kernelName:Gn,backendName:"webgl",kernelFunc:KJ};var qJ=`if (b == 0.0) return NAN; - return mod(a, b);`,jJ=` + `}};var Iee=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mg(o.shape,n,s):new lg(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},UF={kernelName:Kn,backendName:"webgl",kernelFunc:Iee};var vee=`if (b == 0.0) return NAN; + return mod(a, b);`,kee=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+Kr+` + `+Xr+` return result; -`,XJ=nt({opSnippet:qJ,packedOpSnippet:jJ}),$F={kernelName:Ga,backendName:"webgl",kernelFunc:XJ};var mg=class{constructor(e,t,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,o],this.userCode=` +`,Nee=nt({opSnippet:vee,packedOpSnippet:kee}),GF={kernelName:qn,backendName:"webgl",kernelFunc:Nee};var dg=class{constructor(e,t,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,o],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3986,11 +3994,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GZ=ge({opSnippet:UZ}),HD={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}};var YJ=` + `}};var Tee=` if (a == b) { return 1.0; }; -return a / b;`,QJ=` +return a / b;`,_ee=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4008,9 +4016,9 @@ return a / b;`,QJ=` } return result; -`,cv=nt({opSnippet:YJ,packedOpSnippet:QJ,checkOutOfBounds:!0}),EF={kernelName:dn,backendName:"webgl",kernelFunc:cv};var RF="return a - b;",lv=nt({opSnippet:RF,packedOpSnippet:RF,supportsComplex:!0,cpuKernelImpl:YR}),DF={kernelName:Is,backendName:"webgl",kernelFunc:lv};function mv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=pv({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=C.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:t,attrs:{shape:p}}),c=lv({inputs:{a:n,b:u},backend:t}),l=av({inputs:{x:c},backend:t}),m=bp({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:t,attrs:{shape:p}}),f=cv({inputs:{a:l,b:d},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}var AF={kernelName:bs,backendName:"webgl",kernelFunc:mv};function ZJ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:mv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new mg(u,c,s),m=[[a]],d=t.runWebGLProgram(l,[p],"int32",m);return i||t.disposeIntermediateTensorInfo(p),d}var FF={kernelName:Hn,backendName:"webgl",kernelFunc:ZJ};var JJ=Ut+` +`,bv=nt({opSnippet:Tee,packedOpSnippet:_ee,checkOutOfBounds:!0}),HF={kernelName:fn,backendName:"webgl",kernelFunc:bv};var KF="return a - b;",Cv=nt({opSnippet:KF,packedOpSnippet:KF,supportsComplex:!0,cpuKernelImpl:cD}),qF={kernelName:Ts,backendName:"webgl",kernelFunc:Cv};function wv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=yv({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=w.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:t,attrs:{shape:p}}),c=Cv({inputs:{a:n,b:u},backend:t}),l=hv({inputs:{x:c},backend:t}),m=Ip({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:t,attrs:{shape:p}}),f=bv({inputs:{a:l,b:d},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}var jF={kernelName:Is,backendName:"webgl",kernelFunc:wv};function $ee(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:wv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new dg(u,c,s),m=[[a]],d=t.runWebGLProgram(l,[p],"int32",m);return i||t.disposeIntermediateTensorInfo(p),d}var XF={kernelName:jn,backendName:"webgl",kernelFunc:$ee};var Eee=Ut+` return -x; -`,eee=` +`,Ree=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4020,14 +4028,14 @@ return a / b;`,QJ=` result.a = isNaN.a ? x.a : result.a; return result; -`;function tee(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=RR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return P().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,eee):n=new tr(o.shape,JJ),t.runWebGLProgram(n,[o],o.dtype)}var PF={kernelName:oa,backendName:"webgl",kernelFunc:tee};var ree=Wt.nonMaxSuppressionV3Impl;function oee(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=ree(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var OF={kernelName:jn,backendName:"webgl",kernelFunc:oee};var nee=Wt.nonMaxSuppressionV4Impl;function see(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=nee(c,l,a,i,p,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([d]))]}var MF={kernelName:Ha,backendName:"webgl",kernelFunc:see};var aee=Wt.nonMaxSuppressionV5Impl;function iee(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=aee(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var LF={kernelName:Xn,backendName:"webgl",kernelFunc:iee};var dg=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function Dee(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=HR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Fr(o.shape,Ree):n=new rr(o.shape,Eee),t.runWebGLProgram(n,[o],o.dtype)}var YF={kernelName:ua,backendName:"webgl",kernelFunc:Dee};var Aee=Wt.nonMaxSuppressionV3Impl;function Fee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Aee(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var QF={kernelName:Qn,backendName:"webgl",kernelFunc:Fee};var Pee=Wt.nonMaxSuppressionV4Impl;function Oee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=Pee(c,l,a,i,p,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([d]))]}var ZF={kernelName:Xa,backendName:"webgl",kernelFunc:Oee};var Mee=Wt.nonMaxSuppressionV5Impl;function Lee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Mee(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var JF={kernelName:Zn,backendName:"webgl",kernelFunc:Lee};var fg=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${n}), float(${o}), float(index == coords.y))); } - `}};var uee=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new dg(u,a,i,p),l=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},BF={kernelName:Yn,backendName:"webgl",kernelFunc:uee};function rm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=gi({inputs:{input:o},backend:t}),s=rm({inputs:{x:n},backend:t}),a=wp({inputs:{input:o},backend:t}),i=rm({inputs:{x:a},backend:t}),p=Fr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return xi({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var zF={kernelName:fa,backendName:"webgl",kernelFunc:rm};function VF(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=gi({inputs:{input:o},backend:t}),s=VF({inputs:{x:n},backend:t}),a=wp({inputs:{input:o},backend:t}),i=rm({inputs:{x:a},backend:t}),p=Fr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return xi({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var WF={kernelName:na,backendName:"webgl",kernelFunc:VF};function pee(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Qh({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Qh({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=sv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var UF={kernelName:sa,backendName:"webgl",kernelFunc:pee};var fg=class{constructor(e,t,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Re(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` + `}};var Bee=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new fg(u,a,i,p),l=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},e3={kernelName:Jn,backendName:"webgl",kernelFunc:Bee};function rm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=bi({inputs:{input:o},backend:t}),s=rm({inputs:{x:n},backend:t}),a=kp({inputs:{input:o},backend:t}),i=rm({inputs:{x:a},backend:t}),p=Or({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ci({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var t3={kernelName:ba,backendName:"webgl",kernelFunc:rm};function r3(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=bi({inputs:{input:o},backend:t}),s=r3({inputs:{x:n},backend:t}),a=kp({inputs:{input:o},backend:t}),i=rm({inputs:{x:a},backend:t}),p=Or({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ci({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var o3={kernelName:pa,backendName:"webgl",kernelFunc:r3};function zee(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Zh({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Zh({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=fv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var n3={kernelName:ca,backendName:"webgl",kernelFunc:zee};var hg=class{constructor(e,t,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Re(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${a}; int end = ${i}; @@ -4052,7 +4060,7 @@ return a / b;`,QJ=` setOutput(getX(${p})); } } - `}};var hg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),p=Rt("rc",n),u=Rt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; + `}};var gg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),p=Rt("rc",n),u=Rt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; if(${c}) { `,n===1?"":`} rc = outputLoc; @@ -4076,7 +4084,7 @@ return a / b;`,QJ=` ${f} setOutput(result); } - `}};var dv=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return xi({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=P().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hg(n.shape,s,a):new fg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},GF={kernelName:Qn,backendName:"webgl",kernelFunc:dv};var cee=` + `}};var Sv=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ci({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gg(n.shape,s,a):new hg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},s3={kernelName:es,backendName:"webgl",kernelFunc:Sv};var Vee=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4085,7 +4093,7 @@ return a / b;`,QJ=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,lee=` +`,Wee=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -4101,11 +4109,11 @@ return a / b;`,QJ=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+Kr+` + `+Xr+` return result; 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KF={kernelName:es,backendName:"webgl",kernelFunc:dee};function fee(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=FR(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var qF={kernelName:Kp,backendName:"webgl",kernelFunc:fee};function hee(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=PR(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var jF={kernelName:qp,backendName:"webgl",kernelFunc:hee};function gee(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=OR(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var XF={kernelName:jp,backendName:"webgl",kernelFunc:gee};var fv=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=MR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},YF={kernelName:aa,backendName:"webgl",kernelFunc:fv};var xee="return 1.0 / x;",yee=ge({opSnippet:xee}),QF={kernelName:ts,backendName:"webgl",kernelFunc:yee};var bee=Ut+` +`,Uee=nt({opSnippet:Vee,packedOpSnippet:Wee}),a3={kernelName:ts,backendName:"webgl",kernelFunc:Uee};function Gee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=Ct({inputs:{x:n},backend:t,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims("prod",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=qR(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=ti(n.dtype),C=Yr(x,b,"prod",t);d=te({inputs:{x:C},backend:t,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var i3={kernelName:os,backendName:"webgl",kernelFunc:Gee};function Hee(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=jR(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var u3={kernelName:jp,backendName:"webgl",kernelFunc:Hee};function Kee(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=XR(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var p3={kernelName:Xp,backendName:"webgl",kernelFunc:Kee};function qee(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=YR(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var c3={kernelName:Yp,backendName:"webgl",kernelFunc:qee};var Iv=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=QR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},l3={kernelName:la,backendName:"webgl",kernelFunc:Iv};var jee="return 1.0 / x;",Xee=xe({opSnippet:jee}),m3={kernelName:ns,backendName:"webgl",kernelFunc:Xee};var Yee=Ut+` return (x < 0.0) ? 0.0 : x; -`,Cee=` +`,Qee=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4115,9 +4123,9 @@ return a / b;`,QJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,wee=ge({opSnippet:bee,packedOpSnippet:Cee}),ZF={kernelName:rs,backendName:"webgl",kernelFunc:wee};var See=Ut+` +`,Zee=xe({opSnippet:Yee,packedOpSnippet:Qee}),d3={kernelName:ss,backendName:"webgl",kernelFunc:Zee};var Jee=Ut+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Iee=` +`,ete=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4127,7 +4135,7 @@ return a / b;`,QJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,vee=ge({opSnippet:See,packedOpSnippet:Iee}),JF={kernelName:ss,backendName:"webgl",kernelFunc:vee};var gg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,tte=xe({opSnippet:Jee,packedOpSnippet:ete}),f3={kernelName:us,backendName:"webgl",kernelFunc:tte};var xg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); @@ -4160,7 +4168,7 @@ return a / b;`,QJ=` setOutput(newValue); } - `}};var xg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var yg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, @@ -4237,7 +4245,7 @@ return a / b;`,QJ=` setOutput(newValue); } - `}};function kee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=P().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new xg(n.shape,p,u,s,a):new gg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var e3={kernelName:ns,backendName:"webgl",kernelFunc:kee};var yg=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` + `}};function rte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yg(n.shape,p,u,s,a):new xg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var h3={kernelName:is,backendName:"webgl",kernelFunc:rte};var bg=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4318,7 +4326,7 @@ return a / b;`,QJ=` setOutput(accumulator); } - `}};function Nee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new yg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var t3={kernelName:qa,backendName:"webgl",kernelFunc:Nee};var bg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function ote(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new bg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var g3={kernelName:Qa,backendName:"webgl",kernelFunc:ote};var Cg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); @@ -4340,7 +4348,7 @@ return a / b;`,QJ=` setOutput(newValue); } - `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var wg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, @@ -4381,7 +4389,7 @@ return a / b;`,QJ=` setOutput(newValue); } - `}};function Tee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=P().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var r3={kernelName:os,backendName:"webgl",kernelFunc:Tee};var wg=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` + `}};function nte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new wg(n.shape,p,u,s,a):new Cg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var x3={kernelName:as,backendName:"webgl",kernelFunc:nte};var Sg=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4451,7 +4459,7 @@ return a / b;`,QJ=` setOutput(accumulator); } - `}};function _ee(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var o3={kernelName:Ka,backendName:"webgl",kernelFunc:_ee};var Sg=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=` + `}};function ste(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new Sg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var y3={kernelName:Ya,backendName:"webgl",kernelFunc:ste};var Ig=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4461,7 +4469,7 @@ return a / b;`,QJ=` ${a} coords = getOutputCoords(); setOutput(getX(${s})); } - `}};var Ig=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=` + `}};var vg=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4489,7 +4497,7 @@ return a / b;`,QJ=` } setOutput(result); } - `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=e.map((b,w)=>d(w,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function $ee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=P().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ig(n.shape,i):new Sg(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var n3={kernelName:as,backendName:"webgl",kernelFunc:$ee};var vg=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` + `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function ate(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vg(n.shape,i):new Ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var b3={kernelName:ps,backendName:"webgl",kernelFunc:ate};var kg=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4508,7 +4516,7 @@ return a / b;`,QJ=` } setOutput(outputValue); } - `}};var s3={kernelName:_s,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new vg(o.shape,s),[u,c]=C.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Eee=` + `}};var C3={kernelName:Ds,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new kg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var ite=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4523,7 +4531,7 @@ return a / b;`,QJ=` return base + 1.0; } } -`,Ree=ge({opSnippet:Eee}),a3={kernelName:is,backendName:"webgl",kernelFunc:Ree};var Dee="return inversesqrt(x);",Aee=ge({opSnippet:Dee,cpuKernelImpl:LR}),i3={kernelName:us,backendName:"webgl",kernelFunc:Aee};var yu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=t>1?"strides[j]":"strides";this.userCode=` +`,ute=xe({opSnippet:ite}),w3={kernelName:cs,backendName:"webgl",kernelFunc:ute};var pte="return inversesqrt(x);",cte=xe({opSnippet:pte,cpuKernelImpl:ZR}),S3={kernelName:ls,backendName:"webgl",kernelFunc:cte};var Su=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=t>1?"strides[j]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { @@ -4543,7 +4551,7 @@ return a / b;`,QJ=` } setOutput(mix(${g}, sum, float(found))); } - `}};var kg=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + `}};var Ng=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { @@ -4580,7 +4588,7 @@ return a / b;`,QJ=` } setOutput(mix(${g}, sum, found)); } - `}};function Fee(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=C.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g;P().getBool("WEBGL_PACK")?g=new kg(p,i,d.shape.length,f.shape.length,c,m):g=new yu(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var u3={kernelName:ps,backendName:"webgl",kernelFunc:Fee};var Ng=class{constructor(e,t,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=P().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` + `}};function lte(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g;A().getBool("WEBGL_PACK")?g=new Ng(p,i,d.shape.length,f.shape.length,c,m):g=new Su(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var I3={kernelName:ms,backendName:"webgl",kernelFunc:lte};var Tg=class{constructor(e,t,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=A().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4605,7 +4613,7 @@ return a / b;`,QJ=` setOutput(float(findBound(batch, value))); } - `}};function Pee(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Ng(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],"int32",p)}var p3={kernelName:ls,backendName:"webgl",kernelFunc:Pee};var Tg=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Lee=ge({opSnippet:Mee}),l3={kernelName:ms,backendName:"webgl",kernelFunc:Lee};var Bee=Do+` +`,hte=xe({opSnippet:fte}),N3={kernelName:hs,backendName:"webgl",kernelFunc:hte};var gte=Fo+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,zee=` +`,xte=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4633,20 +4641,20 @@ return a / b;`,QJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,Vee=ge({opSnippet:Bee,packedOpSnippet:zee,cpuKernelImpl:zR}),m3={kernelName:hs,backendName:"webgl",kernelFunc:Vee};var Wee=` +`,yte=xe({opSnippet:gte,packedOpSnippet:xte,cpuKernelImpl:eD}),T3={kernelName:bs,backendName:"webgl",kernelFunc:yte};var bte=` if (isnan(x)) { return 0.0; } return sign(x); -`,Uee=ge({opSnippet:Wee}),d3={kernelName:fs,backendName:"webgl",kernelFunc:Uee};var Gee=Do+` +`,Cte=xe({opSnippet:bte}),_3={kernelName:ys,backendName:"webgl",kernelFunc:Cte};var wte=Fo+` return sin(x); -`,Hee=` +`,Ste=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Kr} + ${Xr} return result; -`,Kee=ge({opSnippet:Gee,packedOpSnippet:Hee}),f3={kernelName:ds,backendName:"webgl",kernelFunc:Kee};var qee=` +`,Ite=xe({opSnippet:wte,packedOpSnippet:Ste}),$3={kernelName:gs,backendName:"webgl",kernelFunc:Ite};var vte=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,jee=ge({opSnippet:qee}),h3={kernelName:ja,backendName:"webgl",kernelFunc:jee};var Xee=` +`,kte=xe({opSnippet:vte}),E3={kernelName:xs,backendName:"webgl",kernelFunc:kte};var Nte=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4666,17 +4674,17 @@ return a / b;`,QJ=` result = log(exp_x + 1.0); } return result; -`,Yee=ge({opSnippet:Xee}),g3={kernelName:gs,backendName:"webgl",kernelFunc:Yee};var Qee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},x3={kernelName:ca,backendName:"webgl",kernelFunc:Qee};function Zee(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: +`,Tte=xe({opSnippet:Nte}),R3={kernelName:Cs,backendName:"webgl",kernelFunc:Tte};var _te=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},D3={kernelName:ha,backendName:"webgl",kernelFunc:_te};function $te(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw: ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=WR(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var y3={kernelName:Vi,backendName:"webgl",kernelFunc:Zee};function Jee(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=UR(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var b3={kernelName:Xa,backendName:"webgl",kernelFunc:Jee};function ete(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=rD(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var A3={kernelName:Hi,backendName:"webgl",kernelFunc:$te};function Ete(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=oD(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var F3={kernelName:Za,backendName:"webgl",kernelFunc:Ete};function Rte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=uh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var C3={kernelName:Wi,backendName:"webgl",kernelFunc:ete};function tte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=ph(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var P3={kernelName:Ki,backendName:"webgl",kernelFunc:Rte};function Dte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=uh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var w3={kernelName:Ui,backendName:"webgl",kernelFunc:tte};function rte(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=C.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=t.bufferSync(n),b=t.bufferSync(s),w=y.decodeString(t.readSync(a.dataId)[0]),S=BR(x,b,i,m,c,u,p,l,w,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new yu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var S3={kernelName:Cs,backendName:"webgl",kernelFunc:rte};function ote(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Ls({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var I3={kernelName:la,backendName:"webgl",kernelFunc:ote};var v3="return sqrt(x);",nte=ge({opSnippet:v3,packedOpSnippet:v3,cpuKernelImpl:GR}),k3={kernelName:xs,backendName:"webgl",kernelFunc:nte};var ste="return x * x;",ate=ge({opSnippet:ste}),N3={kernelName:Gi,backendName:"webgl",kernelFunc:ate};var T3="return (a - b) * (a - b);",ite=nt({opSnippet:T3,packedOpSnippet:T3}),_3={kernelName:ws,backendName:"webgl",kernelFunc:ite};function ute(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=t.readSync(n.dataId),a=C.fromUint8ToStringArray(s),i=HR(a,"string",o);return t.makeTensorInfo(n.shape,"string",i)}var $3={kernelName:_u,backendName:"webgl",kernelFunc:ute};function pte({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Ut+` + ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=ph(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var O3={kernelName:qi,backendName:"webgl",kernelFunc:Dte};function Ate(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=t.bufferSync(n),b=t.bufferSync(s),C=y.decodeString(t.readSync(a.dataId)[0]),S=JR(x,b,i,m,c,u,p,l,C,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new Su(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var M3={kernelName:vs,backendName:"webgl",kernelFunc:Ate};function Fte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Ws({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var L3={kernelName:ga,backendName:"webgl",kernelFunc:Fte};var B3="return sqrt(x);",Pte=xe({opSnippet:B3,packedOpSnippet:B3,cpuKernelImpl:nD}),z3={kernelName:ws,backendName:"webgl",kernelFunc:Pte};var Ote="return x * x;",Mte=xe({opSnippet:Ote}),V3={kernelName:ji,backendName:"webgl",kernelFunc:Mte};var W3="return (a - b) * (a - b);",Lte=nt({opSnippet:W3,packedOpSnippet:W3}),U3={kernelName:ks,backendName:"webgl",kernelFunc:Lte};function Bte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=t.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=sD(a,"string",o);return t.makeTensorInfo(n.shape,"string",i)}var G3={kernelName:Du,backendName:"webgl",kernelFunc:Bte};function zte({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Ut+` return x > 0.0 ? 1.0 : float(${e.alpha}); - `,s=new tr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var E3={kernelName:yo,backendName:"webgl",kernelFunc:pte};var _g=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` + `,s=new rr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var H3={kernelName:wo,backendName:"webgl",kernelFunc:zte};var $g=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${e}); ${s} strides = ${s}(${t}); @@ -4684,15 +4692,15 @@ return a / b;`,QJ=` ${a} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function 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Ag=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p="0.0",u="sumValue",c=Math.floor(o/4)*4,l=o%4,m=` + `}};function Zte(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Ag(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],"float32")}var rP={kernelName:Rs,backendName:"webgl",kernelFunc:Zte};function Jte(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Bs(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=dD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var oP={kernelName:Qi,backendName:"webgl",kernelFunc:Jte};function ere(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var nP={kernelName:ya,backendName:"webgl",kernelFunc:ere};var Fg=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p="0.0",u="sumValue",c=Math.floor(o/4)*4,l=o%4,m=` sumValue += dot(values, segFilter); `,d="";s%o>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { @@ -4984,9 +4992,9 @@ return a / b;`,QJ=` } setOutput(${u}); } - `}};function vte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=C.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=C.getInnerMostAxes(1,i)[0]);let 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Nte(r){H3=r.wasm.cwrap(bo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Tte(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=bu[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let 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vp(r){let{inputs:{x:e},backend:t}=r;if(e.dtype==="string")return ir(t.readSync(e.dataId),e.shape,e.dtype);let o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var J3={kernelName:xo,backendName:"wasm",kernelFunc:vp};var eP;function Rte(r){eP=r.wasm.cwrap(ao,null,["number","array","number","number","number","array","number"])}function mo(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=Ate(e.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var tP={kernelName:ao,backendName:"wasm",kernelFunc:mo,setupFunc:Rte};function Tr(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=C.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let c=new Array(n);for(let d=0;d`new shape: ${a}, old shape: ${o.shape}. 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vP;function Yte(r){vP=r.wasm.cwrap(Zo,null,["number","number","boolean","number","number","number"])}function Qte(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=s.shape.reduce((l,m)=>l*m,1)!==0,p=n.shape.length===1?[a]:[n.shape[0],a],u=e.makeOutput(p,s.dtype);function c(l){return e.dataIdMap.get(l.dataId).id}return vP(c(n),a,i,c(s),we[s.dtype],c(u)),u}var kP={kernelName:Zo,backendName:"wasm",setupFunc:Yte,kernelFunc:Qte};function Zte(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.typedArrayFromHeap(o),a=t.typedArrayFromHeap(n),i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeOutput([i.length],"int32",void 0,new Int32Array(i))}var NP={kernelName:Xs,backendName:"wasm",kernelFunc:Zte};function Pr(r){let{inputs:{x:e},attrs:{dtype:t},backend:o}=r,n=o.makeOutput(e.shape,t),s=o.typedArrayFromHeap(e);return o.typedArrayFromHeap(n).set(s),n}var TP={kernelName:ho,backendName:"wasm",kernelFunc:Pr};var _P=Ce(Jo);var $P;function Jte(r){$P=r.wasm.cwrap(go,null,["number","number","number","number"])}function ere(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i=t.dataIdMap.get(n.dataId).id,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(p.dataId).id;return $P(i,s,a,u),p}var EP={kernelName:go,backendName:"wasm",setupFunc:Jte,kernelFunc:ere};function gv(r){let{inputs:e,backend:t}=r,o=y.parseAxisParam(r.attrs.axis,e[0].shape)[0],n=e.map(d=>d.shape);C.assertParamsConsistent(n,o);let s=C.computeOutShape(e.map(d=>d.shape),o),a=e.filter(d=>y.sizeFromShape(d.shape)>0);if(a.length===1)return vp({inputs:{x:a[0]},backend:t});let i=t.makeOutput(s,e[0].dtype);if(y.sizeFromShape(s)===0)return i;if(a[0].dtype==="string"){let d=a.map(w=>{let k=[-1,y.sizeFromShape(w.shape.slice(o))];return zt({inputs:{x:w},backend:t,attrs:{shape:k}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape}));s=C.computeOutShape(d.map(w=>w.shape),1);let h=d[0].shape[0]===1,g=np(f,s,e[0].dtype,h),x=C.computeOutShape(a.map(w=>w.shape),o);i.shape=x;let b=t.dataIdMap.get(i.dataId);return b.stringBytes=C.fromStringArrayToUint8(g),d.forEach(w=>t.disposeData(w.dataId)),i}let p=y.sizeFromShape(a[0].shape.slice(0,o)),u=0,c=a.map(d=>{let f=y.sizeFromShape(d.shape.slice(o));return u+=f,f}),l=a.map(d=>t.typedArrayFromHeap(d)),m=t.typedArrayFromHeap(i);for(let d=0;d`cumprod does not support ${n.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],p),c=n;u!==null&&(c=mo({inputs:{x:n},attrs:{perm:u},backend:t}));let l=C.getInnerMostAxes(1,p)[0];C.assertAxesAreInnerMostDims("cumprod",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;KP(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=C.getUndoAxesPermutation(u);g=mo({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var qP={kernelName:an,backendName:"wasm",setupFunc:dre,kernelFunc:fre};var jP;function hre(r){jP=r.wasm.cwrap(un,null,["number","number","number","number","number","number"])}function gre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],p),c=n;u!==null&&(c=mo({inputs:{x:n},attrs:{perm:u},backend:t}));let l=C.getInnerMostAxes(1,p)[0];C.assertAxesAreInnerMostDims("cumsum",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;jP(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=C.getUndoAxesPermutation(u);g=mo({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var XP={kernelName:un,backendName:"wasm",setupFunc:hre,kernelFunc:gre};var YP;function xre(r){YP=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function yre(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=s.shape.reduce((m,d)=>m*d,1)!==0,u=n.shape.length===1?[a]:[n.shape[0],a],c=e.makeOutput(u,s.dtype);function l(m){return e.dataIdMap.get(m.dataId).id}return YP(l(n),new Uint8Array(new Int32Array(n.shape).buffer),n.shape.length,a,p,l(s),we[s.dtype],i,l(c)),c}var QP={kernelName:Qs,backendName:"wasm",setupFunc:xre,kernelFunc:yre};var ZP;function bre(r){ZP=r.wasm.cwrap(cn,null,["number","number","number","array","number","array","array","number","number"])}function Cre(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=e.makeOutput(f,"float32"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),w=new Uint8Array(new Int32Array(f).buffer),S=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=e.dataIdMap.get(h.dataId).id;return ZP(x,s,a==="NHWC"?1:0,b,n.shape.length-1,w,S,f.length,k),h}var JP={kernelName:cn,backendName:"wasm",setupFunc:bre,kernelFunc:Cre};var eO;function wre(r){eO=r.wasm.cwrap(ln,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=C.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,w=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,F=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let O=o.makeOutput(d.outShape,"float32"),M=o.dataIdMap.get(O.dataId).id;return eO(a,n.shape[0],n.shape[1],n.shape[2],i,f,h,g,x,b,w,F,S,k,_,E,R,D,M),O}var tO={kernelName:ln,backendName:"wasm",setupFunc:wre,kernelFunc:Sre};var rO;function Ire(r){rO=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function vre(r){let{inputs:e,backend:t}=r,{x:o}=e,n=y.sizeFromShape(o.shape),s=t.makeOutput([...o.shape,...o.shape],o.dtype);return rO(t.dataIdMap.get(o.dataId).id,we[o.dtype],n,t.dataIdMap.get(s.dataId).id),s}var oO={kernelName:Zs,backendName:"wasm",setupFunc:Ire,kernelFunc:vre};var nO;function kre(r){nO=r.wasm.cwrap(mn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o;if(n.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(s.shape,s.dtype);return aO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var iO={kernelName:Ai,backendName:"wasm",setupFunc:Tre,kernelFunc:_re};var uO;function $re(r){uO=r.wasm.cwrap(Di,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ere(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(n.shape,n.dtype);return uO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var pO={kernelName:Di,backendName:"wasm",setupFunc:$re,kernelFunc:Ere};var cO=Ce(fn);var lO;function Rre(r){lO=r.wasm.cwrap(Va,null,["number","number","number"])}function Dre(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,"float32"),a=i=>t.dataIdMap.get(i.dataId).id;return lO(a(n),a(o),a(s)),s}var mO={kernelName:Va,backendName:"wasm",setupFunc:Rre,kernelFunc:Dre};var Are=!1,dO=Je(hn,Are,"bool");var fO=Ce(gn,"float32");function Pg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var hO={kernelName:Js,backendName:"wasm",kernelFunc:Pg};var gO=Ce(xn,"float32");function yv(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var xO={kernelName:ea,backendName:"wasm",kernelFunc:yv};var yO;function Fre(r){yO=r.wasm.cwrap(yn,null,["number","number","number","number","number","number"])}function Pre(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return yO(s,i,p,u,c,a),n}var bO={kernelName:yn,backendName:"wasm",kernelFunc:Pre,setupFunc:Fre};var CO=Ce(bn);var Ore=!1,wO=Je(Cn,Ore);var SO;function Mre(r){SO=r.wasm.cwrap(wn,null,["number","number","number","number","number","number","number"])}function Lre(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return SO(c,l,m,d,f,n,g),h}var IO={kernelName:wn,backendName:"wasm",setupFunc:Mre,kernelFunc:Lre};var vO;function Bre(r){vO=r.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=C.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=bu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,w=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,F=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,H=h.inHeight,X=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. 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Kre(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${F} is not in [0, ${c-1}]`)}let l=C.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,w=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return EO(w,we[n.dtype],E,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var RO={kernelName:ta,backendName:"wasm",setupFunc:Hre,kernelFunc:Kre};var qre=!1,DO=Je(In,qre,"bool");var jre=!1,AO=Je(vn,jre,"bool");var FO=Ce(kn,"bool");var PO=Ce(Nn,"bool");var OO=Ce(Tn,"bool");var MO;function Xre(r){MO=r.wasm.cwrap(_n,null,["number","number","number","number"])}function Yre(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,"float32");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;MO(n,we[e.dtype],t,a)}return s}var LO={kernelName:_n,backendName:"wasm",setupFunc:Xre,kernelFunc:Yre};var Qre=!1,BO=Je($n,Qre,"bool");var Zre=!1,zO=Je(En,Zre,"bool");var VO;function Jre(r){VO=r.wasm.cwrap(Rn,null,["number","number","number","number"])}function eoe(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],"float32");return VO(t.dataIdMap.get(i.dataId).id,o,n,a),i}var WO={kernelName:Rn,backendName:"wasm",setupFunc:Jre,kernelFunc:eoe};var UO=Ce(Dn);var GO=Ce(An);var 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coe=!1,tM=Je(Bn,coe);var rM;function loe(r){rM=r.wasm.cwrap(zn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function moe(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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aM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var iM={kernelName:Li,backendName:"wasm",setupFunc:hoe,kernelFunc:goe};var uM;function xoe(r){uM=r.wasm.cwrap(Vn,null,["number, number, number"])}function yoe(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(n.shape,n.dtype);return NO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var TO={kernelName:Oi,backendName:"wasm",setupFunc:poe,kernelFunc:coe};var _O=he(hn);var $O;function loe(r){$O=r.wasm.cwrap(qa,null,["number","number","number"])}function moe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,"float32"),a=i=>t.dataIdMap.get(i.dataId).id;return $O(a(n),a(o),a(s)),s}var EO={kernelName:qa,backendName:"wasm",setupFunc:loe,kernelFunc:moe};var doe=!1,RO=We(xn,doe,"bool");var DO=he(gn);var AO=he(yn,"float32");function Og(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var FO={kernelName:oa,backendName:"wasm",kernelFunc:Og};var PO=he(bn,"float32");function Tv(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var OO={kernelName:na,backendName:"wasm",kernelFunc:Tv};var MO;function foe(r){MO=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number"])}function hoe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return MO(s,i,p,u,c,a),n}var LO={kernelName:Cn,backendName:"wasm",kernelFunc:hoe,setupFunc:foe};var BO=he(wn);var goe=!1,zO=We(Sn,goe);var VO;function xoe(r){VO=r.wasm.cwrap(In,null,["number","number","number","number","number","number","number"])}function yoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return VO(c,l,m,d,f,n,g),h}var WO={kernelName:In,backendName:"wasm",setupFunc:xoe,kernelFunc:yoe};var UO;function boe(r){UO=r.wasm.cwrap(Io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Coe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=Iu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. 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Noe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return XO(C,we[n.dtype],E,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var YO={kernelName:sa,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var Toe=!1,QO=We(kn,Toe,"bool");var _oe=!1,ZO=We(Nn,_oe,"bool");var JO=he(Tn,"bool");var eM=he(_n,"bool");var tM=he($n,"bool");var rM;function $oe(r){rM=r.wasm.cwrap(En,null,["number","number","number","number"])}function Eoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,"float32");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;rM(n,we[e.dtype],t,a)}return s}var oM={kernelName:En,backendName:"wasm",setupFunc:$oe,kernelFunc:Eoe};var Roe=!1,nM=We(Rn,Roe,"bool");var Doe=!1,sM=We(Dn,Doe,"bool");var aM;function Aoe(r){aM=r.wasm.cwrap(An,null,["number","number","number","number"])}function Foe(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],"float32");return aM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var iM={kernelName:An,backendName:"wasm",setupFunc:Aoe,kernelFunc:Foe};var uM=he(Fn);var pM=he(Pn);var Poe=!1,cM=We(On,Poe,"bool");var lM=he(Mn);var Ooe=!1,mM=We(Ln,Ooe,"bool");var Moe=!1,dM=We(w0,Moe,"bool");var fM;function Loe(r){fM=r.wasm.cwrap(Bn,null,["number","number","number","number","number","number","number"])}function Boe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=t.makeOutput(n.shape,n.dtype);return fM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var hM={kernelName:Bn,backendName:"wasm",setupFunc:Loe,kernelFunc:Boe};var gM;function zoe(r){gM=r.wasm.cwrap(ja,null,["number","number","number","number","number","number","number","number","number"])}function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!=="float32"||s.dtype!=="float32"||a.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let l=t.makeOutput(n.shape,n.dtype);return gM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var xM={kernelName:ja,backendName:"wasm",setupFunc:zoe,kernelFunc:Voe};var yM;function Woe(r){yM=r.wasm.cwrap(zn,null,["number","number","number","number"])}function Uoe(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims("max",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;yM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var bM={kernelName:zn,backendName:"wasm",setupFunc:Woe,kernelFunc:Uoe};var Goe=!1,CM=We(Vn,Goe);var wM;function Hoe(r){wM=r.wasm.cwrap(Wn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Koe(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,"int32");return $M(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var EM={kernelName:ia,backendName:"wasm",setupFunc:Joe,kernelFunc:ene};var RM;function tne(r){RM=r.wasm.cwrap(Un,null,["number, number, number"])}function rne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;RM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!=="float32"&&e.disposeData(b.dataId),C}var DM={kernelName:Un,backendName:"wasm",setupFunc:tne,kernelFunc:rne};var AM;function one(r){AM=r.wasm.cwrap(Gn,null,["number","number","number","number"])}function nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;w.assertAxesAreInnerMostDims("min",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:Gn,backendName:"wasm",setupFunc:one,kernelFunc:nne};var sne=!1,PM=We(Hn,sne);var _v;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(_v||(_v={}));var OM;function ane(r){OM=r.wasm.cwrap(Kn,null,["number","array","number","number","array","array","number","number"])}function ine(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(c).buffer),d=new Uint8Array(new 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zM(t.dataIdMap.get(p.dataId).id,u,c,s,a,t.dataIdMap.get(l.dataId).id),i||t.disposeData(p.dataId),l}var VM={kernelName:jn,backendName:"wasm",setupFunc:pne,kernelFunc:cne};var WM=We(qn,!0);var lne=!0,UM=We(Xn,lne);var GM=he(ua);function Kc(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),o=t[0],n=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:o,selectedSize:n,pSelectedScores:s,pValidOutputs:a}}var HM;function mne(r){HM=r.wasm.cwrap(Qn,"number",["number","number","number","number","number"])}function dne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:p}=t,u=e.dataIdMap.get(i.dataId).id,c=e.dataIdMap.get(p.dataId).id,l=HM(u,c,s,n,a),{pSelectedIndices:m,selectedSize:d,pSelectedScores:f,pValidOutputs:h}=Kc(e,l);return e.wasm._free(f),e.wasm._free(h),e.makeOutput([d],"int32",m)}var KM={kernelName:Qn,backendName:"wasm",setupFunc:mne,kernelFunc:dne};var qM;function fne(r){qM=r.wasm.cwrap(Xa,"number",["number","number","number","number","number","bool"])}function hne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=qM(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Kc(e,m);e.wasm._free(h);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([],"int32",g);return[x,b]}var jM={kernelName:Xa,backendName:"wasm",setupFunc:fne,kernelFunc:hne};var XM;function gne(r){XM=r.wasm.cwrap(Zn,"number",["number","number","number","number","number","number"])}function xne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=XM(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Kc(e,m);e.wasm._free(g);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([f],"float32",h);return[x,b]}var YM={kernelName:Zn,backendName:"wasm",setupFunc:gne,kernelFunc:xne};var yne=!1,QM=We(Yn,yne,"bool");var ZM;function bne(r){ZM=r.wasm.cwrap(Jn,null,["number","number","number","number","number"])}function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=t.makeOutput([...n.shape,a],s),c=t.dataIdMap.get(u.dataId).id,m=t.dataIdMap.get(n.dataId).id;return ZM(m,a,i,p,c),u}var JM={kernelName:Jn,backendName:"wasm",setupFunc:bne,kernelFunc:Cne};function wne(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var eL={kernelName:pa,backendName:"wasm",kernelFunc:wne};function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Og({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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Mg={kernelName:es,backendName:"wasm",kernelFunc:vne,setupFunc:Ine};var kne=!1,oL=We(ts,kne);var nL;function Nne(r){nL=r.wasm.cwrap(rs,null,["number","number","number"])}function Tne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),i=t.dataIdMap.get(u.dataId).id);let c=t.makeOutput(o.shape,"float32"),l=t.dataIdMap.get(c.dataId).id;return nL(i,a,l),p.dtype!=="float32"&&t.disposeData(u.dataId),c}var sL={kernelName:rs,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne};var aL;function _ne(r){aL=r.wasm.cwrap(os,null,["number","number","number","number"])}function $ne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;aL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var iL={kernelName:os,backendName:"wasm",setupFunc:_ne,kernelFunc:$ne};var Ene=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cp(o,n,s,a),p=e.makeOutput([i.length],a);return e.typedArrayFromHeap(p).set(i),p},uL={kernelName:la,backendName:"wasm",kernelFunc:Ene};var Rne=!0,pL=We(fn,Rne);var cL=he(ns);var lL=he(ss);var mL=he(us);var dL;function Dne(r){dL=r.wasm.cwrap(is,null,["number","number","number","number","number","number","number","number","number","number"])}function Ane(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let x=h.id,b=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return b;let C=e.dataIdMap.get(b.dataId).id;return dL(x,c,l,m,d,p,u,s?1:0,a?1:0,C),g!=null&&e.disposeData(g.dataId),b}var fL={kernelName:is,backendName:"wasm",setupFunc:Dne,kernelFunc:Ane};var hL;function Fne(r){hL=r.wasm.cwrap(Qa,null,["number","number","number","array","array","boolean"])}function Pne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=t.makeOutput(n.shape,"float32"),p=t.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(u.dataId)),hL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var gL={kernelName:Qa,backendName:"wasm",setupFunc:Fne,kernelFunc:Pne};var xL;function One(r){xL=r.wasm.cwrap(as,null,["number","number","number","number","number","number","number","number","number","number"])}function Mne(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return h;let g=e.dataIdMap.get(n.dataId),x;g.dtype!=="float32"&&(x=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),g=e.dataIdMap.get(x.dataId));let b=g.id,C=e.dataIdMap.get(h.dataId).id;return xL(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&e.disposeData(x.dataId),h}var yL={kernelName:as,backendName:"wasm",setupFunc:One,kernelFunc:Mne};var bL;function Lne(r){bL=r.wasm.cwrap(Ya,null,["number","number","number","array","array","boolean"])}function Bne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=t.makeOutput(n.shape,"float32"),p=t.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(u.dataId)),bL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var CL={kernelName:Ya,backendName:"wasm",setupFunc:Lne,kernelFunc:Bne};var wL;function zne(r){wL=r.wasm.cwrap(ps,null,["number","array","number","array","number","number"])}function Vne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return _p({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);wL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var SL={kernelName:ps,backendName:"wasm",kernelFunc:Vne,setupFunc:zne};var IL;function Wne(r){IL=r.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{radians:s,fillValue:a,center:i}=o,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return IL(u,l,m,d,f,s,h,g,S,C.length,c),p}var vL={kernelName:Ds,backendName:"wasm",kernelFunc:Une,setupFunc:Wne};var kL=he(cs);var NL=he(ls);var TL;function Gne(r){TL=r.wasm.cwrap(ms,null,["number","number","number","number","number","number","array","number","number"])}function Hne(r){let{backend:e,inputs:t,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,i=e.makeOutput(a,s.dtype);if(y.sizeFromShape(a)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=du.calculateShapes(s,n,a),f=e.dataIdMap.get(n.dataId).id,g=e.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(l).buffer),b=e.dataIdMap.get(i.dataId).id;return TL(f,g,we[s.dtype],p,u,c,x,m,b),i}var _L={kernelName:ms,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne};var $L;function Kne(r){$L=r.wasm.cwrap(fs,null,["number","number","number","number","number","number","bool","number"])}function qne(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o;if(n.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${n.dtype} and ${s.dtype}`);let i=t.makeOutput(s.shape,"int32");function p(u){return t.dataIdMap.get(u.dataId).id}return $L(p(n),p(s),n.shape[0],n.shape[1],s.shape[1],we[n.dtype],a==="left",p(i)),i}var EL={kernelName:fs,backendName:"wasm",setupFunc:Kne,kernelFunc:qne};var RL;function jne(r){RL=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Xne(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=t.dataIdMap.get(o.dataId).id,i=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(s.dataId).id,u=t.makeOutput(n.shape,n.dtype),c=t.dataIdMap.get(u.dataId).id,l=o.shape.length,m=n.shape.length,d=l===0||l>1||m===1?1:y.sizeFromShape(n.shape.slice(1));return RL(a,i,p,d,c),u}var DL={kernelName:da,backendName:"wasm",kernelFunc:Xne,setupFunc:jne};var AL=he(hs);var FL;function Yne(r){FL=r.wasm.cwrap(bs,null,["number","number"])}function Qne(r){let{backend:e,inputs:{x:t}}=r,o=e.dataIdMap.get(t.dataId).id,n=e.makeOutput(t.shape,t.dtype),s=e.dataIdMap.get(n.dataId).id;return y.sizeFromShape(n.shape)===0||FL(o,s),n}var PL={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Yne,kernelFunc:Qne};var OL=he(ys);var ML=he(gs);var LL=he(xs);var BL=he(Cs);function Zne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o,i=y.sizeFromShape(s),p=[[0,0]];p.push(...a);for(let _=1+s.length;_0?p+1:0;if(c<0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let l=n.shape.slice();l[0]=c;let m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=t.dataIdMap.get(a.dataId).id,h=t.makeOutput(l,n.dtype),g=t.dataIdMap.get(h.dataId).id,x=t.makeOutput([4],"int32"),b=t.dataIdMap.get(x.dataId).id;CL(m,we[n.dtype],n.shape[0],d,f,g,b,e,0);let w=t.readSync(x.dataId),S;switch(w[0]){case 0:{S=C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{S=C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:S=C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(w[1],w[2]);break;case 3:S=C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w[1],w[2],w[3]);break;default:S=""}if(t.disposeData(x.dataId),S)throw t.disposeData(h.dataId),new Error(S);return h}function bne(r){return Lg(r,!0)}var wL={kernelName:Wi,backendName:"wasm",setupFunc:Mg,kernelFunc:bne};function Cne(r){return Lg(r,!1)}var SL={kernelName:Ui,backendName:"wasm",setupFunc:Mg,kernelFunc:Cne};var IL;function wne(r){IL=r.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Sne(r){let{backend:e,inputs:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,p=e.makeOutput(i,a.dtype);if(y.sizeFromShape(i)===0)return p;let{sliceRank:u,numUpdates:c,sliceSize:l,strides:m,outputSize:d}=C.calculateShapes(s,n,i),f=e.dataIdMap.get(n.dataId).id,h=e.dataIdMap.get(s.dataId).id,g=e.dataIdMap.get(a.dataId).id,x=new Uint8Array(new Int32Array(m).buffer),b=e.dataIdMap.get(p.dataId).id;return IL(f,h,s.shape.length,g,we[a.dtype],u,c,l,x,d,b),p}var vL={kernelName:Cs,backendName:"wasm",setupFunc:wne,kernelFunc:Sne};function Ine(r){let{inputs:e,attrs:t,backend:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=t,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=new Array(n.shape.length).fill(0),c=n.shape.slice();return p.map(l=>{let m=[...c];m[i]=l;let d=Ao({inputs:{x:n},attrs:{begin:u,size:m},backend:o});return u[i]+=l,d})}var kL={kernelName:la,backendName:"wasm",kernelFunc:Ine};var NL=Ce(xs);var TL=Ce(Gi);var vne=!0,_L=Je(ws,vne);var $L;function kne(r){$L=r.wasm.cwrap(yo,null,["number","number","number","number"])}function 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s;if(o==null)s=this.write(n!=null?n:null,e,t);else{let a=this.dataIdNextNumber++;s={id:a},this.dataIdMap.set(s,{id:a,memoryOffset:o,shape:e,dtype:t,refCount:1});let i=y.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,i,o)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:o}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(o),a=y.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,s,a);case"int32":return new Int32Array(n,s,a);case"bool":return new Uint8Array(n,s,a);default:throw new Error(`Unknown dtype ${t}`)}}};function Dse(r){return(e,t)=>(y.fetch(r,{credentials:"same-origin"}).then(o=>{o.ok||e.env.a(`failed to load wasm binary file at '${r}'`),o.arrayBuffer().then(n=>{WebAssembly.instantiate(n,e).then(s=>{t(s.instance,s.module)})})}),{})}function RB(r,e,t){if(Wg!=null)return Wg;let o="tfjs-backend-wasm.wasm";return r&&e?o="tfjs-backend-wasm-threaded-simd.wasm":r&&(o="tfjs-backend-wasm-simd.wasm"),nm!=null&&nm[o]!