From de01483bc55960910672512acbdd7d6bd3e279cf Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sun, 11 Oct 2020 19:22:43 -0400 Subject: [PATCH] initial public commit --- .eslintrc.json | 54 + .gitignore | 1 + README.md | 173 + demo/index.html | 25 + demo/index.js | 120 + dist/human.esm.js | 2640 +++ dist/human.esm.js.map | 7 + dist/human.js | 4006 ++++ dist/human.js.map | 7 + models/blazeface/group1-shard1of1.bin | Bin 0 -> 401768 bytes models/blazeface/model.json | 1 + models/facemesh/group1-shard1of1.bin | Bin 0 -> 2955780 bytes models/facemesh/model.json | 1 + models/handdetect/anchors.json | 17666 ++++++++++++++++ models/handdetect/group1-shard1of2.bin | Bin 0 -> 4194304 bytes models/handdetect/group1-shard2of2.bin | Bin 0 -> 2836760 bytes models/handdetect/model.json | 11712 ++++++++++ models/handskeleton/group1-shard1of2.bin | Bin 0 -> 4194304 bytes models/handskeleton/group1-shard2of2.bin | Bin 0 -> 1307976 bytes models/handskeleton/model.json | 11619 ++++++++++ models/iris/group1-shard1of1.bin | Bin 0 -> 2599092 bytes models/iris/model.json | 1 + models/posenet/group1-shard1of2.bin | Bin 0 -> 4194304 bytes models/posenet/group1-shard2of2.bin | Bin 0 -> 838476 bytes models/posenet/model.json | 1 + models/ssrnet-age/imdb/group1-shard1of1.bin | Bin 0 -> 161240 bytes models/ssrnet-age/imdb/model.json | 1 + models/ssrnet-age/wiki/group1-shard1of1.bin | Bin 0 -> 161240 bytes models/ssrnet-age/wiki/model.json | 1 + .../ssrnet-gender/imdb/group1-shard1of1.bin | Bin 0 -> 161236 bytes models/ssrnet-gender/imdb/model.json | 1 + .../ssrnet-gender/wiki/group1-shard1of1.bin | Bin 0 -> 161236 bytes models/ssrnet-gender/wiki/model.json | 1 + package-lock.json | 1994 ++ package.json | 51 + src/blazeface/box.js | 20 + src/blazeface/face.js | 188 + src/blazeface/index.js | 12 + src/config.js | 58 + src/facemesh/box.js | 51 + src/facemesh/index.js | 77 + src/facemesh/keypoints.js | 38 + src/facemesh/pipeline.js | 301 + src/facemesh/util.js | 88 + src/facemesh/uvcoords.js | 470 + src/handpose/box.js | 65 + src/handpose/hand.js | 107 + src/handpose/index.js | 93 + src/handpose/keypoints.js | 8 + src/handpose/pipeline.js | 193 + src/handpose/util.js | 68 + src/image.js | 127 + src/index.js | 81 + src/posenet/buildParts.js | 46 + src/posenet/decodeMultiple.js | 104 + src/posenet/decodePose.js | 84 + src/posenet/decodeSingle.js | 59 + src/posenet/decoders.js | 60 + src/posenet/heapSort.js | 72 + src/posenet/index.js | 22 + src/posenet/keypoints.js | 61 + src/posenet/modelBase.js | 55 + src/posenet/modelMobileNet.js | 17 + src/posenet/modelPoseNet.js | 113 + src/posenet/modelWeights.js | 28 + src/posenet/util.js | 118 + src/posenet/vectors.js | 52 + src/ssrnet/index.js | 50 + src/triangulation.js | 169 + wiki/group1-shard1of1.bin | Bin 0 -> 161240 bytes wiki/model.json | 1 + 71 files changed, 53239 insertions(+) create mode 100644 .eslintrc.json create mode 100644 .gitignore create mode 100644 README.md create mode 100644 demo/index.html create mode 100644 demo/index.js create mode 100644 dist/human.esm.js create mode 100644 dist/human.esm.js.map create mode 100644 dist/human.js create mode 100644 dist/human.js.map create mode 100644 models/blazeface/group1-shard1of1.bin create mode 100644 models/blazeface/model.json create mode 100644 models/facemesh/group1-shard1of1.bin create mode 100644 models/facemesh/model.json create mode 100644 models/handdetect/anchors.json create mode 100644 models/handdetect/group1-shard1of2.bin create mode 100644 models/handdetect/group1-shard2of2.bin create mode 100644 models/handdetect/model.json create mode 100644 models/handskeleton/group1-shard1of2.bin create mode 100644 models/handskeleton/group1-shard2of2.bin create mode 100644 models/handskeleton/model.json create mode 100644 models/iris/group1-shard1of1.bin create mode 100644 models/iris/model.json create mode 100644 models/posenet/group1-shard1of2.bin create mode 100644 models/posenet/group1-shard2of2.bin create mode 100644 models/posenet/model.json create mode 100644 models/ssrnet-age/imdb/group1-shard1of1.bin create mode 100644 models/ssrnet-age/imdb/model.json create mode 100644 models/ssrnet-age/wiki/group1-shard1of1.bin create mode 100644 models/ssrnet-age/wiki/model.json create mode 100644 models/ssrnet-gender/imdb/group1-shard1of1.bin create mode 100644 models/ssrnet-gender/imdb/model.json create mode 100644 models/ssrnet-gender/wiki/group1-shard1of1.bin create mode 100644 models/ssrnet-gender/wiki/model.json create mode 100644 package-lock.json create mode 100644 package.json create mode 100644 src/blazeface/box.js create mode 100644 src/blazeface/face.js create mode 100644 src/blazeface/index.js create mode 100644 src/config.js create mode 100644 src/facemesh/box.js create mode 100644 src/facemesh/index.js create mode 100644 src/facemesh/keypoints.js create mode 100644 src/facemesh/pipeline.js create mode 100644 src/facemesh/util.js create mode 100644 src/facemesh/uvcoords.js create mode 100644 src/handpose/box.js create mode 100644 src/handpose/hand.js create mode 100644 src/handpose/index.js create mode 100644 src/handpose/keypoints.js create mode 100644 src/handpose/pipeline.js create mode 100644 src/handpose/util.js create mode 100644 src/image.js create mode 100644 src/index.js create mode 100644 src/posenet/buildParts.js create mode 100644 src/posenet/decodeMultiple.js create mode 100644 src/posenet/decodePose.js create mode 100644 src/posenet/decodeSingle.js create mode 100644 src/posenet/decoders.js create mode 100644 src/posenet/heapSort.js create mode 100644 src/posenet/index.js create mode 100644 src/posenet/keypoints.js create mode 100644 src/posenet/modelBase.js create mode 100644 src/posenet/modelMobileNet.js create mode 100644 src/posenet/modelPoseNet.js create mode 100644 src/posenet/modelWeights.js create mode 100644 src/posenet/util.js create mode 100644 src/posenet/vectors.js create mode 100644 src/ssrnet/index.js create mode 100644 src/triangulation.js create mode 100644 wiki/group1-shard1of1.bin create mode 100644 wiki/model.json diff --git a/.eslintrc.json b/.eslintrc.json new file mode 100644 index 00000000..e8937215 --- /dev/null +++ b/.eslintrc.json @@ -0,0 +1,54 @@ +{ + "globals": {}, + "env": { + "browser": true, + "commonjs": true, + "es6": true, + "node": true, + "jquery": true, + "es2020": true + }, + "parserOptions": { "ecmaVersion": 2020 }, + "plugins": [ ], + "extends": [ + "eslint:recommended", + "plugin:import/errors", + "plugin:import/warnings", + "plugin:node/recommended", + "plugin:promise/recommended", + "plugin:json/recommended-with-comments", + "airbnb-base" + ], + "ignorePatterns": [ "dist", "assets", "media", "models", "node_modules" ], + "rules": { + "max-len": [1, 275, 3], + "camelcase": "off", + "guard-for-in": "off", + "prefer-template":"off", + "import/extensions": "off", + "func-names": "off", + "no-await-in-loop": "off", + "no-bitwise": "off", + "no-case-declarations":"off", + "no-continue": "off", + "no-loop-func": "off", + "no-mixed-operators": "off", + "no-param-reassign":"off", + "no-plusplus": "off", + "dot-notation": "off", + "no-restricted-globals": "off", + "no-restricted-syntax": "off", + "no-underscore-dangle": "off", + "newline-per-chained-call": "off", + "node/no-unsupported-features/es-syntax": "off", + "node/shebang": "off", + "object-curly-newline": "off", + "prefer-destructuring": "off", + "promise/always-return": "off", + "promise/catch-or-return": "off", + "promise/no-nesting": "off", + "import/no-absolute-path": "off", + "no-regex-spaces": "off", + "radix": "off" + } +} \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..3c3629e6 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +node_modules diff --git a/README.md b/README.md new file mode 100644 index 00000000..d514a2ad --- /dev/null +++ b/README.md @@ -0,0 +1,173 @@ +# Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking and Age & Gender Prediction + +URL: + +*Suggestions are welcome!* + +## Credits + +This is an amalgamation of multiple existing models: + +- Face Detection: [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view) +- Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view) +- Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view) +- Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view) +- Body Pose Detection: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5) +- Age & Gender Prediction: [**SSR-Net**](https://github.com/shamangary/SSR-Net) + +## Install + +```shell +npm install @vladmandic/human +``` + +All pre-trained models are included in folder `/models` (25MB total) + +## Demo + +Demo is included in `/demo` + +## Requirements + +`Human` library is based on [TensorFlow/JS (TFJS)](js.tensorflow.org), but does not package it to allow for indepdenent version management - import `tfjs` before importing `Human` + +## Usage + +`Human` library does not require special initialization. +All configuration is done in a single JSON object and all model weights will be dynamically loaded upon their first usage(and only then, `Human` will not load weights that it doesn't need according to configuration). + +There is only *ONE* method you need: + +```js +import * as tf from '@tensorflow/tfjs'; +import human from '@vladmandic/human'; + +// 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D +// 'options': optional parameter used to override any options present in default configuration +const results = await human.detect(image, options?) +``` + +Additionally, `Human` library exposes two classes: + +```js +human.defaults // default configuration object +human.models // dynamically maintained object of any loaded models +``` + +## Configuration + +Below is output of `human.defaults` object +Any property can be overriden by passing user object during `human.detect()` +Note that user object and default configuration are merged using deep-merge, so you do not need to redefine entire configuration + +```js +human.defaults = { + face: { + enabled: true, + detector: { + modelPath: '/models/human/blazeface/model.json', + maxFaces: 10, + skipFrames: 5, + minConfidence: 0.8, + iouThreshold: 0.3, + scoreThreshold: 0.75, + }, + mesh: { + enabled: true, + modelPath: '/models/human/facemesh/model.json', + }, + iris: { + enabled: true, + modelPath: '/models/human/iris/model.json', + }, + age: { + enabled: true, + modelPath: '/models/human/ssrnet-imdb-age/model.json', + skipFrames: 5, + }, + gender: { + enabled: true, + modelPath: '/models/human/ssrnet-imdb-gender/model.json', + }, + }, + body: { + enabled: true, + modelPath: '/models/human/posenet/model.json', + maxDetections: 5, + scoreThreshold: 0.75, + nmsRadius: 20, + }, + hand: { + enabled: true, + skipFrames: 5, + minConfidence: 0.8, + iouThreshold: 0.3, + scoreThreshold: 0.75, + detector: { + anchors: '/models/human/handdetect/anchors.json', + modelPath: '/models/human/handdetect/model.json', + }, + skeleton: { + modelPath: '/models/human/handskeleton/model.json', + }, + }, +}; +``` + +Where: +- `enabled`: controls if specified modul is enabled (note: module is not loaded until it is required) +- `modelPath`: path to specific pre-trained model weights +- `maxFaces`, `maxDetections`: how many faces or people are we trying to analyze. limiting number in busy scenes will result in higher performance +- `skipFrames`: how many frames to skip before re-running bounding box detection (e.g., face position does not move fast within a video, so it's ok to use previously detected face position and just run face geometry analysis) +- `minConfidence`: threshold for discarding a prediction +- `iouThreshold`: threshold for deciding whether boxes overlap too much in non-maximum suppression +- `scoreThreshold`: threshold for deciding when to remove boxes based on score in non-maximum suppression +- `nmsRadius`: radius for deciding points are too close in non-maximum suppression + +## Outputs + +Result of `humand.detect()` is a single object that includes data for all enabled modules and all detected objects: + +```js +result = { + face: // + [ + { + confidence: // + box: // + mesh: // (468 base points & 10 iris points) + annotations: // (32 base annotated landmarks & 2 iris annotations) + iris: // (relative distance of iris to camera, multiple by focal lenght to get actual distance) + age: // (estimated age) + gender: // (male or female) + } + ], + body: // + [ + { + score: // , + keypoints: // (17 annotated landmarks) + } + ], + hand: // + [ + confidence: // , + box: // , + landmarks: // (21 points) + annotations: // ]> (5 annotated landmakrs) + ] +} +``` + +## Performance + +Of course, performance will vary depending on your hardware, but also on number of enabled modules as well as their parameters. +For example, on a low-end nVidia GTX1050 it can perform face detection at 50+ FPS, but drop to <5 FPS if all modules are enabled. + +## Todo + +- Improve detection of smaller faces, add BlazeFace back model +- Create demo, host it on gitpages +- Implement draw helper functions +- Sample Images +- Rename human to human diff --git a/demo/index.html b/demo/index.html new file mode 100644 index 00000000..b8481df3 --- /dev/null +++ b/demo/index.html @@ -0,0 +1,25 @@ + + + + + + + +
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+ + + diff --git a/demo/index.js b/demo/index.js new file mode 100644 index 00000000..30c35746 --- /dev/null +++ b/demo/index.js @@ -0,0 +1,120 @@ +/* global tf, ScatterGL, dat */ + +import human from '../dist/human.esm.js'; + +const state = { + backend: 'webgl', + triangulateMesh: true, + renderPointcloud: true, + stop: false, + videoSize: 700, +}; +const options = { +}; + +let ctx; +let videoWidth; +let videoHeight; +let video; +let canvas; +let scatterGLHasInitialized = false; +let scatterGL; + +async function renderPrediction() { + const predictions = await human.detect(video); + ctx.drawImage(video, 0, 0, videoWidth, videoHeight, 0, 0, canvas.width, canvas.height); + const div = document.getElementById('faces'); + div.innerHTML = ''; + for (const prediction of predictions) { + div.appendChild(prediction.canvas); + ctx.beginPath(); + ctx.rect(prediction.box[0], prediction.box[1], prediction.box[2], prediction.box[3]); + ctx.font = 'small-caps 1rem "Segoe UI"'; + ctx.fillText(`${prediction.gender} ${prediction.age}`, prediction.box[0] + 2, prediction.box[1] + 16, prediction.box[2]); + ctx.stroke(); + if (state.triangulateMesh) { + for (let i = 0; i < human.triangulation.length / 3; i++) { + const points = [human.triangulation[i * 3], human.triangulation[i * 3 + 1], human.triangulation[i * 3 + 2]].map((index) => prediction.mesh[index]); + const region = new Path2D(); + region.moveTo(points[0][0], points[0][1]); + for (let j = 1; i < points.length; j++) region.lineTo(points[j][0], points[j][1]); + region.closePath(); + ctx.stroke(region); + } + } else { + for (let i = 0; i < prediction.mesh.length; i++) { + const x = prediction.mesh[i][0]; + const y = prediction.mesh[i][1]; + ctx.beginPath(); + ctx.arc(x, y, 1 /* radius */, 0, 2 * Math.PI); + ctx.fill(); + } + } + if (state.renderPointcloud && scatterGL != null) { + const pointsData = predictions.map((pred) => pred.mesh.map((point) => ([-point[0], -point[1], -point[2]]))); + let flattenedPointsData = []; + for (let i = 0; i < pointsData.length; i++) { + flattenedPointsData = flattenedPointsData.concat(pointsData[i]); + } + const dataset = new ScatterGL.Dataset(flattenedPointsData); + if (!scatterGLHasInitialized) scatterGL.render(dataset); + else scatterGL.updateDataset(dataset); + scatterGLHasInitialized = true; + } + } + if (!state.stop) requestAnimationFrame(renderPrediction); +} + +function setupDatGui() { + const gui = new dat.GUI(); + gui.add(state, 'stop').onChange(() => { renderPrediction(); }); + gui.add(state, 'backend', ['webgl', 'cpu']).onChange((backend) => { tf.setBackend(backend); }); + gui.add(options, 'maxFaces', 1, 100, 1).onChange(() => { human.load(options); }); + gui.add(options, 'detectionConfidence', 0, 1, 0.05).onChange(() => { human.load(options); }); + gui.add(options, 'iouThreshold', 0, 1, 0.05).onChange(() => { human.load(options); }); + gui.add(options, 'scoreThreshold', 0, 1, 0.05).onChange(() => { human.load(options); }); + gui.add(state, 'triangulateMesh'); + gui.add(state, 'renderPointcloud').onChange((render) => { document.querySelector('#scatter-gl-container').style.display = render ? 'inline-block' : 'none'; }); +} + +async function setupCamera() { + video = document.getElementById('video'); + const stream = await navigator.mediaDevices.getUserMedia({ + audio: false, + video: { facingMode: 'user', width: state.videoSize, height: state.videoSize }, + }); + video.srcObject = stream; + return new Promise((resolve) => { + video.onloadedmetadata = () => resolve(video); + }); +} + +async function main() { + await tf.setBackend(state.backend); + setupDatGui(); + await setupCamera(); + video.play(); + videoWidth = video.videoWidth; + videoHeight = video.videoHeight; + video.width = videoWidth; + video.height = videoHeight; + canvas = document.getElementById('output'); + canvas.width = videoWidth; + canvas.height = videoHeight; + const canvasContainer = document.querySelector('.canvas-wrapper'); + canvasContainer.style = `width: ${videoWidth}px; height: ${videoHeight}px`; + ctx = canvas.getContext('2d'); + // ctx.translate(canvas.width, 0); + // ctx.scale(-1, 1); + ctx.fillStyle = '#32EEDB'; + ctx.strokeStyle = '#32EEDB'; + ctx.lineWidth = 0.5; + human.load(options); + renderPrediction(); + if (state.renderPointcloud) { + document.querySelector('#scatter-gl-container').style = `width: ${state.videoSize}px; height: ${state.videoSize}px;`; + scatterGL = new ScatterGL(document.querySelector('#scatter-gl-container'), { rotateOnStart: false, selectEnabled: false }); + } +} + +main(); diff --git a/dist/human.esm.js b/dist/human.esm.js new file mode 100644 index 00000000..f5244a22 --- /dev/null +++ b/dist/human.esm.js @@ -0,0 +1,2640 @@ +var __defineProperty = Object.defineProperty; +var __commonJS = (callback, module) => () => { + if (!module) { + module = {exports: {}}; + callback(module.exports, module); + } + return module.exports; +}; +var __markAsModule = (target) => { + return __defineProperty(target, "__esModule", {value: true}); +}; +var __export = (target, all) => { + __markAsModule(target); + for (var name in all) + __defineProperty(target, name, {get: all[name], enumerable: true}); +}; + +// src/blazeface/box.js +var require_box = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + exports.disposeBox = (box) => { + box.startEndTensor.dispose(); + box.startPoint.dispose(); + box.endPoint.dispose(); + }; + exports.createBox = (startEndTensor) => ({ + startEndTensor, + startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), + endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) + }); + exports.scaleBox = (box, factors) => { + const starts = tf.mul(box.startPoint, factors); + const ends = tf.mul(box.endPoint, factors); + const newCoordinates = tf.concat2d([starts, ends], 1); + return exports.createBox(newCoordinates); + }; +}); + +// src/blazeface/face.js +var require_face = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const bounding = require_box(); + const ANCHORS_CONFIG = { + strides: [8, 16], + anchors: [2, 6] + }; + const NUM_LANDMARKS = 6; + function generateAnchors(width, height, outputSpec) { + const anchors = []; + for (let i = 0; i < outputSpec.strides.length; i++) { + const stride = outputSpec.strides[i]; + const gridRows = Math.floor((height + stride - 1) / stride); + const gridCols = Math.floor((width + stride - 1) / stride); + const anchorsNum = outputSpec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) { + anchors.push([anchorX, anchorY]); + } + } + } + } + return anchors; + } + function decodeBounds(boxOutputs, anchors, inputSize) { + const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]); + const centers = tf.add(boxStarts, anchors); + const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]); + const boxSizesNormalized = tf.div(boxSizes, inputSize); + const centersNormalized = tf.div(centers, inputSize); + const halfBoxSize = tf.div(boxSizesNormalized, 2); + const starts = tf.sub(centersNormalized, halfBoxSize); + const ends = tf.add(centersNormalized, halfBoxSize); + const startNormalized = tf.mul(starts, inputSize); + const endNormalized = tf.mul(ends, inputSize); + const concatAxis = 1; + return tf.concat2d([startNormalized, endNormalized], concatAxis); + } + function scaleBoxFromPrediction(face, scaleFactor) { + return tf.tidy(() => { + const box = face["box"] ? face["box"] : face; + return bounding.scaleBox(box, scaleFactor).startEndTensor.squeeze(); + }); + } + class BlazeFaceModel { + constructor(model, config) { + this.blazeFaceModel = model; + this.width = config.detector.inputSize; + this.height = config.detector.inputSize; + this.maxFaces = config.detector.maxFaces; + this.anchorsData = generateAnchors(config.detector.inputSize, config.detector.inputSize, ANCHORS_CONFIG); + this.anchors = tf.tensor2d(this.anchorsData); + this.inputSizeData = [config.detector.inputSize, config.detector.inputSize]; + this.inputSize = tf.tensor1d([config.detector.inputSize, config.detector.inputSize]); + this.iouThreshold = config.detector.iouThreshold; + this.scoreThreshold = config.detector.scoreThreshold; + } + async getBoundingBoxes(inputImage, returnTensors, annotateBoxes = true) { + const [detectedOutputs, boxes, scores] = tf.tidy(() => { + const resizedImage = inputImage.resizeBilinear([this.width, this.height]); + const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2); + const batchedPrediction = this.blazeFaceModel.predict(normalizedImage); + const prediction = batchedPrediction.squeeze(); + const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize); + const logits = tf.slice(prediction, [0, 0], [-1, 1]); + const scoresOut = tf.sigmoid(logits).squeeze(); + return [prediction, decodedBounds, scoresOut]; + }); + const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold); + const boxIndices = await boxIndicesTensor.array(); + boxIndicesTensor.dispose(); + let boundingBoxes = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1])); + if (!returnTensors) { + boundingBoxes = await Promise.all(boundingBoxes.map(async (boundingBox) => { + const vals = await boundingBox.array(); + boundingBox.dispose(); + return vals; + })); + } + const originalHeight = inputImage.shape[1]; + const originalWidth = inputImage.shape[2]; + let scaleFactor; + if (returnTensors) { + scaleFactor = tf.div([originalWidth, originalHeight], this.inputSize); + } else { + scaleFactor = [ + originalWidth / this.inputSizeData[0], + originalHeight / this.inputSizeData[1] + ]; + } + const annotatedBoxes = []; + for (let i = 0; i < boundingBoxes.length; i++) { + const boundingBox = boundingBoxes[i]; + const annotatedBox = tf.tidy(() => { + const box = boundingBox instanceof tf.Tensor ? bounding.createBox(boundingBox) : bounding.createBox(tf.tensor2d(boundingBox)); + if (!annotateBoxes) { + return box; + } + const boxIndex = boxIndices[i]; + let anchor; + if (returnTensors) { + anchor = this.anchors.slice([boxIndex, 0], [1, 2]); + } else { + anchor = this.anchorsData[boxIndex]; + } + const landmarks = tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]); + const probability = tf.slice(scores, [boxIndex], [1]); + return {box, landmarks, probability, anchor}; + }); + annotatedBoxes.push(annotatedBox); + } + boxes.dispose(); + scores.dispose(); + detectedOutputs.dispose(); + return { + boxes: annotatedBoxes, + scaleFactor + }; + } + async estimateFaces(input, returnTensors = false, annotateBoxes = true) { + const image = tf.tidy(() => { + if (!(input instanceof tf.Tensor)) { + input = tf.browser.fromPixels(input); + } + return input.toFloat().expandDims(0); + }); + const {boxes, scaleFactor} = await this.getBoundingBoxes(image, returnTensors, annotateBoxes); + image.dispose(); + if (returnTensors) { + return boxes.map((face) => { + const scaledBox = scaleBoxFromPrediction(face, scaleFactor); + const normalizedFace = { + topLeft: scaledBox.slice([0], [2]), + bottomRight: scaledBox.slice([2], [2]) + }; + if (annotateBoxes) { + const {landmarks, probability, anchor} = face; + const normalizedLandmarks = landmarks.add(anchor).mul(scaleFactor); + normalizedFace.landmarks = normalizedLandmarks; + normalizedFace.probability = probability; + } + return normalizedFace; + }); + } + return Promise.all(boxes.map(async (face) => { + const scaledBox = scaleBoxFromPrediction(face, scaleFactor); + let normalizedFace; + if (!annotateBoxes) { + const boxData = await scaledBox.array(); + normalizedFace = { + topLeft: boxData.slice(0, 2), + bottomRight: boxData.slice(2) + }; + } else { + const [landmarkData, boxData, probabilityData] = await Promise.all([face.landmarks, scaledBox, face.probability].map(async (d) => d.array())); + const anchor = face.anchor; + const [scaleFactorX, scaleFactorY] = scaleFactor; + const scaledLandmarks = landmarkData.map((landmark) => [ + (landmark[0] + anchor[0]) * scaleFactorX, + (landmark[1] + anchor[1]) * scaleFactorY + ]); + normalizedFace = { + topLeft: boxData.slice(0, 2), + bottomRight: boxData.slice(2), + landmarks: scaledLandmarks, + probability: probabilityData + }; + bounding.disposeBox(face.box); + face.landmarks.dispose(); + face.probability.dispose(); + } + scaledBox.dispose(); + return normalizedFace; + })); + } + } + exports.BlazeFaceModel = BlazeFaceModel; +}); + +// src/blazeface/index.js +var require_blazeface = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const face = require_face(); + async function load(config) { + const blazeface = await tf.loadGraphModel(config.detector.modelPath, {fromTFHub: config.detector.modelPath.includes("tfhub.dev")}); + const model = new face.BlazeFaceModel(blazeface, config); + return model; + } + exports.load = load; + const face_2 = require_face(); + Object.defineProperty(exports, "BlazeFaceModel", {enumerable: true, get() { + return face_2.BlazeFaceModel; + }}); +}); + +// src/facemesh/keypoints.js +var require_keypoints = __commonJS((exports) => { + exports.MESH_ANNOTATIONS = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], + lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] + }; +}); + +// src/facemesh/box.js +var require_box2 = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + function scaleBoxCoordinates(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + return {startPoint, endPoint}; + } + exports.scaleBoxCoordinates = scaleBoxCoordinates; + function getBoxSize(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; + } + exports.getBoxSize = getBoxSize; + function getBoxCenter(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; + } + exports.getBoxCenter = getBoxCenter; + function cutBoxFromImageAndResize(box, image, cropSize) { + const h = image.shape[1]; + const w = image.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tf.image.cropAndResize(image, boxes, [0], cropSize); + } + exports.cutBoxFromImageAndResize = cutBoxFromImageAndResize; + function enlargeBox(box, factor = 1.5) { + const center = getBoxCenter(box); + const size = getBoxSize(box); + const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return {startPoint, endPoint, landmarks: box.landmarks}; + } + exports.enlargeBox = enlargeBox; + function squarifyBox(box) { + const centers = getBoxCenter(box); + const size = getBoxSize(box); + const maxEdge = Math.max(...size); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return {startPoint, endPoint, landmarks: box.landmarks}; + } + exports.squarifyBox = squarifyBox; +}); + +// src/facemesh/util.js +var require_util = __commonJS((exports) => { + exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; + function normalizeRadians(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); + } + exports.normalizeRadians = normalizeRadians; + function computeRotation(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians(radians); + } + exports.computeRotation = computeRotation; + function radToDegrees(rad) { + return rad * 180 / Math.PI; + } + exports.radToDegrees = radToDegrees; + function buildTranslationMatrix(x, y) { + return [[1, 0, x], [0, 1, y], [0, 0, 1]]; + } + function dot(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; + } + exports.dot = dot; + function getColumnFrom2DArr(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; + } + exports.getColumnFrom2DArr = getColumnFrom2DArr; + function multiplyTransformMatrices(mat1, mat2) { + const product = []; + const size = mat1.length; + for (let row = 0; row < size; row++) { + product.push([]); + for (let col = 0; col < size; col++) { + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + } + return product; + } + function buildRotationMatrix(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); + } + exports.buildRotationMatrix = buildRotationMatrix; + function invertTransformMatrix(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot(rotationComponent[0], translationComponent), + -dot(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; + } + exports.invertTransformMatrix = invertTransformMatrix; + function rotatePoint(homogeneousCoordinate, rotationMatrix) { + return [ + dot(homogeneousCoordinate, rotationMatrix[0]), + dot(homogeneousCoordinate, rotationMatrix[1]) + ]; + } + exports.rotatePoint = rotatePoint; + function xyDistanceBetweenPoints(a, b) { + return Math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2); + } + exports.xyDistanceBetweenPoints = xyDistanceBetweenPoints; +}); + +// src/facemesh/pipeline.js +var require_pipeline = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const bounding = require_box2(); + const keypoints = require_keypoints(); + const util = require_util(); + const LANDMARKS_COUNT = 468; + const UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.25; + const MESH_MOUTH_INDEX = 13; + const MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, keypoints.MESH_ANNOTATIONS["midwayBetweenEyes"][0]]; + const BLAZEFACE_MOUTH_INDEX = 3; + const BLAZEFACE_NOSE_INDEX = 2; + const BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX]; + const LEFT_EYE_OUTLINE = keypoints.MESH_ANNOTATIONS["leftEyeLower0"]; + const LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]]; + const RIGHT_EYE_OUTLINE = keypoints.MESH_ANNOTATIONS["rightEyeLower0"]; + const RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]]; + const IRIS_UPPER_CENTER_INDEX = 3; + const IRIS_LOWER_CENTER_INDEX = 4; + const IRIS_IRIS_INDEX = 71; + const IRIS_NUM_COORDINATES = 76; + const ENLARGE_EYE_RATIO = 2.3; + const IRIS_MODEL_INPUT_SIZE = 64; + const MESH_TO_IRIS_INDICES_MAP = [ + {key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15]}, + {key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31]}, + {key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47]}, + {key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8]}, + {key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24]}, + {key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40]}, + {key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62]}, + {key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70]}, + {key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53]} + ]; + function replaceRawCoordinates(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < MESH_TO_IRIS_INDICES_MAP.length; i++) { + const {key, indices} = MESH_TO_IRIS_INDICES_MAP[i]; + const originalIndices = keypoints.MESH_ANNOTATIONS[`${prefix}${key}`]; + const shouldReplaceAllKeys = keys == null; + if (shouldReplaceAllKeys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index][0], + newCoords[index][1], + (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } + } + class Pipeline { + constructor(boundingBoxDetector, meshDetector, irisModel, config) { + this.regionsOfInterest = []; + this.runsWithoutFaceDetector = 0; + this.boundingBoxDetector = boundingBoxDetector; + this.meshDetector = meshDetector; + this.irisModel = irisModel; + this.meshWidth = config.mesh.inputSize; + this.meshHeight = config.mesh.inputSize; + this.skipFrames = config.detector.skipFrames; + this.maxFaces = config.detector.maxFaces; + } + transformRawCoords(rawCoords, box, angle, rotationMatrix) { + const boxSize = bounding.getBoxSize({startPoint: box.startPoint, endPoint: box.endPoint}); + const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.meshWidth / 2), + scaleFactor[1] * (coord[1] - this.meshHeight / 2), + coord[2] + ]); + const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => [...util.rotatePoint(coord, coordsRotationMatrix), coord[2]]); + const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix); + const boxCenter = [...bounding.getBoxCenter({startPoint: box.startPoint, endPoint: box.endPoint}), 1]; + const originalBoxCenter = [ + util.dot(boxCenter, inverseRotationMatrix[0]), + util.dot(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + coord[0] + originalBoxCenter[0], + coord[1] + originalBoxCenter[1], + coord[2] + ]); + } + getLeftToRightEyeDepthDifference(rawCoords) { + const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2]; + const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2]; + return leftEyeZ - rightEyeZ; + } + getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) { + const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), ENLARGE_EYE_RATIO)); + const boxSize = bounding.getBoxSize(box); + let crop = tf.image.cropAndResize(face, [[ + box.startPoint[1] / this.meshHeight, + box.startPoint[0] / this.meshWidth, + box.endPoint[1] / this.meshHeight, + box.endPoint[0] / this.meshWidth + ]], [0], [IRIS_MODEL_INPUT_SIZE, IRIS_MODEL_INPUT_SIZE]); + if (flip) { + crop = tf.image.flipLeftRight(crop); + } + return {box, boxSize, crop}; + } + getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) { + const eyeRawCoords = []; + for (let i = 0; i < IRIS_NUM_COORDINATES; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / IRIS_MODEL_INPUT_SIZE : x / IRIS_MODEL_INPUT_SIZE) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / IRIS_MODEL_INPUT_SIZE * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return {rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX)}; + } + getAdjustedIrisCoords(rawCoords, irisCoords, direction) { + const upperCenterZ = rawCoords[keypoints.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2]; + const lowerCenterZ = rawCoords[keypoints.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); + } + async predict(input, predictIrises, predictMesh) { + if (this.shouldUpdateRegionsOfInterest()) { + const returnTensors = false; + const annotateFace = true; + const {boxes, scaleFactor} = await this.boundingBoxDetector.getBoundingBoxes(input, returnTensors, annotateFace); + if (boxes.length === 0) { + this.regionsOfInterest = []; + return null; + } + const scaledBoxes = boxes.map((prediction) => { + const predictionBoxCPU = { + startPoint: prediction.box.startPoint.squeeze().arraySync(), + endPoint: prediction.box.endPoint.squeeze().arraySync() + }; + const scaledBox = bounding.scaleBoxCoordinates(predictionBoxCPU, scaleFactor); + const enlargedBox = bounding.enlargeBox(scaledBox); + return { + ...enlargedBox, + landmarks: prediction.landmarks.arraySync() + }; + }); + boxes.forEach((box) => { + if (box != null && box.startPoint != null) { + box.startEndTensor.dispose(); + box.startPoint.dispose(); + box.endPoint.dispose(); + } + }); + this.updateRegionsOfInterest(scaledBoxes); + this.runsWithoutFaceDetector = 0; + } else { + this.runsWithoutFaceDetector++; + } + return tf.tidy(() => this.regionsOfInterest.map((box, i) => { + let angle = 0; + const boxLandmarksFromMeshModel = box.landmarks.length >= LANDMARKS_COUNT; + let [indexOfMouth, indexOfForehead] = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES; + if (boxLandmarksFromMeshModel === false) { + [indexOfMouth, indexOfForehead] = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES; + } + angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]); + const faceCenter = bounding.getBoxCenter({startPoint: box.startPoint, endPoint: box.endPoint}); + const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]]; + let rotatedImage = input; + let rotationMatrix = util.IDENTITY_MATRIX; + if (angle !== 0) { + rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); + rotationMatrix = util.buildRotationMatrix(-angle, faceCenter); + } + const boxCPU = {startPoint: box.startPoint, endPoint: box.endPoint}; + const face = bounding.cutBoxFromImageAndResize(boxCPU, rotatedImage, [this.meshHeight, this.meshWidth]).div(255); + const [, flag, coords] = this.meshDetector.predict(face); + const coordsReshaped = tf.reshape(coords, [-1, 3]); + let rawCoords = coordsReshaped.arraySync(); + if (predictIrises) { + const {box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop} = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true); + const {box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop} = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]); + const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop])); + const eyePredictionsData = eyePredictions.dataSync(); + const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3); + const {rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords} = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3); + const {rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords} = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize); + const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left"); + replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right"); + } else if (leftToRightEyeDepthDifference < 1) { + replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + } + const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix); + tf.dispose(rawCoords); + const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData)); + if (predictMesh) { + const transformedCoords = tf.tensor2d(transformedCoordsData); + this.regionsOfInterest[i] = {...landmarksBox, landmarks: transformedCoords.arraySync()}; + const prediction2 = { + coords: transformedCoords, + box: landmarksBox, + confidence: flag.squeeze(), + image: face + }; + return prediction2; + } + const prediction = { + coords: null, + box: landmarksBox, + confidence: flag.squeeze(), + image: face + }; + return prediction; + })); + } + updateRegionsOfInterest(boxes) { + for (let i = 0; i < boxes.length; i++) { + const box = boxes[i]; + const previousBox = this.regionsOfInterest[i]; + let iou = 0; + if (previousBox && previousBox.startPoint) { + const [boxStartX, boxStartY] = box.startPoint; + const [boxEndX, boxEndY] = box.endPoint; + const [previousBoxStartX, previousBoxStartY] = previousBox.startPoint; + const [previousBoxEndX, previousBoxEndY] = previousBox.endPoint; + const xStartMax = Math.max(boxStartX, previousBoxStartX); + const yStartMax = Math.max(boxStartY, previousBoxStartY); + const xEndMin = Math.min(boxEndX, previousBoxEndX); + const yEndMin = Math.min(boxEndY, previousBoxEndY); + const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax); + const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY); + const previousBoxArea = (previousBoxEndX - previousBoxStartX) * (previousBoxEndY - boxStartY); + iou = intersection / (boxArea + previousBoxArea - intersection); + } + if (iou < UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD) { + this.regionsOfInterest[i] = box; + } + } + this.regionsOfInterest = this.regionsOfInterest.slice(0, boxes.length); + } + clearRegionOfInterest(index) { + if (this.regionsOfInterest[index] != null) { + this.regionsOfInterest = [ + ...this.regionsOfInterest.slice(0, index), + ...this.regionsOfInterest.slice(index + 1) + ]; + } + } + shouldUpdateRegionsOfInterest() { + const roisCount = this.regionsOfInterest.length; + const noROIs = roisCount === 0; + if (this.maxFaces === 1 || noROIs) { + return noROIs; + } + return roisCount !== this.maxFaces && this.runsWithoutFaceDetector >= this.skipFrames; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return {startPoint, endPoint}; + } + } + exports.Pipeline = Pipeline; +}); + +// src/facemesh/uvcoords.js +var require_uvcoords = __commonJS((exports) => { + exports.UV_COORDS = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 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+ [0.611334979534149, 0.362284004688263], + [0.634037971496582, 0.355970978736877], + [0.656635999679565, 0.355356991291046], + [0.681214988231659, 0.35834002494812], + [0.698584973812103, 0.363156020641327], + [0.941866993904114, 0.319076001644135], + [0.698584973812103, 0.387449026107788], + [0.584177017211914, 0.624107003211975], + [0.554318010807037, 0.566076993942261], + [0.534153997898102, 0.62064003944397], + [0.711217999458313, 0.819975018501282], + [0.664629995822906, 0.852871000766754], + [0.559099972248077, 0.902631998062134], + [0.871706008911133, 0.791940987110138], + [0.591234028339386, 0.373893976211548], + [0.544341027736664, 0.451583981513977], + [0.624562978744507, 0.924192011356354], + [0.88577002286911, 0.615028977394104], + [0.551338016986847, 0.695277988910675], + [0.551980018615723, 0.704632043838501], + [0.552887976169586, 0.715808033943176], + [0.555167973041534, 0.730794012546539], + [0.569944024085999, 0.767035007476807], + [0.593203008174896, 0.685675978660583], + [0.599261999130249, 0.681069016456604], + [0.607599973678589, 0.677703022956848], + [0.631937980651855, 0.663500010967255], + [0.752032995223999, 0.601315021514893], + [0.547226011753082, 0.420395016670227], + [0.563543975353241, 0.359827995300293], + [0.583841025829315, 0.368713974952698], + [0.586614012718201, 0.692366003990173], + [0.771915018558502, 0.683578014373779], + [0.531597018241882, 0.352482974529266], + [0.588370978832245, 0.804440975189209], + [0.52079701423645, 0.442565023899078], + [0.567984998226166, 0.493479013442993], + [0.543282985687256, 0.819254994392395], + [0.655317008495331, 0.745514988899231], + [0.621008992195129, 0.574018001556396], + [0.625559985637665, 0.78031200170517], + [0.680198013782501, 0.570719003677368], + [0.64276397228241, 0.604337990283966], + [0.704662978649139, 0.621529996395111], + [0.552012026309967, 0.862591981887817], + [0.589071989059448, 0.508637011051178], + [0.685944974422455, 0.775357007980347], + [0.645735025405884, 0.812640011310577], + [0.675342977046967, 0.703978002071381], + [0.810858011245728, 0.646304965019226], + [0.72012197971344, 0.714666962623596], + [0.866151988506317, 0.682704985141754], + [0.663187026977539, 0.644596993923187], + [0.570082008838654, 0.466325998306274], + [0.544561982154846, 0.548375964164734], + [0.562758982181549, 0.558784961700439], + [0.531987011432648, 0.530140042304993], + [0.585271000862122, 0.335177004337311], + [0.622952997684479, 0.32277899980545], + [0.655896008014679, 0.320163011550903], + [0.687132000923157, 0.322345972061157], + [0.716481983661652, 0.333200991153717], + [0.758756995201111, 0.382786989212036], + [0.897013008594513, 0.468769013881683], + [0.732392013072968, 0.424547016620636], + [0.70211398601532, 0.433162987232208], + [0.66652500629425, 0.433866024017334], + [0.633504986763, 0.426087975502014], + [0.603875994682312, 0.416586995124817], + [0.579657971858978, 0.409945011138916], + [0.992439985275269, 0.480777025222778], + [0.567192018032074, 0.569419980049133], + [0.54136598110199, 0.478899002075195], + [0.526564002037048, 0.546118021011353], + [0.523913025856018, 0.563830018043518], + [0.531529009342194, 0.555056989192963], + [0.566035985946655, 0.582329034805298], + [0.51631098985672, 0.563053965568542], + [0.5174720287323, 0.577877044677734], + [0.573594987392426, 0.389806985855103], + [0.560697972774506, 0.395331978797913], + [0.549755990505219, 0.399751007556915], + [0.710287988185883, 0.368252992630005], + [0.723330020904541, 0.363372981548309] + ]; +}); + +// src/facemesh/index.js +var require_facemesh = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const blazeface = require_blazeface(); + const keypoints = require_keypoints(); + const pipe = require_pipeline(); + const uv_coords = require_uvcoords(); + exports.uv_coords = uv_coords; + async function loadDetectorModel(config) { + return blazeface.load(config); + } + async function loadMeshModel(modelUrl) { + return tf.loadGraphModel(modelUrl, {fromTFHub: modelUrl.includes("tfhub.dev")}); + } + async function loadIrisModel(modelUrl) { + return tf.loadGraphModel(modelUrl, {fromTFHub: modelUrl.includes("tfhub.dev")}); + } + async function load(config) { + const models = await Promise.all([ + loadDetectorModel(config), + loadMeshModel(config.mesh.modelPath), + loadIrisModel(config.iris.modelPath) + ]); + const faceMesh = new MediaPipeFaceMesh(models[0], models[1], models[2], config); + return faceMesh; + } + exports.load = load; + class MediaPipeFaceMesh { + constructor(blazeFace, blazeMeshModel, irisModel, config) { + this.pipeline = new pipe.Pipeline(blazeFace, blazeMeshModel, irisModel, config); + this.config = config; + } + async estimateFaces(input, config) { + if (config) + this.config = config; + const image = tf.tidy(() => { + if (!