=null?nm[o]:t+o}async function AB(){let[r,e]=await Promise.all([A().getAsync("WASM_HAS_SIMD_SUPPORT"),A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((t,o)=>{let n={};n.locateFile=(i,p)=>{if(i.endsWith(".worker.js")){let u=DB.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?RB(r,e,om!=null?om:p):p+i},zv&&(n.instantiateWasm=Dse(RB(r,e,om!=null?om:"")));let s=!1;n.onAbort=()=>{if(s||sm)return;sm=!0,o({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let a;e&&r&&Wg==null?(n.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+EB.toString()],{type:"text/javascript"}),a=EB(n)):a=Rse(n),a.then(i=>{s=!0,sm=!1;let p=null;i.tfjs={init:i.cwrap("init",null,[]),initWithThreadsCount:i.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:i.cwrap("get_threads_count","number",[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",p,["number"]),dispose:i.cwrap("dispose",p,[])},t({wasm:i})}).catch(o)})}function Ase(r,e){switch(e){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${e}`)}}var Fse=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Wg=null,om=null,nm={},sm=!1,zv=!1;function Pse(r,e=!1){if(_w("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),sm)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Wg=r,zv=e}function Ose(r,e=!1){if(sm)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")om=r;else{nm=r;let t=Fse.filter(o=>nm[o]==null);if(t.length>0)throw new Error(`There were no entries found for the following binaries: ${t.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}zv=e}var FB=-1,Mv=-1;function Mse(r){FB=r}function Lse(){if(Mv===-1)throw new Error("WASM backend not initialized.");return Mv}var Bse="4.7.0";var zse=2;nu("wasm",async()=>{let{wasm:r}=await AB();return new am(r)},zse);var go=A();go.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);go.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);go.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!0);go.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);go.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);go.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);go.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);go.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>0);go.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);go.registerFlag("WEBGPU_PRINT_SHADER",()=>"");go.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY",()=>!1);var Ug=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}};var Gg=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t,o=!1,n=!0){let s,a=PB(e,t);return n?(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).length>0?(s=this.freeBuffers.get(a).pop(),this.numFreeBuffers--):(s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o}),this.numBytesAllocated+=e)):(s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o}),this.numBytesAllocated+=e),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.usedBuffers.get(a).push(s),this.numUsedBuffers++,this.numBytesUsed+=e,s}releaseBuffer(e,t=!0){if(this.freeBuffers.size===0)return;let o=e.size,n=e.usage,s=PB(o,n),a=this.usedBuffers.get(s),i=a.indexOf(e);if(i<0)throw new Error("Cannot find the 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s=MB(o),a=e*t*s,i=OB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=OB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(p,1);let u=MB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function OB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function MB(r){if(r==="rgba8unorm")return 16;throw new Error(`${r} is not supported!`)}function LB(r,e){if(Math.max(...r)>5)throw new Error("Cannot symbolically compute strides for rank > 6 tensor.");let t=r.length,o="xyzwuv",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Us=(r,e,t)=>t==="int32"?`atomicAdd(${r}, bitcast(${e}));`:` { var oldValue = 0; loop { @@ -4998,12 +5006,12 @@ return a / b;`,QJ=` } oldValue = res.old_value; } - }`;var xB=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=nse(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=r.createComputePipeline({compute:{module:i,entryPoint:"_start"},label:e.constructor.name,layout:"auto"}),u=P().get("WEBGPU_PRINT_SHADER");if(u!==""){u=u.toLowerCase();let c=u.split(",");(u==="all"||c.some(l=>n.toLowerCase().includes(l)))&&(console.group(n),console.debug(a),console.groupEnd())}return p},Ae=(r,e="f32")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function Nt(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Po(r){if(r===0)return"x";if(r===1)return"y";if(r===2)return"z";if(r===3)return"w";if(r===4)return"u";if(r===5)return"v";throw Error(`Index ${r} is not yet supported`)}function K(...r){let e;switch(r.length){case 0:e=` + }`;var WB=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=Wse(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=A().get("WEBGPU_PRINT_SHADER");if(p!==""){p=p.toLowerCase();let u=p.split(",");(p==="all"||u.some(c=>e.shaderKey.toLowerCase().includes(c)))&&(console.group(e.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:"_start"},label:e.constructor.name,layout:"auto"}):r.createComputePipeline({compute:{module:i,entryPoint:"_start"},label:e.constructor.name,layout:"auto"})},Ae=(r,e="f32")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function ht(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return"x";if(r===1)return"y";if(r===2)return"z";if(r===3)return"w";if(r===4)return"u";if(r===5)return"v";throw Error(`Index ${r} is not yet supported`)}function H(...r){let e;switch(r.length){case 0:e=` fn main() `;break;case 1:e=` fn main(${r[0]} : i32) - `;break;default:throw Error("Unreachable")}return e}function fB(r,e){let t;return t=` - ${ose(e)} + `;break;default:throw Error("Unreachable")}return e}function BB(r,e){let t;return t=` + ${Vse(e)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @@ -5016,9 +5024,9 @@ return a / b;`,QJ=` workgroupId = WorkgroupId; ${r?"main(getGlobalIndex());":"main();"}; } - `,t}function ose(r){return` + `,t}function Vse(r){return` @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]}) -`}function nse(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(` +`}function Wse(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(` var localId: vec3; var localIndex: u32; @@ -5028,7 +5036,7 @@ return a / b;`,QJ=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${bB(t)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${GB(t)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} @@ -5040,23 +5048,23 @@ return a / b;`,QJ=` outShapeStrides : vec2, }; - @group(0) @binding(0) var result: array<${kp(e.dtype,t.outputComponent)}>; + @group(0) @binding(0) var result: array<${$p(e.dtype,t.outputComponent)}>; @group(0) @binding(2) var uniforms: Uniform; - `);let f=gB(t);return[hB,o.join(` -`),Dv(e.shape),t.getUserCode(),fB(f,t)].join(` -`)}let s,a,i="struct Uniforms { NAN : f32, INFINITY : f32, ";t.variableNames.forEach((f,h)=>{let g=Nt(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=Nt(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=Nt(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=Nt(s),i+=` - outShapeStrides: ${a}, `,t.size&&(i+="size : i32, "),t.uniforms&&(i+=t.uniforms),i+="};",i=mse(i),o.push(i),t.atomic?o.push(` + `);let f=VB(t);return[zB,o.join(` +`),im(e.shape),t.getUserCode(),BB(f,t)].join(` +`)}let s,a,i="struct Uniforms { NAN : f32, INFINITY : f32, ";t.variableNames.forEach((f,h)=>{let g=ht(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ht(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ht(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=ht(s),i+=` + outShapeStrides: ${a}, `,t.size&&(i+="size : i32, "),t.uniforms&&(i+=t.uniforms),i+="};",i=Yse(i),o.push(i),t.atomic?o.push(` @group(0) @binding(0) var result: array>; `):o.push(` - @group(0) @binding(0) var result: array<${kp(e.dtype,t.outputComponent)}>; + @group(0) @binding(0) var result: array<${$p(e.dtype,t.outputComponent)}>; `),t.variableNames.forEach((f,h)=>{o.push(` - @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?kp(r[h].dtype,t.variableComponents[h]):kp(r[h].dtype,t.outputComponent)}>; + @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?$p(r[h].dtype,t.variableComponents[h]):$p(r[h].dtype,t.outputComponent)}>; `)}),i!==""&&o.push(` @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms; - `);let u=pse(e.shape,t.dispatchLayout),c=[hB,o.join(` -`)+sse,Dv(e.shape),u,cse(e.shape.length)];t.atomic||c.push(lse(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${Dv(r[h].shape,f)}`)});let l=r.map((f,h)=>use(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(` -`);c.push(l),c.push(t.getUserCode());let m=gB(t);return c.push(fB(m,t)),c.join(` -`)}function yB(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>C.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=bB(r)?"flatDispatch":"";return n+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+e.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,n}var hB=` + `);let u=qse(e.shape,t.dispatchLayout),c=[zB,o.join(` +`)+Use,im(e.shape),u,jse(e.shape.length)];t.atomic||c.push(Xse(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${im(r[h].shape,f)}`)});let l=r.map((f,h)=>Kse(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(` +`);c.push(l),c.push(t.getUserCode());let m=VB(t);return c.push(BB(m,t)),c.join(` +`)}function UB(r,e,t){let o=r.shaderKey;if(r.isFromPixels)return o;let n=[],s=[];e.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(t.shape),s.push(t.dtype);let a=e.map(c=>w.getBroadcastDims(c.shape,t.shape)),i=e.map(c=>y.arraysEqual(c.shape,t.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=GB(r)?"flatDispatch":"";return o+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+n.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,o}var zB=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -5115,19 +5123,19 @@ return a / b;`,QJ=` let floatToUint: vec4 = bitcast>(val); return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } -`,sse=` +`,Use=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function Dv(r,e=""){let t=r.length,o=e!==""?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",n=e!==""?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:"outShapeStrides";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=Nt(t),i=[];for(let u=0;u vec2 { +`;function im(r,e=""){let t=r.length,o=e!==""?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",n=e!==""?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:"outShapeStrides";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ht(t),i=[];for(let u=0;u vec2 { let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n}; return vec2(d0, d1); - }`;let p;return p="var index2 = index;"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Po(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Po(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Po(c)}`;return`${l}; ${m};`}).join(""),` + }`;let p;return p="var index2 = index;"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(""),` fn ${o}(index : i32) -> ${a} { ${p} return ${a}(${i.join(",")}); } - `}function ase(r,e){let t=r.name,o=r.shape.length,n=Nt(o),s="get"+t.charAt(0).toUpperCase()+t.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return` + `}function Gse(r,e){let t=r.name,o=r.shape.length,n=ht(o),s="get"+t.charAt(0).toUpperCase()+t.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return` fn ${s}() -> ${Ae(e)} { return ${Ae(e)}(${t}[0]); } @@ -5136,7 +5144,7 @@ return a / b;`,QJ=` return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})${e===1?"":` / ${e}`}]); } - `}function ise(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=e.length,u=Nt(p);if(y.arraysEqual(r.shape,e)&&o)return` + `}function Hse(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=e.length,u=ht(p);if(y.arraysEqual(r.shape,e)&&o)return` fn ${a}Index(globalIndex : i32) -> ${Ae(t)} { return ${Ae(t)}(${n}[globalIndex]); } @@ -5144,7 +5152,7 @@ return a / b;`,QJ=` fn ${a}Coords(coords : ${u}) -> ${Ae(t)} { return ${Ae(t)}(${n}[${p>1?"getOutputIndexFromCoords(coords)":"coords"}${t===1?"":` / ${t}`}]); } - `;let c=C.getBroadcastDims(r.shape,e),l=p-i,m="";if(i===0)return` + `;let c=w.getBroadcastDims(r.shape,e),l=p-i,m="";if(i===0)return` fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{ return get${s}(); } @@ -5152,8 +5160,8 @@ return a / b;`,QJ=` fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{ return get${s}(); } - `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${Po(g+l)} = 0;`).join(` -`);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=Nt(i),x=r.shape.map((b,w)=>`coords.${Po(w+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return` + `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(` +`);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=ht(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return` fn ${a}Index(globalIndex : i32) -> ${Ae(t)} { var coords = getCoordsFromIndex(globalIndex); ${m} @@ -5165,13 +5173,13 @@ return a / b;`,QJ=` ${m} return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?"":` / ${t}`}]); } -`}function use(r,e,t,o){let n=ase(r,t);return r.shape.length<=e.length&&(n+=ise(r,e,t,o)),n}function pse(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return"";if(t.length===s)return`fn getOutputCoords() -> ${Nt(s)}{ +`}function Kse(r,e,t,o){let n=Gse(r,t);return r.shape.length<=e.length&&(n+=Hse(r,e,t,o)),n}function qse(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return"";if(t.length===s)return`fn getOutputCoords() -> ${ht(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } - `;let i="",p=[t,o,n];for(let m=0;m ${c} { + `;let i="",p=[t,o,n];for(let m=0;m ${c} { ${i} -`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(",")}); }`,l}function cse(r){let e="";switch(r){case 0:case 1:e+=` +`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(",")}); }`,l}function jse(r){let e="";switch(r){case 0:case 1:e+=` fn getOutputIndexFromCoords(coords : i32) -> i32 { return coords; } @@ -5205,14 +5213,14 @@ return a / b;`,QJ=` coords.u * uniforms.outShapeStrides.u + coords.v; } - `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function bB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function kp(r,e=1){if(r==="float32")return Ae(e,"f32");if(r==="int32"||r==="bool")return Ae(e,"i32");throw new Error(`type ${r} is not supported.`)}function lse(r,e,t){let o=r.length,n=kp(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) { + `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function GB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function $p(r,e=1){if(r==="float32")return Ae(e,"f32");if(r==="int32"||r==="bool")return Ae(e,"i32");throw new Error(`type ${r} is not supported.`)}function Xse(r,e,t){let o=r.length,n=$p(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,"i32")}) { result[flatIndex] = ${n}(value); } - `;if(o>=2){let a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=Nt(o);s+=` + `;if(o>=2){let a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=ht(o);s+=` fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(", ")}, value : ${Ae(t)}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndex(flatIndex${t===1?"":` / ${t}`}, value); @@ -5221,17 +5229,12 @@ return a / b;`,QJ=` let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndexI32(flatIndex${t===1?"":` / ${t}`}, value); } - `}return s}function mse(r){let e=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(e,o=>"@align(16) "+o);let t=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function gB(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var Fv={};He(Fv,{GPUBytesPerElement:()=>Hg,MatMulProgramType:()=>Oo,assertNotComplex:()=>cm,computeDispatch:()=>q,computeWorkPerThreadForConv2d:()=>um,computeWorkgroupInfoForMatMul:()=>Av,computeWorkgroupSizeForConv2d:()=>im,flatDispatchLayout:()=>Z,isWebGPUSupported:()=>pm,tilesFitEvenlyIntoShape:()=>fse});var Np=r=>{let e=1;for(let t=0;tt%r[o]===0)}function q(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Np(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Np(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Np(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Av(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function im(r,e,t=!1){if(t)return[8,8,1];let o=Np(r.x.map(s=>e[s])),n=Np(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function um(r,e,t=!1){if(t)return[4,4,1];let o=Np(r.x.map(s=>e[s])),n=Np(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Z(r){return{x:r.map((e,t)=>t)}}function Hg(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function pm(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function cm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Oo;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Oo||(Oo={}));var hse=P().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),gse=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Cu=class extends ro{nextDataId(){return Cu.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!pm())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new Wg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ug(this.device),this.textureManager=new Gg(this.device),this.tensorMap=new Lo(this,ur()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),P().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),P().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype==="string")return o;if(t.dtype==="complex64"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(h,g).buffer,"float32");return this.convertAndCacheOnCPU(e,x),x}let s=["opaque","premultiplied"],a=t.resourceInfo,i=a.size;y.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.ensureComputePassEnded(),m.map((h,g)=>{let x=h.getContext("webgpu");return x.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,F)=>{this.ensureCommandEncoderReady(),this.currentCommandEncoder.copyBufferToTexture({buffer:a.buffer,bytesPerRow:x,offset:F},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext("2d",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,F,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=C.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=y.convertBackendValuesAndArrayBuffer(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,o){let n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=Hg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=Hg(t.dtype)*y.sizeFromShape(t.shape),n;if(t.values){if(n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage(),!0),n.mapState==="unmapped"){let s=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),a=s.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o),this.stagingPendingDisposal.push({size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s})}else{let s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n}}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(u=>{u.data.length===0&&(u.data=[1]);let c;switch(u.data.length){case 1:c=4;break;case 2:c=8;break;case 3:c=16;break;case 4:c=16;break;case 5:c=16;break;case 6:c=16;break;default:y.assert(!1,()=>`Unsupported ${u.data.length}D shape`)}(o===5||o===6)&&(c=16),c>s&&(s=c),t=Math.ceil(t/c)*c,o=u.data.length,n.push(t),t+=u.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((u,c)=>{let l=n[c];u.type==="int32"?new Int32Array(a,l,u.data.length).set(u.data):u.type==="uint32"?new Uint32Array(a,l,u.data.length).set(u.data):new Float32Array(a,l,u.data.length).set(u.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,a,0,t);let p={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(p),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=gse(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(s).map(g=>g.shape);let h="int32";if(i.map(g=>{a.push({type:h,data:g});let x=y.computeStrides(g);a.push({type:h,data:x})}),e.size){let g=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.outputComponent?g/e.outputComponent:g]})}}let p=t.map((h,g)=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=yB(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=xB(this.device,e,p,s,u),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),P().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=hse){return P().getBool("WEBGPU_CPU_FORWARD")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){let r={powerPreference:P().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has("timestamp-query-inside-passes")&&(t.requiredFeatures=["timestamp-query-inside-passes"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize,maxBufferSize:o.maxBufferSize,maxComputeWorkgroupSizeX:o.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:o.maxComputeInvocationsPerWorkgroup};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Cu(n,s)},3);var fe;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.ELU_DER=5]="ELU_DER",r[r.EQUAL=6]="EQUAL",r[r.GREATER=7]="GREATER",r[r.GREATER_EQUAL=8]="GREATER_EQUAL",r[r.INT_DIV=9]="INT_DIV",r[r.LESS=10]="LESS",r[r.LESS_EQUAL=11]="LESS_EQUAL",r[r.LOGICAL_AND=12]="LOGICAL_AND",r[r.LOGICAL_OR=13]="LOGICAL_OR",r[r.MAX=14]="MAX",r[r.MIN=15]="MIN",r[r.MOD=16]="MOD",r[r.MUL=17]="MUL",r[r.NOT_EQUAL=18]="NOT_EQUAL",r[r.POW=19]="POW",r[r.PRELU=20]="PRELU",r[r.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",r[r.SUB=22]="SUB"})(fe||(fe={}));var xse=` - resultTemp = select(resultTemp, valueForNaN, isNaN | isnan(a) | isnan(b));`,yse=` - resultTemp = select( - resultTemp, vec4(valueForNaN), - vec4(isNaN) | isnanVec4(a) | isnanVec4(b)); - `,bse="return a + b;",Cse="var resultTemp = atan2(a, b);",wse="return areal * breal - aimag * bimag;",Sse="return areal * bimag + aimag * breal;",Ise="return a / b;",vse="return select(a * (b + 1.0), a, b >= 0.);",kse="return select(a * (b + vec4(1.0)), a, b >= vec4(0.));",Nse="return f32(a == b);",Tse="return vec4(a == b);",_se="return f32(a > b);",$se="return vec4(a > b);",Ese="return f32(a >= b);",Rse="return vec4(a >= b);",Dse=` + `}return s}function Yse(r){let e=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(e,o=>"@align(16) "+o);let t=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function VB(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var Wv={};Ke(Wv,{GPUBytesPerElement:()=>Kg,MatMulProgramType:()=>Mo,assertNotComplex:()=>lm,computeDispatch:()=>K,computeWorkPerThreadForConv2d:()=>pm,computeWorkgroupInfoForMatMul:()=>Vv,computeWorkgroupSizeForConv2d:()=>um,flatDispatchLayout:()=>X,isWebGPUSupported:()=>cm,tilesFitEvenlyIntoShape:()=>Zse});var Ep=r=>{let e=1;for(let t=0;tt%r[o]===0)}function K(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ep(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ep(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ep(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Vv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function um(r,e,t=!1){if(t)return[8,8,1];let o=Ep(r.x.map(s=>e[s])),n=Ep(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function pm(r,e,t=!1){if(t)return[4,4,1];let o=Ep(r.x.map(s=>e[s])),n=Ep(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function Kg(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function cm(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function lm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Mo||(Mo={}));var Jse=A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),eae=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},vu=class extends so{nextDataId(){return vu.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!cm())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Ug(t),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Gg(this.device),this.textureManager=new Hg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){if(A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype==="string")return o;if(t.dtype==="complex64"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,"float32");return this.convertAndCacheOnCPU(e,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],a=t.resource,i=a.size;y.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext("webgpu");return x.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext("2d",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=Kg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=Kg(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState==="unmapped"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type==="int32"?new Int32Array(a,c,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=eae(this.device,e);let a=t.map((p,u)=>{if(p.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:e.variableNames[u]}});e.shaderKey=UB(e,a,s);let i=A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=WB(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],a=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),a=o.concat(t).map(m=>m.shape);let l="int32";if(a.map(m=>{s.push({type:l,data:m});let d=y.computeStrides(m);s.push({type:l,data:d})}),e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:l,data:[e.outputComponent?m/e.outputComponent:m]})}}n&&(s=[...s,...n]);let i=[this.tensorToBinding(t),...o.map(l=>this.tensorToBinding(l)),this.makeUniforms(s)];o.forEach(l=>{this.commandQueueOwnedIds.add(l.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let p=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:i.map((l,m)=>({binding:m,resource:l}))}),u=this.activeTimers!=null;this.ensureCommandEncoderReady();let c={};u&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),c.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:"beginning"},{querySet:this.querySet,queryIndex:1,location:"end"}],this.computePassEncoder=this.commandEncoder.beginComputePass(c)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(c)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,p),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(u||A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass)&&(this.endComputePassEncoder(),u?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=Jse){return A().getBool("WEBGPU_CPU_FORWARD")&&e.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has("timestamp-query")&&(t.requiredFeatures=["timestamp-query"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize,maxBufferSize:o.maxBufferSize,maxComputeWorkgroupSizeX:o.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:o.maxComputeInvocationsPerWorkgroup};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new vu(n,s)},3);var fe;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.ELU_DER=5]="ELU_DER",r[r.EQUAL=6]="EQUAL",r[r.GREATER=7]="GREATER",r[r.GREATER_EQUAL=8]="GREATER_EQUAL",r[r.INT_DIV=9]="INT_DIV",r[r.LESS=10]="LESS",r[r.LESS_EQUAL=11]="LESS_EQUAL",r[r.LOGICAL_AND=12]="LOGICAL_AND",r[r.LOGICAL_OR=13]="LOGICAL_OR",r[r.MAX=14]="MAX",r[r.MIN=15]="MIN",r[r.MOD=16]="MOD",r[r.MUL=17]="MUL",r[r.NOT_EQUAL=18]="NOT_EQUAL",r[r.POW=19]="POW",r[r.PRELU=20]="PRELU",r[r.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",r[r.SUB=22]="SUB"})(fe||(fe={}));var tae="return a + b;",rae="var resultTemp = atan2(a, b);",oae="return areal * breal - aimag * bimag;",nae="return areal * bimag + aimag * breal;",sae="return a / b;",aae="return select(a * (b + 1.0), a, b >= 0.);",iae="return select(a * (b + vec4(1.0)), a, b >= vec4(0.));",uae="return f32(a == b);",pae="return vec4(a == b);",cae="return f32(a > b);",lae="return vec4(a > b);",mae="return f32(a >= b);",dae="return vec4(a >= b);",fae=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); -`,Ase=` +`,hae=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); @@ -5252,14 +5255,14 @@ return a / b;`,QJ=` resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); -`,Fse="return f32(a < b);",Pse="return vec4(a < b);",Ose="return f32(a <= b);",Mse="return vec4(a <= b);",Lse="return f32(a >= 1.0 && b >= 1.0);",Bse=`return (vec4(a >= vec4(1.0)) * - vec4(b >= vec4(1.0)));`,zse="return f32(a >= 1.0 || b >= 1.0);",Vse=`return min(vec4(a >= vec4(1.0)) + - vec4(b >= vec4(1.0)), vec4(1.0));`,Wse="var resultTemp = max(a, b);",Use="var resultTemp = min(a, b);",Gse=` +`,gae="return f32(a < b);",xae="return vec4(a < b);",yae="return f32(a <= b);",bae="return vec4(a <= b);",Cae="return f32(a >= 1.0 && b >= 1.0);",wae=`return (vec4(a >= vec4(1.0)) * + vec4(b >= vec4(1.0)));`,Sae="return f32(a >= 1.0 || b >= 1.0);",Iae=`return min(vec4(a >= vec4(1.0)) + + vec4(b >= vec4(1.0)), vec4(1.0));`,vae="var resultTemp = max(a, b);",kae="var resultTemp = min(a, b);",Nae=` let isNaN = b == 0.; var resultTemp = a % b; resultTemp = select((resultTemp + b) % b, resultTemp, (a < 0. && b < 0.) || (a >= 0. && b > 0.)); -`,Hse=` +`,Tae=` let isNaN = !vec4(b); var resultTemp = vec4(a % b); if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { @@ -5274,20 +5277,20 @@ return a / b;`,QJ=` if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) { resultTemp[3] = (resultTemp[3] + b[3]) % b[3]; } -`,Kse="return a * b;",qse=` +`,_ae="return a * b;",$ae=` var resultTemp = f32(a != b); let valueForNaN = 1.0; -`,jse=` +`,Eae=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; -`,Xse=` +`,Rae=` let isNaN = a < 0.0 && floor(b) < b; if (b == 0.0) { return 1.0; } var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b), round(abs(b) % 2.0) != 1.0); -`,Yse=` +`,Dae=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -5308,38 +5311,48 @@ return a / b;`,QJ=` resultTemp.a = 1.0; } let isNaN = (a < vec4(0.0)) & (floor(b) < b); -`,Qse="if (a < 0.0) { return b * a; } return a;",Zse=` +`,Aae="if (a < 0.0) { return b * a; } return a;",Fae=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Jse="return (a - b) * (a - b);",eae="return a - b;";function Kc(r,e){do{let t;switch(r){case fe.ATAN2:t=Cse;break;case fe.MAX:t=Wse;break;case fe.MIN:t=Use;break;case fe.MOD:t=e?Hse:Gse;break;case fe.NOT_EQUAL:t=e?jse:qse;break;case fe.POW:t=e?Yse:Xse;break;default:continue}return` +`,Pae="return (a - b) * (a - b);",Oae="return a - b;";function qc(r,e){let t;do{switch(r){case fe.ATAN2:t=rae;break;case fe.MAX:t=vae;break;case fe.MIN:t=kae;break;case fe.MOD:t=e?Tae:Nae;break;case fe.NOT_EQUAL:t=e?Eae:$ae;break;case fe.POW:t=e?Dae:Rae;break;default:continue}let o,n,s;return e?(o="isnanVec4",n="vec4",s="vec4"):(o="isnan",n="f32",s="bool"),` + let aIsNaN = ${o}(a); + let aPostLegalization = select(a, ${n}(42), aIsNaN); + let bIsNaN = ${o}(b); + let bPostLegalization = select(b, ${n}(42), bIsNaN); let isNaN = false; let valueForNaN = uniforms.NAN; { + let a = aPostLegalization; + let b = bPostLegalization; ${t} - ${e?yse:xse} - return resultTemp; + return select( + resultTemp, ${n}(valueForNaN), + ${s}(isNaN) | aIsNaN | bIsNaN); } - `}while(!1);switch(r){case fe.ADD:return bse;case fe.COMPLEX_MULTIPLY_IMAG:return Sse;case fe.COMPLEX_MULTIPLY_REAL:return wse;case fe.DIV:return Ise;case fe.ELU_DER:return e?kse:vse;case fe.EQUAL:return e?Tse:Nse;case fe.GREATER:return e?$se:_se;case fe.GREATER_EQUAL:return e?Rse:Ese;case fe.INT_DIV:return e?Ase:Dse;case fe.LESS:return e?Pse:Fse;case fe.LESS_EQUAL:return e?Mse:Ose;case fe.LOGICAL_AND:return e?Bse:Lse;case fe.LOGICAL_OR:return e?Vse:zse;case fe.MUL:return Kse;case fe.PRELU:return e?Zse:Qse;case fe.SQUARED_DIFFERENCE:return Jse;case fe.SUB:return eae;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.ROUND=27]="ROUND",r[r.RSQRT=28]="RSQRT",r[r.SELU=29]="SELU",r[r.SIGMOID=30]="SIGMOID",r[r.SIGN=31]="SIGN",r[r.SIN=32]="SIN",r[r.SINH=33]="SINH",r[r.SOFTPLUS=34]="SOFTPLUS",r[r.SQRT=35]="SQRT",r[r.SQUARE=36]="SQUARE",r[r.STEP=37]="STEP",r[r.TAN=38]="TAN",r[r.TANH=39]="TANH",r[r.TO_INT=40]="TO_INT"})(Q||(Q={}));var tae="return abs(a);",rae=` + `}while(!1);switch(r){case fe.ADD:return tae;case fe.COMPLEX_MULTIPLY_IMAG:return nae;case fe.COMPLEX_MULTIPLY_REAL:return oae;case fe.DIV:return sae;case fe.ELU_DER:return e?iae:aae;case fe.EQUAL:return e?pae:uae;case fe.GREATER:return e?lae:cae;case fe.GREATER_EQUAL:return e?dae:mae;case fe.INT_DIV:return e?hae:fae;case fe.LESS:return e?xae:gae;case fe.LESS_EQUAL:return e?bae:yae;case fe.LOGICAL_AND:return e?wae:Cae;case fe.LOGICAL_OR:return e?Iae:Sae;case fe.MUL:return _ae;case fe.PRELU:return e?Fae:Aae;case fe.SQUARED_DIFFERENCE:return Pae;case fe.SUB:return Oae;default:}return` + ${t} + return resultTemp; + `}var Z;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.ROUND=27]="ROUND",r[r.RSQRT=28]="RSQRT",r[r.SELU=29]="SELU",r[r.SIGMOID=30]="SIGMOID",r[r.SIGN=31]="SIGN",r[r.SIN=32]="SIN",r[r.SINH=33]="SINH",r[r.SOFTPLUS=34]="SOFTPLUS",r[r.SQRT=35]="SQRT",r[r.SQUARE=36]="SQUARE",r[r.STEP=37]="STEP",r[r.TAN=38]="TAN",r[r.TANH=39]="TANH",r[r.TO_INT=40]="TO_INT"})(Z||(Z={}));var Mae="return abs(a);",Lae=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); -`,oae=` +`,Bae=` if (a < 1.) { return uniforms.NAN; } return acosh(a); -`,nae=` +`,zae=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); -`,sae="return asinh(a);",aae=` +`,Vae="return asinh(a);",Wae=` if (isnan(a)) { return uniforms.NAN; } return atan(a); -`,iae=` +`,Uae=` if (abs(a) > 1.) { return uniforms.NAN; } @@ -5350,10 +5363,10 @@ return a / b;`,QJ=` return -uniforms.INFINITY; } return atanh(a); -`,uae="return ceil(a);",pae="return cos(a);",cae=` +`,Gae="return ceil(a);",Hae="return cos(a);",Kae=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; -`,lae="return exp(a) - 1.0;",mae="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",dae=` +`,qae="return exp(a) - 1.0;",jae="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Xae=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -5368,40 +5381,40 @@ return a / b;`,QJ=` resFloat.a = a.a; } return resFloat; -`,fae=` +`,Yae=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - let p = ${C.ERF_P}; - let a1 = ${C.ERF_A1}; - let a2 = ${C.ERF_A2}; - let a3 = ${C.ERF_A3}; - let a4 = ${C.ERF_A4}; - let a5 = ${C.ERF_A5}; + let p = ${w.ERF_P}; + let a1 = ${w.ERF_A1}; + let a2 = ${w.ERF_A2}; + let a3 = ${w.ERF_A3}; + let a4 = ${w.ERF_A4}; + let a5 = ${w.ERF_A5}; let sign = sign(a); let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); -`,hae="return exp(a);",gae="return floor(a);",xae="return f32(!isnan(a) && !isinf(a));",yae="return f32(isinf(a));",bae="return f32(isnan(a));",Cae="return a;",wae=`if (a < 0.0) { return uniforms.NAN; } - return log(a);`,Sae=` +`,Qae="return exp(a);",Zae="return floor(a);",Jae="return f32(!isnan(a) && !isinf(a));",eie="return f32(isinf(a));",tie="return f32(isnan(a));",rie="return a;",oie=`if (a < 0.0) { return uniforms.NAN; } + return log(a);`,nie=` if (isnan(a)) { return a; } return log(1.0 + a); -`,Iae="return f32(!(a >= 1.0));",vae="return -a;",kae="if (a < 0.0) { return uniforms.alpha * a; } return a;",Nae=` +`,sie="return f32(!(a >= 1.0));",aie="return -a;",iie="if (a < 0.0) { return uniforms.alpha * a; } return a;",uie=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Tae="return 1.0 / a;",_ae="return select(a, 0.0, a < 0.0);",$ae="return clamp(a, 0.0, 6.0);",Eae="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",Rae=` +`,pie="return 1.0 / a;",cie="return select(a, 0.0, a < 0.0);",lie="return clamp(a, 0.0, 6.0);",mie="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",die=` return select(a, vec4(0.0), a < vec4(0.0)); -`,Dae="return round(a);",Aae="return inverseSqrt(a);",Fae=` +`,fie="return round(a);",hie="return inverseSqrt(a);",gie=` if (a >= 0.0) { - return ${C.SELU_SCALE} * a; + return ${w.SELU_SCALE} * a; } else { - return ${C.SELU_SCALEALPHA} * (exp(a) - 1.0); + return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0); } -`,Pae="return 1.0 / (1.0 + exp(-1.0 * a));",Oae="return sign(a);",Mae="return sin(a);",Lae=` +`,xie="return 1.0 / (1.0 + exp(-1.0 * a));",yie="return sign(a);",bie="return sin(a);",Cie=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; -`,Bae=` +`,wie=` let epsilon = 1.1920928955078125e-7; let threshold = log(epsilon) + 2.0; @@ -5416,26 +5429,26 @@ return a / b;`,QJ=` } else { return log(exp_a + 1.0); } -`,zae="return sqrt(a);",Vae="return a * a;",Wae=` +`,Sie="return sqrt(a);",Iie="return a * a;",vie=` if (isnan(a)) { return a; } return select(uniforms.stepAlpha, 1.0, a > 0.0); -`,Uae="return tan(a);",Gae=` +`,kie="return tan(a);",Nie=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,Hae="return f32(i32((a)));";function yi(r,e){switch(r){case Q.ABS:return tae;case Q.ACOS:return rae;case Q.ACOSH:return oae;case Q.ASIN:return nae;case Q.ASINH:return sae;case Q.ATAN:return aae;case Q.ATANH:return iae;case Q.COS:return pae;case Q.COSH:return cae;case Q.CEIL:return uae;case Q.ELU:return e?dae:mae;case Q.ERF:return fae;case Q.EXP:return hae;case Q.EXPM1:return lae;case Q.FLOOR:return gae;case Q.IS_FINITE:return xae;case Q.IS_INF:return yae;case Q.IS_NAN:return bae;case Q.LINEAR:return Cae;case Q.LOG:return wae;case Q.LOG1P:return Sae;case Q.LOGICAL_NOT:return Iae;case Q.NEG:return vae;case Q.LEAKYRELU:return e?Nae:kae;case Q.RECIPROCAL:return Tae;case Q.RELU:return e?Rae:_ae;case Q.RELU6:return e?Eae:$ae;case Q.ROUND:return Dae;case Q.RSQRT:return Aae;case Q.SELU:return Fae;case Q.SIGMOID:return Pae;case Q.SIGN:return Oae;case Q.SIN:return Mae;case Q.SINH:return Lae;case Q.SOFTPLUS:return Bae;case Q.SQRT:return zae;case Q.SQUARE:return Vae;case Q.STEP:return Wae;case Q.TAN:return Uae;case Q.TANH:return Gae;case Q.TO_INT:return Hae;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=yi(Q.LINEAR);else if(r==="relu")n=yi(Q.RELU,t);else if(r==="elu")n=yi(Q.ELU,t);else if(r==="relu6")n=yi(Q.RELU6,t);else if(r==="prelu")n=Kc(fe.PRELU,t);else if(r==="sigmoid")n=yi(Q.SIGMOID,t);else if(r==="leakyrelu")n=yi(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i="";return e?i=` +`,Tie="return f32(i32((a)));";function wi(r,e){switch(r){case Z.ABS:return Mae;case Z.ACOS:return Lae;case Z.ACOSH:return Bae;case Z.ASIN:return zae;case Z.ASINH:return Vae;case Z.ATAN:return Wae;case Z.ATANH:return Uae;case Z.COS:return Hae;case Z.COSH:return Kae;case Z.CEIL:return Gae;case Z.ELU:return e?Xae:jae;case Z.ERF:return Yae;case Z.EXP:return Qae;case Z.EXPM1:return qae;case Z.FLOOR:return Zae;case Z.IS_FINITE:return Jae;case Z.IS_INF:return eie;case Z.IS_NAN:return tie;case Z.LINEAR:return rie;case Z.LOG:return oie;case Z.LOG1P:return nie;case Z.LOGICAL_NOT:return sie;case Z.NEG:return aie;case Z.LEAKYRELU:return e?uie:iie;case Z.RECIPROCAL:return pie;case Z.RELU:return e?die:cie;case Z.RELU6:return e?mie:lie;case Z.ROUND:return fie;case Z.RSQRT:return hie;case Z.SELU:return gie;case Z.SIGMOID:return xie;case Z.SIGN:return yie;case Z.SIN:return bie;case Z.SINH:return Cie;case Z.SOFTPLUS:return wie;case Z.SQRT:return Sie;case Z.SQUARE:return Iie;case Z.STEP:return vie;case Z.TAN:return kie;case Z.TANH:return Nie;case Z.TO_INT:return Tie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=wi(Z.LINEAR);else if(r==="relu")n=wi(Z.RELU,t);else if(r==="elu")n=wi(Z.ELU,t);else if(r==="relu6")n=wi(Z.RELU6,t);else if(r==="prelu")n=qc(fe.PRELU,t);else if(r==="sigmoid")n=wi(Z.SIGMOID,t);else if(r==="leakyrelu")n=wi(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i="";return e?i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); ${n} }`:i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { ${n} - }`,i}function jr(r,e){return` + }`,i}function Qr(r,e){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${e?"value = activation(value, coords);":""} - `}function Pv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=` + `}function Uv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=` ${r?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,i=e?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` @@ -5457,19 +5470,19 @@ return a / b;`,QJ=` ${i} return value; } - `}function lm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return` - ${Pv(t,o,n,s,a,i)} + `}function mm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return` + ${Uv(t,o,n,s,a,i)} fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ae(i)}) { let col = colIn * ${i}; ${n&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${jr(r,e)} + ${Qr(r,e)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } - `}var Kae=(r,e)=>r?` + `}var _ie=(r,e)=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart / ${e} + inputCol); @@ -5477,7 +5490,7 @@ return a / b;`,QJ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, kStart / ${e} + inputCol); - `,qae=(r,e,t)=>r?` + `,$ie=(r,e,t)=>r?` let ACached0 = mm_Asub[k * ${e}][localRow]; let ACached1 = mm_Asub[k * ${e} + 1][localRow]; let ACached2 = mm_Asub[k * ${e} + 2][localRow]; @@ -5494,13 +5507,13 @@ return a / b;`,QJ=` acc[i] = fma(BCached1, vec4(ACached.y), acc[i]); acc[i] = fma(BCached2, vec4(ACached.z), acc[i]); ${e===3?"":"acc[i] = fma(BCached3, vec4(ACached.w), acc[i]);"} - }`;function Tp(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. + }`;function Rp(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. Otherwise, innerElementSize ${l} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),` var mm_Asub : array, ${u/l}>, ${c}>; var mm_Bsub : array, ${p/r[0]}>, ${o}>; - ${K()} { + ${H()} { let localRow = i32(localId.y); let tileRow = localRow * ${d}; let tileCol = i32(localId.x); @@ -5524,7 +5537,7 @@ return a / b;`,QJ=` for (var innerRow = 0; innerRow < ${d}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${Kae(t,l)} + ${_ie(t,l)} } // Load one tile of B into local memory. @@ -5543,7 +5556,7 @@ return a / b;`,QJ=` let BCached2 = mm_Bsub[k * ${l} + 2][tileCol]; ${l===3?"":`let BCached3 = mm_Bsub[k * ${l} + 3][tileCol];`} - ${qae(t,l,d)} + ${$ie(t,l,d)} } workgroupBarrier(); @@ -5552,7 +5565,7 @@ return a / b;`,QJ=` for (var innerRow = 0; innerRow < ${d}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var CB=r=>r?` + }`}var HB=r=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -5560,7 +5573,7 @@ return a / b;`,QJ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,jae=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function _p(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?` + `,Eie=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Dp(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${p}; @@ -5571,7 +5584,7 @@ return a / b;`,QJ=` // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) { for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) { - ${CB(t)} + ${HB(t)} } } // Load one tile of B into local memory. @@ -5626,7 +5639,7 @@ return a / b;`,QJ=` for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${CB(t)} + ${HB(t)} } } @@ -5651,7 +5664,7 @@ return a / b;`,QJ=` } for (var innerRow = 0; innerRow < ${h}; innerRow++) { - ${jae(t)} + ${Eie(t)} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); @@ -5672,7 +5685,7 @@ return a / b;`,QJ=` var mm_Asub : array, ${l}>; var mm_Bsub : array, ${o}>; - ${K()} { + ${H()} { let batch = ${n?"0":"i32(globalId.z)"}; let batchA = ${n||!i?"batch":"batch % uniforms.aShape[0]"}; let batchB = ${n||!i?"batch":"batch % uniforms.bShape[0]"}; @@ -5689,7 +5702,7 @@ return a / b;`,QJ=` } ${x} } - `}var Xae=r=>r?` + `}var Rie=r=>r?` mm_readA(batchA, colA, globalRow), mm_readA(batchA, colA + 1, globalRow), mm_readA(batchA, colA + 2, globalRow), @@ -5699,10 +5712,10 @@ return a / b;`,QJ=` mm_readA(batchA, globalRow, colA + 1), mm_readA(batchA, globalRow, colA + 2), mm_readA(batchA, globalRow, colA + 3) - `;function Yae(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return` + `;function Die(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return` var mm_Asub : array, ${r[0]}>; - ${K()} { + ${H()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); @@ -5718,7 +5731,7 @@ return a / b;`,QJ=` for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. let colA = t * ${t} + tileCol * 4; - mm_Asub[tileCol] = vec4(${Xae(e)}); + mm_Asub[tileCol] = vec4(${Rie(e)}); workgroupBarrier(); // Compute acc values for a single thread. @@ -5738,13 +5751,13 @@ return a / b;`,QJ=` mm_write(batch, globalRow, globalCol, acc); } - `}var Kg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Av(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` + `}var qg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Vv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${lm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Yae(this.workgroupSize,this.transposeA):_p(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} - `}};function Qae(r){return` + ${mm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?Rp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Die(this.workgroupSize,this.transposeA):Dp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + `}};function Aie(r){return` var sumValues : array; - ${K()} { + ${H()} { let coords = getOutputCoords(); let batch = coords[0]; let batchA = batch % uniforms.aShape[0]; @@ -5775,11 +5788,11 @@ return a / b;`,QJ=` mm_write(batch, row, col, sum); } } - `}var qg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return` + `}var jg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights)} - ${lm(this.addBias,this.activation,this.transposeA,this.transposeB)} - ${Qae(this.workgroupSize[0])} - `}};function Zae(r){let e=r[1],t=r[0],o=e>t?e:t;return` + ${mm(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Aie(this.workgroupSize[0])} + `}};function Fie(r){let e=r[1],t=r[0],o=e>t?e:t;return` var mm_Asub : array, ${e}>; var mm_Bsub : array, ${o}>; @@ -5789,7 +5802,7 @@ return a / b;`,QJ=` // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. - ${K()} { + ${H()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); @@ -5831,12 +5844,12 @@ return a / b;`,QJ=` mm_write(batch, globalRow, globalCol, acc); } - `}var jg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` + `}var Xg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights)} - ${lm(this.addBias,this.activation,this.transposeA,this.transposeB)} - ${Zae(this.workgroupSize)} - `}};var Xg=class{constructor(e,t,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=q(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` - ${Pv(!1,this.transposeB,!1,!1,!1,e)} + ${mm(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Fie(this.workgroupSize)} + `}};var Yg=class{constructor(e,t,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=K(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` + ${Uv(!1,this.transposeB,!1,!1,!1,e)} fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Ae(e)}) { let col = colIn * ${e}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { @@ -5845,34 +5858,34 @@ return a / b;`,QJ=` // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${e}; i = i + 1) { - ${Bs("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} + ${Us("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} } } } - ${e===4?Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):_p(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} - `}},Yg=class{constructor(e,t=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` + ${e===4?Rp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Dp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + `}},Qg=class{constructor(e,t=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights)} - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${jr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtIndex(index, value); } } - `}};var Qg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` - ${K("index")} { + `}};var Zg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } - `}};function Vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Qg(o),i=[{type:"float32",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var wB={kernelName:ea,backendName:"webgpu",kernelFunc:Vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var SB={kernelName:ia,backendName:"webgpu",kernelFunc:pe};function $p({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],F=Math.max(x,b),O=[E,R],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],L,B,z=[F,d,f],U=P().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(U<0){let H=P().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),X=H>0?H:n.thresholdToIncreaseWorkgroups,J=F*Math.ceil(d/32)*Math.ceil(f/32);J<=X||d<=8&&J<=X*2?F*d*f<=128?U=Oo.MatMulReduceProgram:F===1&&m>=2e3?U=Oo.MatMulSplitKProgram:U=Oo.MatMulSmallOutputSizeProgram:U=Oo.MatMulPackedProgram}switch(U){case Oo.MatMulReduceProgram:L=new qg(z,t,o,s,p,a);break;case Oo.MatMulSplitKProgram:{if(B=Vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Xg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let X=new Yg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p==="leakyrelu"&&(J=[{type:"float32",data:[i]}],X.uniforms+=" alpha : f32,");let ne=n.runWebGPUProgram(X,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Oo.MatMulSmallOutputSizeProgram:L=new jg(k,_,z,t,o,s,p,a);break;case Oo.MatMulPackedProgram:let H=n.adapterInfo.isIntel();L=new Kg(k,z,t,o,s,p,a,H);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let H of D)n.disposeData(H.dataId);return j}function Jae(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return $p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var IB={kernelName:bo,backendName:"webgpu",kernelFunc:Jae};var mm=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,o),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` + `}};function Vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Zg(o),i=[{type:"float32",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var KB={kernelName:na,backendName:"webgpu",kernelFunc:Vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var qB={kernelName:ma,backendName:"webgpu",kernelFunc:pe};function Ap({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Ir.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],L,B,z=[P,d,f],U=A().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(U<0){let q=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new jg(z,t,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=Vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Yg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Qg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p==="leakyrelu"&&(J=[{type:"float32",data:[i]}],Y.uniforms+=" alpha : f32,");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Xg(k,_,z,t,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new qg(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Pie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Ap({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var jB={kernelName:So,backendName:"webgpu",kernelFunc:Pie};var dm=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${Kc(this.op,!1)} + ${qc(this.op,!1)} } - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); @@ -5881,16 +5894,16 @@ return a / b;`,QJ=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}};var bi=class{constructor(e,t,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,o),this.dispatchLayout=Z(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",o=` + `}};var Si=class{constructor(e,t,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",o=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { - ${Kc(this.op,this.outputComponent===4)} + ${qc(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${o} var sharedBuf : array; - ${K("index")} { + ${H("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { @@ -5906,7 +5919,7 @@ return a / b;`,QJ=` } `}else e=` ${o} - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index * ${this.outputComponent}); let a = ${t}(getAByOutputCoords(coords)); @@ -5914,18 +5927,18 @@ return a / b;`,QJ=` setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var vB={kernelName:xo,backendName:"webgpu",kernelFunc:At};function fo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var kB={kernelName:Ti,backendName:"webgpu",kernelFunc:fo};var Xr=class{constructor(e,t,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return` + `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var XB={kernelName:Co,backendName:"webgpu",kernelFunc:At};function xo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var YB={kernelName:Ri,backendName:"webgpu",kernelFunc:xo};var Zr=class{constructor(e,t,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { - ${yi(this.op,!1)} + ${wi(this.op,!1)} } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } - `}};function xe({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Xr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,w={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new bi(r,a.shape,i.shape);return p.runWebGPUProgram(k,[w,S],dt(x.dtype,b.dtype))});else{let g=new mm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new mm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=fo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?C.fromUint8ToStringArray(l):l,f=a.dtype==="string"?C.