(input instanceof tf.Tensor)) { + input = tf.browser.fromPixels(input); + } + return input.toFloat().expandDims(0); + }); + const results = []; + const predictions = await this.pipeline.predict(image, this.config.iris.enabled, this.config.mesh.enabled); + image.dispose(); + if (!predictions) + return results; + for (const prediction of predictions) { + const confidence = prediction.confidence.arraySync(); + if (confidence >= this.config.detector.minConfidence) { + const result = { + confidence: confidence || 0, + box: prediction.box ? [prediction.box.startPoint[0], prediction.box.startPoint[1], prediction.box.endPoint[0] - prediction.box.startPoint[0], prediction.box.endPoint[1] - prediction.box.startPoint[1]] : 0, + mesh: prediction.coords ? prediction.coords.arraySync() : null, + image: prediction.image ? tf.clone(prediction.image) : null + }; + const annotations = {}; + if (result.mesh && result.mesh.length > 0) { + for (const key in keypoints.MESH_ANNOTATIONS) { + if (this.config.iris.enabled || key.includes("Iris") === false) { + annotations[key] = keypoints.MESH_ANNOTATIONS[key].map((index) => result.mesh[index]); + } + } + } + result["annotations"] = annotations; + results.push(result); + } + tf.dispose(prediction.confidence); + tf.dispose(prediction.image); + tf.dispose(prediction.coords); + } + return results; + } + } + exports.MediaPipeFaceMesh = MediaPipeFaceMesh; +}); + +// src/ssrnet/index.js +var require_ssrnet = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const models = {}; + let last = {age: 0, gender: ""}; + let frame = 0; + async function getImage(image, size) { + const tensor = tf.tidy(() => { + const buffer = tf.browser.fromPixels(image); + const resize = tf.image.resizeBilinear(buffer, [size, size]); + const expand = tf.cast(tf.expandDims(resize, 0), "float32"); + return expand; + }); + return tensor; + } + async function predict(image, config) { + frame += 1; + if (frame >= config.face.age.skipFrames) { + frame = 0; + return last; + } + if (!models.age && config.face.age.enabled) + models.age = await tf.loadGraphModel(config.face.age.modelPath); + if (!models.gender && config.face.gender.enabled) + models.gender = await tf.loadGraphModel(config.face.gender.modelPath); + let enhance; + if (image instanceof tf.Tensor) { + const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false); + enhance = tf.mul(resize, [255]); + tf.dispose(resize); + } else { + enhance = await getImage(image, config.face.age.inputSize); + } + const obj = {}; + if (config.face.age.enabled) { + const ageT = await models.age.predict(enhance); + obj.age = Math.trunc(10 * ageT.dataSync()[0]) / 10; + tf.dispose(ageT); + } + if (config.face.gender.enabled) { + const genderT = await models.gender.predict(enhance); + obj.gender = Math.trunc(100 * genderT.dataSync()[0]) < 50 ? "female" : "male"; + tf.dispose(genderT); + } + tf.dispose(enhance); + last = obj; + return obj; + } + exports.predict = predict; +}); + +// src/posenet/modelBase.js +var require_modelBase = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + class BaseModel { + constructor(model, outputStride) { + this.model = model; + this.outputStride = outputStride; + const inputShape = this.model.inputs[0].shape; + tf.util.assert(inputShape[1] === -1 && inputShape[2] === -1, () => `Input shape [${inputShape[1]}, ${inputShape[2]}] must both be equal to or -1`); + } + predict(input) { + return tf.tidy(() => { + const asFloat = this.preprocessInput(input.toFloat()); + const asBatch = asFloat.expandDims(0); + const results = this.model.predict(asBatch); + const results3d = results.map((y) => y.squeeze([0])); + const namedResults = this.nameOutputResults(results3d); + return { + heatmapScores: namedResults.heatmap.sigmoid(), + offsets: namedResults.offsets, + displacementFwd: namedResults.displacementFwd, + displacementBwd: namedResults.displacementBwd + }; + }); + } + dispose() { + this.model.dispose(); + } + } + exports.BaseModel = BaseModel; +}); + +// src/posenet/modelMobileNet.js +var require_modelMobileNet = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const modelBase = require_modelBase(); + class MobileNet extends modelBase.BaseModel { + preprocessInput(input) { + return tf.tidy(() => tf.div(input, 127.5).sub(1)); + } + nameOutputResults(results) { + const [offsets, heatmap, displacementFwd, displacementBwd] = results; + return {offsets, heatmap, displacementFwd, displacementBwd}; + } + } + exports.MobileNet = MobileNet; +}); + +// src/posenet/heapSort.js +var require_heapSort = __commonJS((exports) => { + function half(k) { + return Math.floor(k / 2); + } + class MaxHeap { + constructor(maxSize, getElementValue) { + this.priorityQueue = new Array(maxSize); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max; + } + empty() { + return this.numberOfElements === -1; + } + size() { + return this.numberOfElements + 1; + } + all() { + return this.priorityQueue.slice(0, this.numberOfElements + 1); + } + max() { + return this.priorityQueue[0]; + } + swim(k) { + while (k > 0 && this.less(half(k), k)) { + this.exchange(k, half(k)); + k = half(k); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t; + } + } + exports.MaxHeap = MaxHeap; +}); + +// src/posenet/buildParts.js +var require_buildParts = __commonJS((exports) => { + const heapSort = require_heapSort(); + function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score) { + localMaximum = false; + break; + } + } + if (!localMaximum) { + break; + } + } + return localMaximum; + } + function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({score}) => score); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score = scores.get(heatmapY, heatmapX, keypointId); + if (score < scoreThreshold) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) { + queue.enqueue({score, part: {heatmapY, heatmapX, id: keypointId}}); + } + } + } + } + return queue; + } + exports.buildPartWithScoreQueue = buildPartWithScoreQueue; +}); + +// src/posenet/keypoints.js +var require_keypoints2 = __commonJS((exports) => { + exports.partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" + ]; + exports.NUM_KEYPOINTS = exports.partNames.length; + exports.partIds = exports.partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; + }, {}); + const connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] + ]; + exports.poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] + ]; + exports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [exports.partIds[jointNameA], exports.partIds[jointNameB]]); + exports.partChannels = [ + "left_face", + "right_face", + "right_upper_leg_front", + "right_lower_leg_back", + "right_upper_leg_back", + "left_lower_leg_front", + "left_upper_leg_front", + "left_upper_leg_back", + "left_lower_leg_back", + "right_feet", + "right_lower_leg_front", + "left_feet", + "torso_front", + "torso_back", + "right_upper_arm_front", + "right_upper_arm_back", + "right_lower_arm_back", + "left_lower_arm_front", + "left_upper_arm_front", + "left_upper_arm_back", + "left_lower_arm_back", + "right_hand", + "right_lower_arm_front", + "left_hand" + ]; +}); + +// src/posenet/vectors.js +var require_vectors = __commonJS((exports) => { + const kpt = require_keypoints2(); + function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS) + }; + } + exports.getOffsetPoint = getOffsetPoint; + function getImageCoords(part, outputStride, offsets) { + const {heatmapY, heatmapX, id: keypoint} = part; + const {y, x} = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride + x, + y: part.heatmapY * outputStride + y + }; + } + exports.getImageCoords = getImageCoords; + function fillArray(element, size) { + const result = new Array(size); + for (let i = 0; i < size; i++) { + result[i] = element; + } + return result; + } + exports.fillArray = fillArray; + function clamp(a, min, max) { + if (a < min) + return min; + if (a > max) + return max; + return a; + } + exports.clamp = clamp; + function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; + } + exports.squaredDistance = squaredDistance; + function addVectors(a, b) { + return {x: a.x + b.x, y: a.y + b.y}; + } + exports.addVectors = addVectors; + function clampVector(a, min, max) { + return {y: clamp(a.y, min, max), x: clamp(a.x, min, max)}; + } + exports.clampVector = clampVector; +}); + +// src/posenet/decodePose.js +var require_decodePose = __commonJS((exports) => { + const keypoints = require_keypoints2(); + const vectors = require_vectors(); + const parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => [keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]); + const parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId); + const childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId); + function getDisplacement(edgeId, point, displacements) { + const numEdges = displacements.shape[2] / 2; + return { + y: displacements.get(point.y, point.x, edgeId), + x: displacements.get(point.y, point.x, numEdges + edgeId) + }; + } + function getStridedIndexNearPoint(point, outputStride, height, width) { + return { + y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1), + x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1) + }; + } + function traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) { + const [height, width] = scoresBuffer.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width); + const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements); + const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width); + const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets); + targetKeypoint = vectors.addVectors({ + x: targetKeypointIndices.x * outputStride, + y: targetKeypointIndices.y * outputStride + }, {x: offsetPoint.x, y: offsetPoint.y}); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width); + const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId); + return {position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score}; + } + function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) { + const numParts = scores.shape[2]; + const numEdges = parentToChildEdges.length; + const instanceKeypoints = new Array(numParts); + const {part: rootPart, score: rootScore} = root; + const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets); + instanceKeypoints[rootPart.id] = { + score: rootScore, + part: keypoints.partNames[rootPart.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceKeypointId = parentToChildEdges[edge]; + const targetKeypointId = childToParentEdges[edge]; + if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) { + instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceKeypointId = childToParentEdges[edge]; + const targetKeypointId = parentToChildEdges[edge]; + if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) { + instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd); + } + } + return instanceKeypoints; + } + exports.decodePose = decodePose; +}); + +// src/posenet/decodeMultiple.js +var require_decodeMultiple = __commonJS((exports) => { + const buildParts = require_buildParts(); + const decodePose = require_decodePose(); + const vectors = require_vectors(); + function withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, {x, y}, keypointId) { + return poses.some(({keypoints}) => { + const correspondingKeypoint = keypoints[keypointId].position; + return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); + } + function getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) { + const notOverlappedKeypointScores = instanceKeypoints.reduce((result, {position, score}, keypointId) => { + if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) { + result += score; + } + return result; + }, 0); + return notOverlappedKeypointScores / instanceKeypoints.length; + } + const kLocalMaximumRadius = 1; + function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, maxPoseDetections, scoreThreshold = 0.5, nmsRadius = 20) { + const poses = []; + const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer); + const squaredNmsRadius = nmsRadius * nmsRadius; + while (poses.length < maxPoseDetections && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = vectors.getImageCoords(root.part, outputStride, offsetsBuffer); + if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) + continue; + const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, outputStride, displacementsFwdBuffer, displacementsBwdBuffer); + const score = getInstanceScore(poses, squaredNmsRadius, keypoints); + poses.push({keypoints, score}); + } + return poses; + } + exports.decodeMultiplePoses = decodeMultiplePoses; +}); + +// src/posenet/decoders.js +var require_decoders = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const kpt = require_keypoints2(); + function getPointsConfidence(heatmapScores, heatMapCoords) { + const numKeypoints = heatMapCoords.shape[0]; + const result = new Float32Array(numKeypoints); + for (let keypoint = 0; keypoint < numKeypoints; keypoint++) { + const y = heatMapCoords.get(keypoint, 0); + const x = heatMapCoords.get(keypoint, 1); + result[keypoint] = heatmapScores.get(y, x, keypoint); + } + return result; + } + exports.getPointsConfidence = getPointsConfidence; + function getOffsetPoint(y, x, keypoint, offsetsBuffer) { + return { + y: offsetsBuffer.get(y, x, keypoint), + x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS) + }; + } + function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) { + const result = []; + for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) { + const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf(); + const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf(); + const {x, y} = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer); + result.push(y); + result.push(x); + } + return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]); + } + exports.getOffsetVectors = getOffsetVectors; + function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) { + return tf.tidy(() => { + const offsetVectors = getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer); + return heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, "int32")).toFloat().add(offsetVectors); + }); + } + exports.getOffsetPoints = getOffsetPoints; + function mod(a, b) { + return tf.tidy(() => { + const floored = a.div(tf.scalar(b, "int32")); + return a.sub(floored.mul(tf.scalar(b, "int32"))); + }); + } + function argmax2d(inputs) { + const [height, width, depth] = inputs.shape; + return tf.tidy(() => { + const reshaped = inputs.reshape([height * width, depth]); + const coords = reshaped.argMax(0); + const yCoords = coords.div(tf.scalar(width, "int32")).expandDims(1); + const xCoords = mod(coords, width).expandDims(1); + return tf.concat([yCoords, xCoords], 1); + }); + } + exports.argmax2d = argmax2d; +}); + +// src/posenet/decodeSingle.js +var require_decodeSingle = __commonJS((exports) => { + const kpt = require_keypoints2(); + const decoders = require_decoders(); + async function decodeSinglePose(heatmapScores, offsets, outputStride) { + let totalScore = 0; + const heatmapValues = decoders.argmax2d(heatmapScores); + const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]); + const scoresBuffer = allTensorBuffers[0]; + const offsetsBuffer = allTensorBuffers[1]; + const heatmapValuesBuffer = allTensorBuffers[2]; + const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, outputStride, offsetsBuffer); + const offsetPointsBuffer = await offsetPoints.buffer(); + const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer)); + const keypoints = keypointConfidence.map((score, keypointId) => { + totalScore += score; + return { + position: { + y: offsetPointsBuffer.get(keypointId, 0), + x: offsetPointsBuffer.get(keypointId, 1) + }, + part: kpt.partNames[keypointId], + score + }; + }); + heatmapValues.dispose(); + offsetPoints.dispose(); + return {keypoints, score: totalScore / keypoints.length}; + } + exports.decodeSinglePose = decodeSinglePose; +}); + +// src/posenet/util.js +var require_util2 = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const kpt = require_keypoints2(); + function eitherPointDoesntMeetConfidence(a, b, minConfidence) { + return a < minConfidence || b < minConfidence; + } + function getAdjacentKeyPoints(keypoints, minConfidence) { + return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => { + if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) { + return result; + } + result.push([keypoints[leftJoint], keypoints[rightJoint]]); + return result; + }, []); + } + exports.getAdjacentKeyPoints = getAdjacentKeyPoints; + const {NEGATIVE_INFINITY, POSITIVE_INFINITY} = Number; + function getBoundingBox(keypoints) { + return keypoints.reduce(({maxX, maxY, minX, minY}, {position: {x, y}}) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: NEGATIVE_INFINITY, + maxY: NEGATIVE_INFINITY, + minX: POSITIVE_INFINITY, + minY: POSITIVE_INFINITY + }); + } + exports.getBoundingBox = getBoundingBox; + function getBoundingBoxPoints(keypoints) { + const {minX, minY, maxX, maxY} = getBoundingBox(keypoints); + return [{x: minX, y: minY}, {x: maxX, y: minY}, {x: maxX, y: maxY}, {x: minX, y: maxY}]; + } + exports.getBoundingBoxPoints = getBoundingBoxPoints; + async function toTensorBuffers3D(tensors) { + return Promise.all(tensors.map((tensor) => tensor.buffer())); + } + exports.toTensorBuffers3D = toTensorBuffers3D; + function scalePose(pose, scaleY, scaleX, offsetY = 0, offsetX = 0) { + return { + score: pose.score, + keypoints: pose.keypoints.map(({score, part, position}) => ({ + score, + part, + position: { + x: position.x * scaleX + offsetX, + y: position.y * scaleY + offsetY + } + })) + }; + } + exports.scalePose = scalePose; + function scalePoses(poses, scaleY, scaleX, offsetY = 0, offsetX = 0) { + if (scaleX === 1 && scaleY === 1 && offsetY === 0 && offsetX === 0) { + return poses; + } + return poses.map((pose) => scalePose(pose, scaleY, scaleX, offsetY, offsetX)); + } + exports.scalePoses = scalePoses; + function getInputTensorDimensions(input) { + return input instanceof tf.Tensor ? [input.shape[0], input.shape[1]] : [input.height, input.width]; + } + exports.getInputTensorDimensions = getInputTensorDimensions; + function toInputTensor(input) { + return input instanceof tf.Tensor ? input : tf.browser.fromPixels(input); + } + exports.toInputTensor = toInputTensor; + function toResizedInputTensor(input, resizeHeight, resizeWidth) { + return tf.tidy(() => { + const imageTensor = toInputTensor(input); + return imageTensor.resizeBilinear([resizeHeight, resizeWidth]); + }); + } + exports.toResizedInputTensor = toResizedInputTensor; + function padAndResizeTo(input, [targetH, targetW]) { + const [height, width] = getInputTensorDimensions(input); + const targetAspect = targetW / targetH; + const aspect = width / height; + let [padT, padB, padL, padR] = [0, 0, 0, 0]; + if (aspect < targetAspect) { + padT = 0; + padB = 0; + padL = Math.round(0.5 * (targetAspect * height - width)); + padR = Math.round(0.5 * (targetAspect * height - width)); + } else { + padT = Math.round(0.5 * (1 / targetAspect * width - height)); + padB = Math.round(0.5 * (1 / targetAspect * width - height)); + padL = 0; + padR = 0; + } + const resized = tf.tidy(() => { + let imageTensor = toInputTensor(input); + imageTensor = tf.pad3d(imageTensor, [[padT, padB], [padL, padR], [0, 0]]); + return imageTensor.resizeBilinear([targetH, targetW]); + }); + return {resized, padding: {top: padT, left: padL, right: padR, bottom: padB}}; + } + exports.padAndResizeTo = padAndResizeTo; + function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth], padding) { + const scaleY = (height + padding.top + padding.bottom) / inputResolutionHeight; + const scaleX = (width + padding.left + padding.right) / inputResolutionWidth; + const scaledPoses = scalePoses(poses, scaleY, scaleX, -padding.top, -padding.left); + return scaledPoses; + } + exports.scaleAndFlipPoses = scaleAndFlipPoses; +}); + +// src/posenet/modelPoseNet.js +var require_modelPoseNet = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const modelMobileNet = require_modelMobileNet(); + const decodeMultiple = require_decodeMultiple(); + const decodeSingle = require_decodeSingle(); + const util = require_util2(); + class PoseNet { + constructor(net, inputResolution) { + this.baseModel = net; + this.inputResolution = inputResolution; + } + async estimateMultiplePoses(input, config) { + const outputStride = this.baseModel.outputStride; + const inputResolution = this.inputResolution; + const [height, width] = util.getInputTensorDimensions(input); + const {resized, padding} = util.padAndResizeTo(input, [inputResolution, inputResolution]); + const {heatmapScores, offsets, displacementFwd, displacementBwd} = this.baseModel.predict(resized); + const allTensorBuffers = await util.toTensorBuffers3D([heatmapScores, offsets, displacementFwd, displacementBwd]); + const scoresBuffer = allTensorBuffers[0]; + const offsetsBuffer = allTensorBuffers[1]; + const displacementsFwdBuffer = allTensorBuffers[2]; + const displacementsBwdBuffer = allTensorBuffers[3]; + const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, config.maxDetections, config.scoreThreshold, config.nmsRadius); + const resultPoses = util.scaleAndFlipPoses(poses, [height, width], [inputResolution, inputResolution], padding); + heatmapScores.dispose(); + offsets.dispose(); + displacementFwd.dispose(); + displacementBwd.dispose(); + resized.dispose(); + return resultPoses; + } + async estimateSinglePose(input) { + const outputStride = this.baseModel.outputStride; + const inputResolution = this.inputResolution; + const [height, width] = util.getInputTensorDimensions(input); + const {resized, padding} = util.padAndResizeTo(input, inputResolution); + const {heatmapScores, offsets, displacementFwd, displacementBwd} = this.baseModel.predict(resized); + const pose = await decodeSingle.decodeSinglePose(heatmapScores, offsets, outputStride); + const poses = [pose]; + const resultPoses = util.scaleAndFlipPoses(poses, [height, width], [inputResolution, inputResolution], padding); + heatmapScores.dispose(); + offsets.dispose(); + displacementFwd.dispose(); + displacementBwd.dispose(); + resized.dispose(); + return resultPoses[0]; + } + dispose() { + this.baseModel.dispose(); + } + } + exports.PoseNet = PoseNet; + async function loadMobileNet(config) { + const outputStride = config.outputStride; + const graphModel = await tf.loadGraphModel(config.modelPath); + const mobilenet = new modelMobileNet.MobileNet(graphModel, outputStride); + return new PoseNet(mobilenet, config.inputResolution); + } + async function load(config) { + return loadMobileNet(config); + } + exports.load = load; +}); + +// src/posenet/index.js +var require_posenet = __commonJS((exports) => { + const modelMobileNet = require_modelMobileNet(); + const modelPoseNet = require_modelPoseNet(); + const decodeMultiple = require_decodeMultiple(); + const decodeSingle = require_decodeSingle(); + const keypoints = require_keypoints2(); + const util = require_util2(); + exports.load = modelPoseNet.load; + exports.PoseNet = modelPoseNet.PoseNet; + exports.MobileNet = modelMobileNet.MobileNet; + exports.decodeMultiplePoses = decodeMultiple.decodeMultiplePoses; + exports.decodeSinglePose = decodeSingle.decodeSinglePose; + exports.partChannels = keypoints.partChannels; + exports.partIds = keypoints.partIds; + exports.partNames = keypoints.partNames; + exports.poseChain = keypoints.poseChain; + exports.getAdjacentKeyPoints = util.getAdjacentKeyPoints; + exports.getBoundingBox = util.getBoundingBox; + exports.getBoundingBoxPoints = util.getBoundingBoxPoints; + exports.scaleAndFlipPoses = util.scaleAndFlipPoses; + exports.scalePose = util.scalePose; +}); + +// src/handpose/box.js +var require_box3 = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + function getBoxSize(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; + } + exports.getBoxSize = getBoxSize; + function getBoxCenter(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; + } + exports.getBoxCenter = getBoxCenter; + function cutBoxFromImageAndResize(box, image, cropSize) { + const h = image.shape[1]; + const w = image.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tf.image.cropAndResize(image, boxes, [0], cropSize); + } + exports.cutBoxFromImageAndResize = cutBoxFromImageAndResize; + function scaleBoxCoordinates(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + const palmLandmarks = box.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return {startPoint, endPoint, palmLandmarks}; + } + exports.scaleBoxCoordinates = scaleBoxCoordinates; + function enlargeBox(box, factor = 1.5) { + const center = getBoxCenter(box); + const size = getBoxSize(box); + const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return {startPoint, endPoint, palmLandmarks: box.palmLandmarks}; + } + exports.enlargeBox = enlargeBox; + function squarifyBox(box) { + const centers = getBoxCenter(box); + const size = getBoxSize(box); + const maxEdge = Math.max(...size); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return {startPoint, endPoint, palmLandmarks: box.palmLandmarks}; + } + exports.squarifyBox = squarifyBox; + function shiftBox(box, shiftFactor) { + const boxSize = [ + box.endPoint[0] - box.startPoint[0], + box.endPoint[1] - box.startPoint[1] + ]; + const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]]; + const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]]; + const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]]; + return {startPoint, endPoint, palmLandmarks: box.palmLandmarks}; + } + exports.shiftBox = shiftBox; +}); + +// src/handpose/hand.js +var require_hand = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const bounding = require_box3(); + class HandDetector { + constructor(model, width, height, anchors, iouThreshold, scoreThreshold) { + this.model = model; + this.width = width; + this.height = height; + this.iouThreshold = iouThreshold; + this.scoreThreshold = scoreThreshold; + this.anchors = anchors.map((anchor) => [anchor.x_center, anchor.y_center]); + this.anchorsTensor = tf.tensor2d(this.anchors); + this.inputSizeTensor = tf.tensor1d([width, height]); + this.doubleInputSizeTensor = tf.tensor1d([width * 2, height * 2]); + } + normalizeBoxes(boxes) { + return tf.tidy(() => { + const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]); + const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]); + const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor); + const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor); + const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor); + const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor); + return tf.concat2d([startPoints, endPoints], 1); + }); + } + normalizeLandmarks(rawPalmLandmarks, index) { + return tf.tidy(() => { + const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]); + return tf.mul(landmarks, this.inputSizeTensor); + }); + } + async getBoundingBoxes(input) { + const normalizedInput = tf.tidy(() => tf.mul(tf.sub(input, 0.5), 2)); + let batchedPrediction; + if (tf.getBackend() === "webgl") { + const savedWebglPackDepthwiseConvFlag = tf.env().get("WEBGL_PACK_DEPTHWISECONV"); + tf.env().set("WEBGL_PACK_DEPTHWISECONV", true); + batchedPrediction = this.model.predict(normalizedInput); + tf.env().set("WEBGL_PACK_DEPTHWISECONV", savedWebglPackDepthwiseConvFlag); + } else { + batchedPrediction = this.model.predict(normalizedInput); + } + const prediction = batchedPrediction.squeeze(); + const scores = tf.tidy(() => tf.sigmoid(tf.slice(prediction, [0, 0], [-1, 1])).squeeze()); + const rawBoxes = tf.slice(prediction, [0, 1], [-1, 4]); + const boxes = this.normalizeBoxes(rawBoxes); + const boxesWithHandsTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, 1, this.iouThreshold, this.scoreThreshold); + const boxesWithHands = await boxesWithHandsTensor.array(); + const toDispose = [ + normalizedInput, + batchedPrediction, + boxesWithHandsTensor, + prediction, + boxes, + rawBoxes, + scores + ]; + if (boxesWithHands.length === 0) { + toDispose.forEach((tensor) => tensor.dispose()); + return null; + } + const boxIndex = boxesWithHands[0]; + const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]); + const rawPalmLandmarks = tf.slice(prediction, [boxIndex, 5], [1, 14]); + const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([ + -1, + 2 + ])); + toDispose.push(rawPalmLandmarks); + toDispose.forEach((tensor) => tensor.dispose()); + return {boxes: matchingBox, palmLandmarks}; + } + async estimateHandBounds(input) { + const inputHeight = input.shape[1]; + const inputWidth = input.shape[2]; + const image = tf.tidy(() => input.resizeBilinear([this.width, this.height]).div(255)); + const prediction = await this.getBoundingBoxes(image); + if (prediction === null) { + image.dispose(); + return null; + } + const boundingBoxes = prediction.boxes.arraySync(); + const startPoint = boundingBoxes[0].slice(0, 2); + const endPoint = boundingBoxes[0].slice(2, 4); + const palmLandmarks = prediction.palmLandmarks.arraySync(); + image.dispose(); + prediction.boxes.dispose(); + prediction.palmLandmarks.dispose(); + return bounding.scaleBoxCoordinates({startPoint, endPoint, palmLandmarks}, [inputWidth / this.width, inputHeight / this.height]); + } + } + exports.HandDetector = HandDetector; +}); + +// src/handpose/keypoints.js +var require_keypoints3 = __commonJS((exports) => { + exports.MESH_ANNOTATIONS = { + thumb: [1, 2, 3, 4], + indexFinger: [5, 6, 7, 8], + middleFinger: [9, 10, 11, 12], + ringFinger: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palmBase: [0] + }; +}); + +// src/handpose/util.js +var require_util3 = __commonJS((exports) => { + function normalizeRadians(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); + } + exports.normalizeRadians = normalizeRadians; + function computeRotation(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians(radians); + } + exports.computeRotation = computeRotation; + const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; + function dot(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; + } + exports.dot = dot; + function getColumnFrom2DArr(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; + } + exports.getColumnFrom2DArr = getColumnFrom2DArr; + function multiplyTransformMatrices(mat1, mat2) { + const product = []; + const size = mat1.length; + for (let row = 0; row < size; row++) { + product.push([]); + for (let col = 0; col < size; col++) { + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + } + return product; + } + function buildRotationMatrix(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); + } + exports.buildRotationMatrix = buildRotationMatrix; + function invertTransformMatrix(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot(rotationComponent[0], translationComponent), + -dot(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; + } + exports.invertTransformMatrix = invertTransformMatrix; + function rotatePoint(homogeneousCoordinate, rotationMatrix) { + return [ + dot(homogeneousCoordinate, rotationMatrix[0]), + dot(homogeneousCoordinate, rotationMatrix[1]) + ]; + } + exports.rotatePoint = rotatePoint; +}); + +// src/handpose/pipeline.js +var require_pipeline2 = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const bounding = require_box3(); + const util = require_util3(); + const UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.8; + const PALM_BOX_SHIFT_VECTOR = [0, -0.4]; + const PALM_BOX_ENLARGE_FACTOR = 3; + const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; + const HAND_BOX_ENLARGE_FACTOR = 1.65; + const PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2]; + const PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0; + const PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2; + class HandPipeline { + constructor(boundingBoxDetector, meshDetector, meshWidth, meshHeight, maxContinuousChecks, detectionConfidence) { + this.regionsOfInterest = []; + this.runsWithoutHandDetector = 0; + this.boundingBoxDetector = boundingBoxDetector; + this.meshDetector = meshDetector; + this.maxContinuousChecks = maxContinuousChecks; + this.detectionConfidence = detectionConfidence; + this.meshWidth = meshWidth; + this.meshHeight = meshHeight; + this.maxHandsNumber = 1; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => { + const homogeneousCoordinate = [...coord, 1]; + return util.rotatePoint(homogeneousCoordinate, rotationMatrix); + }); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return bounding.enlargeBox(bounding.squarifyBox(bounding.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = bounding.enlargeBox(bounding.squarifyBox(bounding.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR); + const palmLandmarks = []; + for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) { + palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2)); + } + boxAroundHand.palmLandmarks = palmLandmarks; + return boxAroundHand; + } + transformRawCoords(rawCoords, box, angle, rotationMatrix) { + const boxSize = bounding.getBoxSize(box); + const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.meshWidth / 2), + scaleFactor[1] * (coord[1] - this.meshHeight / 2), + coord[2] + ]); + const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = util.rotatePoint(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix); + const boxCenter = [...bounding.getBoxCenter(box), 1]; + const originalBoxCenter = [ + util.dot(boxCenter, inverseRotationMatrix[0]), + util.dot(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + coord[0] + originalBoxCenter[0], + coord[1] + originalBoxCenter[1], + coord[2] + ]); + } + async estimateHand(image, config) { + const useFreshBox = this.shouldUpdateRegionsOfInterest(); + if (useFreshBox === true) { + const boundingBoxPrediction = await this.boundingBoxDetector.estimateHandBounds(image); + if (boundingBoxPrediction === null) { + image.dispose(); + this.regionsOfInterest = []; + return null; + } + this.updateRegionsOfInterest(boundingBoxPrediction, true); + this.runsWithoutHandDetector = 0; + } else { + this.runsWithoutHandDetector++; + } + const currentBox = this.regionsOfInterest[0]; + const angle = util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]); + const palmCenter = bounding.getBoxCenter(currentBox); + const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]]; + const rotatedImage = tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized); + const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter); + const box = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = bounding.cutBoxFromImageAndResize(box, rotatedImage, [this.meshWidth, this.meshHeight]); + const handImage = croppedInput.div(255); + croppedInput.dispose(); + rotatedImage.dispose(); + let prediction; + if (tf.getBackend() === "webgl") { + const savedWebglPackDepthwiseConvFlag = tf.env().get("WEBGL_PACK_DEPTHWISECONV"); + tf.env().set("WEBGL_PACK_DEPTHWISECONV", true); + prediction = this.meshDetector.predict(handImage); + tf.env().set("WEBGL_PACK_DEPTHWISECONV", savedWebglPackDepthwiseConvFlag); + } else { + prediction = this.meshDetector.predict(handImage); + } + const [flag, keypoints] = prediction; + handImage.dispose(); + const flagValue = flag.dataSync()[0]; + flag.dispose(); + if (flagValue < config.minConfidence) { + keypoints.dispose(); + this.regionsOfInterest = []; + return null; + } + const keypointsReshaped = tf.reshape(keypoints, [-1, 3]); + const rawCoords = keypointsReshaped.arraySync(); + keypoints.dispose(); + keypointsReshaped.dispose(); + const coords = this.transformRawCoords(rawCoords, box, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords); + this.updateRegionsOfInterest(nextBoundingBox, false); + const result = { + landmarks: coords, + confidence: flagValue, + box: { + topLeft: nextBoundingBox.startPoint, + bottomRight: nextBoundingBox.endPoint + } + }; + return result; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return {startPoint, endPoint}; + } + updateRegionsOfInterest(box, forceUpdate) { + if (forceUpdate) { + this.regionsOfInterest = [box]; + } else { + const previousBox = this.regionsOfInterest[0]; + let iou = 0; + if (previousBox != null && previousBox.startPoint != null) { + const [boxStartX, boxStartY] = box.startPoint; + const [boxEndX, boxEndY] = box.endPoint; + const [previousBoxStartX, previousBoxStartY] = previousBox.startPoint; + const [previousBoxEndX, previousBoxEndY] = previousBox.endPoint; + const xStartMax = Math.max(boxStartX, previousBoxStartX); + const yStartMax = Math.max(boxStartY, previousBoxStartY); + const xEndMin = Math.min(boxEndX, previousBoxEndX); + const yEndMin = Math.min(boxEndY, previousBoxEndY); + const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax); + const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY); + const previousBoxArea = (previousBoxEndX - previousBoxStartX) * (previousBoxEndY - boxStartY); + iou = intersection / (boxArea + previousBoxArea - intersection); + } + this.regionsOfInterest[0] = iou > UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD ? previousBox : box; + } + } + shouldUpdateRegionsOfInterest() { + const roisCount = this.regionsOfInterest.length; + return roisCount !== this.maxHandsNumber || this.runsWithoutHandDetector >= this.maxContinuousChecks; + } + } + exports.HandPipeline = HandPipeline; +}); + +// src/handpose/index.js +var require_handpose = __commonJS((exports) => { + const tf = require("@tensorflow/tfjs"); + const hand = require_hand(); + const keypoints = require_keypoints3(); + const pipe = require_pipeline2(); + async function loadHandDetectorModel(url) { + return tf.loadGraphModel(url, {fromTFHub: url.includes("tfhub.dev")}); + } + async function loadHandPoseModel(url) { + return tf.loadGraphModel(url, {fromTFHub: url.includes("tfhub.dev")}); + } + async function loadAnchors(url) { + return tf.util.fetch(url).then((d) => d.json()); + } + async function load(config) { + const [ANCHORS, handDetectorModel, handPoseModel] = await Promise.all([ + loadAnchors(config.detector.anchors), + loadHandDetectorModel(config.detector.modelPath), + loadHandPoseModel(config.skeleton.modelPath) + ]); + const detector = new hand.HandDetector(handDetectorModel, config.inputSize, config.inputSize, ANCHORS, config.iouThreshold, config.scoreThreshold); + const pipeline = new pipe.HandPipeline(detector, handPoseModel, config.inputSize, config.inputSize, config.skipFrames, config.minConfidence); + const handpose = new HandPose(pipeline); + return handpose; + } + exports.load = load; + class HandPose { + constructor(pipeline) { + this.pipeline = pipeline; + } + static getAnnotations() { + return keypoints.MESH_ANNOTATIONS; + } + async estimateHands(input, config) { + const image = tf.tidy(() => { + if (!