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new bi(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:NB,castImpl:TB,ceilImpl:_B,concatImpl:$B,equalImpl:EB,expImpl:RB,expm1Impl:DB,floorImpl:AB,floorDivImpl:FB,gatherNdImpl:PB,gatherV2Impl:OB,greaterEqualImpl:MB,greaterImpl:LB,lessEqualImpl:BB,lessImpl:zB,logImpl:VB,maxImpl:WB,maximumImpl:UB,minimumImpl:GB,multiplyImpl:HB,negImpl:KB,notEqualImpl:qB,prodImpl:jB,rangeImpl:XB,rsqrtImpl:YB,scatterImpl:QB,simpleAbsImpl:ZB,sliceImpl:JB,stridedSliceImpl:ez,stringNGramsImpl:tz,subImpl:rz,tileImpl:oz,topKImpl:nz,transposeImpl:sz,uniqueImpl:o3t}=Sc;var eie=xe({opType:Q.ABS,cpuKernelImpl:ZB}),az={kernelName:Gs,backendName:"webgpu",kernelFunc:eie};var tie=xe({opType:Q.ACOS}),iz={kernelName:zo,backendName:"webgpu",kernelFunc:tie};var rie=xe({opType:Q.ACOSH}),uz={kernelName:Vo,backendName:"webgpu",kernelFunc:rie};var oie=et({opType:fe.ADD,cpuKernelImpl:NB,supportsComplex:!0}),pz={kernelName:no,backendName:"webgpu",kernelFunc:oie};var Zg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(" + ");return` - ${K("index")} { + `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Zr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new dm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new dm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?w.fromUint8ToStringArray(l):l,f=a.dtype==="string"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:QB,castImpl:ZB,ceilImpl:JB,concatImpl:ez,equalImpl:tz,expImpl:rz,expm1Impl:oz,floorImpl:nz,floorDivImpl:sz,gatherNdImpl:az,gatherV2Impl:iz,greaterEqualImpl:uz,greaterImpl:pz,lessEqualImpl:cz,lessImpl:lz,logImpl:mz,maxImpl:dz,maximumImpl:fz,minimumImpl:hz,multiplyImpl:gz,negImpl:xz,notEqualImpl:yz,prodImpl:bz,rangeImpl:Cz,rsqrtImpl:wz,scatterImpl:Sz,simpleAbsImpl:Iz,sliceImpl:vz,stridedSliceImpl:kz,stringNGramsImpl:Nz,subImpl:Tz,tileImpl:_z,topKImpl:$z,transposeImpl:Ez,uniqueImpl:NPt}=Ic;var Oie=ye({opType:Z.ABS,cpuKernelImpl:Iz}),Rz={kernelName:js,backendName:"webgpu",kernelFunc:Oie};var Mie=ye({opType:Z.ACOS}),Dz={kernelName:Vo,backendName:"webgpu",kernelFunc:Mie};var Lie=ye({opType:Z.ACOSH}),Az={kernelName:Wo,backendName:"webgpu",kernelFunc:Lie};var Bie=et({opType:fe.ADD,cpuKernelImpl:QB,supportsComplex:!0}),Fz={kernelName:io,backendName:"webgpu",kernelFunc:Bie};var Jg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(" + ");return` + ${H("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { @@ -5936,9 +5949,9 @@ return a / b;`,QJ=` } } } - `}};function nie(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Zg(s);return t.runWebGPUProgram(a,o,n)}var cz={kernelName:Wo,backendName:"webgpu",kernelFunc:nie};var Jg=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` + `}};function zie(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Jg(s);return t.runWebGPUProgram(a,o,n)}var Pz={kernelName:Uo,backendName:"webgpu",kernelFunc:zie};var ex=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; - ${K()} { + ${H()} { var x = i32(workgroupId.x) * ${e} + i32(localId.x); var y = i32(workgroupId.y) * ${e} + i32(localId.y); let width = uniforms.outShape[0]; @@ -5955,18 +5968,18 @@ return a / b;`,QJ=` [localId.y]); } } - `}};var ex=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",o=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` + `}};function Gv(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",o=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) @@ -5983,7 +5996,7 @@ return a / b;`,QJ=` let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } - ${K("index")} { + ${H("index")} { let outputIndex = index / ${o}; let offset = getOffset(outputIndex); var bestValue = ${t}; @@ -6014,7 +6027,7 @@ return a / b;`,QJ=` ${n} } } - `}};function Yr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=C.getAxesPermutation(p,s),c=r;u!=null&&(c=rr({inputs:{x:r},attrs:{perm:u},backend:n}),p=C.getInnerMostAxes(p.length,s),a.push(c)),C.assertAxesAreInnerMostDims(o,p,s);let[l,m]=C.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=C.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=WB(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:w}=jB(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,w,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},w=o==="mean"?"float32":Za(r.dtype),S=[{type:"int32",data:[h]}],k=new tx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],w,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function aie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Yr(n,a,s,"all",t)}var mz={kernelName:Uo,backendName:"webgpu",kernelFunc:aie};function iie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Yr(n,a,s,"any",t)}var dz={kernelName:Go,backendName:"webgpu",kernelFunc:iie};var qc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=C.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=Z(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type="plain",this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=q(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Po(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;sn.disposeData(h.dataId)),f}function Vie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Jr(n,a,s,"all",t)}var Mz={kernelName:Go,backendName:"webgpu",kernelFunc:Vie};function Wie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Jr(n,a,s,"any",t)}var Lz={kernelName:Ho,backendName:"webgpu",kernelFunc:Wie};var jc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type="plain",this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=K(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s u32 { return ((a - 1u) / b + 1u); } @@ -6024,7 +6037,7 @@ return a / b;`,QJ=` var xBestValues : array; `} - ${K("index")} { + ${H("index")} { let outputIndex = index / ${e}; let reduceLength = ${t()}; @@ -6064,7 +6077,7 @@ return a / b;`,QJ=` } } `:` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; @@ -6080,8 +6093,8 @@ return a / b;`,QJ=` setOutputAtIndexI32(index, bestIndex); } } - `}};function uie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=rr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new qc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var fz={kernelName:Hs,backendName:"webgpu",kernelFunc:uie};function pie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=rr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new qc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var hz={kernelName:Ks,backendName:"webgpu",kernelFunc:pie};var cie=xe({opType:Q.ASIN}),gz={kernelName:Ho,backendName:"webgpu",kernelFunc:cie};var lie=xe({opType:Q.ASINH}),xz={kernelName:Ko,backendName:"webgpu",kernelFunc:lie};var mie=xe({opType:Q.ATAN}),yz={kernelName:qo,backendName:"webgpu",kernelFunc:mie};var die=et({opType:fe.ATAN2}),bz={kernelName:Xo,backendName:"webgpu",kernelFunc:die};var fie=xe({opType:Q.ATANH}),Cz={kernelName:jo,backendName:"webgpu",kernelFunc:fie};var rx=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` - ${K("index")} { + `}};function Uie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var Bz={kernelName:Xs,backendName:"webgpu",kernelFunc:Uie};function Gie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var zz={kernelName:Ys,backendName:"webgpu",kernelFunc:Gie};var Hie=ye({opType:Z.ASIN}),Vz={kernelName:Ko,backendName:"webgpu",kernelFunc:Hie};var Kie=ye({opType:Z.ASINH}),Wz={kernelName:qo,backendName:"webgpu",kernelFunc:Kie};var qie=ye({opType:Z.ATAN}),Uz={kernelName:jo,backendName:"webgpu",kernelFunc:qie};var jie=et({opType:fe.ATAN2}),Gz={kernelName:Yo,backendName:"webgpu",kernelFunc:jie};var Xie=ye({opType:Z.ATANH}),Hz={kernelName:Xo,backendName:"webgpu",kernelFunc:Xie};var ox=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6095,13 +6108,13 @@ return a / b;`,QJ=` setOutputAtIndex(index, value); } } - `}};var Da=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}};var Ma=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; maxPosition = ${this.flattenPositions?this.includeBatchIndex?"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d":"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d":"wR * uniforms.filterDims.y + wC"}; }`:e="resultValue = max(value, resultValue);";let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6136,13 +6149,13 @@ return a / b;`,QJ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}},wu=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},ku=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; maxPosition = ${this.flattenPositions?this.includeBatchIndex?"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch":"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch":"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC"}; }`:e="resultValue = max(value, resultValue);";let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -6185,9 +6198,9 @@ return a / b;`,QJ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}};function Ov(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Yr(n,s,a,"max",t)}var wz={kernelName:Ln,backendName:"webgpu",kernelFunc:Ov};function Mv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Yr(n,a,s,"mean",t)}var Sz={kernelName:Vn,backendName:"webgpu",kernelFunc:Mv};function ox(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=Mv({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=Ov({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new rx(e):(t==="avg"?n=new Da(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Da(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function hie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=C.computePool2DInfo(n.shape,s,a,u,i,p);return ox(n,c,"avg",t)}var Iz={kernelName:Yo,backendName:"webgpu",kernelFunc:hie};function gie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var vz={kernelName:qs,backendName:"webgpu",kernelFunc:gie};var nx=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` - ${K("index")} { + `}};function Hv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Jr(n,s,a,"max",t)}var Kz={kernelName:zn,backendName:"webgpu",kernelFunc:Hv};function Kv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Jr(n,a,s,"mean",t)}var qz={kernelName:Un,backendName:"webgpu",kernelFunc:Kv};function nx(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=Kv({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=Hv({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new ox(e):(t==="avg"?n=new Ma(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Ma(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Yie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return nx(n,c,"avg",t)}var jz={kernelName:Qo,backendName:"webgpu",kernelFunc:Yie};function Qie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new ku(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var Xz={kernelName:Qs,backendName:"webgpu",kernelFunc:Qie};var sx=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6224,9 +6237,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}},sx=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, - outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` - ${K("index")} { + `}},ax=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -6273,8 +6286,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}};function xie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=C.computePool3DInfo(a.shape,i,p,1,u,c),m=new sx(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:"int32",data:[l.outDepth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"float32",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var kz={kernelName:Ni,backendName:"webgpu",kernelFunc:xie};function yie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;cm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=C.computePool2DInfo(a.shape,i,p,1,u),l=new nx(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var Nz={kernelName:Gp,backendName:"webgpu",kernelFunc:yie};function bie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return $p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var Tz={kernelName:Qo,backendName:"webgpu",kernelFunc:bie};var ax=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Nt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Nt(this.rank),t=Cie(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${Lv[a]} = uniforms.start.${Po(a)} + coords.${Lv[a]};`),` - ${K("index")} { + `}};function Zie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ax(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:"int32",data:[l.outDepth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"float32",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var Yz={kernelName:Ei,backendName:"webgpu",kernelFunc:Zie};function Jie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;lm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new sx(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var Qz={kernelName:$i,backendName:"webgpu",kernelFunc:Jie};function eue(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Ap({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var Zz={kernelName:Zo,backendName:"webgpu",kernelFunc:eue};var ix=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ht(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=ht(this.rank),t=tue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${qv[a]} = uniforms.start.${Oo(a)} + coords.${qv[a]};`),` + ${H("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); @@ -6283,17 +6296,17 @@ return a / b;`,QJ=` setOutputAtIndex(index, getSource(${t})); } } - `}},Lv=["x","y","z","w","u","v"];function Cie(r){if(r===1)return"sourceLoc";if(r<=6)return Lv.slice(0,r).map(e=>`sourceLoc.${e}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function zs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.tensorMap.get(n.dataId),m=JB(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new ax(i,p),c=[{type:"int32",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var _z={kernelName:pa,backendName:"webgpu",kernelFunc:zs};var wie=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,w)=>b*w),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),c=C.getReshapedPermuted(n.shape,s,i),l=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=rr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=zs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},$z={kernelName:js,backendName:"webgpu",kernelFunc:wie};var Sie=` + `}},qv=["x","y","z","w","u","v"];function tue(r){if(r===1)return"sourceLoc";if(r<=6)return qv.slice(0,r).map(e=>`sourceLoc.${e}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function Gs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.tensorMap.get(n.dataId),m=vz(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new ix(i,p),c=[{type:"int32",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var Jz={kernelName:fa,backendName:"webgpu",kernelFunc:Gs};var rue=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=yr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},eV={kernelName:Zs,backendName:"webgpu",kernelFunc:rue};var oue=` fn bincount_write(index: i32, value: f32) { - ${Bs("&result[index]","value","float32")} + ${Us("&result[index]","value","float32")} } -`,Iie=` +`,nue=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } -`,jc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` - ${this.binaryOutput?Iie:Sie} - ${K("index")} { +`,Xc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` + ${this.binaryOutput?nue:oue} + ${H("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { @@ -6309,8 +6322,8 @@ return a / b;`,QJ=` } }`} } - `}};function vie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=Vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new jc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var Ez={kernelName:Zo,backendName:"webgpu",kernelFunc:vie};var ix=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` - ${K("index")} { + `}};function sue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=Vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Xc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var tV={kernelName:Jo,backendName:"webgpu",kernelFunc:sue};var ux=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { var s0 = 1.0; var s1 = 1.0; @@ -6334,8 +6347,8 @@ return a / b;`,QJ=` } } } - `}};function kie(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=C.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],"int32",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new ix(i),u=[{type:"int32",data:[s]},{type:"int32",data:[a]}];return t.runWebGPUProgram(p,[o,n],"int32",u)}var Rz={kernelName:Xs,backendName:"webgpu",kernelFunc:kie};var Bv=et({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:qB}),Dz={kernelName:qn,backendName:"webgpu",kernelFunc:Bv};function Ci(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var Az={kernelName:zi,backendName:"webgpu",kernelFunc:Ci};function Fz(r,e){let t=new Xr(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function zv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return At({inputs:{x:n},backend:t});let a=Wr(n.shape),i=zv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=fo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Ci({inputs:{input:n},backend:t}),i=zv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=TB(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return Fz(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=Bv({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var Pz={kernelName:ho,backendName:"webgpu",kernelFunc:zv};var Nie=xe({opType:Q.CEIL,cpuKernelImpl:_B}),Oz={kernelName:Jo,backendName:"webgpu",kernelFunc:Nie};var ux=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` - ${K("index")} { + `}};function aue(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],"int32",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new ux(i),u=[{type:"int32",data:[s]},{type:"int32",data:[a]}];return t.runWebGPUProgram(p,[o,n],"int32",u)}var rV={kernelName:Js,backendName:"webgpu",kernelFunc:aue};var jv=et({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:yz}),oV={kernelName:Yn,backendName:"webgpu",kernelFunc:jv};function Ii(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var nV={kernelName:Gi,backendName:"webgpu",kernelFunc:Ii};function sV(r,e){let t=new Zr(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Xv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return At({inputs:{x:n},backend:t});let a=Gr(n.shape),i=Xv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=xo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Ii({inputs:{input:n},backend:t}),i=Xv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=ZB(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return sV(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=jv({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var aV={kernelName:yo,backendName:"webgpu",kernelFunc:Xv};var iue=ye({opType:Z.CEIL,cpuKernelImpl:JB}),iV={kernelName:en,backendName:"webgpu",kernelFunc:iue};var px=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + ${H("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); var clampedValue = clamp( @@ -6344,8 +6357,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, clampedValue); } } - `}};var px=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` - ${K("index")} { + `}};var cx=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` + ${H("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { @@ -6355,8 +6368,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function Tie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new ux(n.shape):i=new px(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var Mz={kernelName:go,backendName:"webgpu",kernelFunc:Tie};var cx=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` - ${K("index")} { + `}};function uue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new px(n.shape):i=new cx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var uV={kernelName:bo,backendName:"webgpu",kernelFunc:uue};var lx=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); let im = abs(getImagByOutputIndex(index)); @@ -6367,8 +6380,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } - `}};function Lz(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function _ie(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new cx(o.shape),a=[Lz(o,n.complexTensorInfos.real),Lz(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var Bz={kernelName:_i,backendName:"webgpu",kernelFunc:_ie};var lx=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;s`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;sCi({inputs:{input:w},backend:t})),h=r.map(w=>Ep({inputs:{input:w},backend:t})),g=Xc(f,e,t),x=Xc(h,e,t),b=fo({inputs:{real:g,imag:x},backend:t});return f.forEach(w=>t.disposeData(w.dataId)),h.forEach(w=>t.disposeData(w.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=C.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=$B(h,g,o,x),w=C.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(w,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new lx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function $ie(r,e,t){let o=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function Vv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Xc(p,s,t)}var Vz={kernelName:Ys,backendName:"webgpu",kernelFunc:Vv};function Eie(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?` + `}};function Fp(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var lV={kernelName:Vi,backendName:"webgpu",kernelFunc:Fp};function Yc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let f=r.map(C=>Ii({inputs:{input:C},backend:t})),h=r.map(C=>Fp({inputs:{input:C},backend:t})),g=Yc(f,e,t),x=Yc(h,e,t),b=xo({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=ez(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new mx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function cue(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function Yv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Yc(p,s,t)}var mV={kernelName:ea,backendName:"webgpu",kernelFunc:Yv};function lue(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6416,7 +6429,7 @@ return a / b;`,QJ=` let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${c(i)} } - return resData;`,w=r?e&&o?` + return resData;`,C=r?e&&o?` let col = colIn * ${i}; ${b}`:` let col = colIn * ${i}; @@ -6433,11 +6446,11 @@ return a / b;`,QJ=` return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return` ${dr(s,a,u===4,4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} { - ${r?w:S} + ${r?C:S} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} { - ${r?S:w} + ${r?S:C} } fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) { @@ -6447,13 +6460,13 @@ return a / b;`,QJ=` var value = valueIn; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${d} - ${jr(n,s)} + ${Qr(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var mx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=im(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=um(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Tp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):_p(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` - ${Eie(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} + }`}var dx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=um(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Rp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Dp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + ${lue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} - `}};var dx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` + `}};var fx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); @@ -6475,11 +6488,11 @@ return a / b;`,QJ=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${jr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } - ${K("index")} { + ${H("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} @@ -6499,9 +6512,9 @@ return a / b;`,QJ=` } writeResult(batch, outRow, outCol, outChannel, acc); } - `}};var fx=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, - inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` - ${K("index")} { + `}};var hx=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, + inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` + ${H("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; @@ -6523,8 +6536,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, value); } } - `}};function hx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Rie({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat==="channelsLast",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=hx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=hx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=$p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Die({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,w=b==="channelsLast",S=p*u*c,k=h*f,_=w?[t.batchSize,k,S]:[t.batchSize,S,k],E=new fx(_,w),R=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),F=[];F.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(F.push(O),s!=null){let U=hx(s.shape,w);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),F.push(s))}if(n!=null){let U=hx(n.shape,w);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),F.push(n))}let B=$p({a:w?D:O,b:w?O:D,transposeA:!w,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});F.push(B);for(let U of F)o.disposeData(U.dataId);return z}function gx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat==="channelsLast",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=P().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type==="SAME"||t.padInfo.type==="VALID")))return Rie({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=P().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(P().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return Die({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:"int32",data:[t.filterHeight,t.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[t.strideHeight,t.strideWidth]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new dx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[E]},{type:"int32",data:[R]});let D=o.adapterInfo.isIntel();g=new mx(t,_,E,R,p,i,u,D)}let w=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),w.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),w.push(s)),S.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of w)o.disposeData(_.dataId);return k}function Aie(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return gx({x:n,filter:s,convInfo:m,backend:o})}var Wz={kernelName:en,backendName:"webgpu",kernelFunc:Aie};var xx=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=` - ${K()} { + `}};function gx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function mue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat==="channelsLast",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=gx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=gx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Ap({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function due({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b==="channelsLast",S=p*u*c,k=h*f,_=C?[t.batchSize,k,S]:[t.batchSize,S,k],E=new hx(_,C),R=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=gx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=gx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=Ap({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function xx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat==="channelsLast",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type==="SAME"||t.padInfo.type==="VALID")))return mue({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return due({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:"int32",data:[t.filterHeight,t.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[t.strideHeight,t.strideWidth]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new fx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[E]},{type:"int32",data:[R]});let D=o.adapterInfo.isIntel();g=new dx(t,_,E,R,p,i,u,D)}let C=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function fue(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return xx({x:n,filter:s,convInfo:m,backend:o})}var dV={kernelName:tn,backendName:"webgpu",kernelFunc:fue};var yx=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=` + ${H()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; @@ -6625,7 +6638,7 @@ return a / b;`,QJ=` `;return this.isVec4?` ${n} `:` - ${K("index")} { + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6666,8 +6679,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}},yx=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` - ${K("index")} { + `}},bx=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; @@ -6707,9 +6720,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}},bx=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, - outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` - ${K("index")} { + `}},Cx=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, + outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wF = coords.x; @@ -6748,9 +6761,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}},Cx=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, - outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` - ${K("index")} { + `}},wx=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, + outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -6802,7 +6815,7 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}};function Fie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new yx(m),f=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]},{type:"int32",data:[m.inHeight]},{type:"int32",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var Uz={kernelName:$i,backendName:"webgpu",kernelFunc:Fie};function Pie(r=4){let e=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + `}};function hue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new bx(m),f=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]},{type:"int32",data:[m.inHeight]},{type:"int32",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var fV={kernelName:Ai,backendName:"webgpu",kernelFunc:hue};function gue(r=4){let e=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); @@ -6865,11 +6878,11 @@ return a / b;`,QJ=` col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value; } - }`}var wx=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=im(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=um(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Tp(this.elementsPerThread,this.workgroupSize):_p(this.elementsPerThread,this.workgroupSize);return` - ${Pie(this.isVec4?4:1)} + }`}var Sx=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=um(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Rp(this.elementsPerThread,this.workgroupSize):Dp(this.elementsPerThread,this.workgroupSize);return` + ${gue(this.isVec4?4:1)} ${e} - `}};function Oie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(P().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.dataFormat!=="channelsLast")f=new xx(m);else{f=new wx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return t.runWebGPUProgram(f,[n,s],"float32",d)}var Gz={kernelName:tn,backendName:"webgpu",kernelFunc:Oie};var Sx=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` - ${K("index")} { + `}};function xue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.dataFormat!=="channelsLast")f=new yx(m);else{f=new Sx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return t.runWebGPUProgram(f,[n,s],"float32",d)}var hV={kernelName:rn,backendName:"webgpu",kernelFunc:xue};var Ix=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords.x; @@ -6950,8 +6963,8 @@ return a / b;`,QJ=` } setOutputAtIndex(index, dotProd); } - }`}};function Mie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new Sx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var Hz={kernelName:rn,backendName:"webgpu",kernelFunc:Mie};function Lie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=C.computeConv3DInfo(n.shape,p,a,1,i),c=new bx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var Kz={kernelName:za,backendName:"webgpu",kernelFunc:Lie};function Bie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=C.computeConv3DInfo(p,s.shape,a,1,i),c=new Cx(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var qz={kernelName:on,backendName:"webgpu",kernelFunc:Bie};var zie=xe({opType:Q.COS}),jz={kernelName:nn,backendName:"webgpu",kernelFunc:zie};var Vie=xe({opType:Q.COSH}),Xz={kernelName:sn,backendName:"webgpu",kernelFunc:Vie};var Ix=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` - ${K("index")} { + }`}};function yue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new Ix(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var gV={kernelName:on,backendName:"webgpu",kernelFunc:yue};function bue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Cx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var xV={kernelName:Ka,backendName:"webgpu",kernelFunc:bue};function Cue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new wx(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var yV={kernelName:nn,backendName:"webgpu",kernelFunc:Cue};var wue=ye({opType:Z.COS}),bV={kernelName:sn,backendName:"webgpu",kernelFunc:wue};var Sue=ye({opType:Z.COSH}),CV={kernelName:an,backendName:"webgpu",kernelFunc:Sue};var vx=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${o}); @@ -7006,24 +7019,24 @@ return a / b;`,QJ=` } } } - `}};var Wie=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Ix(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],"float32",l)},Yz={kernelName:pn,backendName:"webgpu",kernelFunc:Wie};var Rp;(function(r){r.Prod="*",r.Sum="+"})(Rp||(Rp={}));var dm=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Rp.Prod?"1.0":"0.0",o=this.exclusive?t:`getX(${Qz(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` - ${K("index")} { + `}};var Iue=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new vx(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],"float32",l)},wV={kernelName:cn,backendName:"webgpu",kernelFunc:Iue};var Pp;(function(r){r.Prod="*",r.Sum="+"})(Pp||(Pp={}));var fm=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Pp.Prod?"1.0":"0.0",o=this.exclusive?t:`getX(${SV(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` + ${H("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${Zz(e,"coords",this.op)}; + let end = ${IV(e,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; - ${Zz(e,"coords",this.op)} = idx; - val ${this.op}= getX(${Qz(e,"coords",this.op)}); + ${IV(e,"coords",this.op)} = idx; + val ${this.op}= getX(${SV(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function Qz(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zz(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function vx(r,e,t,o,n,s){let a=e.shape.length,i=C.getAxesPermutation([o],a),p=e;i!=null&&(p=rr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new dm(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new dm(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=C.getUndoAxesPermutation(i),d=rr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function Uie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return vx(Rp.Prod,n,t,s,a,i)}var Jz={kernelName:an,backendName:"webgpu",kernelFunc:Uie};function Gie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return vx(Rp.Sum,n,t,s,a,i)}var eV={kernelName:un,backendName:"webgpu",kernelFunc:Gie};function Hie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=Vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new jc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var tV={kernelName:Qs,backendName:"webgpu",kernelFunc:Hie};var kx=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` - ${K("index")} { + `}};function SV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function IV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function kx(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=yr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new fm(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new fm(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=yr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return kx(Pp.Prod,n,t,s,a,i)}var vV={kernelName:un,backendName:"webgpu",kernelFunc:vue};function kue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return kx(Pp.Sum,n,t,s,a,i)}var kV={kernelName:pn,backendName:"webgpu",kernelFunc:kue};function Nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=Vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Xc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var NV={kernelName:ta,backendName:"webgpu",kernelFunc:Nue};var Nx=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -7042,7 +7055,7 @@ return a / b;`,QJ=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Kie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new kx(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var rV={kernelName:cn,backendName:"webgpu",kernelFunc:Kie};var Nx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Tue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new Nx(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var TV={kernelName:ln,backendName:"webgpu",kernelFunc:Tue};var Tx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` ${dr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${o}>; @@ -7056,7 +7069,7 @@ return a / b;`,QJ=` return value; } - ${K()} { + ${H()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pads; @@ -7099,12 +7112,12 @@ return a / b;`,QJ=` value = fma(xVal, wVal, value); } } - ${jr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};var Yc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` + `}};var Qc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=K(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` ${dr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); @@ -7114,11 +7127,16 @@ return a / b;`,QJ=` return value; } - ${K()} { - let batch = i32(globalId.z) / uniforms.outShape[1]; - let r = i32(globalId.z) % uniforms.outShape[1]; - let c = i32(globalId.y) * ${this.workPerThread}; - let d1 = i32(globalId.x) * 4; + ${H("index")} { + let width0 = uniforms.outShape[3] / ${this.outputComponent}; + let d1 = (index % width0) * ${this.outputComponent}; + var index1 = index / width0; + let width1 = uniforms.virtualWidth / ${this.workPerThread}; + let c = (index1 % width1) * ${this.workPerThread}; + index1 = index1 / width1; + let r = index1 % uniforms.outShape[1]; + let batch = index1 / uniforms.outShape[1]; + let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads; let xRCorner = xRCCorner.x; @@ -7149,16 +7167,16 @@ return a / b;`,QJ=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${jr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}};var Qc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, - filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` + `}};var Zc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, + filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${dr(this.activation,this.hasPreluActivation,!1,4)} - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -7217,13 +7235,13 @@ return a / b;`,QJ=` } } } - ${jr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function qie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=C.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=C.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Nx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Yc(d):(g=new Qc(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var oV={kernelName:ln,backendName:"webgpu",kernelFunc:qie};var Tx=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, - outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` - ${K("index")} { + `}};function _ue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Tx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Qc(d),f.push({type:"int32",data:[g.virtualWidth]})):(g=new Zc(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var _V={kernelName:mn,backendName:"webgpu",kernelFunc:_ue};var _x=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, + outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; @@ -7257,9 +7275,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}},_x=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` - ${K("index")} { + `}},$x=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -7300,16 +7318,16 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}};function jie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=C.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Tx(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.inHeight]},{type:"int32",data:[l.inWidth]},{type:"int32",data:[l.batchSize]},{type:"int32",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],"float32",d)}var nV={kernelName:Ei,backendName:"webgpu",kernelFunc:jie};function Xie(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=C.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new _x(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var sV={kernelName:Ri,backendName:"webgpu",kernelFunc:Xie};var $x=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` - ${K("index")} { + `}};function $ue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new _x(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.inHeight]},{type:"int32",data:[l.inWidth]},{type:"int32",data:[l.batchSize]},{type:"int32",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],"float32",d)}var $V={kernelName:Fi,backendName:"webgpu",kernelFunc:$ue};function Eue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new $x(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var EV={kernelName:Pi,backendName:"webgpu",kernelFunc:Eue};var Ex=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); setOutputAtIndex(index, value); } } - `}};function Yie(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new $x(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var aV={kernelName:Zs,backendName:"webgpu",kernelFunc:Yie};var Ex=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` - ${K("index")} { + `}};function Rue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Ex(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var RV={kernelName:ra,backendName:"webgpu",kernelFunc:Rue};var Rx=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; let coords = getOutputCoords(); @@ -7340,9 +7358,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, curVal); } } - `}};function Qie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new Ex(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var iV={kernelName:mn,backendName:"webgpu",kernelFunc:Qie};var Rx=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(e.outShape),this.dispatch=q(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 + `}};function Due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new Rx(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var DV={kernelName:dn,backendName:"webgpu",kernelFunc:Due};var Dx=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=K(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${t} type.`);this.type=t,this.shaderKey="dilation2DBackpropInput"}getUserCode(){return` - ${K("index")} { + ${H("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; @@ -7381,12 +7399,12 @@ return a / b;`,QJ=` let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${Bs("&result[flatIndexIn]","value",this.type)} + ${Us("&result[flatIndexIn]","value",this.type)} } } - `}},Dx=class{constructor(e,t,o){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=Z(e.outShape),this.dispatch=q(this.dispatchLayout,e.outShape,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 + `}},Ax=class{constructor(e,t,o){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=K(this.dispatchLayout,e.outShape,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 types, does not support ${o} type.`);this.type=o,this.shaderKey="dilation2DBackpropFilter"}getUserCode(){return` - ${K("index")} { + ${H("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; @@ -7424,10 +7442,10 @@ return a / b;`,QJ=` let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${Bs("&result[flatIndexIn]","value",this.type)} + ${Us("&result[flatIndexIn]","value",this.type)} } } - `}};function Zie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=s.dtype,m=new Dx(c,s.shape,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var uV={kernelName:Ai,backendName:"webgpu",kernelFunc:Zie};function Jie(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=n.dtype,m=new Rx(c,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var pV={kernelName:Di,backendName:"webgpu",kernelFunc:Jie};var Wv=et({opType:fe.MUL,cpuKernelImpl:HB,supportsComplex:!0}),cV={kernelName:Kn,backendName:"webgpu",kernelFunc:Wv};function Uv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Yr(n,s,a,"sum",t)}var lV={kernelName:ys,backendName:"webgpu",kernelFunc:Uv};function eue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=C.decodeEinsumEquation(n,s.length);C.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=C.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Uv({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var mV={kernelName:Fi,backendName:"webgpu",kernelFunc:eue};var tue=xe({opType:Q.ELU}),dV={kernelName:fn,backendName:"webgpu",kernelFunc:tue};var rue=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new bi(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},fV={kernelName:Va,backendName:"webgpu",kernelFunc:rue};var oue=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:EB}),hV={kernelName:hn,backendName:"webgpu",kernelFunc:oue};var nue=xe({opType:Q.ERF}),gV={kernelName:Wa,backendName:"webgpu",kernelFunc:nue};var sue=xe({opType:Q.EXP,cpuKernelImpl:RB,dtype:"float32"}),xV={kernelName:gn,backendName:"webgpu",kernelFunc:sue};function Ax(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var yV={kernelName:Js,backendName:"webgpu",kernelFunc:Ax};var aue=xe({opType:Q.EXPM1,cpuKernelImpl:DB}),bV={kernelName:xn,backendName:"webgpu",kernelFunc:aue};var fm=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=s.dtype,m=new Ax(c,s.shape,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var AV={kernelName:Mi,backendName:"webgpu",kernelFunc:Aue};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=n.dtype,m=new Dx(c,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=Vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var FV={kernelName:Oi,backendName:"webgpu",kernelFunc:Fue};var Qv=et({opType:fe.MUL,cpuKernelImpl:gz,supportsComplex:!0}),PV={kernelName:Xn,backendName:"webgpu",kernelFunc:Qv};function Zv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Jr(n,s,a,"sum",t)}var OV={kernelName:Ss,backendName:"webgpu",kernelFunc:Zv};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Zv({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var MV={kernelName:Li,backendName:"webgpu",kernelFunc:Pue};var Oue=ye({opType:Z.ELU}),LV={kernelName:hn,backendName:"webgpu",kernelFunc:Oue};var Mue=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Si(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},BV={kernelName:qa,backendName:"webgpu",kernelFunc:Mue};var Lue=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:tz}),zV={kernelName:xn,backendName:"webgpu",kernelFunc:Lue};var Bue=ye({opType:Z.ERF}),VV={kernelName:gn,backendName:"webgpu",kernelFunc:Bue};var zue=ye({opType:Z.EXP,cpuKernelImpl:rz,dtype:"float32"}),WV={kernelName:yn,backendName:"webgpu",kernelFunc:zue};function Fx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var UV={kernelName:oa,backendName:"webgpu",kernelFunc:Fx};var Vue=ye({opType:Z.EXPM1,cpuKernelImpl:oz}),GV={kernelName:bn,backendName:"webgpu",kernelFunc:Vue};var hm=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -7454,14 +7472,14 @@ return a / b;`,QJ=` return result; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function Fx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new fm("real",u),l=new fm("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=fo({inputs:{real:g,imag:x},backend:t});i.push(b);let w=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),w}function iue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Fx(o,!1,t)}var CV={kernelName:Pi,backendName:"webgpu",kernelFunc:iue};var Px=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` - ${K("index")} { + `}};function Px(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new hm("real",u),l=new hm("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function Wue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Px(o,!1,t)}var HV={kernelName:Bi,backendName:"webgpu",kernelFunc:Wue};var Ox=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; @@ -7469,9 +7487,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, outputValue); } } - `}};var wV={kernelName:yn,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Px(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var uue=xe({opType:Q.FLOOR,cpuKernelImpl:AB}),SV={kernelName:bn,backendName:"webgpu",kernelFunc:uue};var pue=et({opType:fe.INT_DIV,cpuKernelImpl:FB,dtype:"int32"}),IV={kernelName:Cn,backendName:"webgpu",kernelFunc:pue};var Ox=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}};var KV={kernelName:Cn,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Uue=ye({opType:Z.FLOOR,cpuKernelImpl:nz}),qV={kernelName:wn,backendName:"webgpu",kernelFunc:Uue};var Gue=et({opType:fe.INT_DIV,cpuKernelImpl:sz,dtype:"int32"}),jV={kernelName:Sn,backendName:"webgpu",kernelFunc:Gue};var Mx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; - ${K("index")} { + ${H("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); @@ -7481,8 +7499,8 @@ return a / b;`,QJ=` } } } - `}};var vV={kernelName:$u,backendName:"webgpu",kernelFunc:cue},Zc,Gv=P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function cue(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!="undefined"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d)b={width:c,height:l,format:null,usage:null,texture:t.device.importExternalTexture({source:n})};else{if(f){let L=P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Zc==null||L!==Gv)&&(Gv=L,Zc=document.createElement("canvas").getContext("2d",{willReadFrequently:Gv})),Zc.canvas.width=c,Zc.canvas.height=l,Zc.drawImage(n,0,0,c,l),n=Zc.canvas}let F=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O="rgba8unorm",M=t.textureManager.acquireTexture(m[1],m[0],O,F);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:O,usage:F,texture:M}}let w=y.sizeFromShape(m),S=y.computeStrides(m),k=new Ox(m,s,d),_=[{type:"uint32",data:[w]},{type:"uint32",data:[s]},{type:"uint32",data:[...S]}],E=t.makeTensorInfo([l,c],"int32"),R=t.tensorMap.get(E.dataId);R.resourceInfo=b;let D=t.runWebGPUProgram(k,[E],"int32",_);return t.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,w=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}};var kV={kernelName:wn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Mx(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function lue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return gx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var NV={kernelName:Co,backendName:"webgpu",kernelFunc:lue};function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(C.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=C.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let w=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?S=new Yc(h,x,m,b):(S=new Qc(h,x,m,b),w.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(w.push({type:"float32",data:[d]}),S.uniforms+=" alpha : f32,"),t.runWebGPUProgram(S,g,"float32",w)}var TV={kernelName:wo,backendName:"webgpu",kernelFunc:mue};var Lx=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Nt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` - ${K("index")} { + `}};var YV={kernelName:In,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Lx(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return xx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var QV={kernelName:Io,backendName:"webgpu",kernelFunc:Kue};function que(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Qc(h,x,m,b),C.push({type:"int32",data:[S.virtualWidth]})):(S=new Zc(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),S.uniforms+=" alpha : f32,"),t.runWebGPUProgram(S,g,"float32",C)}var ZV={kernelName:vo,backendName:"webgpu",kernelFunc:que};var Bx=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ht(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; @@ -7508,8 +7526,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function due(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=C.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=t.readSync(n.dataId),w=t.bufferSync(o),S=PB(b,w,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Lx(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var _V={kernelName:Sn,backendName:"webgpu",kernelFunc:due};var Bx=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=fue(this.aShape);return` - ${K("index")} { + `}};function jue(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=t.readSync(n.dataId),C=t.bufferSync(o),S=az(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Bx(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var JV={kernelName:vn,backendName:"webgpu",kernelFunc:jue};var zx=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=Xue(this.aShape);return` + ${H("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); @@ -7517,13 +7535,13 @@ return a / b;`,QJ=` setOutputAtIndex(index, inBounds * getA(${e})); } } - `}};function fue(r){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Bx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var $V={kernelName:ta,backendName:"webgpu",kernelFunc:Hv};var hue=et({opType:fe.GREATER,cpuKernelImpl:LB,dtype:"bool"}),EV={kernelName:In,backendName:"webgpu",kernelFunc:hue};var gue=et({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:MB}),RV={kernelName:vn,backendName:"webgpu",kernelFunc:gue};function xue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Fx(o,!0,t)}var DV={kernelName:Oi,backendName:"webgpu",kernelFunc:xue};var yue=xe({opType:Q.IS_FINITE,dtype:"bool"}),AV={kernelName:kn,backendName:"webgpu",kernelFunc:yue};var bue=xe({opType:Q.IS_INF,dtype:"bool"}),FV={kernelName:Nn,backendName:"webgpu",kernelFunc:bue};var Cue=xe({opType:Q.IS_NAN,dtype:"bool"}),PV={kernelName:Tn,backendName:"webgpu",kernelFunc:Cue};function wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Xr(n.shape,Q.LEAKYRELU,"alpha : f32,");return t.runWebGPUProgram(i,[n],"float32",a)}var OV={kernelName:_n,backendName:"webgpu",kernelFunc:wue};var Sue=et({opType:fe.LESS,dtype:"bool",cpuKernelImpl:zB}),MV={kernelName:$n,backendName:"webgpu",kernelFunc:Sue};var Iue=et({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:BB}),LV={kernelName:En,backendName:"webgpu",kernelFunc:Iue};var zx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` - ${K("index")} { + `}};function Xue(r){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new zx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var eW={kernelName:sa,backendName:"webgpu",kernelFunc:e0};var Yue=et({opType:fe.GREATER,cpuKernelImpl:pz,dtype:"bool"}),tW={kernelName:kn,backendName:"webgpu",kernelFunc:Yue};var Que=et({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:uz}),rW={kernelName:Nn,backendName:"webgpu",kernelFunc:Que};function Zue(r){let{inputs:e,backend:t}=r,{input:o}=e;return Px(o,!0,t)}var oW={kernelName:zi,backendName:"webgpu",kernelFunc:Zue};var Jue=ye({opType:Z.IS_FINITE,dtype:"bool"}),nW={kernelName:Tn,backendName:"webgpu",kernelFunc:Jue};var epe=ye({opType:Z.IS_INF,dtype:"bool"}),sW={kernelName:_n,backendName:"webgpu",kernelFunc:epe};var tpe=ye({opType:Z.IS_NAN,dtype:"bool"}),aW={kernelName:$n,backendName:"webgpu",kernelFunc:tpe};function rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Zr(n.shape,Z.LEAKYRELU,"alpha : f32,");return t.runWebGPUProgram(i,[n],"float32",a)}var iW={kernelName:En,backendName:"webgpu",kernelFunc:rpe};var ope=et({opType:fe.LESS,dtype:"bool",cpuKernelImpl:lz}),uW={kernelName:Rn,backendName:"webgpu",kernelFunc:ope};var npe=et({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:cz}),pW={kernelName:Dn,backendName:"webgpu",kernelFunc:npe};var Vx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } - `}};function vue(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new zx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return e.runWebGPUProgram(i,[],"float32",p)}var BV={kernelName:Rn,backendName:"webgpu",kernelFunc:vue};var kue=xe({opType:Q.LOG,cpuKernelImpl:VB}),zV={kernelName:Dn,backendName:"webgpu",kernelFunc:kue};var Nue=xe({opType:Q.LOG1P}),VV={kernelName:An,backendName:"webgpu",kernelFunc:Nue};var Tue=et({opType:fe.LOGICAL_AND,dtype:"bool"}),WV={kernelName:Fn,backendName:"webgpu",kernelFunc:Tue};var _ue=xe({opType:Q.LOGICAL_NOT}),UV={kernelName:Pn,backendName:"webgpu",kernelFunc:_ue};var $ue=et({opType:fe.LOGICAL_OR}),GV={kernelName:On,backendName:"webgpu",kernelFunc:$ue};var HV=` + `}};function spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Vx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return e.runWebGPUProgram(i,[],"float32",p)}var cW={kernelName:An,backendName:"webgpu",kernelFunc:spe};var ape=ye({opType:Z.LOG,cpuKernelImpl:mz}),lW={kernelName:Fn,backendName:"webgpu",kernelFunc:ape};var ipe=ye({opType:Z.LOG1P}),mW={kernelName:Pn,backendName:"webgpu",kernelFunc:ipe};var upe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),dW={kernelName:On,backendName:"webgpu",kernelFunc:upe};var ppe=ye({opType:Z.LOGICAL_NOT}),fW={kernelName:Mn,backendName:"webgpu",kernelFunc:ppe};var cpe=et({opType:fe.LOGICAL_OR}),hW={kernelName:Ln,backendName:"webgpu",kernelFunc:cpe};var gW=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -7533,8 +7551,8 @@ return a / b;`,QJ=` } else { powValue = exp(log(basis) * (-uniforms.beta)); } -`,Vx=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` - ${K("index")} { +`,Wx=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -7551,17 +7569,17 @@ return a / b;`,QJ=` sum = sum + z * z; } } - ${HV} + ${gW} setOutputAtIndex(index, x * powValue); } } - `}},Wx=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=q(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` + `}},Ux=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=K(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` var lrnSub: array; const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; const maxAllowRadius = ${this.maxAllowRadius}; - ${K()} { + ${H()} { let localDepth = i32(localId.x); let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; let xDepth = workgroupDepth + localDepth - maxAllowRadius; @@ -7584,12 +7602,12 @@ return a / b;`,QJ=` let z = lrnSub[index + i]; sum = sum + z * z; } - ${HV} + ${gW} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } - } `}};function Eue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Vx(n.shape):u=new Wx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var KV={kernelName:Mn,backendName:"webgpu",kernelFunc:Eue};var Ux=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` - ${K("index")} { + } `}};function lpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Wx(n.shape):u=new Ux(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var xW={kernelName:Bn,backendName:"webgpu",kernelFunc:lpe};var Gx=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -7638,9 +7656,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, result); } } - `}};function Rue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Ux(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var qV={kernelName:Ua,backendName:"webgpu",kernelFunc:Rue};var Due=et({opType:fe.MAX,cpuKernelImpl:UB}),jV={kernelName:Bn,backendName:"webgpu",kernelFunc:Due};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=C.computePool2DInfo(n.shape,s,a,u,i,p);return ox(n,c,"max",t)}var XV={kernelName:zn,backendName:"webgpu",kernelFunc:Aue};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=C.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,"max"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var YV={kernelName:ra,backendName:"webgpu",kernelFunc:Fue};var Gx=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, - outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` - ${K("index")} { + `}};function mpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Gx(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var yW={kernelName:ja,backendName:"webgpu",kernelFunc:mpe};var dpe=et({opType:fe.MAX,cpuKernelImpl:fz}),bW={kernelName:Vn,backendName:"webgpu",kernelFunc:dpe};function fpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return nx(n,c,"max",t)}var CW={kernelName:Wn,backendName:"webgpu",kernelFunc:fpe};function hpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new ku(l,"max"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var wW={kernelName:aa,backendName:"webgpu",kernelFunc:hpe};var Hx=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -7683,9 +7701,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}},Hx=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, - outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` - ${K("index")} { + `}},Kx=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; @@ -7741,8 +7759,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, dotProd); } } - `}};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,l,u,c),d=new wu(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],"int32",f),g=new Hx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var QV={kernelName:Li,backendName:"webgpu",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;cm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=C.computePool2DInfo(i.shape,p,u,1,c,l),d=new Da(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],"int32",f),g=new Gx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var ZV={kernelName:Hp,backendName:"webgpu",kernelFunc:Oue};function Mue(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Da(c,"max",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Da(c,"max",!0,!0,i);let f=t.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var JV={kernelName:Bi,backendName:"webgpu",kernelFunc:Mue};function Lue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Yr(n,s,a,"min",t)}var eW={kernelName:Wn,backendName:"webgpu",kernelFunc:Lue};var Bue=et({opType:fe.MIN,cpuKernelImpl:GB}),tW={kernelName:Un,backendName:"webgpu",kernelFunc:Bue};var Kx=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Nt(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${K("index")} { + `}};function gpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new ku(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],"int32",f),g=new Kx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var SW={kernelName:Ui,backendName:"webgpu",kernelFunc:gpe};function xpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;lm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ma(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],"int32",f),g=new Hx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var IW={kernelName:Wi,backendName:"webgpu",kernelFunc:xpe};function ype(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ma(c,"max",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ma(c,"max",!0,!0,i);let f=t.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var vW={kernelName:ia,backendName:"webgpu",kernelFunc:ype};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Jr(n,s,a,"min",t)}var kW={kernelName:Gn,backendName:"webgpu",kernelFunc:bpe};var Cpe=et({opType:fe.MIN,cpuKernelImpl:hz}),NW={kernelName:Hn,backendName:"webgpu",kernelFunc:Cpe};var qx=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=ht(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + ${H("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${o}); @@ -7758,7 +7776,7 @@ return a / b;`,QJ=` setOutputAtIndex(index, getX(${p})); } } - `}};var rW={kernelName:Gn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new Kx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var zue=et({opType:fe.MOD}),oW={kernelName:Ga,backendName:"webgpu",kernelFunc:zue};var qx=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` + `}};var TW={kernelName:Kn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new qx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var wpe=et({opType:fe.MOD}),_W={kernelName:qn,backendName:"webgpu",kernelFunc:wpe};var jx=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { @@ -7769,7 +7787,7 @@ return a / b;`,QJ=` return fract((p3.x + p3.y) * p3.z); } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -7791,12 +7809,12 @@ return a / b;`,QJ=` setOutputAtIndexI32(index, uniforms.numOutcomes - 1); } } - `}};var jx=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` + `}};var Xx=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` var buf : array; var rowMaxShared : f32; var rowSumShared : f32; const blockSize = ${this.workgroupSize[0]}; - ${K("index")} { + ${H("index")} { let row = index / blockSize; let tid = i32(localId.x); let cols = uniforms.outShape[1]; @@ -7850,31 +7868,32 @@ return a / b;`,QJ=` setOutputAtCoords(row, col, value); } } - `}};function Kv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new jx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var nW={kernelName:bs,backendName:"webgpu",kernelFunc:Kv};function Vue(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Kv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new qx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=t.runWebGPUProgram(l,[p],"int32",m);return i||t.disposeData(p.dataId),d}var sW={kernelName:Hn,backendName:"webgpu",kernelFunc:Vue};function Wue(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=KB(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Xr(o.shape,Q.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var aW={kernelName:oa,backendName:"webgpu",kernelFunc:Wue};function Uue(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var iW={kernelName:jn,backendName:"webgpu",kernelFunc:Uue};function Gue(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var uW={kernelName:Xn,backendName:"webgpu",kernelFunc:Gue};var Xx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` - ${K("index")} { + `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Xx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var $W={kernelName:Is,backendName:"webgpu",kernelFunc:t0};function Spe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:t0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new jx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=t.runWebGPUProgram(l,[p],"int32",m);return i||t.disposeData(p.dataId),d}var EW={kernelName:jn,backendName:"webgpu",kernelFunc:Spe};function Ipe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=xz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Zr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var RW={kernelName:ua,backendName:"webgpu",kernelFunc:Ipe};function vpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var DW={kernelName:Qn,backendName:"webgpu",kernelFunc:vpe};function kpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var AW={kernelName:Zn,backendName:"webgpu",kernelFunc:kpe};var Yx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + ${H("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Xx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var pW={kernelName:Yn,backendName:"webgpu",kernelFunc:Hue};function hm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ci({inputs:{input:o},backend:t}),s=hm({inputs:{x:n},backend:t}),a=Ep({inputs:{input:o},backend:t}),i=hm({inputs:{x:a},backend:t}),p=fo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var cW={kernelName:fa,backendName:"webgpu",kernelFunc:hm};function lW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ci({inputs:{input:o},backend:t}),s=lW({inputs:{x:n},backend:t}),a=Ep({inputs:{input:o},backend:t}),i=hm({inputs:{x:a},backend:t}),p=fo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var mW={kernelName:na,backendName:"webgpu",kernelFunc:lW};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Ax({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Ax({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=Vv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var dW={kernelName:sa,backendName:"webgpu",kernelFunc:Kue};var Yx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Nt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(","),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",p=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${K("index")} { + `}};function Npe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Yx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var FW={kernelName:Jn,backendName:"webgpu",kernelFunc:Npe};function gm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ii({inputs:{input:o},backend:t}),s=gm({inputs:{x:n},backend:t}),a=Fp({inputs:{input:o},backend:t}),i=gm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var PW={kernelName:ba,backendName:"webgpu",kernelFunc:gm};function OW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ii({inputs:{input:o},backend:t}),s=OW({inputs:{x:n},backend:t}),a=Fp({inputs:{input:o},backend:t}),i=gm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var MW={kernelName:pa,backendName:"webgpu",kernelFunc:OW};function Tpe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Fx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Fx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=Yv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var LW={kernelName:ca,backendName:"webgpu",kernelFunc:Tpe};function r0(r,e=!1){let t=r.length,o=ht(t),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(","),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:""}`).join(","),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?"any(paddedCoords < start)":"paddedCoords < start",u=t>1?"any(paddedCoords >= end)":"paddedCoords >= end",c=t>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,t):"coords";return` + let start = ${a}; + let end = ${i}; + if (${p} || ${u}) { + setOutputAtIndex(index, ${e?0:"uniforms.constantValue"}); + } else { + let coords = paddedCoords - start; + setOutputAtIndex(index, getX(${c})); + } + `}var Qx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { - let start = ${s}; - let end = ${a}; - let outC = getCoordsFromIndex(index); - - if (${i} || ${p}) { - setOutputAtIndex(index, uniforms.constantValue); - } else { - let coords = outC - start; - setOutputAtIndex(index, getX(${u})); - } + let paddedCoords = getCoordsFromIndex(index); + ${r0(this.xShape)} } } - `}};var qv=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new Yx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},fW={kernelName:Qn,backendName:"webgpu",kernelFunc:qv};var que=et({opType:fe.POW}),hW={kernelName:Zn,backendName:"webgpu",kernelFunc:que};function jue(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new bi(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var gW={kernelName:Jn,backendName:"webgpu",kernelFunc:jue};function Xue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Yr(n,s,a,"prod",t)}var xW={kernelName:es,backendName:"webgpu",kernelFunc:Xue};var Yue=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=XB(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},yW={kernelName:aa,backendName:"webgpu",kernelFunc:Yue};var Que=et({opType:fe.DIV}),bW={kernelName:dn,backendName:"webgpu",kernelFunc:Que};var Zue=xe({opType:Q.RECIPROCAL}),CW={kernelName:ts,backendName:"webgpu",kernelFunc:Zue};var Jue=xe({opType:Q.RELU}),wW={kernelName:rs,backendName:"webgpu",kernelFunc:Jue};var epe=xe({opType:Q.RELU6}),SW={kernelName:ss,backendName:"webgpu",kernelFunc:epe};var Qx=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` - ${K("index")} { + `}};var _pe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new Qx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},BW={kernelName:es,backendName:"webgpu",kernelFunc:_pe};var $pe=et({opType:fe.POW}),zW={kernelName:ts,backendName:"webgpu",kernelFunc:$pe};function Epe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Si(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var VW={kernelName:rs,backendName:"webgpu",kernelFunc:Epe};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Jr(n,s,a,"prod",t)}var WW={kernelName:os,backendName:"webgpu",kernelFunc:Rpe};var Dpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Cz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},UW={kernelName:la,backendName:"webgpu",kernelFunc:Dpe};var Ape=et({opType:fe.DIV}),GW={kernelName:fn,backendName:"webgpu",kernelFunc:Ape};var Fpe=ye({opType:Z.RECIPROCAL}),HW={kernelName:ns,backendName:"webgpu",kernelFunc:Fpe};var Ppe=ye({opType:Z.RELU}),KW={kernelName:ss,backendName:"webgpu",kernelFunc:Ppe};var Ope=ye({opType:Z.RELU6}),qW={kernelName:us,backendName:"webgpu",kernelFunc:Ope};var Zx=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -7916,9 +7935,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, newValue); } } - `}};function tpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new Qx(n.shape,p,u);return t.runWebGPUProgram(f,[n],"float32",d)}var IW={kernelName:ns,backendName:"webgpu",kernelFunc:tpe};var Zx=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, - invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return` - ${K("index")} { + `}};function Mpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new Zx(n.shape,p,u);return t.runWebGPUProgram(f,[n],"float32",d)}var jW={kernelName:is,backendName:"webgpu",kernelFunc:Mpe};var Jx=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, + invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -7991,8 +8010,8 @@ return a / b;`,QJ=` setOutputAtIndex(index, accumulator); } } - `}};function rpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,w=new Zx(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[d]},{type:"float32",data:[f]},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return t.runWebGPUProgram(w,[s],s.dtype,S)}var vW={kernelName:qa,backendName:"webgpu",kernelFunc:rpe};var Jx=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` - ${K("index")} { + `}};function Lpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new Jx(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[d]},{type:"float32",data:[f]},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var XW={kernelName:Qa,backendName:"webgpu",kernelFunc:Lpe};var ey=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -8022,9 +8041,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, newValue); } } - `}};function ope(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new Jx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var kW={kernelName:os,backendName:"webgpu",kernelFunc:ope};var ey=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, - winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return` - ${K("index")} { + `}};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new ey(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var YW={kernelName:as,backendName:"webgpu",kernelFunc:Bpe};var ty=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, + winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; @@ -8082,7 +8101,7 @@ return a / b;`,QJ=` setOutputAtIndex(index, accumulator); } } - `}};function npe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,w=new ey(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return t.runWebGPUProgram(w,[s],s.dtype,S)}var NW={kernelName:Ka,backendName:"webgpu",kernelFunc:npe};var ty=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function zpe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ty(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var QW={kernelName:Ya,backendName:"webgpu",kernelFunc:zpe};var ry=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -8104,7 +8123,7 @@ return a / b;`,QJ=` return reverseCoords; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); @@ -8112,9 +8131,9 @@ return a / b;`,QJ=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function spe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new ty(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var TW={kernelName:as,backendName:"webgpu",kernelFunc:spe};var ry=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function Vpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new ry(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var ZW={kernelName:ps,backendName:"webgpu",kernelFunc:Vpe};var oy=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * @@ -8133,7 +8152,7 @@ return a / b;`,QJ=` setOutputAtIndex(index, outputValue); } } - `}};var _W={kernelName:_s,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new ry(o.shape,s),[u,c]=C.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var ape=xe({opType:Q.ROUND}),$W={kernelName:is,backendName:"webgpu",kernelFunc:ape};var ipe=xe({opType:Q.RSQRT,cpuKernelImpl:YB}),EW={kernelName:us,backendName:"webgpu",kernelFunc:ipe};var Aa=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=Z(e),this.dispatch=q(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Nt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` + `}};var JW={kernelName:Ds,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new oy(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Wpe=ye({opType:Z.ROUND}),eU={kernelName:cs,backendName:"webgpu",kernelFunc:Wpe};var Upe=ye({opType:Z.RSQRT,cpuKernelImpl:wz}),tU={kernelName:ls,backendName:"webgpu",kernelFunc:Upe};var La=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=K(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=ht(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -8150,7 +8169,7 @@ return a / b;`,QJ=` } `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(", ")})`;return` ${s} - ${K("index")} { + ${H("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; @@ -8159,12 +8178,12 @@ return a / b;`,QJ=` flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = - ${kp(this.type)}(${i}); + ${$p(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices?Bs("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices?Us("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function upe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=C.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=Vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],w=new Aa(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(w,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var RW={kernelName:ps,backendName:"webgpu",kernelFunc:upe};var oy=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` + }`}};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=Vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new La(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var rU={kernelName:ms,backendName:"webgpu",kernelFunc:Gpe};var ny=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -8179,15 +8198,15 @@ return a / b;`,QJ=` return right; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function ppe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new oy([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],"int32",p)}var DW={kernelName:ls,backendName:"webgpu",kernelFunc:ppe};var ny=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},zW={kernelName:ca,backendName:"webgpu",kernelFunc:xpe};var sy=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ht(this.outputShape.length),t=Gv(this.newDim);return` + ${im(this.paddedXShape,"PaddedX")} + ${H("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape); + let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); + ${r0(this.xShape,!0)} + } + } + `}};var Jpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new ay(n.shape,u,p,c,l,d.length),h=[{type:"int32",data:c},{type:"int32",data:d}];p.map(b=>h.push({type:"int32",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},lU={kernelName:ha,backendName:"webgpu",kernelFunc:Jpe};var iy=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let 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WW={kernelName:Cs,backendName:"webgpu",kernelFunc:bpe};function Cpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=zs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var UW={kernelName:la,backendName:"webgpu",kernelFunc:Cpe};var wpe=xe({opType:Q.SQRT}),GW={kernelName:xs,backendName:"webgpu",kernelFunc:wpe};var HW={kernelName:Gi,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Xr(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Spe=et({opType:fe.SQUARED_DIFFERENCE}),KW={kernelName:ws,backendName:"webgpu",kernelFunc:Spe};function Ipe({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Xr(o.shape,Q.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var qW={kernelName:yo,backendName:"webgpu",kernelFunc:Ipe};var ay=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Nt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` - ${K("index")} { + `}};function ece(r,e=""){if(r>=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let 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f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=xm({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let R=new La([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new La([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new La([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),E}var 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yU={kernelName:wo,backendName:"webgpu",kernelFunc:sce};var uy=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ht(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } - `}};function 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ice(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Nz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var CU={kernelName:xa,backendName:"webgpu",kernelFunc:ice};var uce=et({opType:fe.SUB,cpuKernelImpl:Tz,supportsComplex:!0}),wU={kernelName:Ts,backendName:"webgpu",kernelFunc:uce};var pce=ye({opType:Z.TAN}),SU={kernelName:_s,backendName:"webgpu",kernelFunc:pce};var cce=ye({opType:Z.TANH}),IU={kernelName:$s,backendName:"webgpu",kernelFunc:cce};function lce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=xm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new La([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach(E=>t.disposeData(E.dataId)),_}var vU={kernelName:ds,backendName:"webgpu",kernelFunc:lce};var py=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -8280,8 +8309,8 @@ return a / b;`,QJ=` } } } - `}},uy=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` - ${K("index")} { + `}},cy=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` + ${H("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -8339,7 +8368,7 @@ return a / b;`,QJ=` } } } - `}};function Jc(r,e){e!==null&&r.disposeData(e.dataId)}function eU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new iy(E),O=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,"int32",O),Jc(t,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new uy([c,k/2]),D=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],F=f;f=t.runWebGPUProgram(E,_,"int32",D),Jc(t,F);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=zs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),Jc(t,x);let b=Hv({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});Jc(t,l);let w=i.slice(0,-1);w.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:w},backend:t}),Jc(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:w},backend:t}),Jc(t,S),[b,f]}var tU={kernelName:Ns,backendName:"webgpu",kernelFunc:Epe};var py=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Z(this.outputShape),this.dispatch=q(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` + `}};function el(r,e){e!==null&&r.disposeData(e.dataId)}function kU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new py(E),O=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,"int32",O),el(t,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new cy([c,k/2]),D=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],P=f;f=t.runWebGPUProgram(E,_,"int32",D),el(t,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Gs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),el(t,x);let b=e0({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});el(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:t}),el(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:t}),el(t,S),[b,f]}var NU={kernelName:Es,backendName:"webgpu",kernelFunc:mce};var ly=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=K(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { @@ -8403,7 +8432,7 @@ return a / b;`,QJ=` return outputValue; } - ${K("index")} { + ${H("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; @@ -8455,9 +8484,9 @@ return a / b;`,QJ=` setOutputAtIndex(index, outputValue); } } - `}};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new py(g),b=a==="nearest"?1:2,w;switch(i){case"constant":w=1;break;case"reflect":w=2;break;case"wrap":w=3;break;case"nearest":w=4;break;default:w=1;break}let S=[{type:"int32",data:[b]},{type:"int32",data:[w]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",S)}var rU={kernelName:Ts,backendName:"webgpu",kernelFunc:Rpe};function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var oU={kernelName:da,backendName:"webgpu",kernelFunc:Dpe};var cy=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=Z(e),this.dispatch=q(this.dispatchLayout,e,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`UnsortedSegmentSum only supports float32 and int32 + `}};function dce(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new ly(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let S=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",S)}var TU={kernelName:Rs,backendName:"webgpu",kernelFunc:dce};function fce(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var _U={kernelName:ya,backendName:"webgpu",kernelFunc:fce};var my=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=K(this.dispatchLayout,e,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`UnsortedSegmentSum only supports float32 and int32 types, does not support ${o} type.`);this.type=o,this.shaderKey="unsortedSegmentSum"}getUserCode(){return` - ${K("index")} { + ${H("index")} { if (index < uniforms.xSize) { let coords = getXCoordsFromIndex(index); let b = coords[0]; @@ -8468,8 +8497,8 @@ return a / b;`,QJ=` let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments; let value = getX(b, inCol); - ${Bs("&result[flatIndex]","value",this.type)} + ${Us("&result[flatIndex]","value",this.type)} } } } - `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=C.getAxesPermutation([u],i),l=n;c!=null&&(l=rr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=C.getInnerMostAxes(1,i)[0]);let m=C.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=Vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new cy(f.shape,g,h),w=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,w,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=C.getUndoAxesPermutation(c);_=rr({inputs:{x:_},backend:t,attrs:{perm:E}})}return p.forEach(E=>t.disposeData(E.dataId)),_}var nU={kernelName:ji,backendName:"webgpu",kernelFunc:Ape};var Fpe=[IB,az,iz,uz,pz,cz,mz,dz,fz,hz,gz,xz,yz,bz,Cz,Iz,vz,kz,Nz,Tz,$z,Ez,Rz,Pz,Oz,Mz,kB,Bz,Vz,Wz,Uz,Gz,Hz,Kz,qz,jz,Xz,Yz,Jz,eV,tV,rV,nV,sV,oV,aV,iV,uV,pV,mV,dV,fV,hV,gV,xV,yV,bV,CV,wB,wV,vV,SV,IV,kV,NV,TV,_V,$V,EV,RV,vB,DV,zz,AV,FV,PV,OV,MV,LV,BV,VV,zV,WV,UV,GV,KV,qV,wz,jV,XV,ZV,YV,QV,JV,Sz,eW,tW,rW,oW,sW,cV,aW,iW,uW,Dz,pW,mW,dW,fW,hW,gW,xW,yW,Az,bW,CW,wW,SW,SB,IW,vW,kW,NW,TW,_W,$W,EW,RW,DW,AW,FW,PW,OW,MW,LW,_z,qW,jW,XW,nW,BW,zW,WW,UW,GW,HW,KW,YW,lV,QW,ZW,JW,VW,tU,rU,lz,oU,nU,cW];for(let r of Fpe)Ya(r);var sU="4.5.0",Ppe="4.5.0",Ope="4.5.0",Mpe="4.5.0",Lpe="4.5.0",Bpe="0.0.1-alpha.20",zpe={tfjs:sU,"tfjs-core":sU,"tfjs-converter":Ppe,"tfjs-backend-cpu":Ope,"tfjs-backend-webgl":Mpe,"tfjs-backend-wasm":Lpe,"tfjs-backend-webgpu":Bpe};export{Gs as Abs,zo as Acos,Vo as Acosh,Yu as AdadeltaOptimizer,Qu as AdagradOptimizer,Zu as AdamOptimizer,Ju as AdamaxOptimizer,no as Add,Wo as AddN,Uo as All,Go as Any,Hs as ArgMax,Ks as ArgMin,Ho as Asin,Ko as Asinh,qo as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,qs as AvgPool3D,Ni as AvgPool3DGrad,Gp as AvgPoolGrad,am as BackendWasm,Qo as BatchMatMul,js as BatchToSpaceND,Zo as Bincount,ml as BitwiseAnd,Xs as BroadcastArgs,Kpe as BroadcastTo,ho as Cast,Jo as Ceil,go as ClipByValue,Ti as Complex,_i as ComplexAbs,Ys as Concat,en as Conv2D,$i as Conv2DBackpropFilter,tn as Conv2DBackpropInput,rn as Conv3D,za as Conv3DBackpropFilterV2,on as Conv3DBackpropInputV2,nn as Cos,sn as Cosh,pn as CropAndResize,an as Cumprod,un as Cumsum,Lo as DataStorage,Qs as DenseBincount,cn as DepthToSpace,ln as DepthwiseConv2dNative,Ei as DepthwiseConv2dNativeBackpropFilter,Ri as DepthwiseConv2dNativeBackpropInput,Zs as Diag,mn as Dilation2D,Ai as Dilation2DBackpropFilter,Di as Dilation2DBackpropInput,WC as ENV,Fi as Einsum,fn as Elu,Va as EluGrad,cl as Environment,hn as Equal,Wa as Erf,gn as Exp,Js as ExpandDims,xn as Expm1,Pi as FFT,ea as Fill,yn as FlipLeftRight,bn as Floor,Cn as FloorDiv,$u as FromPixels,wn as FusedBatchNorm,Co as FusedConv2D,wo as FusedDepthwiseConv2D,xp as GPGPUContext,Sn as GatherNd,ta as GatherV2,Ol as GraphModel,In as Greater,vn as GreaterEqual,Oi as IFFT,xo as Identity,Mi as Imag,kn as IsFinite,Nn as IsInf,Tn as IsNan,ro as KernelBackend,Mn as LRN,Ua as LRNGrad,_n as LeakyRelu,$n as Less,En as LessEqual,Rn as LinSpace,Dn as Log,An as Log1p,qpe as LogSoftmax,Fn as LogicalAnd,Pn as LogicalNot,On as LogicalOr,m0 as LogicalXor,jpe as LowerBound,lu as MathBackendCPU,hu as MathBackendWebGL,Xpe as MatrixBandPart,Ln as Max,zn as MaxPool,ra as MaxPool3D,Li as MaxPool3DGrad,Hp as MaxPoolGrad,Bi as MaxPoolWithArgmax,Bn as Maximum,Vn as Mean,Wn as Min,Un as Minimum,Gn as MirrorPad,Ga as Mod,ep as MomentumOptimizer,Hn as Multinomial,Kn as Multiply,oa as Neg,jn as NonMaxSuppressionV3,Ha as NonMaxSuppressionV4,Xn as NonMaxSuppressionV5,qn as NotEqual,pw as OP_SCOPE_SUFFIX,Yn as OneHot,na as OnesLike,kr as Optimizer,Dl as OptimizerConstructors,sa as Pack,Qn as PadV2,Ype as Pool,Zn as Pow,Jn as Prelu,es as Prod,tp as RMSPropOptimizer,Kp as RaggedGather,qp as RaggedRange,jp as RaggedTensorToTensor,aa as Range,JC as Rank,zi as Real,dn as RealDiv,ts as Reciprocal,Et as Reduction,rs as Relu,ss as Relu6,ia as Reshape,ns as ResizeBilinear,qa as ResizeBilinearGrad,os as ResizeNearestNeighbor,Ka as ResizeNearestNeighborGrad,as as Reverse,_s as RotateWithOffset,is as Round,us as Rsqrt,ii as SGDOptimizer,ps as ScatterNd,ls as SearchSorted,ua as Select,ms as Selu,hs as Sigmoid,fs as Sign,ds as Sin,ja as Sinh,pa as Slice,bs as Softmax,gs as Softplus,ca as SpaceToBatchND,Vi as SparseFillEmptyRows,Xa as SparseReshape,Wi as SparseSegmentMean,Ui as SparseSegmentSum,Cs as SparseToDense,la as SplitV,xs as Sqrt,Gi as Square,ws as SquaredDifference,_u as StaticRegexReplace,yo as Step,Ss as StridedSlice,ma as StringNGrams,Hi as StringSplit,Ki as StringToHashBucketFast,Is as Sub,ys as Sum,vs as Tan,ks as Tanh,pt as Tensor,tt as TensorBuffer,cs as TensorScatterUpdate,so as Tile,Ns as TopK,Ts as Transform,ao as Transpose,qi as Unique,da as Unpack,ji as UnsortedSegmentSum,Qpe as UpperBound,Qa as Variable,Cu as WebGPUBackend,fa as ZerosLike,bo as _FusedMatMul,Zt as abs,ik as acos,uk as acosh,be as add,pk as addN,ck as all,lk as any,mk as argMax,dk as argMin,fk as asin,hk as asinh,gk as atan,xk as atan2,yk as atanh,cd as avgPool,wk as avgPool3d,Tme as backend,C as backend_util,Sk as basicLSTMCell,tu as batchNorm,vk as batchNorm2d,kk as batchNorm3d,Nk as batchNorm4d,ld as batchToSpaceND,md as bincount,Tk as bitwiseAnd,qq as booleanMaskAsync,_k as broadcastArgs,ru as broadcastTo,Sr as broadcast_util,MN as browser,me as buffer,Ye as cast,$k as ceil,Ek as clipByValue,Vr as clone,$r as complex,yt as concat,Rk as concat1d,Dk as concat2d,Ak as concat3d,Fk as concat4d,Pk as conv1d,ou as conv2d,Ok as conv2dTranspose,Mk as conv3d,Bk as conv3dTranspose,sce as copyRegisteredKernels,zk as cos,Vk as cosh,_l as cosineWindow,Wk as cumprod,Uk as cumsum,Ir as customGrad,Gk as denseBincount,bw as deprecationWarn,Hk as depthToSpace,ac as depthwiseConv2d,KX as deregisterOp,Zi as device_util,Kk as diag,qk as dilation2d,gme as disableDeprecationWarnings,Ot as dispose,xme as disposeVariables,Ke as div,Xk as divNoNan,Yk as dot,s6 as dropout,Qk as einsum,gd as elu,hme as enableDebugMode,fme as enableProdMode,Pw as enclosingPowerOfTwo,ur as engine,Zk as ensureShape,P as env,hd as equal,Jk as erf,r2 as euclideanNorm,ko as exp,oi as expandDims,o2 as expm1,xd as eye,pc as fft,Sa as fill,kme as findBackend,Nme as findBackendFactory,yd as floor,pd as floorDiv,hD as forceHalfFloat,Ow as fused,bd as gather,o6 as gatherND,of as gather_util,Ime as getBackend,HC as getGradient,fl as getKernel,Km as getKernelsForBackend,ese as getThreadsCount,qI as gpgpu_util,YH as grad,QH as grads,Bu as greater,Cd as greaterEqual,Hu as ifft,su as imag,uj as image,i6 as inTopKAsync,pi as io,Wd as irfft,n2 as isFinite,s2 as isInf,a2 as isNaN,Er as keep,Wt as kernel_impls,wd as leakyRelu,kl as less,ic as lessEqual,pj as linalg,i2 as linspace,W5 as loadGraphModel,U5 as loadGraphModelSync,u2 as localResponseNormalization,ni as log,Sd as log1p,p2 as logSigmoid,c2 as logSoftmax,kd as logSumExp,zu as logicalAnd,Nd as logicalNot,Td as logicalOr,l2 as logicalXor,cj as losses,m2 as lowerBound,Qe as matMul,PN as math,Ia as max,$d as maxPool,d2 as maxPool3d,f2 as maxPoolWithArgmax,Ed as maximum,Vu as mean,yme as memory,h2 as meshgrid,vl as min,Wu as minimum,g2 as mirrorPad,x2 as mod,y2 as moments,Yq as movingAverage,se as mul,b2 as multiRNNCell,C2 as multinomial,pr as neg,Kw as nextFrame,Lu as norm,Rd as notEqual,Tl as oneHot,va as ones,w2 as onesLike,N as op,S2 as outerProduct,ka as pad,I2 as pad1d,v2 as pad2d,k2 as pad3d,N2 as pad4d,T2 as pool,ri as pow,Ad as prelu,ud as print,_2 as prod,bme as profile,$2 as raggedGather,E2 as raggedRange,R2 as raggedTensorToTensor,D2 as rand,J2 as randomGamma,Bd as randomNormal,e1 as randomStandardNormal,uc as randomUniform,t1 as randomUniformInt,au as range,Sme as ready,si as real,r1 as reciprocal,eu as registerBackend,rce as registerGradient,Ya as registerKernel,HX as registerOp,iu as relu,zd as relu6,vme as removeBackend,W as reshape,uo as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,cc as rfft,Vd as round,i1 as rsqrt,ke as scalar,Zq as scatterND,pu as scatter_util,Nl as searchSorted,u1 as selu,p1 as separableConv2d,vN as serialization,wme as setBackend,_me as setPlatform,Jne as setThreadsCount,Qne as setWasmPath,Zne as setWasmPaths,iI as setWebGLContext,c1 as setdiff1dAsync,Sc as shared,wa as sigmoid,l1 as sign,ij as signal,m1 as sin,d1 as sinh,qe as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ct as slice_util,y1 as softmax,vd as softplus,Dd as spaceToBatchND,lj as sparse,t6 as sparseToDense,aj as spectral,ai as split,Rr as sqrt,Jt as square,Ud as squaredDifference,lc as squeeze,vr as stack,Gd as step,b1 as stridedSlice,mj as string,Te as sub,ot as sum,Za as sumOutType,C1 as tan,Il as tanh,ir as tensor,xr as tensor1d,uu as tensor2d,Hd as tensor3d,w1 as tensor4d,S1 as tensor5d,I1 as tensor6d,k1 as tensorScatterUpdate,M0 as tensor_util,Z2 as test_util,De as tidy,nu as tile,Cme as time,N1 as topk,CUe as train,dc as transpose,T1 as truncatedNormal,_1 as unique,nce as unregisterGradient,oce as unregisterKernel,$1 as unsortedSegmentSum,po as unstack,dt as upcastType,E1 as upperBound,y as util,ZH as valueAndGrad,JH as valueAndGrads,R1 as variable,vw as variableGrads,zpe as version,H5 as version_converter,Vj as version_core,I8 as version_cpu,tse as version_wasm,xZ as version_webgl,cst as webgl,_c as webgl_util,Fv as webgpu_util,io as where,qd as whereAsync,Wr as zeros,Ht as zerosLike}; + `}};function hce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=yr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=Vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new my(f.shape,g,h),C=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=yr({inputs:{x:_},backend:t,attrs:{perm:E}})}return p.forEach(E=>t.disposeData(E.dataId)),_}var $U={kernelName:Zi,backendName:"webgpu",kernelFunc:hce};var gce=[jB,Rz,Dz,Az,Fz,Pz,Mz,Lz,Bz,zz,Vz,Wz,Uz,Gz,Hz,jz,Xz,Yz,Qz,Zz,eV,tV,rV,aV,iV,uV,YB,cV,mV,dV,fV,hV,gV,xV,yV,bV,CV,wV,vV,kV,NV,TV,$V,EV,_V,RV,DV,AV,FV,MV,LV,BV,zV,VV,WV,UV,GV,HV,KB,KV,XV,qV,jV,YV,QV,ZV,JV,eW,tW,rW,XB,oW,lV,nW,sW,aW,iW,uW,pW,cW,mW,lW,dW,fW,hW,xW,yW,Kz,bW,CW,IW,wW,SW,vW,qz,kW,NW,TW,_W,EW,PV,RW,DW,AW,oV,FW,MW,LW,BW,zW,VW,WW,UW,nV,GW,HW,KW,qW,qB,jW,XW,YW,QW,ZW,JW,eU,tU,rU,oU,nU,sU,aU,iU,uU,pU,Jz,yU,bU,CU,$W,cU,lU,dU,fU,hU,gU,xU,wU,OV,SU,IU,vU,mU,NU,TU,Oz,_U,$U,PW];for(let r of gce)Ja(r);var EU="4.7.0",xce="4.7.0",yce="4.7.0",bce="4.7.0",Cce="4.7.0",wce="4.7.0",Sce={tfjs:EU,"tfjs-core":EU,"tfjs-converter":xce,"tfjs-backend-cpu":yce,"tfjs-backend-webgl":bce,"tfjs-backend-wasm":Cce,"tfjs-backend-webgpu":wce};export{js as Abs,Vo as Acos,Wo as Acosh,ep as AdadeltaOptimizer,tp as AdagradOptimizer,rp as AdamOptimizer,op as AdamaxOptimizer,io as Add,Uo as AddN,Go as All,Ho as Any,Xs as ArgMax,Ys as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Qs as AvgPool3D,Ei as AvgPool3DGrad,$i as AvgPoolGrad,am as BackendWasm,Zo as BatchMatMul,Zs as BatchToSpaceND,Jo as Bincount,Ha as BitwiseAnd,Js as BroadcastArgs,_ce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Ri as Complex,Di as ComplexAbs,ea as Concat,tn as Conv2D,Ai as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,Ka as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ta as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Fi as DepthwiseConv2dNativeBackpropFilter,Pi as DepthwiseConv2dNativeBackpropInput,ra as Diag,dn as Dilation2D,Mi as Dilation2DBackpropFilter,Oi as Dilation2DBackpropInput,qm as Draw,QC as ENV,Li as Einsum,hn as Elu,qa as EluGrad,ll as Environment,xn as Equal,gn as Erf,yn as Exp,oa as ExpandDims,bn as Expm1,Bi as FFT,na as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Au as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,wp as GPGPUContext,vn as GatherNd,sa as GatherV2,Ol as GraphModel,kn as Greater,Nn as GreaterEqual,zi as IFFT,Co as Identity,Vi as Imag,Tn as IsFinite,_n as IsInf,$n as IsNan,so as KernelBackend,Bn as LRN,ja as LRNGrad,En as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,$ce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,w0 as LogicalXor,Ece as LowerBound,hu as MathBackendCPU,bu as MathBackendWebGL,Rce as MatrixBandPart,zn as Max,Wn as MaxPool,aa as MaxPool3D,Ui as MaxPool3DGrad,Wi as MaxPoolGrad,ia as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,np as MomentumOptimizer,jn as Multinomial,Xn as Multiply,ua as Neg,Qn as NonMaxSuppressionV3,Xa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,yw as OP_SCOPE_SUFFIX,Jn as OneHot,pa as OnesLike,Nr as Optimizer,Dl as OptimizerConstructors,ca as Pack,es as PadV2,Dce as Pool,ts as Pow,rs as Prelu,os as Prod,sp as RMSPropOptimizer,jp as RaggedGather,Xp as RaggedRange,Yp as RaggedTensorToTensor,la as Range,uw as Rank,Gi as Real,fn as RealDiv,ns as Reciprocal,Et as Reduction,ss as Relu,us as Relu6,ma as Reshape,is as ResizeBilinear,Qa as ResizeBilinearGrad,as as ResizeNearestNeighbor,Ya as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,ci as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,da as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,fa as Slice,Is as Softmax,Cs as Softplus,ha as SpaceToBatchND,Hi as SparseFillEmptyRows,Za as SparseReshape,Ki as SparseSegmentMean,qi as SparseSegmentSum,vs as SparseToDense,ga as SplitV,ws as Sqrt,ji as Square,ks as SquaredDifference,Du as StaticRegexReplace,wo as Step,Ns as StridedSlice,xa as StringNGrams,Xi as StringSplit,Yi as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,$s as Tanh,ut as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,uo as Tile,Es as TopK,Rs as Transform,po as Transpose,Qi as Unique,ya as Unpack,Zi as UnsortedSegmentSum,Ace as UpperBound,ei as Variable,vu as WebGPUBackend,ba as ZerosLike,So as _FusedMatMul,Jt as abs,xk as acos,yk as acosh,Ce as add,bk as addN,Ck as all,wk as any,Sk as argMax,Ik as argMin,vk as asin,kk as asinh,Nk as atan,Tk as atan2,_k as atanh,md as avgPool,Rk as avgPool3d,dde as backend,w as backend_util,Dk as basicLSTMCell,su as batchNorm,Fk as batchNorm2d,Pk as batchNorm3d,Ok as batchNorm4d,dd as batchToSpaceND,fd as bincount,Mk as bitwiseAnd,b6 as booleanMaskAsync,Lk as broadcastArgs,au as broadcastTo,Ir as broadcast_util,XN as browser,me as buffer,qe as cast,Bk as ceil,zk as clipByValue,Ur as clone,Er as complex,bt as concat,Vk as concat1d,Wk as concat2d,Uk as concat3d,Gk as concat4d,Hk as conv1d,iu as conv2d,Kk as conv2dTranspose,qk as conv3d,Xk as conv3dTranspose,Vce as copyRegisteredKernels,Yk as cos,Qk as cosh,_l as cosineWindow,Zk as cumprod,Jk as cumsum,vr as customGrad,e2 as denseBincount,_w as deprecationWarn,t2 as depthToSpace,ic as depthwiseConv2d,b5 as deregisterOp,ru as device_util,r2 as diag,o2 as dilation2d,rde as disableDeprecationWarnings,Ot as dispose,ode as disposeVariables,je as div,s2 as divNoNan,a2 as dot,R6 as dropout,i2 as einsum,yd as elu,tde as enableDebugMode,ede as enableProdMode,Gw as enclosingPowerOfTwo,ur as engine,u2 as ensureShape,A as env,xd as equal,p2 as erf,m2 as euclideanNorm,_o as exp,ai as expandDims,d2 as expm1,bd as eye,cc as fft,Ta as fill,lde as findBackend,mde as findBackendFactory,Cd as floor,ld as floorDiv,$D as forceHalfFloat,Hw as fused,wd as gather,$6 as gatherND,nf as gather_util,pde as getBackend,ew as getGradient,fl as getKernel,jm as getKernelsForBackend,Lse as getThreadsCount,rv as gpgpu_util,SK as grad,IK as grads,Uu as greater,Sd as greaterEqual,Xu as ifft,pu as imag,Fj as image,A6 as inTopKAsync,mi as io,Gd as irfft,f2 as isFinite,h2 as isInf,g2 as isNaN,Rr as keep,Wt as kernel_impls,Id as leakyRelu,kl as less,uc as lessEqual,Pj as linalg,x2 as linspace,h8 as loadGraphModel,g8 as loadGraphModelSync,y2 as localResponseNormalization,ii as log,vd as log1p,b2 as logSigmoid,C2 as logSoftmax,Td as logSumExp,Gu as logicalAnd,_d as logicalNot,$d as logicalOr,w2 as logicalXor,Oj as losses,S2 as lowerBound,Ze as matMul,HN as math,_a as max,Rd as maxPool,I2 as maxPool3d,v2 as maxPoolWithArgmax,Dd as maximum,Hu as mean,nde as memory,k2 as meshgrid,vl as min,Ku as minimum,N2 as mirrorPad,T2 as mod,_2 as moments,S6 as movingAverage,se as mul,$2 as multiRNNCell,E2 as multinomial,pr as neg,tS as nextFrame,Wu as norm,Ad as notEqual,Tl as oneHot,$a as ones,R2 as onesLike,N as op,D2 as outerProduct,Ea as pad,A2 as pad1d,F2 as pad2d,P2 as pad3d,O2 as pad4d,M2 as pool,si as pow,Pd as prelu,cd as print,L2 as prod,sde as profile,B2 as raggedGather,z2 as raggedRange,V2 as raggedTensorToTensor,W2 as rand,p1 as randomGamma,Vd as randomNormal,c1 as randomStandardNormal,pc as randomUniform,l1 as randomUniformInt,cu as range,ude as ready,ui as real,m1 as reciprocal,nu as registerBackend,Lce as registerGradient,Ja as registerKernel,y5 as registerOp,lu as relu,Wd as relu6,cde as removeBackend,W as reshape,lo as reverse,d1 as reverse1d,f1 as reverse2d,h1 as reverse3d,g1 as reverse4d,lc as rfft,Ud as round,x1 as rsqrt,ke as scalar,v6 as scatterND,du as scatter_util,Nl as searchSorted,y1 as selu,b1 as separableConv2d,FN as serialization,ide as setBackend,fde as setPlatform,Mse as setThreadsCount,Pse as setWasmPath,Ose as setWasmPaths,gI as setWebGLContext,C1 as setdiff1dAsync,Ic as shared,Na as sigmoid,w1 as sign,Aj as signal,S1 as sin,I1 as sinh,Xe as slice,v1 as slice1d,k1 as slice2d,N1 as slice3d,T1 as slice4d,ct as slice_util,_1 as softmax,Nd as softplus,Fd as spaceToBatchND,Mj as sparse,T6 as sparseToDense,Dj as spectral,pi as split,Dr as sqrt,er as square,Hd as squaredDifference,mc as squeeze,kr as stack,Kd as step,$1 as stridedSlice,Lj as string,Te as sub,ot as sum,ti as sumOutType,E1 as tan,Il as tanh,ir as tensor,xr as tensor1d,mu as tensor2d,qd as tensor3d,R1 as tensor4d,D1 as tensor5d,A1 as tensor6d,P1 as tensorScatterUpdate,K0 as tensor_util,u1 as test_util,De as tidy,uu as tile,ade as time,O1 as topk,uGe as train,fc as transpose,M1 as truncatedNormal,L1 as unique,zce as unregisterGradient,Bce as unregisterKernel,B1 as unsortedSegmentSum,mo as unstack,dt as upcastType,z1 as upperBound,y as util,vK as valueAndGrad,kK as valueAndGrads,V1 as variable,Aw as variableGrads,Sce as version,y8 as version_converter,fX as version_core,Y8 as version_cpu,Bse as version_wasm,HZ as version_webgl,tat as webgl,$c as webgl_util,Wv as webgpu_util,co as where,Xd as whereAsync,Gr as zeros,Ht as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index cccba4b0..dd2b1bce 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,4 @@ author: ' */ -var e="4.5.0";var s="4.5.0";var t="4.5.0";var n="4.5.0";var r="4.5.0";var i="0.0.1-alpha.20";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; +var e="4.7.0";var s="4.7.0";var t="4.7.0";var n="4.7.0";var r="4.7.0";var i="4.7.0";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; diff --git a/src/draw/primitives.ts b/src/draw/primitives.ts index 5fbd7d8a..53db3c78 100644 --- a/src/draw/primitives.ts +++ b/src/draw/primitives.ts @@ -7,7 +7,7 @@ export const getCanvasContext = (input: AnyCanvas) => { if (!input) log('draw error: invalid canvas'); else if (!input.getContext) log('draw error: canvas context not defined'); else { - const ctx = input.getContext('2d'); + const ctx = input.getContext('2d', { willReadFrequently: true }); if (!ctx) log('draw error: cannot get canvas context'); else return ctx; } diff --git a/src/face/facemesh.ts b/src/face/facemesh.ts index 1c1237ca..f02cdae2 100644 --- a/src/face/facemesh.ts +++ b/src/face/facemesh.ts @@ -98,6 +98,7 @@ export async function predict(input: Tensor4D, config: Config): Promise [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]); @@ -127,6 +128,7 @@ export async function predict(input: Tensor4D, config: Config): Promise t.shape[t.shape.length - 1] === 266) as Tensor; const contoursData = contoursT && await contoursT.data(); // 133 x 2d points diff --git a/src/hand/handpose.ts b/src/hand/handpose.ts index 853a5b5a..2be4d982 100644 --- a/src/hand/handpose.ts +++ b/src/hand/handpose.ts @@ -27,7 +27,13 @@ let handDetectorModel: GraphModel | null; let handPoseModel: GraphModel | null; let handPipeline: handpipeline.HandPipeline; +export function initPipeline() { + const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined; + if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel); +} + export async function predict(input: Tensor, config: Config): Promise { + if (!handPipeline) initPipeline(); const predictions = await handPipeline.estimateHands(input, config); if (!predictions) return []; const hands: HandResult[] = []; @@ -82,21 +88,16 @@ export async function predict(input: Tensor, config: Config): Promise { - if (env.initial) { - handDetectorModel = null; - handPoseModel = null; - } - if (!handDetectorModel || !handPoseModel) { - [handDetectorModel, handPoseModel] = await Promise.all([ - config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null, - config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null, - ]); - } else { - if (config.debug) log('cached model:', handDetectorModel['modelUrl']); - if (config.debug) log('cached model:', handPoseModel['modelUrl']); - } - const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined; - if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel); - return [handDetectorModel, handPoseModel]; +export async function loadDetect(config: Config): Promise { + if (env.initial) handDetectorModel = null; + if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath); + else if (config.debug) log('cached model:', handDetectorModel['modelUrl']); + return handDetectorModel; +} + +export async function loadSkeleton(config: Config): Promise { + if (env.initial) handPoseModel = null; + if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath); + else if (config.debug) log('cached model:', handPoseModel['modelUrl']); + return handPoseModel; } diff --git a/src/hand/handposepipeline.ts b/src/hand/handposepipeline.ts index 2a5bc6b6..7d08d4b4 100644 --- a/src/hand/handposepipeline.ts +++ b/src/hand/handposepipeline.ts @@ -94,10 +94,11 @@ export class HandPipeline { const skipTime = (config.hand.skipTime || 0) > (now() - lastTime); const skipFrame = this.skipped < (config.hand.skipFrames || 0); if (config.skipAllowed && skipTime && skipFrame) { + this.skipped++; + } else { boxes = await this.handDetector.predict(image, config); this.skipped = 0; } - if (config.skipAllowed) this.skipped++; // if detector result count doesn't match current working set, use it to reset current working