(input instanceof tf.Tensor)) { + input = tf.browser.fromPixels(input); + } + return input.toFloat().expandDims(0); + }); + const prediction = await this.pipeline.estimateHand(image, config); + image.dispose(); + if (!prediction) + return []; + const annotations = {}; + for (const key of Object.keys(keypoints.MESH_ANNOTATIONS)) { + annotations[key] = keypoints.MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]); + } + return [{ + confidence: prediction.confidence || 0, + box: prediction.box ? [prediction.box.topLeft[0], prediction.box.topLeft[1], prediction.box.bottomRight[0] - prediction.box.topLeft[0], prediction.box.bottomRight[1] - prediction.box.topLeft[1]] : 0, + landmarks: prediction.landmarks, + annotations + }]; + } + } + exports.HandPose = HandPose; +}); + +// src/config.js +var require_config = __commonJS((exports) => { + __export(exports, { + default: () => config_default + }); + var config_default = { + face: { + enabled: true, + detector: { + modelPath: "/models/blazeface/model.json", + inputSize: 128, + maxFaces: 10, + skipFrames: 5, + minConfidence: 0.8, + iouThreshold: 0.3, + scoreThreshold: 0.75 + }, + mesh: { + enabled: true, + modelPath: "/models/facemesh/model.json", + inputSize: 192 + }, + iris: { + enabled: true, + modelPath: "/models/iris/model.json", + inputSize: 192 + }, + age: { + enabled: true, + modelPath: "/models/ssrnet-imdb-age/model.json", + inputSize: 64, + skipFrames: 5 + }, + gender: { + enabled: true, + modelPath: "/models/ssrnet-imdb-gender/model.json" + } + }, + body: { + enabled: true, + modelPath: "/models/posenet/model.json", + inputResolution: 257, + outputStride: 16, + maxDetections: 5, + scoreThreshold: 0.75, + nmsRadius: 20 + }, + hand: { + enabled: true, + inputSize: 256, + skipFrames: 5, + minConfidence: 0.8, + iouThreshold: 0.3, + scoreThreshold: 0.75, + detector: { + anchors: "/models/handdetect/anchors.json", + modelPath: "/models/handdetect/model.json" + }, + skeleton: { + modelPath: "/models/handskeleton/model.json" + } + } + }; +}); + +// src/index.js +var require_src = __commonJS((exports) => { + const facemesh = require_facemesh(); + const ssrnet = require_ssrnet(); + const posenet = require_posenet(); + const handpose = require_handpose(); + const defaults = require_config().default; + const models = { + facemesh: null, + blazeface: null, + ssrnet: null, + iris: null + }; + function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) { + prev[key] = pVal.concat(...oVal); + } else if (isObject(pVal) && isObject(oVal)) { + prev[key] = mergeDeep(pVal, oVal); + } else { + prev[key] = oVal; + } + }); + return prev; + }, {}); + } + async function detect(input, userConfig) { + const config = mergeDeep(defaults, userConfig); + let poseRes = []; + if (config.body.enabled) { + if (!models.posenet) + models.posenet = await posenet.load(config.body); + poseRes = await models.posenet.estimateMultiplePoses(input, config.body); + } + let handRes = []; + if (config.hand.enabled) { + if (!models.handpose) + models.handpose = await handpose.load(config.hand); + handRes = await models.handpose.estimateHands(input, config.hand); + } + const faceRes = []; + if (config.face.enabled) { + if (!models.facemesh) + models.facemesh = await facemesh.load(config.face); + const faces = await models.facemesh.estimateFaces(input, config.face); + for (const face of faces) { + const ssrdata = config.face.age.enabled || config.face.gender.enabled ? await ssrnet.predict(face.image, config) : {}; + const iris = face.annotations.leftEyeIris && face.annotations.rightEyeIris ? Math.max(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0], face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0]) : 0; + faceRes.push({ + confidence: face.confidence, + box: face.box, + mesh: face.mesh, + annotations: face.annotations, + age: ssrdata.age, + gender: ssrdata.gender, + iris: iris !== 0 ? Math.trunc(100 * 11.7 / iris) / 100 : 0 + }); + } + } + return {face: faceRes, body: poseRes, hand: handRes}; + } + exports.detect = detect; + exports.defaults = defaults; + exports.models = models; +}); +export default require_src(); +//# sourceMappingURL=human.esm.js.map diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map new file mode 100644 index 00000000..ee5f1c51 --- /dev/null +++ b/dist/human.esm.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["src/blazeface/box.js", "src/blazeface/face.js", "src/blazeface/index.js", "src/facemesh/keypoints.js", "src/facemesh/box.js", "src/facemesh/util.js", "src/facemesh/pipeline.js", "src/facemesh/uvcoords.js", "src/facemesh/index.js", "src/ssrnet/index.js", "src/posenet/modelBase.js", "src/posenet/modelMobileNet.js", "src/posenet/heapSort.js", "src/posenet/buildParts.js", "src/posenet/keypoints.js", "src/posenet/vectors.js", "src/posenet/decodePose.js", "src/posenet/decodeMultiple.js", "src/posenet/decoders.js", "src/posenet/decodeSingle.js", "src/posenet/util.js", "src/posenet/modelPoseNet.js", "src/posenet/index.js", "src/handpose/box.js", "src/handpose/hand.js", "src/handpose/keypoints.js", "src/handpose/util.js", "src/handpose/pipeline.js", "src/handpose/index.js", "src/config.js", "src/index.js"], + "sourcesContent": ["const tf = require('@tensorflow/tfjs');\n\nexports.disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nexports.createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nexports.scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return exports.createBox(newCoordinates);\n};\n", "const tf = require('@tensorflow/tfjs');\nconst bounding = require('./box');\n\nconst ANCHORS_CONFIG = {\n strides: [8, 16],\n anchors: [2, 6],\n};\nconst NUM_LANDMARKS = 6;\nfunction generateAnchors(width, height, outputSpec) {\n const anchors = [];\n for (let i = 0; i < outputSpec.strides.length; i++) {\n const stride = outputSpec.strides[i];\n const gridRows = Math.floor((height + stride - 1) / stride);\n const gridCols = Math.floor((width + stride - 1) / stride);\n const anchorsNum = outputSpec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return bounding.scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.detector.inputSize;\n this.height = config.detector.inputSize;\n this.maxFaces = config.detector.maxFaces;\n this.anchorsData = generateAnchors(config.detector.inputSize, config.detector.inputSize, ANCHORS_CONFIG);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSizeData = [config.detector.inputSize, config.detector.inputSize];\n this.inputSize = tf.tensor1d([config.detector.inputSize, config.detector.inputSize]);\n this.iouThreshold = config.detector.iouThreshold;\n this.scoreThreshold = config.detector.scoreThreshold;\n }\n\n async getBoundingBoxes(inputImage, returnTensors, annotateBoxes = true) {\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n // [1, 897, 17] 1 = batch, 897 = number of anchors\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n const prediction = batchedPrediction.squeeze();\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold);\n const boxIndices = await boxIndicesTensor.array();\n boxIndicesTensor.dispose();\n let boundingBoxes = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n if (!returnTensors) {\n boundingBoxes = await Promise.all(boundingBoxes.map(async (boundingBox) => {\n const vals = await boundingBox.array();\n boundingBox.dispose();\n return vals;\n }));\n }\n const originalHeight = inputImage.shape[1];\n const originalWidth = inputImage.shape[2];\n let scaleFactor;\n if (returnTensors) {\n scaleFactor = tf.div([originalWidth, originalHeight], this.inputSize);\n } else {\n scaleFactor = [\n originalWidth / this.inputSizeData[0],\n originalHeight / this.inputSizeData[1],\n ];\n }\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boundingBox = boundingBoxes[i];\n const annotatedBox = tf.tidy(() => {\n const box = boundingBox instanceof tf.Tensor\n ? bounding.createBox(boundingBox)\n : bounding.createBox(tf.tensor2d(boundingBox));\n if (!annotateBoxes) {\n return box;\n }\n const boxIndex = boxIndices[i];\n let anchor;\n if (returnTensors) {\n anchor = this.anchors.slice([boxIndex, 0], [1, 2]);\n } else {\n anchor = this.anchorsData[boxIndex];\n }\n const landmarks = tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1])\n .squeeze()\n .reshape([NUM_LANDMARKS, -1]);\n const probability = tf.slice(scores, [boxIndex], [1]);\n return { box, landmarks, probability, anchor };\n });\n annotatedBoxes.push(annotatedBox);\n }\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor,\n };\n }\n\n async estimateFaces(input, returnTensors = false, annotateBoxes = true) {\n const image = tf.tidy(() => {\n if (!(input instanceof tf.Tensor)) {\n input = tf.browser.fromPixels(input);\n }\n return input.toFloat().expandDims(0);\n });\n const { boxes, scaleFactor } = await this.getBoundingBoxes(image, returnTensors, annotateBoxes);\n image.dispose();\n if (returnTensors) {\n return boxes.map((face) => {\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n const normalizedFace = {\n topLeft: scaledBox.slice([0], [2]),\n bottomRight: scaledBox.slice([2], [2]),\n };\n if (annotateBoxes) {\n const { landmarks, probability, anchor } = face;\n const normalizedLandmarks = landmarks.add(anchor).mul(scaleFactor);\n normalizedFace.landmarks = normalizedLandmarks;\n normalizedFace.probability = probability;\n }\n return normalizedFace;\n });\n }\n return Promise.all(boxes.map(async (face) => {\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n let normalizedFace;\n if (!annotateBoxes) {\n const boxData = await scaledBox.array();\n normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n };\n } else {\n const [landmarkData, boxData, probabilityData] = await Promise.all([face.landmarks, scaledBox, face.probability].map(async (d) => d.array()));\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n bounding.disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n }\n scaledBox.dispose();\n return normalizedFace;\n }));\n }\n}\nexports.BlazeFaceModel = BlazeFaceModel;\n", "const tf = require('@tensorflow/tfjs');\nconst face = require('./face');\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.detector.modelPath, { fromTFHub: config.detector.modelPath.includes('tfhub.dev') });\n const model = new face.BlazeFaceModel(blazeface, config);\n return model;\n}\nexports.load = load;\nconst face_2 = require('./face');\n\nObject.defineProperty(exports, 'BlazeFaceModel', { enumerable: true, get() { return face_2.BlazeFaceModel; } });\n", "exports.MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n", "const tf = require('@tensorflow/tfjs');\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "/* eslint-disable class-methods-use-this */\nconst tf = require('@tensorflow/tfjs');\nconst bounding = require('./box');\nconst keypoints = require('./keypoints');\nconst util = require('./util');\n\nconst LANDMARKS_COUNT = 468;\nconst UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.25;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, keypoints.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = keypoints.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = keypoints.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\nconst ENLARGE_EYE_RATIO = 2.3; // Factor by which to enlarge the box around the eye landmarks so the input region matches the expectations of the iris model.\nconst IRIS_MODEL_INPUT_SIZE = 64;\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = keypoints.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.regionsOfInterest = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.mesh.inputSize;\n this.meshHeight = config.mesh.inputSize;\n this.skipFrames = config.detector.skipFrames;\n this.maxFaces = config.detector.maxFaces;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]]));\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => ([\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1], coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), ENLARGE_EYE_RATIO));\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [IRIS_MODEL_INPUT_SIZE, IRIS_MODEL_INPUT_SIZE]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / IRIS_MODEL_INPUT_SIZE))\n : (x / IRIS_MODEL_INPUT_SIZE)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / IRIS_MODEL_INPUT_SIZE) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[keypoints.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[keypoints.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, predictIrises, predictMesh) {\n if (this.shouldUpdateRegionsOfInterest()) {\n const returnTensors = false;\n const annotateFace = true;\n const { boxes, scaleFactor } = await this.boundingBoxDetector.getBoundingBoxes(input, returnTensors, annotateFace);\n if (boxes.length === 0) {\n this.regionsOfInterest = [];\n return null;\n }\n const scaledBoxes = boxes.map((prediction) => {\n const predictionBoxCPU = {\n startPoint: prediction.box.startPoint.squeeze().arraySync(),\n endPoint: prediction.box.endPoint.squeeze().arraySync(),\n };\n const scaledBox = bounding.scaleBoxCoordinates(predictionBoxCPU, scaleFactor);\n const enlargedBox = bounding.enlargeBox(scaledBox);\n return {\n ...enlargedBox,\n landmarks: prediction.landmarks.arraySync(),\n };\n });\n boxes.forEach((box) => {\n if (box != null && box.startPoint != null) {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n }\n });\n this.updateRegionsOfInterest(scaledBoxes);\n this.runsWithoutFaceDetector = 0;\n } else {\n this.runsWithoutFaceDetector++;\n }\n return tf.tidy(() => this.regionsOfInterest.map((box, i) => {\n let angle = 0;\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n const boxLandmarksFromMeshModel = box.landmarks.length >= LANDMARKS_COUNT;\n let [indexOfMouth, indexOfForehead] = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n if (boxLandmarksFromMeshModel === false) {\n [indexOfMouth, indexOfForehead] = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n }\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n let rotatedImage = input;\n let rotationMatrix = util.IDENTITY_MATRIX;\n if (angle !== 0) {\n rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n }\n const boxCPU = { startPoint: box.startPoint, endPoint: box.endPoint };\n const face = bounding.cutBoxFromImageAndResize(boxCPU, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n // The first returned tensor represents facial contours, which are included in the coordinates.\n const [, flag, coords] = this.meshDetector.predict(face);\n const coordsReshaped = tf.reshape(coords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n if (predictIrises) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = (this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop])));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n tf.dispose(rawCoords);\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n if (predictMesh) {\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n this.regionsOfInterest[i] = { ...landmarksBox, landmarks: transformedCoords.arraySync() };\n const prediction = {\n // coords: tf.tensor2d(rawCoords, [rawCoords.length, 3]),\n coords: transformedCoords,\n box: landmarksBox,\n confidence: flag.squeeze(),\n image: face,\n };\n return prediction;\n }\n const prediction = {\n coords: null,\n // scaledCoords: null,\n box: landmarksBox,\n confidence: flag.squeeze(),\n image: face,\n };\n return prediction;\n }));\n }\n\n // Updates regions of interest if the intersection over union between the incoming and previous regions falls below a threshold.\n updateRegionsOfInterest(boxes) {\n for (let i = 0; i < boxes.length; i++) {\n const box = boxes[i];\n const previousBox = this.regionsOfInterest[i];\n let iou = 0;\n if (previousBox && previousBox.startPoint) {\n const [boxStartX, boxStartY] = box.startPoint;\n const [boxEndX, boxEndY] = box.endPoint;\n const [previousBoxStartX, previousBoxStartY] = previousBox.startPoint;\n const [previousBoxEndX, previousBoxEndY] = previousBox.endPoint;\n const xStartMax = Math.max(boxStartX, previousBoxStartX);\n const yStartMax = Math.max(boxStartY, previousBoxStartY);\n const xEndMin = Math.min(boxEndX, previousBoxEndX);\n const yEndMin = Math.min(boxEndY, previousBoxEndY);\n const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);\n const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);\n const previousBoxArea = (previousBoxEndX - previousBoxStartX) * (previousBoxEndY - boxStartY);\n iou = intersection / (boxArea + previousBoxArea - intersection);\n }\n if (iou < UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD) {\n this.regionsOfInterest[i] = box;\n }\n }\n this.regionsOfInterest = this.regionsOfInterest.slice(0, boxes.length);\n }\n\n clearRegionOfInterest(index) {\n if (this.regionsOfInterest[index] != null) {\n this.regionsOfInterest = [\n ...this.regionsOfInterest.slice(0, index),\n ...this.regionsOfInterest.slice(index + 1),\n ];\n }\n }\n\n shouldUpdateRegionsOfInterest() {\n const roisCount = this.regionsOfInterest.length;\n const noROIs = roisCount === 0;\n if (this.maxFaces === 1 || noROIs) {\n return noROIs;\n }\n return roisCount !== this.maxFaces && this.runsWithoutFaceDetector >= this.skipFrames;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\nexports.Pipeline = Pipeline;\n", "exports.UV_COORDS = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n 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[0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n", "const tf = require('@tensorflow/tfjs');\nconst blazeface = require('../blazeface');\nconst keypoints = require('./keypoints');\nconst pipe = require('./pipeline');\nconst uv_coords = require('./uvcoords');\n\nexports.uv_coords = uv_coords;\n\nasync function loadDetectorModel(config) {\n return blazeface.load(config);\n}\nasync function loadMeshModel(modelUrl) {\n return tf.loadGraphModel(modelUrl, { fromTFHub: modelUrl.includes('tfhub.dev') });\n}\nasync function loadIrisModel(modelUrl) {\n return tf.loadGraphModel(modelUrl, { fromTFHub: modelUrl.includes('tfhub.dev') });\n}\n\nasync function load(config) {\n const models = await Promise.all([\n loadDetectorModel(config),\n loadMeshModel(config.mesh.modelPath),\n loadIrisModel(config.iris.modelPath),\n ]);\n // eslint-disable-next-line no-use-before-define\n const faceMesh = new MediaPipeFaceMesh(models[0], models[1], models[2], config);\n return faceMesh;\n}\nexports.load = load;\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n this.pipeline = new pipe.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n if (config) this.config = config;\n const image = tf.tidy(() => {\n if (!(input instanceof tf.Tensor)) {\n input = tf.browser.fromPixels(input);\n }\n return input.toFloat().expandDims(0);\n });\n const results = [];\n const predictions = await this.pipeline.predict(image, this.config.iris.enabled, this.config.mesh.enabled);\n image.dispose();\n if (!predictions) return results;\n for (const prediction of predictions) {\n const confidence = prediction.confidence.arraySync();\n if (confidence >= this.config.detector.minConfidence) {\n const result = {\n confidence: confidence || 0,\n box: prediction.box ? [prediction.box.startPoint[0], prediction.box.startPoint[1], prediction.box.endPoint[0] - prediction.box.startPoint[0], prediction.box.endPoint[1] - prediction.box.startPoint[1]] : 0,\n mesh: prediction.coords ? prediction.coords.arraySync() : null,\n image: prediction.image ? tf.clone(prediction.image) : null,\n // mesh: prediction.coords.arraySync(),\n };\n const annotations = {};\n if (result.mesh && result.mesh.length > 0) {\n for (const key in keypoints.MESH_ANNOTATIONS) {\n if (this.config.iris.enabled || key.includes('Iris') === false) {\n annotations[key] = keypoints.MESH_ANNOTATIONS[key].map((index) => result.mesh[index]);\n }\n }\n }\n result['annotations'] = annotations;\n results.push(result);\n }\n tf.dispose(prediction.confidence);\n tf.dispose(prediction.image);\n tf.dispose(prediction.coords);\n }\n return results;\n }\n}\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\n", "const tf = require('@tensorflow/tfjs');\n\nconst models = {};\nlet last = { age: 0, gender: '' };\nlet frame = 0;\n\nasync function getImage(image, size) {\n const tensor = tf.tidy(() => {\n const buffer = tf.browser.fromPixels(image);\n const resize = tf.image.resizeBilinear(buffer, [size, size]);\n const expand = tf.cast(tf.expandDims(resize, 0), 'float32');\n // const normalize = tf.mul(expand, [1.0 / 1.0]);\n return expand;\n });\n return tensor;\n}\n\nasync function predict(image, config) {\n frame += 1;\n if (frame >= config.face.age.skipFrames) {\n frame = 0;\n return last;\n }\n if (!models.age && config.face.age.enabled) models.age = await tf.loadGraphModel(config.face.age.modelPath);\n if (!models.gender && config.face.gender.enabled) models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n let enhance;\n if (image instanceof tf.Tensor) {\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n } else {\n enhance = await getImage(image, config.face.age.inputSize);\n }\n const obj = {};\n if (config.face.age.enabled) {\n const ageT = await models.age.predict(enhance);\n obj.age = Math.trunc(10 * ageT.dataSync()[0]) / 10;\n tf.dispose(ageT);\n }\n if (config.face.gender.enabled) {\n const genderT = await models.gender.predict(enhance);\n obj.gender = Math.trunc(100 * genderT.dataSync()[0]) < 50 ? 'female' : 'male';\n tf.dispose(genderT);\n }\n tf.dispose(enhance);\n last = obj;\n return obj;\n}\n\nexports.predict = predict;\n", "const tf = require('@tensorflow/tfjs');\n/**\n * PoseNet supports using various convolution neural network models\n * (e.g. ResNet and MobileNetV1) as its underlying base model.\n * The following BaseModel interface defines a unified interface for\n * creating such PoseNet base models. Currently both MobileNet (in\n * ./mobilenet.ts) and ResNet (in ./resnet.ts) implements the BaseModel\n * interface. New base models that conform to the BaseModel interface can be\n * added to PoseNet.\n */\nclass BaseModel {\n constructor(model, outputStride) {\n this.model = model;\n this.outputStride = outputStride;\n const inputShape = this.model.inputs[0].shape;\n tf.util.assert((inputShape[1] === -1) && (inputShape[2] === -1), () => `Input shape [${inputShape[1]}, ${inputShape[2]}] must both be equal to or -1`);\n }\n\n /**\n * Predicts intermediate Tensor representations.\n *\n * @param input The input RGB image of the base model.\n * A Tensor of shape: [`inputResolution`, `inputResolution`, 3].\n *\n * @return A dictionary of base model's intermediate predictions.\n * The returned dictionary should contains the following elements:\n * heatmapScores: A Tensor3D that represents the heatmapScores.\n * offsets: A Tensor3D that represents the offsets.\n * displacementFwd: A Tensor3D that represents the forward displacement.\n * displacementBwd: A Tensor3D that represents the backward displacement.\n */\n predict(input) {\n return tf.tidy(() => {\n const asFloat = this.preprocessInput(input.toFloat());\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = this.nameOutputResults(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n /**\n * Releases the CPU and GPU memory allocated by the model.