set if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) { diff --git a/src/models.ts b/src/models.ts index ac203361..1e155459 100644 --- a/src/models.ts +++ b/src/models.ts @@ -147,7 +147,11 @@ export class Models { // hand alternatives m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null; m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null; - if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null]; + // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null]; + if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) { + m.handdetect = handpose.loadDetect(this.instance.config); + m.handskeleton = handpose.loadSkeleton(this.instance.config); + } // object detection alternatives m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null; m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null; diff --git a/src/util/webcam.ts b/src/util/webcam.ts index 5b7f9a82..81a92d40 100644 --- a/src/util/webcam.ts +++ b/src/util/webcam.ts @@ -103,7 +103,7 @@ export class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-cl }; /** start method initializizes webcam stream and associates it with a dom video element */ - public start = async (webcamConfig?: Partial): Promise => { + public start = async (webcamConfig?: Partial): Promise => { // set config if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug; if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop; @@ -120,13 +120,13 @@ export class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-cl this.element = el; } else { if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element); - return; + return `webcam error: cannot get dom element: ${webcamConfig.element}`; } } else if (webcamConfig.element instanceof HTMLVideoElement) { this.element = webcamConfig.element; } else { if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element); - return; + return `webcam error: unknown dom element: ${webcamConfig.element}`; } } else { this.element = document.createElement('video'); @@ -156,18 +156,18 @@ export class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-cl // get webcam and set it to run in dom element if (!navigator?.mediaDevices) { - if (this.config.debug) log('webcam', 'no devices'); - return; + if (this.config.debug) log('webcam error', 'no devices'); + return 'webcam error: no devices'; } try { this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints } catch (err) { log('webcam', err); - return; + return `webcam error: ${err}`; } if (!this.stream) { - if (this.config.debug) log('webcam', 'no stream'); - return; + if (this.config.debug) log('webcam error', 'no stream'); + return 'webcam error no stream'; } this.element.srcObject = this.stream; // assign it to dom element const ready = new Promise((resolve) => { // wait until stream is ready @@ -189,6 +189,7 @@ export class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-cl capabilities: this.capabilities, }); } + return `webcam: ${this.label}`; }; /** pause webcam video method */ diff --git a/test/build.log b/test/build.log index 4d3e6c4d..33d33a3c 100644 --- a/test/build.log +++ b/test/build.log @@ -1,51 +1,51 @@ -2023-05-08 09:13:55 DATA:  Build {"name":"@vladmandic/human","version":"3.0.6"} -2023-05-08 09:13:55 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.6"} -2023-05-08 09:13:55 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2023-05-08 09:13:55 INFO:  Toolchain: {"build":"0.9.2","esbuild":"0.17.18","typescript":"5.0.4","typedoc":"0.23.28","eslint":"8.40.0"} -2023-05-08 09:13:55 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} -2023-05-08 09:13:55 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} -2023-05-08 09:13:55 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} -2023-05-08 09:13:55 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":960} -2023-05-08 09:13:55 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":675233,"outputBytes":320463} -2023-05-08 09:13:55 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":968} -2023-05-08 09:13:55 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":675241,"outputBytes":320467} -2023-05-08 09:13:55 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":2006} -2023-05-08 09:13:55 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":676279,"outputBytes":320578} -2023-05-08 09:13:55 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} -2023-05-08 09:13:55 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":674943,"outputBytes":319027} -2023-05-08 09:13:55 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1250365} -2023-05-08 09:13:55 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1924638,"outputBytes":1565102} -2023-05-08 09:13:56 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1924638,"outputBytes":2047423} -2023-05-08 09:13:59 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} -2023-05-08 09:14:01 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} -2023-05-08 09:14:01 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6203,"outputBytes":2926} -2023-05-08 09:14:01 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} -2023-05-08 09:14:01 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} -2023-05-08 09:14:09 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":171,"errors":0,"warnings":0} -2023-05-08 09:14:09 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} -2023-05-08 09:14:09 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} -2023-05-08 09:14:09 INFO:  Done... -2023-05-08 09:14:09 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} -2023-05-08 09:14:09 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} -2023-05-08 09:14:09 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} -2023-05-08 09:14:09 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} -2023-05-08 09:14:09 STATE: Filter: {"input":"types/tfjs-core.d.ts"} -2023-05-08 09:14:10 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":210} -2023-05-08 09:14:10 STATE: Filter: {"input":"types/human.d.ts"} -2023-05-08 09:14:10 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} -2023-05-08 09:14:10 STATE: Write: {"output":"dist/human.esm.d.ts"} -2023-05-08 09:14:10 STATE: Write: {"output":"dist/human.d.ts"} -2023-05-08 09:14:10 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} -2023-05-08 09:14:10 STATE: Write: {"output":"dist/human.node.d.ts"} -2023-05-08 09:14:10 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} -2023-05-08 09:14:10 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} -2023-05-08 09:14:10 STATE: Models {"folder":"./models","models":12} -2023-05-08 09:14:10 STATE: Models {"folder":"../human-models/models","models":44} -2023-05-08 09:14:10 STATE: Models {"folder":"../blazepose/model/","models":4} -2023-05-08 09:14:10 STATE: Models {"folder":"../anti-spoofing/model","models":1} -2023-05-08 09:14:10 STATE: Models {"folder":"../efficientpose/models","models":3} -2023-05-08 09:14:10 STATE: Models {"folder":"../insightface/models","models":5} -2023-05-08 09:14:10 STATE: Models {"folder":"../movenet/models","models":3} -2023-05-08 09:14:10 STATE: Models {"folder":"../nanodet/models","models":4} -2023-05-08 09:14:11 STATE: Models: {"count":58,"totalSize":380063249} -2023-05-08 09:14:11 INFO:  Human Build complete... {"logFile":"test/build.log"} +2023-06-12 13:27:02 DATA:  Build {"name":"@vladmandic/human","version":"3.0.7"} +2023-06-12 13:27:02 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.7"} +2023-06-12 13:27:02 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2023-06-12 13:27:02 INFO:  Toolchain: {"build":"0.9.2","esbuild":"0.17.19","typescript":"5.1.3","typedoc":"0.23.28","eslint":"8.42.0"} +2023-06-12 13:27:02 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} +2023-06-12 13:27:02 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} +2023-06-12 13:27:02 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":352} +2023-06-12 13:27:02 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":560,"outputBytes":951} +2023-06-12 13:27:02 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":675883,"outputBytes":320756} +2023-06-12 13:27:02 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":568,"outputBytes":959} +2023-06-12 13:27:02 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":675891,"outputBytes":320760} +2023-06-12 13:27:02 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":656,"outputBytes":1997} +2023-06-12 13:27:02 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":676929,"outputBytes":320871} +2023-06-12 13:27:02 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1366,"outputBytes":661} +2023-06-12 13:27:02 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":675593,"outputBytes":319319} +2023-06-12 13:27:02 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1366,"outputBytes":1259192} +2023-06-12 13:27:03 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1934124,"outputBytes":1574208} +2023-06-12 13:27:03 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1934124,"outputBytes":2050464} +2023-06-12 13:27:06 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} +2023-06-12 13:27:08 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} +2023-06-12 13:27:08 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} +2023-06-12 13:27:08 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} +2023-06-12 13:27:08 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} +2023-06-12 13:27:17 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":171,"errors":0,"warnings":0} +2023-06-12 13:27:17 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2023-06-12 13:27:17 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} +2023-06-12 13:27:17 INFO:  Done... +2023-06-12 13:27:17 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} +2023-06-12 13:27:17 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} +2023-06-12 13:27:17 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} +2023-06-12 13:27:17 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} +2023-06-12 13:27:17 STATE: Filter: {"input":"types/tfjs-core.d.ts"} +2023-06-12 13:27:18 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":211} +2023-06-12 13:27:18 STATE: Filter: {"input":"types/human.d.ts"} +2023-06-12 13:27:18 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} +2023-06-12 13:27:18 STATE: Write: {"output":"dist/human.esm.d.ts"} +2023-06-12 13:27:18 STATE: Write: {"output":"dist/human.d.ts"} +2023-06-12 13:27:18 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} +2023-06-12 13:27:18 STATE: Write: {"output":"dist/human.node.d.ts"} +2023-06-12 13:27:18 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} +2023-06-12 13:27:18 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} +2023-06-12 13:27:18 STATE: Models {"folder":"./models","models":12} +2023-06-12 13:27:18 STATE: Models {"folder":"../human-models/models","models":44} +2023-06-12 13:27:18 STATE: Models {"folder":"../blazepose/model/","models":4} +2023-06-12 13:27:18 STATE: Models {"folder":"../anti-spoofing/model","models":1} +2023-06-12 13:27:18 STATE: Models {"folder":"../efficientpose/models","models":3} +2023-06-12 13:27:18 STATE: Models {"folder":"../insightface/models","models":5} +2023-06-12 13:27:18 STATE: Models {"folder":"../movenet/models","models":3} +2023-06-12 13:27:18 STATE: Models {"folder":"../nanodet/models","models":4} +2023-06-12 13:27:19 STATE: Models: {"count":58,"totalSize":380063249} +2023-06-12 13:27:19 INFO:  Human Build complete... {"logFile":"test/build.log"} diff --git a/test/test.log b/test/test.log index e6210e8c..6bd7a420 100644 --- a/test/test.log +++ b/test/test.log @@ -1,905 +1,905 @@ -2023-05-08 09:14:14 INFO:  @vladmandic/human version 3.0.6 -2023-05-08 09:14:14 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 -2023-05-08 09:14:14 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] -2023-05-08 09:14:14 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start -2023-05-08 09:14:16 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start -2023-05-08 09:14:16 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start -2023-05-08 09:14:17 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start -2023-05-08 09:14:18 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start -2023-05-08 09:14:19 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start -2023-05-08 09:14:20 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start -2023-05-08 09:14:33 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start -2023-05-08 09:14:34 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start -2023-05-08 09:14:49 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start -2023-05-08 09:14:54 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] -2023-05-08 09:14:54 INFO:  -2023-05-08 09:14:54 INFO:  test-node-load.js start -2023-05-08 09:14:55 INFO:  test-node-load.js load start {"human":"3.0.6","tf":"4.5.0","progress":0} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":21,"progress":0.06164218554150753} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":36,"progress":0.2204080722078057} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":63,"progress":0.4726970548784964} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":77,"progress":0.68355493315821} -2023-05-08 09:14:55 STATE: test-node-load.js passed {"progress":1} -2023-05-08 09:14:55 INFO:  test-node-load.js load final {"progress":1} -2023-05-08 09:14:55 DATA:  test-node-load.js load interval {"elapsed":459,"progress":1} -2023-05-08 09:14:55 INFO:  -2023-05-08 09:14:55 INFO:  test-node-gear.js start -2023-05-08 09:14:55 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] -2023-05-08 09:14:56 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg -2023-05-08 09:14:56 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92,"emotion":[]} -2023-05-08 09:14:56 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg -2023-05-08 09:14:56 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}],"emotion":[]} -2023-05-08 09:14:57 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2023-05-08 09:14:57 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99,"emotion":[]} -2023-05-08 09:14:57 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2023-05-08 09:14:57 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"male","emotion":[]} -2023-05-08 09:14:57 INFO:  -2023-05-08 09:14:57 INFO:  test-backend-node.js start -2023-05-08 09:14:57 INFO:  test-backend-node.js test: configuration validation -2023-05-08 09:14:57 STATE: test-backend-node.js passed: configuration default validation [] -2023-05-08 09:14:57 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2023-05-08 09:14:57 INFO:  test-backend-node.js test: model load -2023-05-08 09:14:57 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2023-05-08 09:14:57 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2023-05-08 09:14:57 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2023-05-08 09:14:57 INFO:  test-backend-node.js test: warmup -2023-05-08 09:14:57 STATE: test-backend-node.js passed: create human -2023-05-08 09:14:57 INFO:  test-backend-node.js human version: 3.0.6 -2023-05-08 09:14:57 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 -2023-05-08 09:14:57 INFO:  test-backend-node.js tfjs version: 4.5.0 -2023-05-08 09:14:57 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.5.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} -2023-05-08 09:14:57 STATE: test-backend-node.js passed: set backend: tensorflow -2023-05-08 09:14:57 STATE: test-backend-node.js tensors 1785 -2023-05-08 09:14:57 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 -2023-05-08 09:14:57 STATE: test-backend-node.js passed: load models 11 -2023-05-08 09:14:57 STATE: test-backend-node.js passed: warmup: none default -2023-05-08 09:14:57 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2023-05-08 09:14:57 DATA:  test-backend-node.js result: performance: load: null total: null -2023-05-08 09:14:57 STATE: test-backend-node.js passed: warmup none result match -2023-05-08 09:14:57 STATE: test-backend-node.js event: image -2023-05-08 09:14:58 STATE: test-backend-node.js event: detect -2023-05-08 09:14:58 STATE: test-backend-node.js event: warmup -2023-05-08 09:14:58 STATE: test-backend-node.js passed: warmup: face default -2023-05-08 09:14:58 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2023-05-08 09:14:58 DATA:  test-backend-node.js result: performance: load: null total: 456 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: warmup face result match -2023-05-08 09:14:58 STATE: test-backend-node.js event: image -2023-05-08 09:14:58 STATE: test-backend-node.js event: detect -2023-05-08 09:14:58 STATE: test-backend-node.js event: warmup -2023-05-08 09:14:58 STATE: test-backend-node.js passed: warmup: body default -2023-05-08 09:14:58 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:14:58 DATA:  test-backend-node.js result: performance: load: null total: 330 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: warmup body result match -2023-05-08 09:14:58 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2023-05-08 09:14:58 INFO:  test-backend-node.js test: details verification -2023-05-08 09:14:58 STATE: test-backend-node.js start default -2023-05-08 09:14:58 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:14:58 STATE: test-backend-node.js event: image -2023-05-08 09:14:58 STATE: test-backend-node.js event: detect -2023-05-08 09:14:58 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default -2023-05-08 09:14:58 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:14:58 DATA:  test-backend-node.js result: performance: load: null total: 328 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face length 1 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face score 1 0.93 1 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face arrays 4 478 1024 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details face liveness 0.83 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details body length 1 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details body 0.92 17 6 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details hand length 1 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details hand 0.51 0.73 point -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details hand arrays 21 5 7 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details gesture length 7 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details object length 1 -2023-05-08 09:14:58 STATE: test-backend-node.js passed: details object 0.72 person -2023-05-08 09:14:59 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2023-05-08 09:14:59 STATE: test-backend-node.js event: image -2023-05-08 09:14:59 STATE: test-backend-node.js event: detect -2023-05-08 09:14:59 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2023-05-08 09:14:59 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2023-05-08 09:14:59 STATE: test-backend-node.js event: image -2023-05-08 09:15:00 STATE: test-backend-node.js event: detect -2023-05-08 09:15:00 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 -2023-05-08 09:15:00 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:00 STATE: test-backend-node.js event: image -2023-05-08 09:15:00 STATE: test-backend-node.js event: detect -2023-05-08 09:15:00 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2023-05-08 09:15:00 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:00 STATE: test-backend-node.js event: image -2023-05-08 09:15:00 STATE: test-backend-node.js event: detect -2023-05-08 09:15:00 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 -2023-05-08 09:15:01 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2023-05-08 09:15:01 STATE: test-backend-node.js event: image -2023-05-08 09:15:01 STATE: test-backend-node.js event: detect -2023-05-08 09:15:01 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2023-05-08 09:15:01 INFO:  test-backend-node.js test default -2023-05-08 09:15:01 STATE: test-backend-node.js start async -2023-05-08 09:15:01 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:01 STATE: test-backend-node.js event: image -2023-05-08 09:15:02 STATE: test-backend-node.js event: detect -2023-05-08 09:15:02 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async -2023-05-08 09:15:02 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:02 DATA:  test-backend-node.js result: performance: load: null total: 342 -2023-05-08 09:15:02 STATE: test-backend-node.js passed: default result face match 1 female 0.97 -2023-05-08 09:15:02 INFO:  test-backend-node.js test sync -2023-05-08 09:15:02 STATE: test-backend-node.js start sync -2023-05-08 09:15:02 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:02 STATE: test-backend-node.js event: image -2023-05-08 09:15:02 STATE: test-backend-node.js event: detect -2023-05-08 09:15:02 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync -2023-05-08 09:15:02 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:02 DATA:  test-backend-node.js result: performance: load: null total: 346 -2023-05-08 09:15:02 STATE: test-backend-node.js passed: default sync 1 female 0.97 -2023-05-08 09:15:02 INFO:  test-backend-node.js test: image process -2023-05-08 09:15:02 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:02 STATE: test-backend-node.js passed: image input null [1,256,256,3] -2023-05-08 09:15:02 INFO:  test-backend-node.js test: image null -2023-05-08 09:15:02 STATE: test-backend-node.js passed: invalid input could not convert input to tensor -2023-05-08 09:15:02 INFO:  test-backend-node.js test face similarity -2023-05-08 09:15:02 STATE: test-backend-node.js start face similarity -2023-05-08 09:15:02 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:02 STATE: test-backend-node.js event: image -2023-05-08 09:15:03 STATE: test-backend-node.js event: detect -2023-05-08 09:15:03 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity -2023-05-08 09:15:03 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-05-08 09:15:03 DATA:  test-backend-node.js result: performance: load: null total: 351 -2023-05-08 09:15:03 STATE: test-backend-node.js start face similarity -2023-05-08 09:15:03 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:03 STATE: test-backend-node.js event: image -2023-05-08 09:15:03 STATE: test-backend-node.js event: detect -2023-05-08 09:15:03 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity -2023-05-08 09:15:03 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:03 DATA:  test-backend-node.js result: performance: load: null total: 312 -2023-05-08 09:15:03 STATE: test-backend-node.js start face similarity -2023-05-08 09:15:03 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-05-08 09:15:03 STATE: test-backend-node.js event: image -2023-05-08 09:15:03 STATE: test-backend-node.js event: detect -2023-05-08 09:15:03 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity -2023-05-08 09:15:03 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2023-05-08 09:15:03 DATA:  test-backend-node.js result: performance: load: null total: 282 -2023-05-08 09:15:03 STATE: test-backend-node.js passed: face descriptor -2023-05-08 09:15:03 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} -2023-05-08 09:15:03 INFO:  test-backend-node.js test object -2023-05-08 09:15:03 STATE: test-backend-node.js start object -2023-05-08 09:15:04 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:04 STATE: test-backend-node.js event: image -2023-05-08 09:15:04 STATE: test-backend-node.js event: detect -2023-05-08 09:15:04 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2023-05-08 09:15:04 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:04 DATA:  test-backend-node.js result: performance: load: null total: 303 -2023-05-08 09:15:04 STATE: test-backend-node.js passed: centernet -2023-05-08 09:15:04 STATE: test-backend-node.js start object -2023-05-08 09:15:05 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:05 STATE: test-backend-node.js event: image -2023-05-08 09:15:05 STATE: test-backend-node.js event: detect -2023-05-08 09:15:05 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2023-05-08 09:15:05 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:05 DATA:  test-backend-node.js result: performance: load: null total: 303 -2023-05-08 09:15:05 STATE: test-backend-node.js passed: nanodet -2023-05-08 09:15:05 INFO:  test-backend-node.js test sensitive -2023-05-08 09:15:05 STATE: test-backend-node.js start sensitive -2023-05-08 09:15:05 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:05 STATE: test-backend-node.js event: image -2023-05-08 09:15:05 STATE: test-backend-node.js event: detect -2023-05-08 09:15:05 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive -2023-05-08 09:15:05 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:05 DATA:  test-backend-node.js result: performance: load: null total: 275 -2023-05-08 09:15:05 STATE: test-backend-node.js passed: sensitive result match -2023-05-08 09:15:05 STATE: test-backend-node.js passed: sensitive face result match -2023-05-08 09:15:05 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2023-05-08 09:15:05 STATE: test-backend-node.js passed: sensitive body result match -2023-05-08 09:15:05 STATE: test-backend-node.js passed: sensitive hand result match -2023-05-08 09:15:05 INFO:  test-backend-node.js test body -2023-05-08 09:15:05 STATE: test-backend-node.js start blazepose -2023-05-08 09:15:07 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:07 STATE: test-backend-node.js event: image -2023-05-08 09:15:07 STATE: test-backend-node.js event: detect -2023-05-08 09:15:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose -2023-05-08 09:15:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2023-05-08 09:15:07 DATA:  test-backend-node.js result: performance: load: null total: 377 -2023-05-08 09:15:07 STATE: test-backend-node.js passed: blazepose -2023-05-08 09:15:07 STATE: test-backend-node.js start efficientpose -2023-05-08 09:15:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:08 STATE: test-backend-node.js event: image -2023-05-08 09:15:08 STATE: test-backend-node.js event: detect -2023-05-08 09:15:08 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose -2023-05-08 09:15:08 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2023-05-08 09:15:08 DATA:  test-backend-node.js result: performance: load: null total: 315 -2023-05-08 09:15:08 STATE: test-backend-node.js passed: efficientpose -2023-05-08 09:15:08 STATE: test-backend-node.js start posenet -2023-05-08 09:15:09 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:09 STATE: test-backend-node.js event: image -2023-05-08 09:15:09 STATE: test-backend-node.js event: detect -2023-05-08 09:15:09 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet -2023-05-08 09:15:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2023-05-08 09:15:09 DATA:  test-backend-node.js result: performance: load: null total: 259 -2023-05-08 09:15:09 STATE: test-backend-node.js passed: posenet -2023-05-08 09:15:09 STATE: test-backend-node.js start movenet -2023-05-08 09:15:09 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:09 STATE: test-backend-node.js event: image -2023-05-08 09:15:10 STATE: test-backend-node.js event: detect -2023-05-08 09:15:10 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet -2023-05-08 09:15:10 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:10 DATA:  test-backend-node.js result: performance: load: null total: 259 -2023-05-08 09:15:10 STATE: test-backend-node.js passed: movenet -2023-05-08 09:15:10 INFO:  test-backend-node.js test face matching -2023-05-08 09:15:10 STATE: test-backend-node.js passed: face database 40 -2023-05-08 09:15:10 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} -2023-05-08 09:15:10 INFO:  test-backend-node.js test face similarity alternative -2023-05-08 09:15:10 STATE: test-backend-node.js start face embeddings -2023-05-08 09:15:10 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:10 STATE: test-backend-node.js event: image -2023-05-08 09:15:11 STATE: test-backend-node.js event: detect -2023-05-08 09:15:11 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-05-08 09:15:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:11 DATA:  test-backend-node.js result: performance: load: null total: 303 -2023-05-08 09:15:11 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} -2023-05-08 09:15:11 STATE: test-backend-node.js start face embeddings -2023-05-08 09:15:11 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:11 STATE: test-backend-node.js event: image -2023-05-08 09:15:12 STATE: test-backend-node.js event: detect -2023-05-08 09:15:12 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-05-08 09:15:12 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:12 DATA:  test-backend-node.js result: performance: load: null total: 309 -2023-05-08 09:15:12 STATE: test-backend-node.js passed: insightface {"embedding":512} -2023-05-08 09:15:12 INFO:  test-backend-node.js test face attention -2023-05-08 09:15:12 STATE: test-backend-node.js start face attention -2023-05-08 09:15:12 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:12 STATE: test-backend-node.js event: image -2023-05-08 09:15:12 STATE: test-backend-node.js event: detect -2023-05-08 09:15:12 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention -2023-05-08 09:15:12 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:12 DATA:  test-backend-node.js result: performance: load: null total: 291 -2023-05-08 09:15:12 STATE: test-backend-node.js passed: face attention -2023-05-08 09:15:12 INFO:  test-backend-node.js test detectors -2023-05-08 09:15:12 STATE: test-backend-node.js start detectors -2023-05-08 09:15:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:13 STATE: test-backend-node.js event: image -2023-05-08 09:15:13 STATE: test-backend-node.js event: detect -2023-05-08 09:15:13 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors -2023-05-08 09:15:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:13 DATA:  test-backend-node.js result: performance: load: null total: 142 -2023-05-08 09:15:13 STATE: test-backend-node.js passed: detector result face match -2023-05-08 09:15:13 STATE: test-backend-node.js passed: detector result hand match -2023-05-08 09:15:13 INFO:  test-backend-node.js test: multi-instance -2023-05-08 09:15:13 STATE: test-backend-node.js start multi instance -2023-05-08 09:15:13 STATE: test-backend-node.js event: image -2023-05-08 09:15:13 STATE: test-backend-node.js event: detect -2023-05-08 09:15:13 STATE: test-backend-node.js passed: detect: random multi instance -2023-05-08 09:15:13 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2023-05-08 09:15:13 DATA:  test-backend-node.js result: performance: load: null total: 132 -2023-05-08 09:15:13 INFO:  test-backend-node.js test: first instance -2023-05-08 09:15:13 STATE: test-backend-node.js start multi instance -2023-05-08 09:15:13 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-05-08 09:15:13 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-05-08 09:15:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:13 DATA:  test-backend-node.js result: performance: load: null total: 130 -2023-05-08 09:15:13 INFO:  test-backend-node.js test: second instance -2023-05-08 09:15:13 STATE: test-backend-node.js start multi instance -2023-05-08 09:15:13 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-05-08 09:15:13 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-05-08 09:15:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:13 DATA:  test-backend-node.js result: performance: load: null total: 126 -2023-05-08 09:15:13 INFO:  test-backend-node.js test: concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js start concurrent -2023-05-08 09:15:13 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:13 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:13 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:14 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-05-08 09:15:14 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-05-08 09:15:14 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-05-08 09:15:14 STATE: test-backend-node.js event: image -2023-05-08 09:15:14 STATE: test-backend-node.js event: image -2023-05-08 09:15:14 STATE: test-backend-node.js event: image -2023-05-08 09:15:15 STATE: test-backend-node.js event: detect -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1144 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1144 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1144 -2023-05-08 09:15:15 STATE: test-backend-node.js event: detect -2023-05-08 09:15:15 STATE: test-backend-node.js event: detect -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1145 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1145 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1145 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1145 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1145 -2023-05-08 09:15:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2023-05-08 09:15:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:15 DATA:  test-backend-node.js result: performance: load: null total: 1145 -2023-05-08 09:15:15 INFO:  test-backend-node.js test: monkey-patch -2023-05-08 09:15:15 STATE: test-backend-node.js event: image -2023-05-08 09:15:15 STATE: test-backend-node.js event: detect -2023-05-08 09:15:15 STATE: test-backend-node.js passed: monkey patch -2023-05-08 09:15:16 STATE: test-backend-node.js passed: segmentation [262144] -2023-05-08 09:15:16 STATE: test-backend-node.js passeed: equal usage -2023-05-08 09:15:16 INFO:  test-backend-node.js test: input compare -2023-05-08 09:15:16 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-05-08 09:15:16 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 -2023-05-08 09:15:16 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} -2023-05-08 09:15:16 INFO:  test-backend-node.js tensors 14855 -2023-05-08 09:15:16 INFO:  test-backend-node.js test complete: 18892 ms -2023-05-08 09:15:16 INFO:  -2023-05-08 09:15:16 INFO:  test-backend-node-gpu.js start -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js test: configuration validation -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: configuration default validation [] -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js test: model load -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js test: warmup -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: create human -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js human version: 3.0.6 -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js tfjs version: 4.5.0 -2023-05-08 09:15:17 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.5.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: set backend: tensorflow -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js tensors 1785 -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: load models 11 -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: warmup: none default -2023-05-08 09:15:17 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2023-05-08 09:15:17 DATA:  test-backend-node-gpu.js result: performance: load: null total: null -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js passed: warmup none result match -2023-05-08 09:15:17 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: warmup -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: warmup: face default -2023-05-08 09:15:19 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2023-05-08 09:15:19 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1593 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: warmup face result match -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: warmup -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: warmup: body default -2023-05-08 09:15:19 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:19 DATA:  test-backend-node-gpu.js result: performance: load: null total: 141 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: warmup body result match -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2023-05-08 09:15:19 INFO:  test-backend-node-gpu.js test: details verification -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js start default -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default -2023-05-08 09:15:19 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:19 DATA:  test-backend-node-gpu.js result: performance: load: null total: 127 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face length 1 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details body length 1 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details hand length 1 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details gesture length 7 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details object length 1 -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: details object 0.72 person -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:19 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2023-05-08 09:15:20 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2023-05-08 09:15:21 INFO:  test-backend-node-gpu.js test default -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js start async -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async -2023-05-08 09:15:21 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:21 DATA:  test-backend-node-gpu.js result: performance: load: null total: 297 -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 -2023-05-08 09:15:21 INFO:  test-backend-node-gpu.js test sync -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js start sync -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:21 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: performance: load: null total: 303 -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 -2023-05-08 09:15:22 INFO:  test-backend-node-gpu.js test: image process -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] -2023-05-08 09:15:22 INFO:  test-backend-node-gpu.js test: image null -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor -2023-05-08 09:15:22 INFO:  test-backend-node-gpu.js test face similarity -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js start face similarity -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: performance: load: null total: 289 -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js start face similarity -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: performance: load: null total: 111 -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js start face similarity -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2023-05-08 09:15:22 DATA:  test-backend-node-gpu.js result: performance: load: null total: 106 -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: face descriptor -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475006597943763,0.5570889682705169],"descriptors":[1024,1024,1024]} -2023-05-08 09:15:22 INFO:  test-backend-node-gpu.js test object -2023-05-08 09:15:22 STATE: test-backend-node-gpu.js start object -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2023-05-08 09:15:23 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:23 DATA:  test-backend-node-gpu.js result: performance: load: null total: 111 -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js passed: centernet -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js start object -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:23 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2023-05-08 09:15:24 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:24 DATA:  test-backend-node-gpu.js result: performance: load: null total: 552 -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: nanodet -2023-05-08 09:15:24 INFO:  test-backend-node-gpu.js test sensitive -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js start sensitive -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive -2023-05-08 09:15:24 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:24 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: sensitive result match -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: sensitive face result match -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: sensitive body result match -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js passed: sensitive hand result match -2023-05-08 09:15:24 INFO:  test-backend-node-gpu.js test body -2023-05-08 09:15:24 STATE: test-backend-node-gpu.js start blazepose -2023-05-08 09:15:26 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:26 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:26 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:26 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose -2023-05-08 09:15:26 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2023-05-08 09:15:26 DATA:  test-backend-node-gpu.js result: performance: load: null total: 392 -2023-05-08 09:15:26 STATE: test-backend-node-gpu.js passed: blazepose -2023-05-08 09:15:26 STATE: test-backend-node-gpu.js start efficientpose -2023-05-08 09:15:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:27 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose -2023-05-08 09:15:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2023-05-08 09:15:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1047 -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: efficientpose -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js start posenet -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet -2023-05-08 09:15:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2023-05-08 09:15:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 117 -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: posenet -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js start movenet -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet -2023-05-08 09:15:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 98 -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: movenet -2023-05-08 09:15:28 INFO:  test-backend-node-gpu.js test face matching -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: face database 40 -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829337456107771}} {"second":{"index":4,"similarity":0.5002921100080195}} {"third":{"index":4,"similarity":0.5402938601022594}} -2023-05-08 09:15:28 INFO:  test-backend-node-gpu.js test face similarity alternative -2023-05-08 09:15:28 STATE: test-backend-node-gpu.js start face embeddings -2023-05-08 09:15:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:29 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:29 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:29 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-05-08 09:15:29 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:29 DATA:  test-backend-node-gpu.js result: performance: load: null total: 291 -2023-05-08 09:15:29 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} -2023-05-08 09:15:29 STATE: test-backend-node-gpu.js start face embeddings -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-05-08 09:15:30 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:30 DATA:  test-backend-node-gpu.js result: performance: load: null total: 191 -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} -2023-05-08 09:15:30 INFO:  test-backend-node-gpu.js test face attention -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js start face attention -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention -2023-05-08 09:15:30 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:30 DATA:  test-backend-node-gpu.js result: performance: load: null total: 230 -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js passed: face attention -2023-05-08 09:15:30 INFO:  test-backend-node-gpu.js test detectors -2023-05-08 09:15:30 STATE: test-backend-node-gpu.js start detectors -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 149 -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: detector result face match -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: detector result hand match -2023-05-08 09:15:31 INFO:  test-backend-node-gpu.js test: multi-instance -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start multi instance -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: detect: random multi instance -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 57 -2023-05-08 09:15:31 INFO:  test-backend-node-gpu.js test: first instance -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start multi instance -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 52 -2023-05-08 09:15:31 INFO:  test-backend-node-gpu.js test: second instance -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start multi instance -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 56 -2023-05-08 09:15:31 INFO:  test-backend-node-gpu.js test: concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js start concurrent -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 416 -2023-05-08 09:15:32 INFO:  test-backend-node-gpu.js test: monkey-patch -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: image -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js event: detect -2023-05-08 09:15:32 STATE: test-backend-node-gpu.js passed: monkey patch -2023-05-08 09:15:33 STATE: test-backend-node-gpu.js passed: segmentation [262144] -2023-05-08 09:15:33 STATE: test-backend-node-gpu.js passeed: equal usage -2023-05-08 09:15:33 INFO:  test-backend-node-gpu.js test: input compare -2023-05-08 09:15:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-05-08 09:15:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2023-05-08 09:15:33 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 -2023-05-08 09:15:33 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} -2023-05-08 09:15:33 INFO:  test-backend-node-gpu.js tensors 14855 -2023-05-08 09:15:33 INFO:  test-backend-node-gpu.js test complete: 16468 ms -2023-05-08 09:15:34 INFO:  -2023-05-08 09:15:34 INFO:  test-backend-node-wasm.js start -2023-05-08 09:15:34 DATA:  test-backend-node-wasm.js stdout: 2023-05-08 09:15:34 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.5.0/dist/ -2023-05-08 09:15:34 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ -2023-05-08 09:15:34 INFO:  test-backend-node-wasm.js test: configuration validation -2023-05-08 09:15:34 STATE: test-backend-node-wasm.js passed: configuration default validation [] -2023-05-08 09:15:34 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2023-05-08 09:15:34 INFO:  test-backend-node-wasm.js test: model load -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"}] -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js test: warmup -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js passed: create human -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js human version: 3.0.6 -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js tfjs version: 4.5.0 -2023-05-08 09:15:35 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.5.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":168} -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js passed: set backend: wasm -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js tensors 1785 -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js passed: load models 11 -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js passed: warmup: none default -2023-05-08 09:15:35 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2023-05-08 09:15:35 DATA:  test-backend-node-wasm.js result: performance: load: null total: null -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js passed: warmup none result match -2023-05-08 09:15:35 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js event: warmup -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js passed: warmup: face default -2023-05-08 09:15:36 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-05-08 09:15:36 DATA:  test-backend-node-wasm.js result: performance: load: null total: 481 -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js passed: warmup face result match -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js event: warmup -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js passed: warmup: body default -2023-05-08 09:15:36 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:36 DATA:  test-backend-node-wasm.js result: performance: load: null total: 316 -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js passed: warmup body result match -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2023-05-08 09:15:36 INFO:  test-backend-node-wasm.js test: details verification -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js start default -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:36 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default -2023-05-08 09:15:37 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:37 DATA:  test-backend-node-wasm.js result: performance: load: null total: 315 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face length 1 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details body length 1 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details hand length 1 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details gesture length 7 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details object length 1 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: details object 0.72 person -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} -2023-05-08 09:15:37 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:38 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2023-05-08 09:15:39 INFO:  test-backend-node-wasm.js test default -2023-05-08 09:15:39 STATE: test-backend-node-wasm.js start async -2023-05-08 09:15:41 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:41 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:41 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async -2023-05-08 09:15:41 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:41 DATA:  test-backend-node-wasm.js result: performance: load: null total: 398 -2023-05-08 09:15:41 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 -2023-05-08 09:15:41 INFO:  test-backend-node-wasm.js test sync -2023-05-08 09:15:41 STATE: test-backend-node-wasm.js start sync -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync -2023-05-08 09:15:43 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:43 DATA:  test-backend-node-wasm.js result: performance: load: null total: 399 -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 -2023-05-08 09:15:43 INFO:  test-backend-node-wasm.js test: image process -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] -2023-05-08 09:15:43 INFO:  test-backend-node-wasm.js test: image null -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor -2023-05-08 09:15:43 INFO:  test-backend-node-wasm.js test face similarity -2023-05-08 09:15:43 STATE: test-backend-node-wasm.js start face similarity -2023-05-08 09:15:44 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-05-08 09:15:44 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity -2023-05-08 09:15:45 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-05-08 09:15:45 DATA:  test-backend-node-wasm.js result: performance: load: null total: 384 -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js start face similarity -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity -2023-05-08 09:15:45 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:45 DATA:  test-backend-node-wasm.js result: performance: load: null total: 283 -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js start face similarity -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2023-05-08 09:15:45 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity -2023-05-08 09:15:46 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2023-05-08 09:15:46 DATA:  test-backend-node-wasm.js result: performance: load: null total: 282 -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js passed: face descriptor -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} -2023-05-08 09:15:46 INFO:  test-backend-node-wasm.js test object -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js start object -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2023-05-08 09:15:46 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:46 DATA:  test-backend-node-wasm.js result: performance: load: null total: 291 -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js passed: centernet -2023-05-08 09:15:46 STATE: test-backend-node-wasm.js start object -2023-05-08 09:15:47 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:47 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:47 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2023-05-08 09:15:47 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2023-05-08 09:15:47 DATA:  test-backend-node-wasm.js result: performance: load: null total: 341 -2023-05-08 09:15:47 STATE: test-backend-node-wasm.js passed: nanodet -2023-05-08 09:15:47 INFO:  test-backend-node-wasm.js test sensitive -2023-05-08 09:15:47 STATE: test-backend-node-wasm.js start sensitive -2023-05-08 09:15:48 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:48 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive -2023-05-08 09:15:49 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:49 DATA:  test-backend-node-wasm.js result: performance: load: null total: 277 -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js passed: sensitive result match -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js passed: sensitive face result match -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js passed: sensitive body result match -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js passed: sensitive hand result match -2023-05-08 09:15:49 INFO:  test-backend-node-wasm.js test body -2023-05-08 09:15:49 STATE: test-backend-node-wasm.js start blazepose -2023-05-08 09:15:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:51 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:51 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:51 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose -2023-05-08 09:15:51 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2023-05-08 09:15:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 424 -2023-05-08 09:15:51 STATE: test-backend-node-wasm.js passed: blazepose -2023-05-08 09:15:51 STATE: test-backend-node-wasm.js start efficientpose -2023-05-08 09:15:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:52 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose -2023-05-08 09:15:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2023-05-08 09:15:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 631 -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js passed: efficientpose -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js start posenet -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet -2023-05-08 09:15:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2023-05-08 09:15:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 249 -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js passed: posenet -2023-05-08 09:15:53 STATE: test-backend-node-wasm.js start movenet -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet -2023-05-08 09:15:54 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-05-08 09:15:54 DATA:  test-backend-node-wasm.js result: performance: load: null total: 208 -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js passed: movenet -2023-05-08 09:15:54 INFO:  test-backend-node-wasm.js test face matching -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js passed: face database 40 -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} -2023-05-08 09:15:54 INFO:  test-backend-node-wasm.js test face similarity alternative -2023-05-08 09:15:54 STATE: test-backend-node-wasm.js start face embeddings -2023-05-08 09:15:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-05-08 09:15:55 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:55 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-05-08 09:15:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 291 -2023-05-08 09:15:55 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} -2023-05-08 09:15:55 STATE: test-backend-node-wasm.js start face embeddings -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js event: detect -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-05-08 09:15:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-05-08 09:15:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 259 -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} -2023-05-08 09:15:56 INFO:  test-backend-node-wasm.js test face attention -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js start face attention -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-05-08 09:15:56 STATE: test-backend-node-wasm.js event: image -2023-05-08 09:15:57 ERROR: test-backend-node-wasm.js failed: testDetect face attention -2023-05-08 09:15:57 ERROR: test-backend-node-wasm.js uncaughtException {"name":"Error","message":"BatchMatMul for non non-float32 tensors not yet supported.","stack":["Error: BatchMatMul for non non-float32 tensors not yet supported."," at Object.batchMatMul [as kernelFunc] (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.5.0_@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.node.js:1221:15)"," at kernelFunc (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4707:32)"," at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4767:27"," at Engine.scopedRun (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4572:23)"," at Engine.runKernelFunc (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4763:14)"," at Engine.runKernel (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4636:21)"," at matMul_ (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.5.0_@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:13409:19)"," at Object.matMul__op [as matMul] (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.5.0_@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:12218:29)"," at executeOp$9 (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.5.0_@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:29523:25)"," at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.5.0_@tensorflow+tfjs-core@4.5.