\n */\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "const tf = require('@tensorflow/tfjs');\nconst modelBase = require('./modelBase');\n\nclass MobileNet extends modelBase.BaseModel {\n // eslint-disable-next-line class-methods-use-this\n preprocessInput(input) {\n // Normalize the pixels [0, 255] to be between [-1, 1].\n return tf.tidy(() => tf.div(input, 127.5).sub(1.0));\n }\n\n // eslint-disable-next-line class-methods-use-this\n nameOutputResults(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n }\n}\nexports.MobileNet = MobileNet;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "const heapSort = require('./heapSort');\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) {\n break;\n }\n }\n return localMaximum;\n}\n/**\n * Builds a priority queue with part candidate positions for a specific image in\n * the batch. For this we find all local maxima in the score maps with score\n * values above a threshold. We create a single priority queue across all parts.\n */\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\nexports.NUM_KEYPOINTS = exports.partNames.length;\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\n/*\n * Define the skeleton. This defines the parent->child relationships of our\n * tree. Arbitrarily this defines the nose as the root of the tree, however\n * since we will infer the displacement for both parent->child and\n * child->parent, we can define the tree root as any node.\n */\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "const kpt = require('./keypoints');\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "const keypoints = require('./keypoints');\nconst vectors = require('./vectors');\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n/**\n * We get a new keypoint along the `edgeId` for the pose instance, assuming\n * that the position of the `idSource` part is already known. For this, we\n * follow the displacement vector from the source to target part (stored in\n * the `i`-t channel of the displacement tensor). The displaced keypoint\n * vector is refined using the offset vector by `offsetRefineStep` times.\n */\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n/**\n * Follows the displacement fields to decode the full pose of the object\n * instance given the position of a part that acts as root.\n *\n * @return An array of decoded keypoints and their scores for a single pose\n */\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward\n // displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward\n // displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n", "const buildParts = require('./buildParts');\nconst decodePose = require('./decodePose');\nconst vectors = require('./vectors');\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n/* Score the newly proposed object instance without taking into account\n * the scores of the parts that overlap with any previously detected\n * instance.\n */\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) {\n result += score;\n }\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n// A point (y, x) is considered as root part candidate if its score is a\n// maximum in a window |y - y'| <= kLocalMaximumRadius, |x - x'| <=\n// kLocalMaximumRadius.\nconst kLocalMaximumRadius = 1;\n/**\n * Detects multiple poses and finds their parts from part scores and\n * displacement vectors. It returns up to `maxDetections` object instance\n * detections in decreasing root score order. It works as follows: We first\n * create a priority queue with local part score maxima above\n * `scoreThreshold`, considering all parts at the same time. Then we\n * iteratively pull the top element of the queue (in decreasing score order)\n * and treat it as a root candidate for a new object instance. To avoid\n * duplicate detections, we reject the root candidate if it is within a disk\n * of `nmsRadius` pixels from the corresponding part of a previously detected\n * instance, which is a form of part-based non-maximum suppression (NMS). If\n * the root candidate passes the NMS check, we start a new object instance\n * detection, treating the corresponding part as root and finding the\n * positions of the remaining parts by following the displacement vectors\n * along the tree-structured part graph. We assign to the newly detected\n * instance a score equal to the sum of scores of its parts which have not\n * been claimed by a previous instance (i.e., those at least `nmsRadius`\n * pixels away from the corresponding part of all previously detected\n * instances), divided by the total number of parts `numParts`.\n *\n * @param heatmapScores 3-D tensor with shape `[height, width, numParts]`.\n * The value of heatmapScores[y, x, k]` is the score of placing the `k`-th\n * object part at position `(y, x)`.\n *\n * @param offsets 3-D tensor with shape `[height, width, numParts * 2]`.\n * The value of [offsets[y, x, k], offsets[y, x, k + numParts]]` is the\n * short range offset vector of the `k`-th object part at heatmap\n * position `(y, x)`.\n *\n * @param displacementsFwd 3-D tensor of shape\n * `[height, width, 2 * num_edges]`, where `num_edges = num_parts - 1` is the\n * number of edges (parent-child pairs) in the tree. It contains the forward\n * displacements between consecutive part from the root towards the leaves.\n *\n * @param displacementsBwd 3-D tensor of shape\n * `[height, width, 2 * num_edges]`, where `num_edges = num_parts - 1` is the\n * number of edges (parent-child pairs) in the tree. It contains the backward\n * displacements between consecutive part from the root towards the leaves.\n *\n * @param outputStride The output stride that was used when feed-forwarding\n * through the PoseNet model. Must be 32, 16, or 8.\n *\n * @param maxPoseDetections Maximum number of returned instance detections per\n * image.\n *\n * @param scoreThreshold Only return instance detections that have root part\n * score greater or equal to this value. Defaults to 0.5.\n *\n * @param nmsRadius Non-maximum suppression part distance. It needs to be\n * strictly positive. Two parts suppress each other if they are less than\n * `nmsRadius` pixels away. Defaults to 20.\n *\n * @return An array of poses and their scores, each containing keypoints and\n * the corresponding keypoint scores.\n */\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, maxPoseDetections, scoreThreshold = 0.5, nmsRadius = 20) {\n const poses = [];\n const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = nmsRadius * nmsRadius;\n // Generate at most maxDetections object instances per image in\n // decreasing root part score order.\n while (poses.length < maxPoseDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it\n // is within a disk of `nmsRadius` pixels from the corresponding part of\n // a previously detected instance.\n const rootImageCoords = vectors.getImageCoords(root.part, outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Start a new detection instance at the position of the root.\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "const tf = require('@tensorflow/tfjs');\nconst kpt = require('./keypoints');\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => {\n const offsetVectors = getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer);\n return heatMapCoordsBuffer.toTensor()\n .mul(tf.scalar(outputStride, 'int32'))\n .toFloat()\n .add(offsetVectors);\n });\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "const kpt = require('./keypoints');\nconst decoders = require('./decoders');\n/**\n * Detects a single pose and finds its parts from part scores and offset\n * vectors. It returns a single pose detection. It works as follows:\n * argmax2d is done on the scores to get the y and x index in the heatmap\n * with the highest score for each part, which is essentially where the\n * part is most likely to exist. This produces a tensor of size 17x2, with\n * each row being the y and x index in the heatmap for each keypoint.\n * The offset vector for each for each part is retrieved by getting the\n * y and x from the offsets corresponding to the y and x index in the\n * heatmap for that part. This produces a tensor of size 17x2, with each\n * row being the offset vector for the corresponding keypoint.\n * To get the keypoint, each part\u2019s heatmap y and x are multiplied\n * by the output stride then added to their corresponding offset vector,\n * which is in the same scale as the original image.\n *\n * @param heatmapScores 3-D tensor with shape `[height, width, numParts]`.\n * The value of heatmapScores[y, x, k]` is the score of placing the `k`-th\n * object part at position `(y, x)`.\n *\n * @param offsets 3-D tensor with shape `[height, width, numParts * 2]`.\n * The value of [offsets[y, x, k], offsets[y, x, k + numParts]]` is the\n * short range offset vector of the `k`-th object part at heatmap\n * position `(y, x)`.\n *\n * @param outputStride The output stride that was used when feed-forwarding\n * through the PoseNet model. Must be 32, 16, or 8.\n *\n * @return A promise that resolves with single pose with a confidence score,\n * which contains an array of keypoints indexed by part id, each with a score\n * and position.\n */\nasync function decodeSinglePose(heatmapScores, offsets, outputStride) {\n let totalScore = 0.0;\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const keypoints = keypointConfidence.map((score, keypointId) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(keypointId, 0),\n x: offsetPointsBuffer.get(keypointId, 1),\n },\n part: kpt.partNames[keypointId],\n score,\n };\n });\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints, score: totalScore / keypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "const tf = require('@tensorflow/tfjs');\nconst kpt = require('./keypoints');\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX, offsetY = 0, offsetX = 0) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: {\n x: position.x * scaleX + offsetX,\n y: position.y * scaleY + offsetY,\n },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction scalePoses(poses, scaleY, scaleX, offsetY = 0, offsetX = 0) {\n if (scaleX === 1 && scaleY === 1 && offsetY === 0 && offsetX === 0) {\n return poses;\n }\n return poses.map((pose) => scalePose(pose, scaleY, scaleX, offsetY, offsetX));\n}\nexports.scalePoses = scalePoses;\n\nfunction getInputTensorDimensions(input) {\n return input instanceof tf.Tensor ? [input.shape[0], input.shape[1]] : [input.height, input.width];\n}\nexports.getInputTensorDimensions = getInputTensorDimensions;\n\nfunction toInputTensor(input) {\n return input instanceof tf.Tensor ? input : tf.browser.fromPixels(input);\n}\nexports.toInputTensor = toInputTensor;\n\nfunction toResizedInputTensor(input, resizeHeight, resizeWidth) {\n return tf.tidy(() => {\n const imageTensor = toInputTensor(input);\n return imageTensor.resizeBilinear([resizeHeight, resizeWidth]);\n });\n}\nexports.toResizedInputTensor = toResizedInputTensor;\n\nfunction padAndResizeTo(input, [targetH, targetW]) {\n const [height, width] = getInputTensorDimensions(input);\n const targetAspect = targetW / targetH;\n const aspect = width / height;\n let [padT, padB, padL, padR] = [0, 0, 0, 0];\n if (aspect < targetAspect) {\n // pads the width\n padT = 0;\n padB = 0;\n padL = Math.round(0.5 * (targetAspect * height - width));\n padR = Math.round(0.5 * (targetAspect * height - width));\n } else {\n // pads the height\n padT = Math.round(0.5 * ((1.0 / targetAspect) * width - height));\n padB = Math.round(0.5 * ((1.0 / targetAspect) * width - height));\n padL = 0;\n padR = 0;\n }\n const resized = tf.tidy(() => {\n let imageTensor = toInputTensor(input);\n imageTensor = tf.pad3d(imageTensor, [[padT, padB], [padL, padR], [0, 0]]);\n return imageTensor.resizeBilinear([targetH, targetW]);\n });\n return { resized, padding: { top: padT, left: padL, right: padR, bottom: padB } };\n}\nexports.padAndResizeTo = padAndResizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth], padding) {\n const scaleY = (height + padding.top + padding.bottom) / (inputResolutionHeight);\n const scaleX = (width + padding.left + padding.right) / (inputResolutionWidth);\n const scaledPoses = scalePoses(poses, scaleY, scaleX, -padding.top, -padding.left);\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "const tf = require('@tensorflow/tfjs');\nconst modelMobileNet = require('./modelMobileNet');\nconst decodeMultiple = require('./decodeMultiple');\nconst decodeSingle = require('./decodeSingle');\nconst util = require('./util');\n\nclass PoseNet {\n constructor(net, inputResolution) {\n this.baseModel = net;\n this.inputResolution = inputResolution;\n }\n\n /**\n * Infer through PoseNet, and estimates multiple poses using the outputs.\n * This does standard ImageNet pre-processing before inferring through the\n * model. The image should pixels should have values [0-255]. It detects\n * multiple poses and finds their parts from part scores and displacement\n * vectors using a fast greedy decoding algorithm. It returns up to\n * `config.maxDetections` object instance detections in decreasing root\n * score order.\n *\n * @param input\n * ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement) The input\n * image to feed through the network.\n *\n * @param config MultiPoseEstimationConfig object that contains parameters\n * for the PoseNet inference using multiple pose estimation.\n *\n * @return An array of poses and their scores, each containing keypoints and\n * the corresponding keypoint scores. The positions of the keypoints are\n * in the same scale as the original image\n */\n async estimateMultiplePoses(input, config) {\n const outputStride = this.baseModel.outputStride;\n const inputResolution = this.inputResolution;\n const [height, width] = util.getInputTensorDimensions(input);\n const { resized, padding } = util.padAndResizeTo(input, [inputResolution, inputResolution]);\n const { heatmapScores, offsets, displacementFwd, displacementBwd } = this.baseModel.predict(resized);\n const allTensorBuffers = await util.toTensorBuffers3D([heatmapScores, offsets, displacementFwd, displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, config.maxDetections, config.scoreThreshold, config.nmsRadius);\n const resultPoses = util.scaleAndFlipPoses(poses, [height, width], [inputResolution, inputResolution], padding);\n heatmapScores.dispose();\n offsets.dispose();\n displacementFwd.dispose();\n displacementBwd.dispose();\n resized.dispose();\n return resultPoses;\n }\n\n /**\n * Infer through PoseNet, and estimates a single pose using the outputs.\n * This does standard ImageNet pre-processing before inferring through the\n * model. The image should pixels should have values [0-255]. It detects\n * multiple poses and finds their parts from part scores and displacement\n * vectors using a fast greedy decoding algorithm. It returns a single pose\n *\n * @param input\n * ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement) The input\n * image to feed through the network.\n *\n * @param config SinglePersonEstimationConfig object that contains\n * parameters for the PoseNet inference using single pose estimation.\n *\n * @return An pose and its scores, containing keypoints and\n * the corresponding keypoint scores. The positions of the keypoints are\n * in the same scale as the original image\n */\n async estimateSinglePose(input) {\n const outputStride = this.baseModel.outputStride;\n const inputResolution = this.inputResolution;\n const [height, width] = util.getInputTensorDimensions(input);\n const { resized, padding } = util.padAndResizeTo(input, inputResolution);\n const { heatmapScores, offsets, displacementFwd, displacementBwd } = this.baseModel.predict(resized);\n const pose = await decodeSingle.decodeSinglePose(heatmapScores, offsets, outputStride);\n const poses = [pose];\n const resultPoses = util.scaleAndFlipPoses(poses, [height, width], [inputResolution, inputResolution], padding);\n heatmapScores.dispose();\n offsets.dispose();\n displacementFwd.dispose();\n displacementBwd.dispose();\n resized.dispose();\n return resultPoses[0];\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\nasync function loadMobileNet(config) {\n const outputStride = config.outputStride;\n const graphModel = await tf.loadGraphModel(config.modelPath);\n const mobilenet = new modelMobileNet.MobileNet(graphModel, outputStride);\n return new PoseNet(mobilenet, config.inputResolution);\n}\n/**\n * Loads the PoseNet model instance from a checkpoint, with the MobileNet architecture. The model to be loaded is configurable using the\n * config dictionary ModelConfig. Please find more details in the documentation of the ModelConfig.\n *\n * @param config ModelConfig dictionary that contains parameters for\n * the PoseNet loading process. Please find more details of each parameters\n * in the documentation of the ModelConfig interface. The predefined\n * `MOBILENET_V1_CONFIG` and `RESNET_CONFIG` can also be used as references\n * for defining your customized config.\n */\nasync function load(config) {\n return loadMobileNet(config);\n}\nexports.load = load;\n", "const modelMobileNet = require('./modelMobileNet');\nconst modelPoseNet = require('./modelPoseNet');\nconst decodeMultiple = require('./decodeMultiple');\nconst decodeSingle = require('./decodeSingle');\nconst keypoints = require('./keypoints');\nconst util = require('./util');\n\nexports.load = modelPoseNet.load;\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.MobileNet = modelMobileNet.MobileNet;\nexports.decodeMultiplePoses = decodeMultiple.decodeMultiplePoses;\nexports.decodeSinglePose = decodeSingle.decodeSinglePose;\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\nexports.getBoundingBox = util.getBoundingBox;\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\nexports.scalePose = util.scalePose;\n", "const tf = require('@tensorflow/tfjs');\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexports.enlargeBox = enlargeBox;\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexports.squarifyBox = squarifyBox;\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0], box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexports.shiftBox = shiftBox;\n", "const tf = require('@tensorflow/tfjs');\nconst bounding = require('./box');\n\nclass HandDetector {\n constructor(model, width, height, anchors, iouThreshold, scoreThreshold) {\n this.model = model;\n this.width = width;\n this.height = height;\n this.iouThreshold = iouThreshold;\n this.scoreThreshold = scoreThreshold;\n this.anchors = anchors.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([width, height]);\n this.doubleInputSizeTensor = tf.tensor1d([width * 2, height * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoundingBoxes(input) {\n const normalizedInput = tf.tidy(() => tf.mul(tf.sub(input, 0.5), 2));\n let batchedPrediction;\n if (tf.getBackend() === 'webgl') {\n // Currently tfjs-core does not pack depthwiseConv because it fails for\n // very large inputs (https://github.com/tensorflow/tfjs/issues/1652).\n // TODO(annxingyuan): call tf.enablePackedDepthwiseConv when available\n // (https://github.com/tensorflow/tfjs/issues/2821)\n const savedWebglPackDepthwiseConvFlag = tf.env().get('WEBGL_PACK_DEPTHWISECONV');\n tf.env().set('WEBGL_PACK_DEPTHWISECONV', true);\n // The model returns a tensor with the following shape:\n // [1 (batch), 2944 (anchor points), 19 (data for each anchor)]\n batchedPrediction = this.model.predict(normalizedInput);\n tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag);\n } else {\n batchedPrediction = this.model.predict(normalizedInput);\n }\n const prediction = batchedPrediction.squeeze();\n // Regression score for each anchor point.\n const scores = tf.tidy(() => tf.sigmoid(tf.slice(prediction, [0, 0], [-1, 1])).squeeze());\n // Bounding box for each anchor point.\n const rawBoxes = tf.slice(prediction, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n const boxesWithHandsTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, 1, this.iouThreshold, this.scoreThreshold);\n const boxesWithHands = await boxesWithHandsTensor.array();\n const toDispose = [\n normalizedInput, batchedPrediction, boxesWithHandsTensor, prediction,\n boxes, rawBoxes, scores,\n ];\n if (boxesWithHands.length === 0) {\n toDispose.forEach((tensor) => tensor.dispose());\n return null;\n }\n const boxIndex = boxesWithHands[0];\n const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(prediction, [boxIndex, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([\n -1, 2,\n ]));\n toDispose.push(rawPalmLandmarks);\n toDispose.forEach((tensor) => tensor.dispose());\n return { boxes: matchingBox, palmLandmarks };\n }\n\n /**\n * Returns a Box identifying the bounding box of a hand within the image.\n * Returns null if there is no hand in the image.\n *\n * @param input The image to classify.\n */\n async estimateHandBounds(input) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([this.width, this.height]).div(255));\n const prediction = await this.getBoundingBoxes(image);\n if (prediction === null) {\n image.dispose();\n return null;\n }\n // Calling arraySync on both boxes and palmLandmarks because the tensors are\n // very small so it's not worth calling await array().\n const boundingBoxes = prediction.boxes.arraySync();\n const startPoint = boundingBoxes[0].slice(0, 2);\n const endPoint = boundingBoxes[0].slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n image.dispose();\n prediction.boxes.dispose();\n prediction.palmLandmarks.dispose();\n return bounding.