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:30103:50"]} -2023-05-08 09:15:57 STATE: all tests complete -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"test-node-gear.js","passed":4,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} -2023-05-08 09:15:57 INFO:  status {"test":"test-backend-node-wasm.js","passed":92,"failed":1} -2023-05-08 09:15:57 INFO:  failures {"count":1} -2023-05-08 09:15:57 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed:","testDetect face attention"]]} +2023-06-12 13:27:26 INFO:  @vladmandic/human version 3.0.7 +2023-06-12 13:27:26 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 +2023-06-12 13:27:26 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] +2023-06-12 13:27:26 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start +2023-06-12 13:27:28 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start +2023-06-12 13:27:28 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start +2023-06-12 13:27:29 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start +2023-06-12 13:27:30 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start +2023-06-12 13:27:31 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start +2023-06-12 13:27:33 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start +2023-06-12 13:27:48 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start +2023-06-12 13:27:49 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start +2023-06-12 13:28:03 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start +2023-06-12 13:28:09 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] +2023-06-12 13:28:09 INFO:  +2023-06-12 13:28:09 INFO:  test-node-load.js start +2023-06-12 13:28:09 INFO:  test-node-load.js load start {"human":"3.0.7","tf":"4.7.0","progress":0} +2023-06-12 13:28:09 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} +2023-06-12 13:28:09 DATA:  test-node-load.js load interval {"elapsed":12,"progress":0} +2023-06-12 13:28:09 DATA:  test-node-load.js load interval {"elapsed":22,"progress":0.06164218554150753} +2023-06-12 13:28:09 DATA:  test-node-load.js load interval {"elapsed":34,"progress":0.12865823126207815} +2023-06-12 13:28:09 DATA:  test-node-load.js load interval {"elapsed":65,"progress":0.4726970548784964} +2023-06-12 13:28:09 DATA:  test-node-load.js load interval {"elapsed":78,"progress":0.68355493315821} +2023-06-12 13:28:10 STATE: test-node-load.js passed {"progress":1} +2023-06-12 13:28:10 INFO:  test-node-load.js load final {"progress":1} +2023-06-12 13:28:10 DATA:  test-node-load.js load interval {"elapsed":511,"progress":1} +2023-06-12 13:28:10 INFO:  +2023-06-12 13:28:10 INFO:  test-node-gear.js start +2023-06-12 13:28:10 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] +2023-06-12 13:28:11 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg +2023-06-12 13:28:11 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92,"emotion":[]} +2023-06-12 13:28:11 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg +2023-06-12 13:28:11 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}],"emotion":[]} +2023-06-12 13:28:12 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2023-06-12 13:28:12 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99,"emotion":[]} +2023-06-12 13:28:12 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2023-06-12 13:28:12 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"male","emotion":[]} +2023-06-12 13:28:12 INFO:  +2023-06-12 13:28:12 INFO:  test-backend-node.js start +2023-06-12 13:28:12 INFO:  test-backend-node.js test: configuration validation +2023-06-12 13:28:12 STATE: test-backend-node.js passed: configuration default validation [] +2023-06-12 13:28:12 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-06-12 13:28:12 INFO:  test-backend-node.js test: model load +2023-06-12 13:28:12 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2023-06-12 13:28:12 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-06-12 13:28:12 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-06-12 13:28:12 INFO:  test-backend-node.js test: warmup +2023-06-12 13:28:12 STATE: test-backend-node.js passed: create human +2023-06-12 13:28:12 INFO:  test-backend-node.js human version: 3.0.7 +2023-06-12 13:28:12 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 +2023-06-12 13:28:12 INFO:  test-backend-node.js tfjs version: 4.7.0 +2023-06-12 13:28:12 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.7.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} +2023-06-12 13:28:12 STATE: test-backend-node.js passed: set backend: tensorflow +2023-06-12 13:28:12 STATE: test-backend-node.js tensors 1785 +2023-06-12 13:28:12 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 +2023-06-12 13:28:12 STATE: test-backend-node.js passed: load models 11 +2023-06-12 13:28:12 STATE: test-backend-node.js passed: warmup: none default +2023-06-12 13:28:12 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-06-12 13:28:12 DATA:  test-backend-node.js result: performance: load: null total: null +2023-06-12 13:28:12 STATE: test-backend-node.js passed: warmup none result match +2023-06-12 13:28:12 STATE: test-backend-node.js event: image +2023-06-12 13:28:13 STATE: test-backend-node.js event: detect +2023-06-12 13:28:13 STATE: test-backend-node.js event: warmup +2023-06-12 13:28:13 STATE: test-backend-node.js passed: warmup: face default +2023-06-12 13:28:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2023-06-12 13:28:13 DATA:  test-backend-node.js result: performance: load: null total: 547 +2023-06-12 13:28:13 STATE: test-backend-node.js passed: warmup face result match +2023-06-12 13:28:13 STATE: test-backend-node.js event: image +2023-06-12 13:28:13 STATE: test-backend-node.js event: detect +2023-06-12 13:28:13 STATE: test-backend-node.js event: warmup +2023-06-12 13:28:13 STATE: test-backend-node.js passed: warmup: body default +2023-06-12 13:28:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:13 DATA:  test-backend-node.js result: performance: load: null total: 392 +2023-06-12 13:28:13 STATE: test-backend-node.js passed: warmup body result match +2023-06-12 13:28:13 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-06-12 13:28:13 INFO:  test-backend-node.js test: details verification +2023-06-12 13:28:13 STATE: test-backend-node.js start default +2023-06-12 13:28:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:13 STATE: test-backend-node.js event: image +2023-06-12 13:28:14 STATE: test-backend-node.js event: detect +2023-06-12 13:28:14 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default +2023-06-12 13:28:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:14 DATA:  test-backend-node.js result: performance: load: null total: 419 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face length 1 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face score 1 0.93 1 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face arrays 4 478 1024 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details face liveness 0.83 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details body length 1 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details body 0.92 17 6 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details hand length 1 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details hand 0.51 0.73 point +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details hand arrays 21 5 7 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details gesture length 7 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details object length 1 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: details object 0.72 person +2023-06-12 13:28:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2023-06-12 13:28:14 STATE: test-backend-node.js event: image +2023-06-12 13:28:14 STATE: test-backend-node.js event: detect +2023-06-12 13:28:14 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-06-12 13:28:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2023-06-12 13:28:14 STATE: test-backend-node.js event: image +2023-06-12 13:28:15 STATE: test-backend-node.js event: detect +2023-06-12 13:28:15 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-06-12 13:28:15 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:15 STATE: test-backend-node.js event: image +2023-06-12 13:28:15 STATE: test-backend-node.js event: detect +2023-06-12 13:28:15 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-06-12 13:28:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:16 STATE: test-backend-node.js event: image +2023-06-12 13:28:16 STATE: test-backend-node.js event: detect +2023-06-12 13:28:16 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-06-12 13:28:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-06-12 13:28:16 STATE: test-backend-node.js event: image +2023-06-12 13:28:17 STATE: test-backend-node.js event: detect +2023-06-12 13:28:17 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-06-12 13:28:17 INFO:  test-backend-node.js test default +2023-06-12 13:28:17 STATE: test-backend-node.js start async +2023-06-12 13:28:17 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:17 STATE: test-backend-node.js event: image +2023-06-12 13:28:17 STATE: test-backend-node.js event: detect +2023-06-12 13:28:17 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async +2023-06-12 13:28:17 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:17 DATA:  test-backend-node.js result: performance: load: null total: 394 +2023-06-12 13:28:17 STATE: test-backend-node.js passed: default result face match 1 female 0.97 +2023-06-12 13:28:17 INFO:  test-backend-node.js test sync +2023-06-12 13:28:17 STATE: test-backend-node.js start sync +2023-06-12 13:28:17 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:17 STATE: test-backend-node.js event: image +2023-06-12 13:28:18 STATE: test-backend-node.js event: detect +2023-06-12 13:28:18 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync +2023-06-12 13:28:18 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:18 DATA:  test-backend-node.js result: performance: load: null total: 399 +2023-06-12 13:28:18 STATE: test-backend-node.js passed: default sync 1 female 0.97 +2023-06-12 13:28:18 INFO:  test-backend-node.js test: image process +2023-06-12 13:28:18 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:18 STATE: test-backend-node.js passed: image input null [1,256,256,3] +2023-06-12 13:28:18 INFO:  test-backend-node.js test: image null +2023-06-12 13:28:18 STATE: test-backend-node.js passed: invalid input could not convert input to tensor +2023-06-12 13:28:18 INFO:  test-backend-node.js test face similarity +2023-06-12 13:28:18 STATE: test-backend-node.js start face similarity +2023-06-12 13:28:18 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:18 STATE: test-backend-node.js event: image +2023-06-12 13:28:18 STATE: test-backend-node.js event: detect +2023-06-12 13:28:18 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity +2023-06-12 13:28:18 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-06-12 13:28:18 DATA:  test-backend-node.js result: performance: load: null total: 406 +2023-06-12 13:28:18 STATE: test-backend-node.js start face similarity +2023-06-12 13:28:19 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:19 STATE: test-backend-node.js event: image +2023-06-12 13:28:19 STATE: test-backend-node.js event: detect +2023-06-12 13:28:19 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity +2023-06-12 13:28:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:19 DATA:  test-backend-node.js result: performance: load: null total: 368 +2023-06-12 13:28:19 STATE: test-backend-node.js start face similarity +2023-06-12 13:28:19 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:19 STATE: test-backend-node.js event: image +2023-06-12 13:28:19 STATE: test-backend-node.js event: detect +2023-06-12 13:28:19 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-06-12 13:28:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-06-12 13:28:19 DATA:  test-backend-node.js result: performance: load: null total: 356 +2023-06-12 13:28:19 STATE: test-backend-node.js passed: face descriptor +2023-06-12 13:28:19 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} +2023-06-12 13:28:19 INFO:  test-backend-node.js test object +2023-06-12 13:28:19 STATE: test-backend-node.js start object +2023-06-12 13:28:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:20 STATE: test-backend-node.js event: image +2023-06-12 13:28:20 STATE: test-backend-node.js event: detect +2023-06-12 13:28:20 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2023-06-12 13:28:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:20 DATA:  test-backend-node.js result: performance: load: null total: 360 +2023-06-12 13:28:20 STATE: test-backend-node.js passed: centernet +2023-06-12 13:28:20 STATE: test-backend-node.js start object +2023-06-12 13:28:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:21 STATE: test-backend-node.js event: image +2023-06-12 13:28:21 STATE: test-backend-node.js event: detect +2023-06-12 13:28:21 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2023-06-12 13:28:21 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:21 DATA:  test-backend-node.js result: performance: load: null total: 375 +2023-06-12 13:28:21 STATE: test-backend-node.js passed: nanodet +2023-06-12 13:28:21 INFO:  test-backend-node.js test sensitive +2023-06-12 13:28:21 STATE: test-backend-node.js start sensitive +2023-06-12 13:28:22 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:22 STATE: test-backend-node.js event: image +2023-06-12 13:28:22 STATE: test-backend-node.js event: detect +2023-06-12 13:28:22 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive +2023-06-12 13:28:22 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:22 DATA:  test-backend-node.js result: performance: load: null total: 326 +2023-06-12 13:28:22 STATE: test-backend-node.js passed: sensitive result match +2023-06-12 13:28:22 STATE: test-backend-node.js passed: sensitive face result match +2023-06-12 13:28:22 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-06-12 13:28:22 STATE: test-backend-node.js passed: sensitive body result match +2023-06-12 13:28:22 STATE: test-backend-node.js passed: sensitive hand result match +2023-06-12 13:28:22 INFO:  test-backend-node.js test body +2023-06-12 13:28:22 STATE: test-backend-node.js start blazepose +2023-06-12 13:28:24 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:24 STATE: test-backend-node.js event: image +2023-06-12 13:28:24 STATE: test-backend-node.js event: detect +2023-06-12 13:28:24 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose +2023-06-12 13:28:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-06-12 13:28:24 DATA:  test-backend-node.js result: performance: load: null total: 435 +2023-06-12 13:28:24 STATE: test-backend-node.js passed: blazepose +2023-06-12 13:28:24 STATE: test-backend-node.js start efficientpose +2023-06-12 13:28:25 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:25 STATE: test-backend-node.js event: image +2023-06-12 13:28:25 STATE: test-backend-node.js event: detect +2023-06-12 13:28:25 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-06-12 13:28:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-06-12 13:28:25 DATA:  test-backend-node.js result: performance: load: null total: 370 +2023-06-12 13:28:25 STATE: test-backend-node.js passed: efficientpose +2023-06-12 13:28:25 STATE: test-backend-node.js start posenet +2023-06-12 13:28:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:26 STATE: test-backend-node.js event: image +2023-06-12 13:28:26 STATE: test-backend-node.js event: detect +2023-06-12 13:28:26 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet +2023-06-12 13:28:26 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-06-12 13:28:26 DATA:  test-backend-node.js result: performance: load: null total: 304 +2023-06-12 13:28:26 STATE: test-backend-node.js passed: posenet +2023-06-12 13:28:26 STATE: test-backend-node.js start movenet +2023-06-12 13:28:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:26 STATE: test-backend-node.js event: image +2023-06-12 13:28:26 STATE: test-backend-node.js event: detect +2023-06-12 13:28:26 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet +2023-06-12 13:28:26 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:26 DATA:  test-backend-node.js result: performance: load: null total: 346 +2023-06-12 13:28:26 STATE: test-backend-node.js passed: movenet +2023-06-12 13:28:26 INFO:  test-backend-node.js test face matching +2023-06-12 13:28:26 STATE: test-backend-node.js passed: face database 40 +2023-06-12 13:28:26 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} +2023-06-12 13:28:26 INFO:  test-backend-node.js test face similarity alternative +2023-06-12 13:28:26 STATE: test-backend-node.js start face embeddings +2023-06-12 13:28:27 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:27 STATE: test-backend-node.js event: image +2023-06-12 13:28:27 STATE: test-backend-node.js event: detect +2023-06-12 13:28:27 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-06-12 13:28:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:27 DATA:  test-backend-node.js result: performance: load: null total: 374 +2023-06-12 13:28:27 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} +2023-06-12 13:28:27 STATE: test-backend-node.js start face embeddings +2023-06-12 13:28:28 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:28 STATE: test-backend-node.js event: image +2023-06-12 13:28:29 STATE: test-backend-node.js event: detect +2023-06-12 13:28:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-06-12 13:28:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:29 DATA:  test-backend-node.js result: performance: load: null total: 395 +2023-06-12 13:28:29 STATE: test-backend-node.js passed: insightface {"embedding":512} +2023-06-12 13:28:29 INFO:  test-backend-node.js test face attention +2023-06-12 13:28:29 STATE: test-backend-node.js start face attention +2023-06-12 13:28:29 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:29 STATE: test-backend-node.js event: image +2023-06-12 13:28:29 STATE: test-backend-node.js event: detect +2023-06-12 13:28:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention +2023-06-12 13:28:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:29 DATA:  test-backend-node.js result: performance: load: null total: 352 +2023-06-12 13:28:29 STATE: test-backend-node.js passed: face attention +2023-06-12 13:28:29 INFO:  test-backend-node.js test detectors +2023-06-12 13:28:29 STATE: test-backend-node.js start detectors +2023-06-12 13:28:30 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:30 STATE: test-backend-node.js event: image +2023-06-12 13:28:30 STATE: test-backend-node.js event: detect +2023-06-12 13:28:30 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors +2023-06-12 13:28:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:30 DATA:  test-backend-node.js result: performance: load: null total: 170 +2023-06-12 13:28:30 STATE: test-backend-node.js passed: detector result face match +2023-06-12 13:28:30 STATE: test-backend-node.js passed: detector result hand match +2023-06-12 13:28:30 INFO:  test-backend-node.js test: multi-instance +2023-06-12 13:28:30 STATE: test-backend-node.js start multi instance +2023-06-12 13:28:30 STATE: test-backend-node.js event: image +2023-06-12 13:28:30 STATE: test-backend-node.js event: detect +2023-06-12 13:28:30 STATE: test-backend-node.js passed: detect: random multi instance +2023-06-12 13:28:30 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-06-12 13:28:30 DATA:  test-backend-node.js result: performance: load: null total: 166 +2023-06-12 13:28:30 INFO:  test-backend-node.js test: first instance +2023-06-12 13:28:30 STATE: test-backend-node.js start multi instance +2023-06-12 13:28:30 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:30 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-06-12 13:28:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:30 DATA:  test-backend-node.js result: performance: load: null total: 172 +2023-06-12 13:28:30 INFO:  test-backend-node.js test: second instance +2023-06-12 13:28:30 STATE: test-backend-node.js start multi instance +2023-06-12 13:28:30 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-06-12 13:28:31 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:31 DATA:  test-backend-node.js result: performance: load: null total: 160 +2023-06-12 13:28:31 INFO:  test-backend-node.js test: concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js start concurrent +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:31 STATE: test-backend-node.js event: image +2023-06-12 13:28:31 STATE: test-backend-node.js event: image +2023-06-12 13:28:31 STATE: test-backend-node.js event: image +2023-06-12 13:28:33 STATE: test-backend-node.js event: detect +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js event: detect +2023-06-12 13:28:33 STATE: test-backend-node.js event: detect +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-06-12 13:28:33 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:33 DATA:  test-backend-node.js result: performance: load: null total: 1409 +2023-06-12 13:28:33 INFO:  test-backend-node.js test: monkey-patch +2023-06-12 13:28:33 STATE: test-backend-node.js event: image +2023-06-12 13:28:33 STATE: test-backend-node.js event: detect +2023-06-12 13:28:33 STATE: test-backend-node.js passed: monkey patch +2023-06-12 13:28:33 STATE: test-backend-node.js passed: segmentation [262144] +2023-06-12 13:28:33 STATE: test-backend-node.js passeed: equal usage +2023-06-12 13:28:33 INFO:  test-backend-node.js test: input compare +2023-06-12 13:28:33 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:34 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:34 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 +2023-06-12 13:28:34 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} +2023-06-12 13:28:34 INFO:  test-backend-node.js tensors 14855 +2023-06-12 13:28:34 INFO:  test-backend-node.js test complete: 21702 ms +2023-06-12 13:28:34 INFO:  +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js start +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js test: configuration validation +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: configuration default validation [] +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js test: model load +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js test: warmup +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: create human +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js human version: 3.0.7 +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js tfjs version: 4.7.0 +2023-06-12 13:28:34 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.7.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: set backend: tensorflow +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js tensors 1785 +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: load models 11 +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: warmup: none default +2023-06-12 13:28:34 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-06-12 13:28:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: null +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js passed: warmup none result match +2023-06-12 13:28:34 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js event: warmup +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js passed: warmup: face default +2023-06-12 13:28:35 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2023-06-12 13:28:35 DATA:  test-backend-node-gpu.js result: performance: load: null total: 556 +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js passed: warmup face result match +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js event: warmup +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js passed: warmup: body default +2023-06-12 13:28:35 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:35 DATA:  test-backend-node-gpu.js result: performance: load: null total: 396 +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js passed: warmup body result match +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-06-12 13:28:35 INFO:  test-backend-node-gpu.js test: details verification +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js start default +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:35 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default +2023-06-12 13:28:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 392 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face length 1 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details body length 1 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details hand length 1 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details gesture length 7 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details object length 1 +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: details object 0.72 person +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:36 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-06-12 13:28:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2023-06-12 13:28:37 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:37 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:37 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-06-12 13:28:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:37 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-06-12 13:28:38 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-06-12 13:28:39 INFO:  test-backend-node-gpu.js test default +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js start async +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async +2023-06-12 13:28:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 395 +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 +2023-06-12 13:28:39 INFO:  test-backend-node-gpu.js test sync +2023-06-12 13:28:39 STATE: test-backend-node-gpu.js start sync +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync +2023-06-12 13:28:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 393 +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 +2023-06-12 13:28:40 INFO:  test-backend-node-gpu.js test: image process +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] +2023-06-12 13:28:40 INFO:  test-backend-node-gpu.js test: image null +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor +2023-06-12 13:28:40 INFO:  test-backend-node-gpu.js test face similarity +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js start face similarity +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity +2023-06-12 13:28:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-06-12 13:28:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 405 +2023-06-12 13:28:40 STATE: test-backend-node-gpu.js start face similarity +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity +2023-06-12 13:28:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 360 +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js start face similarity +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-06-12 13:28:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-06-12 13:28:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 345 +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js passed: face descriptor +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} +2023-06-12 13:28:41 INFO:  test-backend-node-gpu.js test object +2023-06-12 13:28:41 STATE: test-backend-node-gpu.js start object +2023-06-12 13:28:42 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:42 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:42 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2023-06-12 13:28:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 358 +2023-06-12 13:28:42 STATE: test-backend-node-gpu.js passed: centernet +2023-06-12 13:28:42 STATE: test-backend-node-gpu.js start object +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2023-06-12 13:28:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 363 +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js passed: nanodet +2023-06-12 13:28:43 INFO:  test-backend-node-gpu.js test sensitive +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js start sensitive +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:43 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive +2023-06-12 13:28:44 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:44 DATA:  test-backend-node-gpu.js result: performance: load: null total: 367 +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js passed: sensitive result match +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js passed: sensitive face result match +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js passed: sensitive body result match +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js passed: sensitive hand result match +2023-06-12 13:28:44 INFO:  test-backend-node-gpu.js test body +2023-06-12 13:28:44 STATE: test-backend-node-gpu.js start blazepose +2023-06-12 13:28:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:45 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:45 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose +2023-06-12 13:28:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-06-12 13:28:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 457 +2023-06-12 13:28:45 STATE: test-backend-node-gpu.js passed: blazepose +2023-06-12 13:28:45 STATE: test-backend-node-gpu.js start efficientpose +2023-06-12 13:28:46 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:46 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:46 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-06-12 13:28:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-06-12 13:28:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 383 +2023-06-12 13:28:46 STATE: test-backend-node-gpu.js passed: efficientpose +2023-06-12 13:28:46 STATE: test-backend-node-gpu.js start posenet +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet +2023-06-12 13:28:47 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-06-12 13:28:47 DATA:  test-backend-node-gpu.js result: performance: load: null total: 307 +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: posenet +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js start movenet +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet +2023-06-12 13:28:47 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:47 DATA:  test-backend-node-gpu.js result: performance: load: null total: 330 +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: movenet +2023-06-12 13:28:47 INFO:  test-backend-node-gpu.js test face matching +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: face database 40 +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} +2023-06-12 13:28:47 INFO:  test-backend-node-gpu.js test face similarity alternative +2023-06-12 13:28:47 STATE: test-backend-node-gpu.js start face embeddings +2023-06-12 13:28:48 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:48 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:48 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:48 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-06-12 13:28:48 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:48 DATA:  test-backend-node-gpu.js result: performance: load: null total: 375 +2023-06-12 13:28:48 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} +2023-06-12 13:28:48 STATE: test-backend-node-gpu.js start face embeddings +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-06-12 13:28:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 393 +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} +2023-06-12 13:28:49 INFO:  test-backend-node-gpu.js test face attention +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js start face attention +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:49 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention +2023-06-12 13:28:50 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:50 DATA:  test-backend-node-gpu.js result: performance: load: null total: 354 +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js passed: face attention +2023-06-12 13:28:50 INFO:  test-backend-node-gpu.js test detectors +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js start detectors +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors +2023-06-12 13:28:50 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:50 DATA:  test-backend-node-gpu.js result: performance: load: null total: 186 +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js passed: detector result face match +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js passed: detector result hand match +2023-06-12 13:28:50 INFO:  test-backend-node-gpu.js test: multi-instance +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js start multi instance +2023-06-12 13:28:50 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: detect: random multi instance +2023-06-12 13:28:51 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-06-12 13:28:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 159 +2023-06-12 13:28:51 INFO:  test-backend-node-gpu.js test: first instance +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start multi instance +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-06-12 13:28:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 156 +2023-06-12 13:28:51 INFO:  test-backend-node-gpu.js test: second instance +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start multi instance +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-06-12 13:28:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 154 +2023-06-12 13:28:51 INFO:  test-backend-node-gpu.js test: concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js start concurrent +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:52 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:28:53 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1392 +2023-06-12 13:28:53 INFO:  test-backend-node-gpu.js test: monkey-patch +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js event: image +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js event: detect +2023-06-12 13:28:53 STATE: test-backend-node-gpu.js passed: monkey patch +2023-06-12 13:28:54 STATE: test-backend-node-gpu.js passed: segmentation [262144] +2023-06-12 13:28:54 STATE: test-backend-node-gpu.js passeed: equal usage +2023-06-12 13:28:54 INFO:  test-backend-node-gpu.js test: input compare +2023-06-12 13:28:54 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-06-12 13:28:54 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-06-12 13:28:54 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 +2023-06-12 13:28:54 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} +2023-06-12 13:28:54 INFO:  test-backend-node-gpu.js tensors 14855 +2023-06-12 13:28:54 INFO:  test-backend-node-gpu.js test complete: 19773 ms +2023-06-12 13:28:54 INFO:  +2023-06-12 13:28:54 INFO:  test-backend-node-wasm.js start +2023-06-12 13:28:54 DATA:  test-backend-node-wasm.js stdout: 2023-06-12 13:28:54 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.7.0/dist/ +2023-06-12 13:28:54 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ +2023-06-12 13:28:54 INFO:  test-backend-node-wasm.js test: configuration validation +2023-06-12 13:28:54 STATE: test-backend-node-wasm.js passed: configuration default validation [] +2023-06-12 13:28:54 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-06-12 13:28:54 INFO:  test-backend-node-wasm.js test: model load +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"movenet","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"liveness","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"emotion","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"facemesh","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"antispoof","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"centernet","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"handtrack","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"iris","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"faceres","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/faceres.json"}] +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js test: warmup +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: create human +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js human version: 3.0.7 +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js tfjs version: 4.7.0 +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.7.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":174} +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: set backend: wasm +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js tensors 1785 +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: load models 11 +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: warmup: none default +2023-06-12 13:28:56 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-06-12 13:28:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: null +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: warmup none result match +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js event: warmup +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: warmup: face default +2023-06-12 13:28:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-06-12 13:28:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 472 +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: warmup face result match +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js event: warmup +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: warmup: body default +2023-06-12 13:28:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 340 +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js passed: warmup body result match +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-06-12 13:28:56 INFO:  test-backend-node-wasm.js test: details verification +2023-06-12 13:28:56 STATE: test-backend-node-wasm.js start default +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default +2023-06-12 13:28:57 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:28:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 323 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face length 1 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details body length 1 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details hand length 1 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details gesture length 7 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details object length 1 +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: details object 0.72 person +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} +2023-06-12 13:28:57 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:28:58 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-06-12 13:28:59 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:00 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:00 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-06-12 13:29:00 INFO:  test-backend-node-wasm.js test default +2023-06-12 13:29:00 STATE: test-backend-node-wasm.js start async +2023-06-12 13:29:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:01 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:02 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async +2023-06-12 13:29:02 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:29:02 DATA:  test-backend-node-wasm.js result: performance: load: null total: 409 +2023-06-12 13:29:02 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 +2023-06-12 13:29:02 INFO:  test-backend-node-wasm.js test sync +2023-06-12 13:29:02 STATE: test-backend-node-wasm.js start sync +2023-06-12 13:29:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:03 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync +2023-06-12 13:29:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:29:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 429 +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 +2023-06-12 13:29:04 INFO:  test-backend-node-wasm.js test: image process +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] +2023-06-12 13:29:04 INFO:  test-backend-node-wasm.js test: image null +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor +2023-06-12 13:29:04 INFO:  test-backend-node-wasm.js test face similarity +2023-06-12 13:29:04 STATE: test-backend-node-wasm.js start face similarity +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity +2023-06-12 13:29:05 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-06-12 13:29:05 DATA:  test-backend-node-wasm.js result: performance: load: null total: 401 +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js start face similarity +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:05 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity +2023-06-12 13:29:06 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:29:06 DATA:  test-backend-node-wasm.js result: performance: load: null total: 299 +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js start face similarity +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-06-12 13:29:06 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-06-12 13:29:06 DATA:  test-backend-node-wasm.js result: performance: load: null total: 284 +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js passed: face descriptor +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} +2023-06-12 13:29:06 INFO:  test-backend-node-wasm.js test object +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js start object +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:06 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:07 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2023-06-12 13:29:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:29:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 303 +2023-06-12 13:29:07 STATE: test-backend-node-wasm.js passed: centernet +2023-06-12 13:29:07 STATE: test-backend-node-wasm.js start object +2023-06-12 13:29:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:07 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:08 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:08 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2023-06-12 13:29:08 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-06-12 13:29:08 DATA:  test-backend-node-wasm.js result: performance: load: null total: 348 +2023-06-12 13:29:08 STATE: test-backend-node-wasm.js passed: nanodet +2023-06-12 13:29:08 INFO:  test-backend-node-wasm.js test sensitive +2023-06-12 13:29:08 STATE: test-backend-node-wasm.js start sensitive +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive +2023-06-12 13:29:09 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:29:09 DATA:  test-backend-node-wasm.js result: performance: load: null total: 288 +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: sensitive result match +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: sensitive face result match +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: sensitive body result match +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js passed: sensitive hand result match +2023-06-12 13:29:09 INFO:  test-backend-node-wasm.js test body +2023-06-12 13:29:09 STATE: test-backend-node-wasm.js start blazepose +2023-06-12 13:29:12 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:12 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:12 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:12 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose +2023-06-12 13:29:12 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-06-12 13:29:12 DATA:  test-backend-node-wasm.js result: performance: load: null total: 478 +2023-06-12 13:29:12 STATE: test-backend-node-wasm.js passed: blazepose +2023-06-12 13:29:12 STATE: test-backend-node-wasm.js start efficientpose +2023-06-12 13:29:13 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:13 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-06-12 13:29:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-06-12 13:29:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 701 +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js passed: efficientpose +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js start posenet +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet +2023-06-12 13:29:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-06-12 13:29:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 265 +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js passed: posenet +2023-06-12 13:29:14 STATE: test-backend-node-wasm.js start movenet +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet +2023-06-12 13:29:15 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-06-12 13:29:15 DATA:  test-backend-node-wasm.js result: performance: load: null total: 204 +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js passed: movenet +2023-06-12 13:29:15 INFO:  test-backend-node-wasm.js test face matching +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js passed: face database 40 +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} +2023-06-12 13:29:15 INFO:  test-backend-node-wasm.js test face similarity alternative +2023-06-12 13:29:15 STATE: test-backend-node-wasm.js start face embeddings +2023-06-12 13:29:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-06-12 13:29:16 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:16 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:16 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-06-12 13:29:16 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:29:16 DATA:  test-backend-node-wasm.js result: performance: load: null total: 308 +2023-06-12 13:29:16 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} +2023-06-12 13:29:16 STATE: test-backend-node-wasm.js start face embeddings +2023-06-12 13:29:17 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-06-12 13:29:17 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:17 STATE: test-backend-node-wasm.js event: detect +2023-06-12 13:29:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-06-12 13:29:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-06-12 13:29:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 279 +2023-06-12 13:29:17 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} +2023-06-12 13:29:17 INFO:  test-backend-node-wasm.js test face attention +2023-06-12 13:29:17 STATE: test-backend-node-wasm.js start face attention +2023-06-12 13:29:18 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-06-12 13:29:18 STATE: test-backend-node-wasm.js event: image +2023-06-12 13:29:18 ERROR: test-backend-node-wasm.js failed: testDetect face attention +2023-06-12 13:29:18 ERROR: test-backend-node-wasm.js uncaughtException {"name":"Error","message":"BatchMatMul for non non-float32 tensors not yet supported.","stack":["Error: BatchMatMul for non non-float32 tensors not yet supported."," at Object.batchMatMul [as kernelFunc] (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.node.js:1280:15)"," at kernelFunc (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4704:32)"," at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4764:27"," at Engine.scopedRun (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4569:23)"," at Engine.runKernelFunc (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4760:14)"," at Engine.runKernel (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4633:21)"," at matMul_ (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:13405:19)"," at Object.matMul__op [as matMul] (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:12214:29)"," at executeOp$9 (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:29527:25)"," at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:30107:50"]} +2023-06-12 13:29:18 STATE: all tests complete +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"test-node-gear.js","passed":4,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} +2023-06-12 13:29:18 INFO:  status {"test":"test-backend-node-wasm.js","passed":92,"failed":1} +2023-06-12 13:29:18 INFO:  failures {"count":1} +2023-06-12 13:29:18 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed:","testDetect face attention"]]} diff --git a/typedoc/classes/Env.html b/typedoc/classes/Env.html index 10886c78..f9e4fe78 100644 --- a/typedoc/classes/Env.html +++ b/typedoc/classes/Env.html @@ -1,4 +1,4 @@ -Env | @vladmandic/human - v3.0.6
+Env | @vladmandic/human - v3.0.7
  • Preparing search index...
    • -
  • The search index is not available
@vladmandic/human - v3.0.6
+
  • The search index is not available
    • @vladmandic/human - v3.0.7

    Class Env

    @@ -343,7 +343,7 @@

    Modules

      -
    • @vladmandic/human - v3.0.6 +
    • @vladmandic/human - v3.0.7
      • Tensor
      • draw
        • diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html index 664ce981..b6dfbb3b 100644 --- a/typedoc/classes/GraphModel.html +++ b/typedoc/classes/GraphModel.html @@ -1,4 +1,4 @@ -GraphModel | @vladmandic/human - v3.0.6
        +GraphModel | @vladmandic/human - v3.0.7
        • Preparing search index...
          • -
        • The search index is not available
        @vladmandic/human - v3.0.6
        +
      • The search index is not available
      @vladmandic/human - v3.0.7

    Class GraphModel<ModelURL>

    @@ -37,7 +37,7 @@ the command line converter tool and loaded via tf.loadGraphModel.
  • InferenceModel
    • +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:34
    • @@ -97,7 +97,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Optional tfio: __module

    Returns GraphModel<ModelURL>

    +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:66

    • Accessors

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

    Returns string[]

    +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:49

    • inputs

      @@ -116,7 +116,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns TensorInfo[]
    +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:51

    • metadata

      @@ -125,7 +125,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns {}
      +
    • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:54

      • modelSignature

        @@ -134,7 +134,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns {}
        +
      • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:55

        • modelStructuredOutputKeys

          @@ -143,7 +143,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns {}
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:56

          • modelVersion

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

            Returns string

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:48

          • outputNodes

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

            Returns string[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:50

          • outputs

            @@ -168,7 +168,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns TensorInfo[]
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:52

          • weights

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

            Returns NamedTensorsMap

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:53

          • Methods

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

          Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:266

          • disposeIntermediateTensors

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

            Doc

            Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:259

          • execute

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

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:228

          • executeAsync

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

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:245

          • getIntermediateTensors

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

            Doc

            Returns NamedTensorsMap

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:252

          • load

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

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:72

          • loadSync

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

            artifacts: ModelArtifacts

          Returns boolean

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:79

          • predict

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

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:165

          • predictAsync

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

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:206

          • save

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

          Returns Promise<SaveResult>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.7.0_@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:124
          • @@ -449,7 +449,7 @@ scheme-based string shortcut for IOHandler.

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html index c2c16fd8..c7d0c258 100644 --- a/typedoc/classes/Human.html +++ b/typedoc/classes/Human.html @@ -1,4 +1,4 @@ -Human | @vladmandic/human - v3.0.6
              +Human | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Class Human

          @@ -558,7 +558,7 @@ Returns tensor which contains image data in RGBA format

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/classes/Tensor-1.html b/typedoc/classes/Tensor-1.html index 9a3f9501..00b8a411 100644 --- a/typedoc/classes/Tensor-1.html +++ b/typedoc/classes/Tensor-1.html @@ -1,4 +1,4 @@ -Tensor | @vladmandic/human - v3.0.6
              +Tensor | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Class Tensor<R>

          @@ -40,145 +40,145 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0)
          • TensorInfo
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:115
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:130
          • +
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          • +
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          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.d.ts:20
          • +
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          • +
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          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.d.ts:4
          • +
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          • +
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          • +
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          • +
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          • +
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          • +
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          • +
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          • +
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          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.d.ts:21
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.d.ts:4
          • +
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          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.d.ts:5
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.d.ts:20
          • +
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          • +
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          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.d.ts:20
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.d.ts:4
          • +
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.d.ts:20
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.d.ts:4
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          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.d.ts:20
          • @@ -384,7 +384,7 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0)
          id: number

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:156

          • Properties

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:137

          • Readonly dtype

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:143

          • Readonly id

          id: number

          Unique id of this tensor.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:132

          • kept

          kept: boolean

          Whether this tensor has been globally kept.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:147

          • Readonly rankType

          rankType: R

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:145

          • scopeId

          scopeId: number

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:149

          • Readonly shape

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

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:139

          • Readonly size

          size: number

          Number of elements in the tensor.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:141

          • Readonly strides

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

          numpy.ndarray.strides.html

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:155

          • Accessors

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

        • Returns boolean

          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:241

          • rank

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

          • Returns number

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:157

          • Methods

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

          this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.d.ts:21

          • acos

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.d.ts:21

          • acosh

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.d.ts:21

          • add

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.d.ts:21

          • all

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

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.d.ts:5

          • any

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

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.d.ts:5

          • argMax

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

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.d.ts:5

          • argMin

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

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.d.ts:5

          • array

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

            Doc

            Returns Promise<ArrayMap[R]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:175

          • arraySync

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

            Doc

            Returns ArrayMap[R]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:182

          • as1D

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

            T extends Tensor<Rank, T>

          Returns Tensor1D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.d.ts:21

          • as2D

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

            columns: number

          Returns Tensor2D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.d.ts:21

          • as3D

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

            depth: number

          Returns Tensor3D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.d.ts:21

          • as4D

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

            depth2: number

          Returns Tensor4D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.d.ts:21

          • as5D

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

            depth3: number

          Returns Tensor5D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.d.ts:21

          • asScalar

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

            T extends Tensor<Rank, T>

          Returns Scalar

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.d.ts:21

          • asType

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

            dtype: keyof DataTypeMap

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.d.ts:21

          • asin

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.d.ts:21

          • asinh

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.d.ts:21

          • atan

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.d.ts:21

          • atan2

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.d.ts:5

          • atanh

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.d.ts:21

          • avgPool

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

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.d.ts:22

          • batchNorm

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

            Optional varianceEpsilon: number

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.d.ts:5

          • batchToSpaceND

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

            crops: number[][]

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.d.ts:5

          • broadcastTo

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

            shape: ShapeMap[R]

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.d.ts:21

          • buffer

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

            D extends keyof DataTypeMap = "float32"

          Returns Promise<TensorBuffer<R, D>>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:163

          • bufferSync

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

            D extends keyof DataTypeMap = "float32"

          Returns TensorBuffer<R, D>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:168

          • bytes

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

            Returns Promise<Uint8Array | Uint8Array[]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:233

          • cast

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

            dtype: keyof DataTypeMap

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.d.ts:21

          • ceil

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.d.ts:21

          • clipByValue

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

            max: number

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.d.ts:21

          • clone

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:256

          • concat

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

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.d.ts:5

          • conv1d

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

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.d.ts:6

          • conv2d

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

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.d.ts:5

          • conv2dTranspose

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

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.d.ts:5

          • cos

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.d.ts:21

          • cosh

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.d.ts:21

          • cumprod

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

            Optional reverse: boolean

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.d.ts:5

          • cumsum

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

            Optional reverse: boolean

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.d.ts:5

          • data

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

            D extends keyof DataTypeMap = NumericDataType

          Returns Promise<DataTypeMap[D]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:189

          • dataSync

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

            D extends keyof DataTypeMap = NumericDataType

          Returns DataTypeMap[D]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:231

          • dataToGPU

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

            Optional options: DataToGPUWebGLOption

          Returns GPUData

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:224

          • depthToSpace

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

            dataFormat: "NHWC" | "NCHW"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.d.ts:5

          • depthwiseConv2d

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

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.d.ts:5

          • dilation2d

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

            Optional dataFormat: "NHWC"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.d.ts:5

          • dispose

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

            Doc

            Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:239

          • div

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

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.d.ts:21

          • divNoNan

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

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.d.ts:21

          • dot

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

            b: Tensor<Rank> | TensorLike

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.d.ts:5

          • elu

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

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.d.ts:5

          • equal

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

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.d.ts:5

          • erf

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.d.ts:21

          • euclideanNorm

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

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.d.ts:21

          • exp

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.d.ts:21

          • expandDims

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

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.d.ts:5

          • expm1

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.d.ts:21

          • fft

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

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.d.ts:21

          • flatten

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

            T extends Tensor<Rank, T>

          Returns Tensor1D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.d.ts:21

          • floor

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

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.d.ts:21

          • floorDiv

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

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.d.ts:5

          • gather

            @@ -1688,7 +1688,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional batchDims: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.d.ts:21

          • greater

            @@ -1706,7 +1706,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.d.ts:5

          • greaterEqual

            @@ -1724,7 +1724,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.d.ts:5

          • ifft

            @@ -1742,7 +1742,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.d.ts:21

          • irfft

            @@ -1760,7 +1760,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.d.ts:21

          • isFinite

            @@ -1778,7 +1778,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.d.ts:21

          • isInf

            @@ -1796,7 +1796,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.d.ts:21

          • isNaN

            @@ -1814,7 +1814,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.d.ts:21

          • leakyRelu

            @@ -1832,7 +1832,7 @@ parameter, so can not use a user-defined size to create the buffer.

            alpha: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.d.ts:5

          • less

            @@ -1850,7 +1850,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.d.ts:5

          • lessEqual

            @@ -1868,7 +1868,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.d.ts:5

          • localResponseNormalization

            @@ -1892,7 +1892,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional beta: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.d.ts:5

          • log

            @@ -1910,7 +1910,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.d.ts:21

          • log1p

            @@ -1928,7 +1928,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.d.ts:21

          • logSigmoid

            @@ -1946,7 +1946,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.d.ts:21

          • logSoftmax

            @@ -1966,7 +1966,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.d.ts:21

          • logSumExp

            @@ -1988,7 +1988,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.d.ts:5

          • logicalAnd

            @@ -2006,7 +2006,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.d.ts:5

          • logicalNot

            @@ -2019,7 +2019,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.d.ts:5

          • logicalOr

            @@ -2037,7 +2037,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.d.ts:5

          • logicalXor

            @@ -2055,7 +2055,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.d.ts:5

          • matMul

            @@ -2077,7 +2077,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional transposeB: boolean

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.d.ts:5

          • max

            @@ -2097,7 +2097,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.d.ts:21

          • maxPool

            @@ -2121,7 +2121,7 @@ parameter, so can not use a user-defined size to create the buffer.

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.d.ts:22

          • maximum

            @@ -2139,7 +2139,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.d.ts:5

          • mean

            @@ -2159,7 +2159,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.d.ts:5

          • min

            @@ -2179,7 +2179,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.d.ts:21

          • minimum

            @@ -2197,7 +2197,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.d.ts:5

          • mirrorPad

            @@ -2217,7 +2217,7 @@ parameter, so can not use a user-defined size to create the buffer.

            mode: "reflect" | "symmetric"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.d.ts:21

          • mod

            @@ -2235,7 +2235,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.d.ts:5

          • mul

            @@ -2253,7 +2253,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.d.ts:5

          • neg

            @@ -2271,7 +2271,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.d.ts:21

          • norm

            @@ -2293,7 +2293,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.d.ts:21

          • notEqual

            @@ -2311,7 +2311,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.d.ts:5

          • oneHot

            @@ -2328,7 +2328,7 @@ parameter, so can not use a user-defined size to create the buffer.

            offValue: number

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.d.ts:21

          • onesLike

            @@ -2346,7 +2346,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.d.ts:21

          • pad

            @@ -2366,7 +2366,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional constantValue: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.d.ts:21

          • pool

            @@ -2394,7 +2394,7 @@ parameter, so can not use a user-defined size to create the buffer.

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

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.d.ts:22

          • pow

            @@ -2412,7 +2412,7 @@ parameter, so can not use a user-defined size to create the buffer.

            exp: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.d.ts:5

          • prelu

            @@ -2430,7 +2430,7 @@ parameter, so can not use a user-defined size to create the buffer.

            alpha: TensorLike | T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.d.ts:5

          • print

            @@ -2449,7 +2449,7 @@ parameter, so can not use a user-defined size to create the buffer.

          Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:251

          • prod

            @@ -2471,7 +2471,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.d.ts:5

          • reciprocal

            @@ -2489,7 +2489,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.d.ts:21

          • relu

            @@ -2502,7 +2502,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.d.ts:5

          • relu6

            @@ -2515,7 +2515,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.d.ts:5

          • reshape

            @@ -2533,7 +2533,7 @@ parameter, so can not use a user-defined size to create the buffer.

            shape: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.d.ts:5

          • reshapeAs

            @@ -2551,7 +2551,7 @@ parameter, so can not use a user-defined size to create the buffer.

            x: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.d.ts:21

          • resizeBilinear

            @@ -2573,7 +2573,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional halfPixelCenters: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.d.ts:5

          • resizeNearestNeighbor

            @@ -2595,7 +2595,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional halfFloatCenters: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.d.ts:5

          • reverse

            @@ -2615,7 +2615,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number | number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.d.ts:5

          • rfft

            @@ -2633,7 +2633,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.d.ts:21

          • round

            @@ -2651,7 +2651,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.d.ts:21

          • rsqrt

            @@ -2669,7 +2669,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.d.ts:21

          • selu

            @@ -2682,7 +2682,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.d.ts:5

          • separableConv2d

            @@ -2710,7 +2710,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dataFormat: "NHWC" | "NCHW"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.d.ts:5

          • sigmoid

            @@ -2728,7 +2728,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.d.ts:21

          • sign

            @@ -2746,7 +2746,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.d.ts:21

          • sin

            @@ -2764,7 +2764,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.d.ts:21

          • sinh

            @@ -2782,7 +2782,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.d.ts:21

          • slice

            @@ -2804,7 +2804,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional size: number | number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.d.ts:21

          • softmax

            @@ -2824,7 +2824,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dim: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.d.ts:21

          • softplus

            @@ -2842,7 +2842,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.d.ts:21

          • spaceToBatchND

            @@ -2862,7 +2862,7 @@ parameter, so can not use a user-defined size to create the buffer.

            paddings: number[][]

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.d.ts:5

          • split

            @@ -2882,7 +2882,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.d.ts:5

          • sqrt

            @@ -2900,7 +2900,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.d.ts:21

          • square

            @@ -2918,7 +2918,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.d.ts:21

          • squaredDifference

            @@ -2936,7 +2936,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.d.ts:21

          • squeeze

            @@ -2954,7 +2954,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.d.ts:5

          • stack

            @@ -2974,7 +2974,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.d.ts:5

          • step

            @@ -2994,7 +2994,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional alpha: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.d.ts:21

          • stridedSlice

            @@ -3028,7 +3028,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional shrinkAxisMask: number

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.d.ts:21

          • sub

            @@ -3046,7 +3046,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.d.ts:5

          • sum

            @@ -3066,7 +3066,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.d.ts:5

          • tan

            @@ -3084,7 +3084,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.d.ts:21

          • tanh

            @@ -3102,7 +3102,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.d.ts:21

          • throwIfDisposed

            @@ -3110,7 +3110,7 @@ parameter, so can not use a user-defined size to create the buffer.

          • Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:242

          • tile

            @@ -3128,7 +3128,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.d.ts:21

          • toBool

            @@ -3146,7 +3146,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.d.ts:21

          • toFloat

            @@ -3164,7 +3164,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.d.ts:21

          • toInt

            @@ -3182,7 +3182,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.d.ts:21

          • toString

            @@ -3198,7 +3198,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional verbose: boolean

          Returns string

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:262

          • topk

            @@ -3225,7 +3225,7 @@ parameter, so can not use a user-defined size to create the buffer.

          • values: T
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.d.ts:21

          • transpose

            @@ -3243,7 +3243,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional perm: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.d.ts:21

          • unique

            @@ -3268,7 +3268,7 @@ parameter, so can not use a user-defined size to create the buffer.

          • values: T
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.d.ts:21

          • unsortedSegmentSum

            @@ -3290,7 +3290,7 @@ parameter, so can not use a user-defined size to create the buffer.

            numSegments: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.d.ts:21

          • unstack

            @@ -3308,7 +3308,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.d.ts:5

          • variable

            @@ -3325,7 +3325,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dtype: keyof DataTypeMap

          Returns Variable<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:263

          • where

            @@ -3345,7 +3345,7 @@ parameter, so can not use a user-defined size to create the buffer.

            x: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.d.ts:21

          • zerosLike

            @@ -3363,7 +3363,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.d.ts:21
          • @@ -3380,7 +3380,7 @@ parameter, so can not use a user-defined size to create the buffer.