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks }, [inputWidth / this.width, inputHeight / this.height]);\n }\n}\nexports.HandDetector = HandDetector;\n", "exports.MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\nconst buildTranslationMatrix = (x, y) => ([[1, 0, x], [0, 1, y], [0, 0, 1]]);\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n", "const tf = require('@tensorflow/tfjs');\nconst bounding = require('./box');\nconst util = require('./util');\n\nconst UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.8;\nconst PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 3;\nconst HAND_BOX_SHIFT_VECTOR = [0, -0.1];\nconst HAND_BOX_ENLARGE_FACTOR = 1.65;\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass HandPipeline {\n constructor(boundingBoxDetector, meshDetector, meshWidth, meshHeight, maxContinuousChecks, detectionConfidence) {\n // An array of hand bounding boxes.\n this.regionsOfInterest = [];\n this.runsWithoutHandDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.maxContinuousChecks = maxContinuousChecks;\n this.detectionConfidence = detectionConfidence;\n this.meshWidth = meshWidth;\n this.meshHeight = meshHeight;\n this.maxHandsNumber = 1; // TODO(annxingyuan): Add multi-hand support.\n }\n\n // Get the bounding box surrounding the hand, given palm landmarks.\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => {\n const homogeneousCoordinate = [...coord, 1];\n return util.rotatePoint(homogeneousCoordinate, rotationMatrix);\n });\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // boxAroundPalm only surrounds the palm - therefore we shift it\n // upwards so it will capture fingers once enlarged + squarified.\n return bounding.enlargeBox(bounding.squarifyBox(bounding.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n }\n\n // Get the bounding box surrounding the hand, given all hand landmarks.\n getBoxForHandLandmarks(landmarks) {\n // The MediaPipe hand mesh model is trained on hands with empty space\n // around them, so we still need to shift / enlarge boxAroundHand even\n // though it surrounds the entire hand.\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = bounding.enlargeBox(bounding.squarifyBox(bounding.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n boxAroundHand.palmLandmarks = palmLandmarks;\n return boxAroundHand;\n }\n\n // Scale, rotate, and translate raw keypoints from the model so they map to\n // the input coordinates.\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n const boxSize = bounding.getBoxSize(box);\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...bounding.getBoxCenter(box), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0], coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHand(image, config) {\n const useFreshBox = this.shouldUpdateRegionsOfInterest();\n if (useFreshBox === true) {\n const boundingBoxPrediction = await this.boundingBoxDetector.estimateHandBounds(image);\n if (boundingBoxPrediction === null) {\n image.dispose();\n this.regionsOfInterest = [];\n return null;\n }\n this.updateRegionsOfInterest(boundingBoxPrediction, true /* force update */);\n this.runsWithoutHandDetector = 0;\n } else {\n this.runsWithoutHandDetector++;\n }\n // Rotate input so the hand is vertically oriented.\n const currentBox = this.regionsOfInterest[0];\n const angle = util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]);\n const palmCenter = bounding.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized);\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n // The bounding box detector only detects palms, so if we're using a fresh\n // bounding box prediction, we have to construct the hand bounding box from\n // the palm keypoints.\n const box = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = bounding.cutBoxFromImageAndResize(box, rotatedImage, [this.meshWidth, this.meshHeight]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n let prediction;\n if (tf.getBackend() === 'webgl') {\n // Currently tfjs-core does not pack depthwiseConv because it fails for\n // very large inputs (https://github.com/tensorflow/tfjs/issues/1652).\n // TODO(annxingyuan): call tf.enablePackedDepthwiseConv when available\n // (https://github.com/tensorflow/tfjs/issues/2821)\n const savedWebglPackDepthwiseConvFlag = tf.env().get('WEBGL_PACK_DEPTHWISECONV');\n tf.env().set('WEBGL_PACK_DEPTHWISECONV', true);\n prediction = this.meshDetector.predict(handImage);\n tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag);\n } else {\n prediction = this.meshDetector.predict(handImage);\n }\n const [flag, keypoints] = prediction;\n handImage.dispose();\n const flagValue = flag.dataSync()[0];\n flag.dispose();\n if (flagValue < config.minConfidence) {\n keypoints.dispose();\n this.regionsOfInterest = [];\n return null;\n }\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n // Calling arraySync() because the tensor is very small so it's not worth\n // calling await array().\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.updateRegionsOfInterest(nextBoundingBox, false /* force replace */);\n const result = {\n landmarks: coords,\n confidence: flagValue,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n return result;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n // Updates regions of interest if the intersection over union between\n // the incoming and previous regions falls below a threshold.\n updateRegionsOfInterest(box, forceUpdate) {\n if (forceUpdate) {\n this.regionsOfInterest = [box];\n } else {\n const previousBox = this.regionsOfInterest[0];\n let iou = 0;\n if (previousBox != null && previousBox.startPoint != null) {\n const [boxStartX, boxStartY] = box.startPoint;\n const [boxEndX, boxEndY] = box.endPoint;\n const [previousBoxStartX, previousBoxStartY] = previousBox.startPoint;\n const [previousBoxEndX, previousBoxEndY] = previousBox.endPoint;\n const xStartMax = Math.max(boxStartX, previousBoxStartX);\n const yStartMax = Math.max(boxStartY, previousBoxStartY);\n const xEndMin = Math.min(boxEndX, previousBoxEndX);\n const yEndMin = Math.min(boxEndY, previousBoxEndY);\n const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);\n const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);\n const previousBoxArea = (previousBoxEndX - previousBoxStartX) * (previousBoxEndY - boxStartY);\n iou = intersection / (boxArea + previousBoxArea - intersection);\n }\n this.regionsOfInterest[0] = iou > UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD ? previousBox : box;\n }\n }\n\n shouldUpdateRegionsOfInterest() {\n const roisCount = this.regionsOfInterest.length;\n return roisCount !== this.maxHandsNumber || this.runsWithoutHandDetector >= this.maxContinuousChecks;\n }\n}\nexports.HandPipeline = HandPipeline;\n", "const tf = require('@tensorflow/tfjs');\nconst hand = require('./hand');\nconst keypoints = require('./keypoints');\nconst pipe = require('./pipeline');\n\n// Load the bounding box detector model.\nasync function loadHandDetectorModel(url) {\n return tf.loadGraphModel(url, { fromTFHub: url.includes('tfhub.dev') });\n}\n\n// Load the mesh detector model.\nasync function loadHandPoseModel(url) {\n return tf.loadGraphModel(url, { fromTFHub: url.includes('tfhub.dev') });\n}\n\n// In single shot detector pipelines, the output space is discretized into a set\n// of bounding boxes, each of which is assigned a score during prediction. The\n// anchors define the coordinates of these boxes.\nasync function loadAnchors(url) {\n return tf.util\n .fetch(url)\n .then((d) => d.json());\n}\n\n/**\n * Load handpose.\n *\n * @param config A configuration object with the following properties:\n * - `maxContinuousChecks` How many frames to go without running the bounding\n * box detector. Defaults to infinity. Set to a lower value if you want a safety\n * net in case the mesh detector produces consistently flawed predictions.\n * - `detectionConfidence` Threshold for discarding a prediction. Defaults to\n * 0.8.\n * - `iouThreshold` A float representing the threshold for deciding whether\n * boxes overlap too much in non-maximum suppression. Must be between [0, 1].\n * Defaults to 0.3.\n * - `scoreThreshold` A threshold for deciding when to remove boxes based\n * on score in non-maximum suppression. Defaults to 0.75.\n */\nasync function load(config) {\n const [ANCHORS, handDetectorModel, handPoseModel] = await Promise.all([\n loadAnchors(config.detector.anchors),\n loadHandDetectorModel(config.detector.modelPath),\n loadHandPoseModel(config.skeleton.modelPath),\n ]);\n const detector = new hand.HandDetector(handDetectorModel, config.inputSize, config.inputSize, ANCHORS, config.iouThreshold, config.scoreThreshold);\n const pipeline = new pipe.HandPipeline(detector, handPoseModel, config.inputSize, config.inputSize, config.skipFrames, config.minConfidence);\n // eslint-disable-next-line no-use-before-define\n const handpose = new HandPose(pipeline);\n return handpose;\n}\nexports.load = load;\n\nclass HandPose {\n constructor(pipeline) {\n this.pipeline = pipeline;\n }\n\n static getAnnotations() {\n return keypoints.MESH_ANNOTATIONS;\n }\n\n /**\n * Finds hands in the input image.\n *\n * @param input The image to classify. Can be a tensor, DOM element image,\n * video, or canvas.\n * @param flipHorizontal Whether to flip the hand keypoints horizontally.\n * Should be true for videos that are flipped by default (e.g. webcams).\n */\n async estimateHands(input, config) {\n const image = tf.tidy(() => {\n if (!(input instanceof tf.Tensor)) {\n input = tf.browser.fromPixels(input);\n }\n return input.toFloat().expandDims(0);\n });\n const prediction = await this.pipeline.estimateHand(image, config);\n image.dispose();\n if (!prediction) return [];\n const annotations = {};\n for (const key of Object.keys(keypoints.MESH_ANNOTATIONS)) {\n annotations[key] = keypoints.MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n return [{\n confidence: prediction.confidence || 0,\n box: prediction.box ? [prediction.box.topLeft[0], prediction.box.topLeft[1], prediction.box.bottomRight[0] - prediction.box.topLeft[0], prediction.box.bottomRight[1] - prediction.box.topLeft[1]] : 0,\n landmarks: prediction.landmarks,\n annotations,\n }];\n }\n}\nexports.HandPose = HandPose;\n", "export default {\n face: {\n enabled: true, // refers to detector, but since all other face modules rely on detector, it should be a global\n detector: {\n modelPath: '/models/blazeface/model.json',\n inputSize: 128, // fixed value\n maxFaces: 10, // maximum number of faces detected in the input, should be set to the minimum number for performance\n skipFrames: 5, // how many frames to go without running the bounding box detector, only relevant if maxFaces > 1\n minConfidence: 0.8, // threshold for discarding a prediction\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression, must be between [0, 1]\n scoreThreshold: 0.75, // threshold for deciding when to remove boxes based on score in non-maximum suppression\n },\n mesh: {\n enabled: true,\n modelPath: '/models/facemesh/model.json',\n inputSize: 192, // fixed value\n },\n iris: {\n enabled: true,\n modelPath: '/models/iris/model.json',\n inputSize: 192, // fixed value\n },\n age: {\n enabled: true,\n modelPath: '/models/ssrnet-imdb-age/model.json',\n inputSize: 64, // fixed value\n skipFrames: 5,\n },\n gender: {\n enabled: true,\n modelPath: '/models/ssrnet-imdb-gender/model.json',\n },\n },\n body: {\n enabled: true,\n modelPath: '/models/posenet/model.json',\n inputResolution: 257, // fixed value\n outputStride: 16, // fixed value\n maxDetections: 5,\n scoreThreshold: 0.75,\n nmsRadius: 20,\n },\n hand: {\n enabled: true,\n inputSize: 256, // fixed value\n skipFrames: 5,\n minConfidence: 0.8,\n iouThreshold: 0.3,\n scoreThreshold: 0.75,\n detector: {\n anchors: '/models/handdetect/anchors.json',\n modelPath: '/models/handdetect/model.json',\n },\n skeleton: {\n modelPath: '/models/handskeleton/model.json',\n },\n },\n};\n", "const facemesh = require('./facemesh/index.js');\nconst ssrnet = require('./ssrnet/index.js');\nconst posenet = require('./posenet/index.js');\nconst handpose = require('./handpose/index.js');\n// const image = require('./image.js');\n// const triangulation = require('./triangulation.js').default;\nconst defaults = require('./config.js').default;\n\nconst models = {\n facemesh: null,\n blazeface: null,\n ssrnet: null,\n iris: null,\n};\n\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nasync function detect(input, userConfig) {\n const config = mergeDeep(defaults, userConfig);\n\n // run posenet\n let poseRes = [];\n if (config.body.enabled) {\n if (!models.posenet) models.posenet = await posenet.load(config.body);\n poseRes = await models.posenet.estimateMultiplePoses(input, config.body);\n }\n\n // run handpose\n let handRes = [];\n if (config.hand.enabled) {\n if (!models.handpose) models.handpose = await handpose.load(config.hand);\n handRes = await models.handpose.estimateHands(input, config.hand);\n }\n\n // run facemesh, includes blazeface and iris\n const faceRes = [];\n if (config.face.enabled) {\n if (!models.facemesh) models.facemesh = await facemesh.load(config.face);\n const faces = await models.facemesh.estimateFaces(input, config.face);\n for (const face of faces) {\n // run ssr-net age & gender, inherits face from blazeface\n const ssrdata = (config.face.age.enabled || config.face.gender.enabled) ? await ssrnet.predict(face.image, config) : {};\n // iris: array[ bottom, left, top, right, center ]\n const iris = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n ? Math.max(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0], face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0])\n : 0;\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n annotations: face.annotations,\n age: ssrdata.age,\n gender: ssrdata.gender,\n iris: (iris !== 0) ? Math.trunc(100 * 11.7 / iris) / 100 : 0,\n });\n }\n }\n\n // combine results\n return { face: faceRes, body: poseRes, hand: handRes };\n}\n\nexports.detect = detect;\nexports.defaults = defaults;\nexports.models = models;\n"], + "mappings": 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s=O(n,"boxes","nonMaxSuppression"),o=O(t,"scores","nonMaxSuppression"),l=Fr(s,o,e,i,r,a);e=l.maxOutputSize,i=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;var u={boxes:s,scores:o},c={maxOutputSize:e,iouThreshold:i,scoreThreshold:r,softNmsSigma:a},h=z.runKernel(bf,u,c);return{selectedIndices:h[0],selectedScores:h[1]}}var AR=U({nonMaxSuppressionWithScore_:IR});function TR(n,t,e,i,r,a){return i===void 0&&(i=.5),r===void 0&&(r=Number.NEGATIVE_INFINITY),a===void 0&&(a=0),de(this,void 0,void 0,function(){var s,o,l,u,c,h,d;return pe(this,function(p){switch(p.label){case 0:return s=O(n,"boxes","nonMaxSuppressionAsync"),o=O(t,"scores","nonMaxSuppressionAsync"),l=Fr(s,o,e,i,r,a),e=l.maxOutputSize,i=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma,[4,Promise.all([s.data(),o.data()])];case 1:return u=p.sent(),c=u[0],h=u[1],d=av(c,h,e,i,r,a),s!==n&&s.dispose(),o!==t&&o.dispose(),[2,d]}})})}var NR=TR;function xR(n,t,e,i,r,a){i===void 0&&(i=.5),r===void 0&&(r=Number.NEGATIVE_INFINITY),a===void 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i=O(n,"images","resizeBilinear");E(i.rank===3||i.rank===4,function(){return"Error in resizeBilinear: x must be rank 3 or 4, but got "+("rank "+i.rank+".")}),E(t.length===2,function(){return"Error in resizeBilinear: new shape must 2D, but got shape "+(t+".")});var r=i,a=!1;i.rank===3&&(a=!0,r=V(i,[1,i.shape[0],i.shape[1],i.shape[2]]));var s=t[0],o=t[1],l=function(d,p){return p([r]),d.resizeBilinear(r,s,o,e)},u={images:r},c={alignCorners:e,size:t},h=z.runKernelFunc(l,u,null,uu,c);return a?V(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var ov=U({resizeBilinear_:ER});function DR(n,t,e){e===void 0&&(e=!1);var i=O(n,"images","resizeNearestNeighbor");E(i.rank===3||i.rank===4,function(){return"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got "+("rank "+i.rank+".")}),E(t.length===2,function(){return"Error in resizeNearestNeighbor: new shape must 2D, but got shape "+(t+".")}),E(i.dtype==="float32"||i.dtype==="int32",function(){return"`images` must have `int32` or `float32` as 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(i.padding!=null&&i.padding!=="same"&&i.padding!=="valid")throw new M("SeparableConv"+r.rank+"D supports only padding modes: "+("'same' and 'valid', but received "+JSON.stringify(i.padding)));return r.depthMultiplier=i.depthMultiplier==null?1:i.depthMultiplier,r.depthwiseInitializer=et(i.depthwiseInitializer||r.DEFAULT_DEPTHWISE_INITIALIZER),r.depthwiseRegularizer=tt(i.depthwiseRegularizer),r.depthwiseConstraint=yt(i.depthwiseConstraint),r.pointwiseInitializer=et(i.depthwiseInitializer||r.DEFAULT_POINTWISE_INITIALIZER),r.pointwiseRegularizer=tt(i.pointwiseRegularizer),r.pointwiseConstraint=yt(i.pointwiseConstraint),r}return t.prototype.build=function(e){var i;if(e=Ye(e),e.length1&&(t=n.slice(1,n.length)),n=n[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),e=r(e),{inputs:n,initialState:t,constants:e}}function 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this.getClassName()===t.className&&(i.cell={className:this.cell.getClassName(),config:r}),Vt({},r,e,i)},t.fromConfig=function(e,i,r){r===void 0&&(r={});var a=i.cell,s=mn(a,r);return new e(Object.assign(i,{cell:s}))},t.className="RNN",t}(De);y.serialization.registerClass(yi);var Hr=function(n){Q(t,n);function t(){return n!==null&&n.apply(this,arguments)||this}return t}(De),Mh=function(n){Q(t,n);function t(e){var i=n.call(this,e)||this;return 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Got input shapes: "+JSON.stringify(e))},t.prototype.mergeFunction=function(e){var i=this;return y.tidy(function(){return ch(e,i.axis)})},t.prototype.computeOutputShape=function(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new M("A `Concatenate` layer should be called on a list of inputs.");for(var i=e,r=i[0].slice(),a=this.axis<0?r.length+this.axis:this.axis,s=0,o=i.slice(1);s3||t.shape.length>3)throw new Te("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.util.assert(n.shape.length>=2,function(){return"batchDot requires the rank of x to be >= 2, "+("but got "+n.shape.length)}),y.util.assert(n.shape.length>=2,function(){return"batchDot requires the rank of y to be >= 2, "+("but got "+t.shape.length)}),typeof e=="number"&&(e=[e,e]),n.dtype==="complex64"||t.dtype==="complex64")throw new Te("batchDot is not implemented for complex64-type Tensors yet.");var i=n.shape.length,r=t.shape.length;e==null&&(e=[i-1,r-2]);var a=e;return y.tidy(function(){var s;if(i>r){s=i-r;for(var o=[],l=0;li){s=r-i;for(var o=[],l=0;l0){var d=void 0;i>r?d=i+r-3:d=i-1;for(var p=[],l=d;l3||r.length>3)throw new Te("Dot layer does not support tensors of 4D or higher rank yet.");var a=this.interpretAxes(i,r);if(i[a[0]]!==r[a[1]])throw new M("Dimension incompatibility: "+(i[a[0]]+" !== "+r[a[1]]))},t.prototype.mergeFunction=function(e){if(e.length!==2)throw new M("A `Dot` layer must be called on exactly 2 inputs, "+("but received "+e.length+" input(s)."));var i=e[0],r=e[1],a;return Array.isArray(this.axes)?a=this.axes.map(function(s,o){return Va(s,e[o].shape.length)}):a=[Va(this.axes,i.shape.length),Va(this.axes,r.shape.length)],this.normalize&&(i=co(i,a[0]),r=co(r,a[1])),nF(i,r,a)},t.prototype.interpretAxes=function(e,i){var r;return Array.isArray(this.axes)?r=this.axes:r=[Va(this.axes,e.length),Va(this.axes,i.length)],r},t.prototype.computeOutputShape=function(e){y.util.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),function(){return"A `Dot` layer should be called on a list of exactly 2 inputs."