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/classes/WebCam.html b/typedoc/classes/WebCam.html index 9882c4fc..48b799cc 100644 --- a/typedoc/classes/WebCam.html +++ b/typedoc/classes/WebCam.html @@ -1,4 +1,4 @@ -WebCam | @vladmandic/human - v3.0.6
              +WebCam | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Class WebCam

          @@ -197,7 +197,7 @@

          Returns void

          +
        • Defined in src/util/webcam.ts:196

          • play

            @@ -207,11 +207,11 @@

          Returns Promise<void>

          +
        • Defined in src/util/webcam.ts:201

          • start

            - +

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

            @@ -220,7 +220,7 @@
          -

          Returns Promise<void>

          @@ -249,7 +249,7 @@

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/classes/models.Models.html b/typedoc/classes/models.Models.html index 5b75415b..8f6378f9 100644 --- a/typedoc/classes/models.Models.html +++ b/typedoc/classes/models.Models.html @@ -1,4 +1,4 @@ -Models | @vladmandic/human - v3.0.6
              +Models | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Class Models

          @@ -86,7 +86,7 @@

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

          • +
        • Defined in src/models.ts:170

          • load

          +
        • Defined in src/models.ts:181

          • reset

            @@ -131,7 +131,7 @@

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

          +
        • Defined in src/models.ts:187
          • @@ -148,7 +148,7 @@

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/enums/Rank.html b/typedoc/enums/Rank.html index 86c8798c..5cd202df 100644 --- a/typedoc/enums/Rank.html +++ b/typedoc/enums/Rank.html @@ -1,4 +1,4 @@ -Rank | @vladmandic/human - v3.0.6
              +Rank | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Enumeration Rank

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:62
          • @@ -38,37 +38,37 @@

          R0

          R0: "R0"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:63

          • R1

          R1: "R1"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:64

          • R2

          R2: "R2"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:65

          • R3

          R3: "R3"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:66

          • R4

          R4: "R4"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:67

          • R5

          R5: "R5"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:68

          • R6

          R6: "R6"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:69
          • @@ -85,7 +85,7 @@

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.all.html b/typedoc/functions/draw.all.html index d265f743..92c78135 100644 --- a/typedoc/functions/draw.all.html +++ b/typedoc/functions/draw.all.html @@ -1,4 +1,4 @@ -all | @vladmandic/human - v3.0.6
              +all | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function all

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.body.html b/typedoc/functions/draw.body.html index a5337aa2..224d329e 100644 --- a/typedoc/functions/draw.body.html +++ b/typedoc/functions/draw.body.html @@ -1,4 +1,4 @@ -body | @vladmandic/human - v3.0.6
              +body | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function body

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.canvas.html b/typedoc/functions/draw.canvas.html index 480872ab..1af15a50 100644 --- a/typedoc/functions/draw.canvas.html +++ b/typedoc/functions/draw.canvas.html @@ -1,4 +1,4 @@ -canvas | @vladmandic/human - v3.0.6
              +canvas | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function canvas

          @@ -48,7 +48,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.face.html b/typedoc/functions/draw.face.html index 37bfeb21..9192b5ff 100644 --- a/typedoc/functions/draw.face.html +++ b/typedoc/functions/draw.face.html @@ -1,4 +1,4 @@ -face | @vladmandic/human - v3.0.6
              +face | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function face

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.gesture.html b/typedoc/functions/draw.gesture.html index 036edecd..bfdff825 100644 --- a/typedoc/functions/draw.gesture.html +++ b/typedoc/functions/draw.gesture.html @@ -1,4 +1,4 @@ -gesture | @vladmandic/human - v3.0.6
              +gesture | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function gesture

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.hand.html b/typedoc/functions/draw.hand.html index 336d9682..5a20d874 100644 --- a/typedoc/functions/draw.hand.html +++ b/typedoc/functions/draw.hand.html @@ -1,4 +1,4 @@ -hand | @vladmandic/human - v3.0.6
              +hand | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function hand

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.init.html b/typedoc/functions/draw.init.html index 051901fa..710a460e 100644 --- a/typedoc/functions/draw.init.html +++ b/typedoc/functions/draw.init.html @@ -1,4 +1,4 @@ -init | @vladmandic/human - v3.0.6
              +init | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function init

          @@ -41,7 +41,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.object.html b/typedoc/functions/draw.object.html index ad587290..5c63f537 100644 --- a/typedoc/functions/draw.object.html +++ b/typedoc/functions/draw.object.html @@ -1,4 +1,4 @@ -object | @vladmandic/human - v3.0.6
              +object | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function object

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/draw.person.html b/typedoc/functions/draw.person.html index b875c2e2..29025d9b 100644 --- a/typedoc/functions/draw.person.html +++ b/typedoc/functions/draw.person.html @@ -1,4 +1,4 @@ -person | @vladmandic/human - v3.0.6
              +person | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function person

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/empty.html b/typedoc/functions/empty.html index c208a312..bef7f39b 100644 --- a/typedoc/functions/empty.html +++ b/typedoc/functions/empty.html @@ -1,4 +1,4 @@ -empty | @vladmandic/human - v3.0.6
              +empty | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function empty

          @@ -43,7 +43,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/match.distance.html b/typedoc/functions/match.distance.html index c3404388..83412eb9 100644 --- a/typedoc/functions/match.distance.html +++ b/typedoc/functions/match.distance.html @@ -1,4 +1,4 @@ -distance | @vladmandic/human - v3.0.6
              +distance | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function distance

          @@ -58,7 +58,7 @@ default is 20 which normalizes results to similarity above 0.5 can be considered

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/match.find.html b/typedoc/functions/match.find.html index 3bfa73cf..b6747784 100644 --- a/typedoc/functions/match.find.html +++ b/typedoc/functions/match.find.html @@ -1,4 +1,4 @@ -find | @vladmandic/human - v3.0.6
              +find | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function find

          @@ -69,7 +69,7 @@ Returns

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/match.similarity.html b/typedoc/functions/match.similarity.html index 9a0ca643..dc98a0b4 100644 --- a/typedoc/functions/match.similarity.html +++ b/typedoc/functions/match.similarity.html @@ -1,4 +1,4 @@ -similarity | @vladmandic/human - v3.0.6
              +similarity | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function similarity

          @@ -62,7 +62,7 @@ Returns similarity between two face descriptors normalized to 0..1 range where 0

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/functions/models.validateModel.html b/typedoc/functions/models.validateModel.html index c8e54423..3af9ed47 100644 --- a/typedoc/functions/models.validateModel.html +++ b/typedoc/functions/models.validateModel.html @@ -1,4 +1,4 @@ -validateModel | @vladmandic/human - v3.0.6
              +validateModel | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Function validateModel

          @@ -48,7 +48,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/index.html b/typedoc/index.html index 15d1a5e5..b2cfc8ab 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1,4 +1,4 @@ -@vladmandic/human - v3.0.6
              +@vladmandic/human - v3.0.7
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              +
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            @vladmandic/human - v3.0.7
          -

          @vladmandic/human - v3.0.6

          +

          @vladmandic/human - v3.0.7

          @@ -142,7 +142,7 @@

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/BodyConfig.html b/typedoc/interfaces/BodyConfig.html index 47a17288..216f5d51 100644 --- a/typedoc/interfaces/BodyConfig.html +++ b/typedoc/interfaces/BodyConfig.html @@ -1,4 +1,4 @@ -BodyConfig | @vladmandic/human - v3.0.6
              +BodyConfig | @vladmandic/human - v3.0.7
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              +
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          Interface BodyConfig

          @@ -106,7 +106,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/BodyKeypoint.html b/typedoc/interfaces/BodyKeypoint.html index 5664ca0c..2600c3c3 100644 --- a/typedoc/interfaces/BodyKeypoint.html +++ b/typedoc/interfaces/BodyKeypoint.html @@ -1,4 +1,4 @@ -BodyKeypoint | @vladmandic/human - v3.0.6
              +BodyKeypoint | @vladmandic/human - v3.0.7
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          Interface BodyKeypoint

          @@ -90,7 +90,7 @@

          Modules

            -
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            • Tensor
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              • diff --git a/typedoc/interfaces/BodyResult.html b/typedoc/interfaces/BodyResult.html index 6222a2ad..a51fb7c3 100644 --- a/typedoc/interfaces/BodyResult.html +++ b/typedoc/interfaces/BodyResult.html @@ -1,4 +1,4 @@ -BodyResult | @vladmandic/human - v3.0.6
              +BodyResult | @vladmandic/human - v3.0.7
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          Interface BodyResult

          @@ -98,7 +98,7 @@

          Modules

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              • diff --git a/typedoc/interfaces/Config.html b/typedoc/interfaces/Config.html index 44d56447..9a6b3e1c 100644 --- a/typedoc/interfaces/Config.html +++ b/typedoc/interfaces/Config.html @@ -1,4 +1,4 @@ -Config | @vladmandic/human - v3.0.6
              +Config | @vladmandic/human - v3.0.7
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          Interface Config

          @@ -248,7 +248,7 @@ any errors will be printed on console but will be treated as non-fatal

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/DrawOptions.html b/typedoc/interfaces/DrawOptions.html index 5be968e3..78abab00 100644 --- a/typedoc/interfaces/DrawOptions.html +++ b/typedoc/interfaces/DrawOptions.html @@ -1,4 +1,4 @@ -DrawOptions | @vladmandic/human - v3.0.6
              +DrawOptions | @vladmandic/human - v3.0.7
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          Interface DrawOptions

          @@ -261,7 +261,7 @@

          Modules

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              • diff --git a/typedoc/interfaces/FaceAntiSpoofConfig.html b/typedoc/interfaces/FaceAntiSpoofConfig.html index 3c5a6131..7c0e72be 100644 --- a/typedoc/interfaces/FaceAntiSpoofConfig.html +++ b/typedoc/interfaces/FaceAntiSpoofConfig.html @@ -1,4 +1,4 @@ -FaceAntiSpoofConfig | @vladmandic/human - v3.0.6
              +FaceAntiSpoofConfig | @vladmandic/human - v3.0.7
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              +
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          Interface FaceAntiSpoofConfig

          @@ -90,7 +90,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceAttentionConfig.html b/typedoc/interfaces/FaceAttentionConfig.html index cc117eb5..4fcb8a3b 100644 --- a/typedoc/interfaces/FaceAttentionConfig.html +++ b/typedoc/interfaces/FaceAttentionConfig.html @@ -1,4 +1,4 @@ -FaceAttentionConfig | @vladmandic/human - v3.0.6
              +FaceAttentionConfig | @vladmandic/human - v3.0.7
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          Interface FaceAttentionConfig

          @@ -90,7 +90,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
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              • diff --git a/typedoc/interfaces/FaceConfig.html b/typedoc/interfaces/FaceConfig.html index 33abdcde..562709b9 100644 --- a/typedoc/interfaces/FaceConfig.html +++ b/typedoc/interfaces/FaceConfig.html @@ -1,4 +1,4 @@ -FaceConfig | @vladmandic/human - v3.0.6
              +FaceConfig | @vladmandic/human - v3.0.7
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            @vladmandic/human - v3.0.7

          Interface FaceConfig

          @@ -144,7 +144,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
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              • diff --git a/typedoc/interfaces/FaceDescriptionConfig.html b/typedoc/interfaces/FaceDescriptionConfig.html index a547f1e5..74580999 100644 --- a/typedoc/interfaces/FaceDescriptionConfig.html +++ b/typedoc/interfaces/FaceDescriptionConfig.html @@ -1,4 +1,4 @@ -FaceDescriptionConfig | @vladmandic/human - v3.0.6
              +FaceDescriptionConfig | @vladmandic/human - v3.0.7
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          Interface FaceDescriptionConfig

          @@ -101,7 +101,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
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          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceDetectorConfig.html b/typedoc/interfaces/FaceDetectorConfig.html index a34430fa..3b97487a 100644 --- a/typedoc/interfaces/FaceDetectorConfig.html +++ b/typedoc/interfaces/FaceDetectorConfig.html @@ -1,4 +1,4 @@ -FaceDetectorConfig | @vladmandic/human - v3.0.6
              +FaceDetectorConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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          Interface FaceDetectorConfig

          @@ -148,7 +148,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceEmotionConfig.html b/typedoc/interfaces/FaceEmotionConfig.html index c4c49db2..5c22e0b0 100644 --- a/typedoc/interfaces/FaceEmotionConfig.html +++ b/typedoc/interfaces/FaceEmotionConfig.html @@ -1,4 +1,4 @@ -FaceEmotionConfig | @vladmandic/human - v3.0.6
              +FaceEmotionConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FaceEmotionConfig

          @@ -98,7 +98,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceGearConfig.html b/typedoc/interfaces/FaceGearConfig.html index d1f561c9..4d1788d3 100644 --- a/typedoc/interfaces/FaceGearConfig.html +++ b/typedoc/interfaces/FaceGearConfig.html @@ -1,4 +1,4 @@ -FaceGearConfig | @vladmandic/human - v3.0.6
              +FaceGearConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FaceGearConfig

          @@ -98,7 +98,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceIrisConfig.html b/typedoc/interfaces/FaceIrisConfig.html index aa8a62b2..a50f50c0 100644 --- a/typedoc/interfaces/FaceIrisConfig.html +++ b/typedoc/interfaces/FaceIrisConfig.html @@ -1,4 +1,4 @@ -FaceIrisConfig | @vladmandic/human - v3.0.6
              +FaceIrisConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FaceIrisConfig

          @@ -90,7 +90,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceLivenessConfig.html b/typedoc/interfaces/FaceLivenessConfig.html index e54a67ea..efd72263 100644 --- a/typedoc/interfaces/FaceLivenessConfig.html +++ b/typedoc/interfaces/FaceLivenessConfig.html @@ -1,4 +1,4 @@ -FaceLivenessConfig | @vladmandic/human - v3.0.6
              +FaceLivenessConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FaceLivenessConfig

          @@ -90,7 +90,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceMeshConfig.html b/typedoc/interfaces/FaceMeshConfig.html index e1190ed3..a9cd6fa7 100644 --- a/typedoc/interfaces/FaceMeshConfig.html +++ b/typedoc/interfaces/FaceMeshConfig.html @@ -1,4 +1,4 @@ -FaceMeshConfig | @vladmandic/human - v3.0.6
              +FaceMeshConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FaceMeshConfig

          @@ -98,7 +98,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
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              • diff --git a/typedoc/interfaces/FaceResult.html b/typedoc/interfaces/FaceResult.html index 201eb510..606dfc20 100644 --- a/typedoc/interfaces/FaceResult.html +++ b/typedoc/interfaces/FaceResult.html @@ -1,4 +1,4 @@ -FaceResult | @vladmandic/human - v3.0.6
              +FaceResult | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FaceResult

          @@ -222,7 +222,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/FilterConfig.html b/typedoc/interfaces/FilterConfig.html index fcb3db3e..b80abc13 100644 --- a/typedoc/interfaces/FilterConfig.html +++ b/typedoc/interfaces/FilterConfig.html @@ -1,4 +1,4 @@ -FilterConfig | @vladmandic/human - v3.0.6
              +FilterConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface FilterConfig

          @@ -227,7 +227,7 @@

          Modules

            -
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          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/GenericConfig.html b/typedoc/interfaces/GenericConfig.html index 08c74f82..3e7d594c 100644 --- a/typedoc/interfaces/GenericConfig.html +++ b/typedoc/interfaces/GenericConfig.html @@ -1,4 +1,4 @@ -GenericConfig | @vladmandic/human - v3.0.6
              +GenericConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface GenericConfig

          @@ -99,7 +99,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/GestureConfig.html b/typedoc/interfaces/GestureConfig.html index 2025e5da..9ae1fae5 100644 --- a/typedoc/interfaces/GestureConfig.html +++ b/typedoc/interfaces/GestureConfig.html @@ -1,4 +1,4 @@ -GestureConfig | @vladmandic/human - v3.0.6
              +GestureConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
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            @vladmandic/human - v3.0.7

          Interface GestureConfig

          @@ -58,7 +58,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/HandConfig.html b/typedoc/interfaces/HandConfig.html index a7c56c0a..1a8844ca 100644 --- a/typedoc/interfaces/HandConfig.html +++ b/typedoc/interfaces/HandConfig.html @@ -1,4 +1,4 @@ -HandConfig | @vladmandic/human - v3.0.6
              +HandConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
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            @vladmandic/human - v3.0.7

          Interface HandConfig

          @@ -156,7 +156,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/HandResult.html b/typedoc/interfaces/HandResult.html index 1280b4fd..dd6cf8ea 100644 --- a/typedoc/interfaces/HandResult.html +++ b/typedoc/interfaces/HandResult.html @@ -1,4 +1,4 @@ -HandResult | @vladmandic/human - v3.0.6
              +HandResult | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
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            @vladmandic/human - v3.0.7

          Interface HandResult

          @@ -130,7 +130,7 @@

          Modules

            -
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          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/ModelInfo.html b/typedoc/interfaces/ModelInfo.html index cb333d16..44a5b10d 100644 --- a/typedoc/interfaces/ModelInfo.html +++ b/typedoc/interfaces/ModelInfo.html @@ -1,4 +1,4 @@ -ModelInfo | @vladmandic/human - v3.0.6
              +ModelInfo | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface ModelInfo

          @@ -83,7 +83,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/ObjectConfig.html b/typedoc/interfaces/ObjectConfig.html index 7a23b43f..0f1b932c 100644 --- a/typedoc/interfaces/ObjectConfig.html +++ b/typedoc/interfaces/ObjectConfig.html @@ -1,4 +1,4 @@ -ObjectConfig | @vladmandic/human - v3.0.6
              +ObjectConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface ObjectConfig

          @@ -114,7 +114,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/ObjectResult.html b/typedoc/interfaces/ObjectResult.html index df229a31..ff6896aa 100644 --- a/typedoc/interfaces/ObjectResult.html +++ b/typedoc/interfaces/ObjectResult.html @@ -1,4 +1,4 @@ -ObjectResult | @vladmandic/human - v3.0.6
              +ObjectResult | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface ObjectResult

          @@ -98,7 +98,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/PersonResult.html b/typedoc/interfaces/PersonResult.html index 712be28d..78990977 100644 --- a/typedoc/interfaces/PersonResult.html +++ b/typedoc/interfaces/PersonResult.html @@ -1,4 +1,4 @@ -PersonResult | @vladmandic/human - v3.0.6
              +PersonResult | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Interface PersonResult

          @@ -116,7 +116,7 @@

          Modules

            -
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          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/Result.html b/typedoc/interfaces/Result.html index 862c3e92..c54162c6 100644 --- a/typedoc/interfaces/Result.html +++ b/typedoc/interfaces/Result.html @@ -1,4 +1,4 @@ -Result | @vladmandic/human - v3.0.6
              +Result | @vladmandic/human - v3.0.7
              • Preparing search index...
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            @vladmandic/human - v3.0.7

          Interface Result

          @@ -147,7 +147,7 @@

          Modules

            -
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          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/SegmentationConfig.html b/typedoc/interfaces/SegmentationConfig.html index 7e0068b4..72a453e3 100644 --- a/typedoc/interfaces/SegmentationConfig.html +++ b/typedoc/interfaces/SegmentationConfig.html @@ -1,4 +1,4 @@ -SegmentationConfig | @vladmandic/human - v3.0.6
              +SegmentationConfig | @vladmandic/human - v3.0.7
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              @vladmandic/human - v3.0.6
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            @vladmandic/human - v3.0.7

          Interface SegmentationConfig

          @@ -110,7 +110,7 @@ for two-phase models such as face and hand caching applies to bounding boxes det

          Modules

            -
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          • @vladmandic/human - v3.0.7
            • Tensor
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              • diff --git a/typedoc/interfaces/WebCamConfig.html b/typedoc/interfaces/WebCamConfig.html index ed4d46a2..9594a1c4 100644 --- a/typedoc/interfaces/WebCamConfig.html +++ b/typedoc/interfaces/WebCamConfig.html @@ -1,4 +1,4 @@ -WebCamConfig | @vladmandic/human - v3.0.6
              +WebCamConfig | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Interface WebCamConfig

          @@ -111,7 +111,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/models.KernelOps.html b/typedoc/interfaces/models.KernelOps.html index 473ea98f..20a34573 100644 --- a/typedoc/interfaces/models.KernelOps.html +++ b/typedoc/interfaces/models.KernelOps.html @@ -1,4 +1,4 @@ -KernelOps | @vladmandic/human - v3.0.6
              +KernelOps | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Interface KernelOps

          @@ -72,7 +72,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/interfaces/models.ModelStats.html b/typedoc/interfaces/models.ModelStats.html index a80bcefc..2af6b8ce 100644 --- a/typedoc/interfaces/models.ModelStats.html +++ b/typedoc/interfaces/models.ModelStats.html @@ -1,4 +1,4 @@ -ModelStats | @vladmandic/human - v3.0.6
              +ModelStats | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Interface ModelStats

          @@ -93,7 +93,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html index eec44b12..f1b2775a 100644 --- a/typedoc/modules/Tensor.html +++ b/typedoc/modules/Tensor.html @@ -1,4 +1,4 @@ -Tensor | @vladmandic/human - v3.0.6
              +Tensor | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Namespace Tensor

          Explict reexport of main @tensorflow/tfjs types

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:115
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:130
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.d.ts:21
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.d.ts:5
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.d.ts:21
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.d.ts:21
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.d.ts:20
          • @@ -174,7 +174,7 @@

          Modules
            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index 80577f16..7c20eff9 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,4 +1,4 @@ -draw | @vladmandic/human - v3.0.6
              +draw | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Namespace draw

          @@ -55,7 +55,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/modules/match.html b/typedoc/modules/match.html index e495f9cb..20bc22b6 100644 --- a/typedoc/modules/match.html +++ b/typedoc/modules/match.html @@ -1,4 +1,4 @@ -match | @vladmandic/human - v3.0.6
              +match | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Namespace match

          @@ -50,7 +50,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/modules/models.html b/typedoc/modules/models.html index 94f34736..273ec9bb 100644 --- a/typedoc/modules/models.html +++ b/typedoc/modules/models.html @@ -1,4 +1,4 @@ -models | @vladmandic/human - v3.0.6
              +models | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Namespace models

          @@ -52,7 +52,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/AnyCanvas.html b/typedoc/types/AnyCanvas.html index 5f62ae9b..d13dfaaa 100644 --- a/typedoc/types/AnyCanvas.html +++ b/typedoc/types/AnyCanvas.html @@ -1,4 +1,4 @@ -AnyCanvas | @vladmandic/human - v3.0.6
              +AnyCanvas | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Type alias AnyCanvas

          AnyCanvas: HTMLCanvasElement | OffscreenCanvas
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/AnyImage.html b/typedoc/types/AnyImage.html index b3b12ec3..03b9e5f7 100644 --- a/typedoc/types/AnyImage.html +++ b/typedoc/types/AnyImage.html @@ -1,4 +1,4 @@ -AnyImage | @vladmandic/human - v3.0.6
              +AnyImage | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias AnyImage

          AnyImage: HTMLImageElement | typeof Image
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/AnyVideo.html b/typedoc/types/AnyVideo.html index 72c7dc1a..4c5bcbe8 100644 --- a/typedoc/types/AnyVideo.html +++ b/typedoc/types/AnyVideo.html @@ -1,4 +1,4 @@ -AnyVideo | @vladmandic/human - v3.0.6
              +AnyVideo | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias AnyVideo

          AnyVideo: HTMLMediaElement | HTMLVideoElement
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/BackendEnum.html b/typedoc/types/BackendEnum.html index 2564f9e2..cde3d5ed 100644 --- a/typedoc/types/BackendEnum.html +++ b/typedoc/types/BackendEnum.html @@ -1,4 +1,4 @@ -BackendEnum | @vladmandic/human - v3.0.6
              +BackendEnum | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias BackendEnum

          BackendEnum: "" | "cpu" | "wasm" | "webgl" | "humangl" | "tensorflow" | "webgpu"
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/BodyAnnotation.html b/typedoc/types/BodyAnnotation.html index 1c3e7119..176cee61 100644 --- a/typedoc/types/BodyAnnotation.html +++ b/typedoc/types/BodyAnnotation.html @@ -1,4 +1,4 @@ -BodyAnnotation | @vladmandic/human - v3.0.6
              +BodyAnnotation | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
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              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias BodyAnnotation

          BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose

          Type alias BodyAnnotationBlazePose

          BodyAnnotationBlazePose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "leftEye" | "rightEye" | "mouth"

          Type alias BodyAnnotationEfficientPose

          BodyAnnotationEfficientPose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "head"

          Type alias BodyGesture

          BodyGesture: `leaning ${"left" | "right"}` | `raise ${"left" | "right"} hand` | "i give up"
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/BodyLandmark.html b/typedoc/types/BodyLandmark.html index a45cc3d0..a8c75323 100644 --- a/typedoc/types/BodyLandmark.html +++ b/typedoc/types/BodyLandmark.html @@ -1,4 +1,4 @@ -BodyLandmark | @vladmandic/human - v3.0.6
              +BodyLandmark | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias BodyLandmark

          BodyLandmark: BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose

          Type alias BodyLandmarkBlazePose

          BodyLandmarkBlazePose: "nose" | "leftEyeInside" | "leftEye" | "leftEyeOutside" | "rightEyeInside" | "rightEye" | "rightEyeOutside" | "leftEar" | "rightEar" | "leftMouth" | "rightMouth" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftPinky" | "rightPinky" | "leftIndex" | "rightIndex" | "leftThumb" | "rightThumb" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle" | "leftHeel" | "rightHeel" | "leftFoot" | "rightFoot" | "bodyCenter" | "bodyTop" | "leftPalm" | "leftHand" | "rightPalm" | "rightHand"

          Type alias BodyLandmarkEfficientNet

          BodyLandmarkEfficientNet: "head" | "neck" | "rightShoulder" | "rightElbow" | "rightWrist" | "chest" | "leftShoulder" | "leftElbow" | "leftWrist" | "bodyCenter" | "rightHip" | "rightKnee" | "rightAnkle" | "leftHip" | "leftKnee" | "leftAnkle"

          Type alias BodyLandmarkMoveNet

          BodyLandmarkMoveNet: "nose" | "leftEye" | "rightEye" | "leftEar" | "rightEar" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"

          Type alias BodyLandmarkPoseNet

          BodyLandmarkPoseNet: "nose" | "leftEye" | "rightEye" | "leftEar" | "rightEar" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"

          Type alias Box

          Box: [number, number, number, number]
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/Emotion.html b/typedoc/types/Emotion.html index 01a01f30..858160db 100644 --- a/typedoc/types/Emotion.html +++ b/typedoc/types/Emotion.html @@ -1,4 +1,4 @@ -Emotion | @vladmandic/human - v3.0.6
              +Emotion | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
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              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias Emotion

          Emotion: "angry" | "disgust" | "fear" | "happy" | "sad" | "surprise" | "neutral"

          Type alias Events

          Events: "create" | "load" | "image" | "result" | "warmup" | "error"
          @@ -43,7 +43,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/ExternalCanvas.html b/typedoc/types/ExternalCanvas.html index 61475ae7..a3f73010 100644 --- a/typedoc/types/ExternalCanvas.html +++ b/typedoc/types/ExternalCanvas.html @@ -1,4 +1,4 @@ -ExternalCanvas | @vladmandic/human - v3.0.6
              +ExternalCanvas | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias ExternalCanvas

          ExternalCanvas: typeof Canvas
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/FaceGesture.html b/typedoc/types/FaceGesture.html index 31b11abe..b8e17d00 100644 --- a/typedoc/types/FaceGesture.html +++ b/typedoc/types/FaceGesture.html @@ -1,4 +1,4 @@ -FaceGesture | @vladmandic/human - v3.0.6
              +FaceGesture | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias FaceGesture

          FaceGesture: `facing ${"left" | "center" | "right"}` | `blink ${"left" | "right"} eye` | `mouth ${number}% open` | `head ${"up" | "down"}`
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/FaceLandmark.html b/typedoc/types/FaceLandmark.html index 85af7f10..5085f0e9 100644 --- a/typedoc/types/FaceLandmark.html +++ b/typedoc/types/FaceLandmark.html @@ -1,4 +1,4 @@ -FaceLandmark | @vladmandic/human - v3.0.6
              +FaceLandmark | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
              • The search index is not available
              @vladmandic/human - v3.0.6
              +
            • The search index is not available
            @vladmandic/human - v3.0.7

          Type alias FaceLandmark

          FaceLandmark: "leftEye" | "rightEye" | "nose" | "mouth" | "leftEar" | "rightEar" | "symmetryLine" | "silhouette" | "lipsUpperOuter" | "lipsLowerOuter" | "lipsUpperInner" | "lipsLowerInner" | "rightEyeUpper0" | "rightEyeLower0" | "rightEyeUpper1" | "rightEyeLower1" | "rightEyeUpper2" | "rightEyeLower2" | "rightEyeLower3" | "rightEyebrowUpper" | "rightEyebrowLower" | "rightEyeIris" | "leftEyeUpper0" | "leftEyeLower0" | "leftEyeUpper1" | "leftEyeLower1" | "leftEyeUpper2" | "leftEyeLower2" | "leftEyeLower3" | "leftEyebrowUpper" | "leftEyebrowLower" | "leftEyeIris" | "midwayBetweenEyes" | "noseTip" | "noseBottom" | "noseRightCorner" | "noseLeftCorner" | "rightCheek" | "leftCheek"

          Type alias Finger

          Finger: "index" | "middle" | "pinky" | "ring" | "thumb" | "palm"

          Type alias FingerCurl

          FingerCurl: "none" | "half" | "full"

          Type alias FingerDirection

          FingerDirection: "verticalUp" | "verticalDown" | "horizontalLeft" | "horizontalRight" | "diagonalUpRight" | "diagonalUpLeft" | "diagonalDownRight" | "diagonalDownLeft"

          Type alias Gender

          Gender: "male" | "female" | "unknown"

          Type alias GestureResult

          GestureResult: {
              face: number;
              gesture: FaceGesture;
          } | {
              gesture: IrisGesture;
              iris: number;
          } | {
              body: number;
              gesture: BodyGesture;
          } | {
              gesture: HandGesture;
              hand: number;
          }
          @@ -41,7 +41,7 @@ Each result has:

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/HandGesture.html b/typedoc/types/HandGesture.html index 4096e950..c6c22243 100644 --- a/typedoc/types/HandGesture.html +++ b/typedoc/types/HandGesture.html @@ -1,4 +1,4 @@ -HandGesture | @vladmandic/human - v3.0.6
              +HandGesture | @vladmandic/human - v3.0.7
              • Preparing search index...
                • -
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Type alias HandGesture

          HandGesture: `${"thumb" | "index" | "middle" | "ring" | "pinky"} forward` | `${"thumb" | "index" | "middle" | "ring" | "pinky"} up` | "victory" | "thumbs up"
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/HandType.html b/typedoc/types/HandType.html index b6c32078..fe209d54 100644 --- a/typedoc/types/HandType.html +++ b/typedoc/types/HandType.html @@ -1,4 +1,4 @@ -HandType | @vladmandic/human - v3.0.6
              +HandType | @vladmandic/human - v3.0.7
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Type alias HandType

          HandType: "hand" | "fist" | "pinch" | "point" | "face" | "tip" | "pinchtip"

          Type alias ImageObjects

          ImageObjects: ImageData | ImageBitmap
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/Input.html b/typedoc/types/Input.html index 582f54bd..3c6146af 100644 --- a/typedoc/types/Input.html +++ b/typedoc/types/Input.html @@ -1,4 +1,4 @@ -Input | @vladmandic/human - v3.0.6
              +Input | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Type alias Input

          Input: Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/IrisGesture.html b/typedoc/types/IrisGesture.html index 25262bbf..9fb9114e 100644 --- a/typedoc/types/IrisGesture.html +++ b/typedoc/types/IrisGesture.html @@ -1,4 +1,4 @@ -IrisGesture | @vladmandic/human - v3.0.6
              +IrisGesture | @vladmandic/human - v3.0.7
              • Preparing search index...
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              @vladmandic/human - v3.0.6
              +
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            @vladmandic/human - v3.0.7

          Type alias IrisGesture

          IrisGesture: "facing center" | `looking ${"left" | "right" | "up" | "down"}` | "looking center"
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
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              • diff --git a/typedoc/types/ObjectType.html b/typedoc/types/ObjectType.html index 95045c8e..cd603e2e 100644 --- a/typedoc/types/ObjectType.html +++ b/typedoc/types/ObjectType.html @@ -1,4 +1,4 @@ -ObjectType | @vladmandic/human - v3.0.6
              +ObjectType | @vladmandic/human - v3.0.7
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            @vladmandic/human - v3.0.7

          Type alias ObjectType

          ObjectType: "person" | "bicycle" | "car" | "motorcycle" | "airplane" | "bus" | "train" | "truck" | "boat" | "traffic light" | "fire hydrant" | "stop sign" | "parking meter" | "bench" | "bird" | "cat" | "dog" | "horse" | "sheep" | "cow" | "elephant" | "bear" | "zebra" | "giraffe" | "backpack" | "umbrella" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "kite" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "bottle" | "wine glass" | "cup" | "fork" | "knife" | "spoon" | "bowl" | "banana" | "apple" | "sandwich" | "orange" | "broccoli" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "chair" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "toaster" | "sink" | "refrigerator" | "book" | "clock" | "vase" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"

          Type alias Point

          Point: [number, number, number?]
          @@ -36,7 +36,7 @@

          Modules

            -
          • @vladmandic/human - v3.0.6 +
          • @vladmandic/human - v3.0.7
            • Tensor
            • draw
              • diff --git a/typedoc/types/Race.html b/typedoc/types/Race.html index 12a5387d..27c63e33 100644 --- a/typedoc/types/Race.html +++ b/typedoc/types/Race.html @@ -1,4 +1,4 @@ -Race | @vladmandic/human - v3.0.6
              +Race | @vladmandic/human - v3.0.7
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          Type alias Race

          Race: "white" | "black" | "asian" | "indian" | "other"

          Type alias SegmentationEnum

          SegmentationEnum: "default" | "alpha" | "foreground" | "state"
          @@ -36,7 +36,7 @@

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            -
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              • diff --git a/typedoc/types/Tensor1D.html b/typedoc/types/Tensor1D.html index 15ae0970..43dd9e29 100644 --- a/typedoc/types/Tensor1D.html +++ b/typedoc/types/Tensor1D.html @@ -1,4 +1,4 @@ -Tensor1D | @vladmandic/human - v3.0.6
              +Tensor1D | @vladmandic/human - v3.0.7
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          Type alias Tensor1D

          Tensor1D: Tensor<R1>
          @@ -20,7 +20,7 @@

          Doclink

          Tensor

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:280
          • @@ -37,7 +37,7 @@

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              • diff --git a/typedoc/types/Tensor2D.html b/typedoc/types/Tensor2D.html index 2d6e1bce..a022bed4 100644 --- a/typedoc/types/Tensor2D.html +++ b/typedoc/types/Tensor2D.html @@ -1,4 +1,4 @@ -Tensor2D | @vladmandic/human - v3.0.6
              +Tensor2D | @vladmandic/human - v3.0.7
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          Type alias Tensor2D

          Tensor2D: Tensor<R2>
          @@ -20,7 +20,7 @@

          Doclink

          Tensor

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:282
          • @@ -37,7 +37,7 @@

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              • diff --git a/typedoc/types/Tensor3D.html b/typedoc/types/Tensor3D.html index cbcc681f..22a9be35 100644 --- a/typedoc/types/Tensor3D.html +++ b/typedoc/types/Tensor3D.html @@ -1,4 +1,4 @@ -Tensor3D | @vladmandic/human - v3.0.6
              +Tensor3D | @vladmandic/human - v3.0.7
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          Type alias Tensor3D

          Tensor3D: Tensor<R3>
          @@ -20,7 +20,7 @@

          Doclink

          Tensor

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:284
          • @@ -37,7 +37,7 @@

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              • diff --git a/typedoc/types/Tensor4D.html b/typedoc/types/Tensor4D.html index 4086fb4b..3ab6468f 100644 --- a/typedoc/types/Tensor4D.html +++ b/typedoc/types/Tensor4D.html @@ -1,4 +1,4 @@ -Tensor4D | @vladmandic/human - v3.0.6
              +Tensor4D | @vladmandic/human - v3.0.7
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          Type alias Tensor4D

          Tensor4D: Tensor<R4>
          @@ -20,7 +20,7 @@

          Doclink

          Tensor

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:286
          • @@ -37,7 +37,7 @@

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              • diff --git a/typedoc/types/TensorLike.html b/typedoc/types/TensorLike.html index 838e29d1..0f9be55b 100644 --- a/typedoc/types/TensorLike.html +++ b/typedoc/types/TensorLike.html @@ -1,4 +1,4 @@ -TensorLike | @vladmandic/human - v3.0.6
              +TensorLike | @vladmandic/human - v3.0.7
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          Type alias TensorLike

          TensorLike: TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[]
          @@ -20,7 +20,7 @@

          Docalias

          TypedArray|Array

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.7.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:80
          • @@ -37,7 +37,7 @@

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              • diff --git a/typedoc/types/WarmupEnum.html b/typedoc/types/WarmupEnum.html index 4497af55..428e26c0 100644 --- a/typedoc/types/WarmupEnum.html +++ b/typedoc/types/WarmupEnum.html @@ -1,4 +1,4 @@ -WarmupEnum | @vladmandic/human - v3.0.6
              +WarmupEnum | @vladmandic/human - v3.0.7
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          Type alias WarmupEnum

          WarmupEnum: "" | "none" | "face" | "full" | "body"
          @@ -36,7 +36,7 @@

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            -
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            • Tensor
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              • diff --git a/typedoc/types/match.Descriptor.html b/typedoc/types/match.Descriptor.html index 6b5b4cda..2865ab7b 100644 --- a/typedoc/types/match.Descriptor.html +++ b/typedoc/types/match.Descriptor.html @@ -1,4 +1,4 @@ -Descriptor | @vladmandic/human - v3.0.6
              +Descriptor | @vladmandic/human - v3.0.7
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          Type alias Descriptor

          @@ -37,7 +37,7 @@

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            • Tensor
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              • diff --git a/typedoc/types/match.MatchOptions.html b/typedoc/types/match.MatchOptions.html index 97f7cee3..3aeca1c9 100644 --- a/typedoc/types/match.MatchOptions.html +++ b/typedoc/types/match.MatchOptions.html @@ -1,4 +1,4 @@ -MatchOptions | @vladmandic/human - v3.0.6
              +MatchOptions | @vladmandic/human - v3.0.7
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          Type alias MatchOptions

          @@ -35,7 +35,7 @@

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            • Tensor
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              • diff --git a/typedoc/variables/defaults.html b/typedoc/variables/defaults.html index e2270372..b08e1df0 100644 --- a/typedoc/variables/defaults.html +++ b/typedoc/variables/defaults.html @@ -1,4 +1,4 @@ -defaults | @vladmandic/human - v3.0.6
              +defaults | @vladmandic/human - v3.0.7
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          Variable defaultsConst

          defaults: Config = ...
          @@ -38,7 +38,7 @@

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              • diff --git a/typedoc/variables/draw.options.html b/typedoc/variables/draw.options.html index bcc8bf96..bedaea7e 100644 --- a/typedoc/variables/draw.options.html +++ b/typedoc/variables/draw.options.html @@ -1,4 +1,4 @@ -options | @vladmandic/human - v3.0.6
              +options | @vladmandic/human - v3.0.7
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          Variable optionsConst

          @@ -37,7 +37,7 @@

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              • diff --git a/typedoc/variables/env-1.html b/typedoc/variables/env-1.html index ffb209bb..5ca7c13d 100644 --- a/typedoc/variables/env-1.html +++ b/typedoc/variables/env-1.html @@ -1,4 +1,4 @@ -env | @vladmandic/human - v3.0.6
              +env | @vladmandic/human - v3.0.7
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          Variable envConst

          env: Env = ...