});var i=e[0].slice(),r=e[1].slice();if(i.length>3||r.length>3)throw new Te("Dot layer does not support tensors of 4D or higher rank yet.");var a=this.interpretAxes(i,r);i.splice(a[0],1),r.splice(a[1],1),r.splice(0,1);var s=i.concat(r);return s.length===1&&s.push(1),s},t.prototype.computeMask=function(e,i){return null},t.prototype.getConfig=function(){var e={axes:this.axes,normalize:this.normalize},i=n.prototype.getConfig.call(this);return Object.assign(e,i),e},t.className="Dot",t}(Zi);y.serialization.registerClass(pb);var fb=function(n){Q(t,n);function t(e){var i=n.call(this,e)||this;return i.supportsMasking=!0,i.stddev=e.stddev,i}return t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var e=n.prototype.getConfig.call(this),i={stddev:this.stddev};return Object.assign(i,e),i},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){r.invokeCallHook(e,i);var a=xe(e),s=function(){return ro(a.shape,0,r.stddev).add(a)},o=Ba(s,function(){return a},i.training||!1);return o})},t.className="GaussianNoise",t}(De);y.serialization.registerClass(fb);var mb=function(n){Q(t,n);function t(e){var i=n.call(this,e)||this;return i.supportsMasking=!0,i.rate=e.rate,i}return t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var e=n.prototype.getConfig.call(this),i={rate:this.rate};return Object.assign(i,e),i},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){r.invokeCallHook(e,i);var a=xe(e);if(r.rate>0&&r.rate<1){var s=function(){var o=Math.sqrt(r.rate/(1-r.rate));return a.mul(ro(a.shape,1,o))};return Ba(s,function(){return a},i.training||!1)}return a})},t.className="GaussianDropout",t}(De);y.serialization.registerClass(mb);var gb=function(n){Q(t,n);function t(e){var i=n.call(this,e)||this;return i.supportsMasking=!0,i.rate=e.rate,i.noiseShape=e.noiseShape,i}return t.prototype._getNoiseShape=function(e){return this.noiseShape||xe(e).shape},t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var e=n.prototype.getConfig.call(this),i={rate:this.rate};return Object.assign(i,e),i},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){if(r.rate<1&&r.rate>0){var a=r._getNoiseShape(e),s=function(){var o=xe(e),l=1.6732632423543772,u=1.0507009873554805,c=-l*u,h=y.greaterEqual(y.randomUniform(a),r.rate);h=Fa(h,"float32");var d=Math.pow((1-r.rate)*(1+r.rate*Math.pow(c,2)),-.5),p=-d*c*r.rate,f=o.mul(h).add(h.add(-1).mul(c));return f.mul(d).add(p)};return Ba(s,function(){return xe(e)},i.training||!1)}return e})},t.className="AlphaDropout",t}(De);y.serialization.registerClass(gb);function qa(n,t,e,i,r,a){a===void 0&&(a=.001);var s;if(n.rank===2)s=y.batchNorm2d(n,t,e,i,r,a);else if(n.rank===3)s=y.batchNorm3d(n,t,e,i,r,a);else if(n.rank===4)s=y.batchNorm4d(n,t,e,i,r,a);else throw new Te("batchNormalization is not implemented for array of rank "+n.rank+" yet");return s}function iF(n,t,e,i,r){return r===void 0&&(r=.001),y.tidy(function(){var a=y.moments(n,i),s=a.mean,o=a.variance,l=qa(n,s,o,e,t,r);return[l,s,o]})}function rF(n,t,e,i,r){return r===void 0&&(r=.001),y.tidy(function(){for(var a=y.moments(n,i),s=a.mean,o=a.variance,l=[],u=0,c=pn(0,n.rank);u=0?this.axis:this.axis+e.length,a=e[r];if(a==null)throw new M("Axis "+r+" of input tensor should have a defined dimension but the layer received an input with shape "+(JSON.stringify(e)+"."));this.inputSpec=[new At({ndim:e.length,axes:(i={},i[r]=a,i)})];var s=[a];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){var a=i.training==null?!1:i.training,s=xe(e),o=s.shape,l=o.length,u=pn(0,l),c=r.axis>=0?r.axis:r.axis+l;u.splice(c,1);var h=Yi(1,l);h[c]=o[c];var d=u.slice();d.sort();var p=!y.util.arraysEqual(d,pn(0,l).slice(0,l-1)),f=function(){if(p){var L=r.movingMean.read().reshape(h),x=r.movingVariance.read().reshape(h),C=r.center?r.beta.read().reshape(h):null,R=r.scale?r.gamma.read().reshape(h):null;return qa(s,L,x,C,R,r.epsilon)}else return qa(s,r.movingMean.read(),r.movingVariance.read(),r.beta==null?null:r.beta.read(),r.gamma==null?null:r.gamma.read(),r.epsilon)};if(!a)return f();var m=aF(s,r.gamma.read(),r.beta.read(),u,r.epsilon),g=m[0],v=m[1],b=m[2],w=function(L,x,C){y.tidy(function(){var R=1-C,D=L.read(),k=D.sub(x).mul(R);L.write(D.sub(k))})},S=function(){w(r.movingMean,v,r.momentum),w(r.movingVariance,b,r.momentum)};return S(),g})},t.prototype.getConfig=function(){var e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:at(this.betaInitializer),gammaInitializer:at(this.gammaInitializer),movingMeanInitializer:at(this.movingMeanInitializer),movingVarianceInitializer:at(this.movingVarianceInitializer),betaRegularizer:Ke(this.betaRegularizer),gammaRegularizer:Ke(this.gammaRegularizer),betaConstraint:vt(this.betaConstraint),gammaConstraint:vt(this.gammaConstraint)},i=n.prototype.getConfig.call(this);return Object.assign(e,i),e},t.className="BatchNormalization",t}(De);y.serialization.registerClass(vb);var yb=function(n){Q(t,n);function t(e){var i=this;if(e==null&&(e={}),i=n.call(this,e)||this,i.axis=e.axis==null?-1:e.axis,typeof i.axis=="number"){if(!Number.isInteger(i.axis))throw new Error("Expected axis to be an integer, but received "+i.axis)}else if(Array.isArray(i.axis))for(var r=0,a=i.axis;r=i)throw new Error("Invalid axis: "+o)}if(this.axis.length!==hi(this.axis).length)throw new Error("Found duplicate axes in: "+this.axis);var l=this.axis.map(function(c){return e[c]}),u=!0;this.scale?this.gamma=this.addWeight("gamma",l,"float32",this.gammaInitializer,this.gammaRegularizer,u):this.gamma=null,this.center?this.beta=this.addWeight("beta",l,"float32",this.betaInitializer,this.betaRegularizer,u):this.beta=null,this.built=!0},t.prototype.call=function(e,i){var r=this,a=xe(e),s=a.shape,o=s.length;return y.tidy(function(){for(var l=!0,u=y.moments(a,r.axis,l),c=u.mean,h=u.variance,d=Yi(1,o),p=0,f=r.axis;p=0?i=e[2]+this.padding[0][0]+this.padding[0][1]:i=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],i,r]):(e[1]!=null&&e[1]>=0?i=e[1]+this.padding[0][0]+this.padding[0][1]:i=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],i,r,e[3]])},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){return sF(xe(e),r.padding,r.dataFormat)})},t.prototype.getConfig=function(){var e={padding:this.padding,dataFormat:this.dataFormat},i=n.prototype.getConfig.call(this);return Object.assign(e,i),e},t.className="ZeroPadding2D",t}(De);y.serialization.registerClass(bb);function Io(n,t,e,i,r,a){return y.tidy(function(){ut(r),Rv(a),Qt(i),e==null&&(e=[1,1]),i==null&&(i="valid"),r==null&&(r=dn()),a==null&&(a="max"),n=zh(n,r);var s,o=i==="same"?"same":"valid";return a==="max"?s=y.maxPool(n,t,e,o):s=y.avgPool(n,t,e,o),r==="channelsFirst"&&(s=y.transpose(s,[0,3,1,2])),s})}function wb(n,t,e,i,r,a){return y.tidy(function(){ut(r),Rv(a),Qt(i),e==null&&(e=[1,1,1]),i==null&&(i="valid"),r==null&&(r=dn()),a==null&&(a="max"),n=Uy(n,r);var s,o=i==="same"?"same":"valid";return a==="max"?s=y.maxPool3d(n,t,e,o):s=y.avgPool3d(n,t,e,o),r==="channelsFirst"&&(s=y.transpose(s,[0,4,1,2,3])),s})}var Sb=function(n){Q(t,n);function t(e){var i=this;if(e.poolSize==null&&(e.poolSize=2),i=n.call(this,e)||this,typeof e.poolSize=="number")i.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")i.poolSize=e.poolSize;else throw new M("poolSize for 1D convolutional layer must be a number or an Array of a single number, but received "+(""+JSON.stringify(e.poolSize)));if(It(i.poolSize,"poolSize"),e.strides==null)i.strides=i.poolSize;else if(typeof e.strides=="number")i.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")i.strides=e.strides;else throw new M("strides for 1D convolutional layer must be a number or an Array of a single number, but received "+(""+JSON.stringify(e.strides)));return It(i.strides,"strides"),i.padding=e.padding==null?"valid":e.padding,Qt(i.padding),i.inputSpec=[new At({ndim:3})],i}return t.prototype.computeOutputShape=function(e){e=Ye(e);var i=gn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],i,e[2]]},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){r.invokeCallHook(e,i),e=Wa(xe(e),2);var a=r.poolingFunction(xe(e),[r.poolSize[0],1],[r.strides[0],1],r.padding,"channelsLast");return y.squeeze(a,[2])})},t.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},i=n.prototype.getConfig.call(this);return Object.assign(e,i),e},t}(De),Lb=function(n){Q(t,n);function t(e){return n.call(this,e)||this}return t.prototype.poolingFunction=function(e,i,r,a,s){return ut(s),Qt(a),Io(e,i,r,a,s,"max")},t.className="MaxPooling1D",t}(Sb);y.serialization.registerClass(Lb);var Ib=function(n){Q(t,n);function t(e){return n.call(this,e)||this}return t.prototype.poolingFunction=function(e,i,r,a,s){return ut(s),Qt(a),Io(e,i,r,a,s,"avg")},t.className="AveragePooling1D",t}(Sb);y.serialization.registerClass(Ib);var Ab=function(n){Q(t,n);function t(e){var i=this;if(e.poolSize==null&&(e.poolSize=[2,2]),i=n.call(this,e)||this,i.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)i.strides=i.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new M("If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length "+(e.strides.length+"."));i.strides=e.strides}else i.strides=[e.strides,e.strides];return It(i.poolSize,"poolSize"),It(i.strides,"strides"),i.padding=e.padding==null?"valid":e.padding,i.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,ut(i.dataFormat),Qt(i.padding),i.inputSpec=[new At({ndim:4})],i}return t.prototype.computeOutputShape=function(e){e=Ye(e);var i=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return i=gn(i,this.poolSize[0],this.padding,this.strides[0]),r=gn(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],i,r]:[e[0],i,r,e[3]]},t.prototype.call=function(e,i){var r=this;return y.tidy(function(){return r.invokeCallHook(e,i),r.poolingFunction(xe(e),r.poolSize,r.strides,r.padding,r.dataFormat)})},t.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},i=n.prototype.getConfig.call(this);return Object.assign(e,i),e},t}(De),Tb=function(n){Q(t,n);function t(e){return n.call(this,e)||this}return t.prototype.poolingFunction=function(e,i,r,a,s){return ut(s),Qt(a),Io(e,i,r,a,s,"max")},t.className="MaxPooling2D",t}(Ab);y.serialization.registerClass(Tb);var Nb=function(n){Q(t,n);function t(e){return n.call(this,e)||this}return t.prototype.poolingFunction=function(e,i,r,a,s){return ut(s),Qt(a),Io(e,i,r,a,s,"avg")},t.className="AveragePooling2D",t}(Ab);y.serialization.registerClass(Nb);var xb=function(n){Q(t,n);function t(e){var i=this;if(e.poolSize==null&&(e.poolSize=[2,2,2]),i=n.call(this,e)||this,i.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)i.strides=i.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new M("If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length "+(e.strides.length+"."));i.strides=e.strides}else i.strides=[e.strides,e.strides,e.strides];return 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t.size=="number"?t.size:parseInt(t.size,10)}):[]}function td(n,t,e){var i=n[t];return i&&i.shape?Qb(i.shape):e}function nd(n,t,e){var i=n[t];return i?((i.list.f&&i.list.f.length?i.list.f:i.list.i)||[]).map(function(r){return typeof r=="number"?r:parseInt(r,10)}):e}function id(n,t,e,i){i===void 0&&(i=!1);var r=n[t];return r&&r.list&&r.list.s?r.list.s.map(function(a){return Zb(a,i)}):e}function rd(n,t,e){var i=n[t];return i&&i.list&&i.list.shape?i.list.shape.map(function(r){return Qb(r)}):e}function ad(n,t,e){var i=n[t];return i&&i.list&&i.list.b?i.list.b:e}var A4=function(){function n(t,e,i){var r=this;this.node=t,this.tensorMap=e,this.context=i,this.inputs=[],this.attrs={},this.inputs=t.inputNames.map(function(a){return r.getInput(a)}),t.rawAttrs!=null&&(this.attrs=Object.keys(t.rawAttrs).reduce(function(a,s){return a[s]=r.getAttr(s),a},{}))}return n.prototype.getInput=function(t){return Pt(t,this.tensorMap,this.context)},n.prototype.getAttr=function(t,e){var i=this.node.rawAttrs[t];if(i.tensor!=null)return Pt(t,this.tensorMap,this.context);if(i.i!=null||i.f!=null)return Jh(this.node.rawAttrs,t,e);if(i.s!=null)return $h(this.node.rawAttrs,t,e);if(i.b!=null)return Xh(this.node.rawAttrs,t,e);if(i.shape!=null)return td(this.node.rawAttrs,t,e);if(i.type!=null)return Qh(this.node.rawAttrs,t,e);if(i.list!=null){if(i.list.i!=null||i.list.f!=null)return nd(this.node.rawAttrs,t,e);if(i.list.s!=null)return id(this.node.rawAttrs,t,e);if(i.list.shape!=null)return rd(this.node.rawAttrs,t,e);if(i.list.b!=null)return ad(this.node.rawAttrs,t,e);if(i.list.type!=null)return ed(this.node.rawAttrs,t,e)}return e},n}();var T4=function(n,t,e){switch(n.op){case"BiasAdd":case"AddV2":case"Add":return[B.add(A("a",n,t,e),A("b",n,t,e))];case"AddN":return[B.addN(A("tensors",n,t,e))];case"FloorMod":case"Mod":return[B.mod(A("a",n,t,e),A("b",n,t,e))];case"Mul":return[B.mul(A("a",n,t,e),A("b",n,t,e))];case"RealDiv":case"Div":return[B.div(A("a",n,t,e),A("b",n,t,e))];case"DivNoNan":return[B.divNoNan(A("a",n,t,e),A("b",n,t,e))];case"FloorDiv":return[B.floorDiv(A("a",n,t,e),A("b",n,t,e))];case"Sub":return[B.sub(A("a",n,t,e),A("b",n,t,e))];case"Minimum":return[B.minimum(A("a",n,t,e),A("b",n,t,e))];case"Maximum":return[B.maximum(A("a",n,t,e),A("b",n,t,e))];case"Pow":return[B.pow(A("a",n,t,e),A("b",n,t,e))];case"SquaredDifference":return[B.squaredDifference(A("a",n,t,e),A("b",n,t,e))];default:throw TypeError("Node type "+n.op+" is not implemented")}};var N4=function(n,t,e){switch(n.op){case"Abs":case"ComplexAbs":return[B.abs(A("x",n,t,e))];case"Acos":return[B.acos(A("x",n,t,e))];case"Acosh":return[B.acosh(A("x",n,t,e))];case"Asin":return[B.asin(A("x",n,t,e))];case"Asinh":return[B.asinh(A("x",n,t,e))];case"Atan":return[B.atan(A("x",n,t,e))];case"Atan2":return[B.atan2(A("x",n,t,e),A("y",n,t,e))];case"Atanh":return[B.atanh(A("x",n,t,e))];case"Ceil":return[B.ceil(A("x",n,t,e))];case"Complex":return[B.complex(A("real",n,t,e),A("imag",n,t,e))];case"Cos":return[B.cos(A("x",n,t,e))];case"Cosh":return[B.cosh(A("x",n,t,e))];case"Elu":return[B.elu(A("x",n,t,e))];case"Erf":return[B.erf(A("x",n,t,e))];case"Exp":return[B.exp(A("x",n,t,e))];case"Expm1":return[B.expm1(A("x",n,t,e))];case"Floor":return[B.floor(A("x",n,t,e))];case"Log":return[B.log(A("x",n,t,e))];case"Log1p":return[B.log1p(A("x",n,t,e))];case"Imag":return[B.imag(A("x",n,t,e))];case"Neg":return[B.neg(A("x",n,t,e))];case"Reciprocal":return[B.reciprocal(A("x",n,t,e))];case"Real":return[B.real(A("x",n,t,e))];case"Relu":return[B.relu(A("x",n,t,e))];case"Round":return[B.round(A("x",n,t,e))];case"Selu":return[B.selu(A("x",n,t,e))];case"Sigmoid":return[B.sigmoid(A("x",n,t,e))];case"Sin":return[B.sin(A("x",n,t,e))];case"Sign":return[B.sign(A("x",n,t,e))];case"Sinh":return[B.sinh(A("x",n,t,e))];case"Softplus":return[B.softplus(A("x",n,t,e))];case"Sqrt":return[B.sqrt(A("x",n,t,e))];case"Square":return[B.square(A("x",n,t,e))];case"Tanh":return[B.tanh(A("x",n,t,e))];case"Tan":return[B.tan(A("x",n,t,e))];case"Relu6":case"ClipByValue":return[B.clipByValue(A("x",n,t,e),A("clipValueMin",n,t,e),A("clipValueMax",n,t,e))];case"Rsqrt":return[B.rsqrt(Pt(n.inputNames[0],t,e))];case"Prod":return[B.prod(A("x",n,t,e),A("axes",n,t,e))];case"LeakyRelu":return[B.leakyRelu(A("x",n,t,e),A("alpha",n,t,e))];case"Prelu":return[B.prelu(A("x",n,t,e),A("alpha",n,t,e))];default:throw TypeError("Node type "+n.op+" is not implemented")}};function on(n,t,e){e===void 0&&(e=""),B.util.assert(x4(n,t),function(){return e+(" Shapes "+n+" and "+t+" must match")})}function x4(n,t){if(n.length!==t.length)return!1;for(var e=0;e=this.size())throw new Error("Tried to read from index "+t+", but array size is: "+this.size());var e=this.tensors[t];if(e.cleared)throw new Error("TensorArray "+this.name+": Could not read index "+t+" twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).");return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor},n.prototype.readMany=function(t){var e=this;return t.map(function(i){return e.read(i)})},n.prototype.write=function(t,e){if(this.closed_)throw new Error("TensorArray "+this.name+" has already been closed.");if(t<0||!this.dynamicSize&&t>=this.maxSize)throw new Error("Tried to write to index "+t+", but array is not resizeable and size is: "+this.maxSize);var i=this.tensors[t]||{};if(e.dtype!==this.dtype)throw new Error("TensorArray "+this.name+": Could not write to TensorArray index "+t+`, + because the value dtype is `+e.dtype+", but TensorArray dtype is "+this.dtype+".");if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=e.shape),on(this.elementShape,e.shape,"TensorArray "+this.name+": Could not write to TensorArray index "+t+"."),i.read)throw new Error("TensorArray "+this.name+": Could not write to TensorArray index "+t+", because it has already been read.");if(i.written)throw new Error("TensorArray "+this.name+": Could not write to TensorArray index "+t+", because it has already been written.");i.tensor=e,B.keep(e),i.written=!0,this.tensors[t]=i},n.prototype.writeMany=function(t,e){var i=this;if(t.length!==e.length)throw new Error("TensorArray "+this.name+": could not write multiple tensors,"+("because the index size: "+t.length+" is not the same as tensors size: "+e.length+"."));t.forEach(function(r,a){return i.write(r,e[a])})},n.prototype.gather=function(t,e){if(!!e&&e!==this.dtype)throw new Error("TensorArray dtype is "+this.dtype+" but gather requested dtype "+e);if(t)t=t.slice(0,this.size());else{t=[];for(var i=0;i=this.maxSize)throw new Error("Max index must be < array size ("+i+" vs. "+this.maxSize+")");this.writeMany(t,B.unstack(e,0))},n.prototype.split=function(t,e){var i=this;if(e.dtype!==this.dtype)throw new Error("TensorArray dtype is "+this.dtype+" but tensor has dtype "+e.dtype);var r=0,a=t.map(function(c){return r+=c,r});if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to + tensor.shape[0], but sum of lengths is + `+r+", and tensor's shape is: "+e.shape);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error("TensorArray's size is not equal to the size of lengths ("+this.maxSize+" vs. "+t.length+"), and the TensorArray is not marked as dynamically resizeable");var s=r===0?0:e.size/r,o=[];B.tidy(function(){e=B.reshape(e,[1,r,s]);for(var c=0;cthis.maxNumElements)throw new Error("TensorListResize input size "+t+" is greater maxNumElement "+this.maxNumElements+".");this.tensors.length=t},n.prototype.getItem=function(t,e,i){if(i!==this.elementDtype)throw new Error("Invalid data types; op elements "+i+", but list elements "+this.elementDtype);if(t<0||t>this.tensors.length)throw new Error("Trying to access element "+t+" in a list with "+this.tensors.length+" elements.");if(this.tensors[t]==null)throw new Error("element at index "+t+" is null.");return on(this.tensors[t].shape,e,"TensorList shape mismatch: "),this.tensors[t]},n.prototype.setItem=function(t,e){if(e.dtype!==this.elementDtype)throw new Error("Invalid data types; op elements "+e.dtype+", but list elements "+this.elementDtype);if(t<0||this.maxNumElements!==-1&&t>=this.maxNumElements)throw new Error("Trying to set element "+t+" in a list with max "+this.maxNumElements+" elements.");on(this.elementShape,e.shape,"TensorList shape mismatch: "),B.keep(e),this.tensors[t]=e},n.prototype.gather=function(t,e,i){var r=this;if(e!==this.elementDtype)throw new Error("Invalid data types; op elements "+e+", but list elements "+this.elementDtype);return on(this.elementShape,i,"TensorList shape mismatch: "),t=t.slice(0,this.size()),t.length===0?B.tensor([],[0].concat(this.elementShape)):B.tidy(function(){var a=t.map(function(s){return B.reshape(r.tensors[s],i)});return B.stack(a,0)})},n.prototype.concat=function(t,e){var i=this;if(!!t&&t!==this.elementDtype)throw new Error("TensorList dtype is "+this.elementDtype+" but concat requested dtype "+t);return on(this.elementShape,e,"TensorList shape mismatch: "),this.size()===0?B.tensor([],[0].concat(this.elementShape)):B.tidy(function(){var r=i.tensors.map(function(a){return B.reshape(a,e)});return B.concat(r,0)})},n}();function R4(n,t,e){var i=n.dtype;if(n.shape.length<1)throw new Error("Tensor must be at least a vector, but saw shape: "+n.shape);if(n.dtype!==e)throw new Error("Invalid data types; op elements "+n.dtype+", but list elements "+e);var r=n.shape.slice(1);on(r,t,"TensorList shape mismatch: ");var a=B.unstack(n);return new xo(a,t,i)}function O4(n,t,e){return new xo([],n,t,e)}function E4(n,t,e,i){if(t.length!==n.shape[0])throw new Error("Expected len(indices) == tensor.shape[0], but saw: "+t.length+" vs. "+n.shape[0]);var r=Math.max.apply(Math,t);if(i!=null&&i!==-1&&r>=i)throw new Error("Max index must be < array size ("+r+" vs. "+i+")");var a=new xo([],e,n.dtype,i),s=B.unstack(n,0);return t.forEach(function(o,l){a.setItem(o,s[l])}),a}function D4(n,t,e){var i=0,r=t.map(function(u){return i+=u,i});if(i!==n.shape[0])throw new Error(`Expected sum of lengths to be equal to + tensor.shape[0], but sum of lengths is + `+i+", and tensor's shape is: "+n.shape);for(var a=i===0?0:n.size/i,s=B.tidy(function(){var u=[];n=B.reshape(n,[1,i,a]);for(var c=0;c1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")},n.prototype.nextIteration=function(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;var t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")},n.prototype.getWeight=function(t){return this.weightMap[t]},n.prototype.addTensorArray=function(t){this.tensorArrayMap[t.id]=t},n.prototype.getTensorArray=function(t){return this.tensorArrayMap[t]},n.prototype.addTensorList=function(t){this.tensorListMap[t.id]=t},n.prototype.getTensorList=function(t){return this.tensorListMap[t]},n.prototype.dispose=function(t){for(var e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(var e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)},n}();function rw(n,t,e,i){var r=new Set,a=[],s=null,o=null,l=new Set,u=Object.keys(n).map(function(p){return Yt(p)[0]}),c=[];i!=null&&(c=i.map(function(p){return Yt(p.name)[0]}));for(var h=t.slice();h.length>0;){var d=h.pop();if((iw(d)||$4(d))&&(s==null&&(s=d,o=s.children.map(function(p){return p.name}).filter(function(p){return r.has(p)}))),r.add(d.name),e[d.name]!=null)continue;if(u.indexOf(d.name)!==-1)continue;if(c.indexOf(d.name)!==-1)continue;if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(function(p){if(l.has(p.name))return;l.add(p.name),h.push(p)})}return{inputs:n,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:s,syncInputs:o}}function X4(n,t,e){var i=e.usedNodes,r=e.inputs,a=[],s=Object.keys(r).map(function(h){return Yt(h)[0]}).map(function(h){return n.nodes[h]}),o=n.initNodes;s.forEach(function(h){i.has(h.name)&&a.push(h)}),n.weights.forEach(function(h){i.has(h.name)&&a.push(h)}),o!=null&&o.forEach(function(h){i.has(h.name)&&a.push(h)});for(var l=new Set,u=[];a.length>0;){var c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(function(h){!l.has(h.name)&&i.has(h.name)&&h.inputs.every(function(d){return l.has(d.name)})&&a.push(h)})}return u}var J4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Z4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function iw(n){return J4.indexOf(n.op)>=0}function $4(n){return Z4.indexOf(n.op)>=0}var aw=function(){function n(t,e){var i=this;this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(function(r){i._functionExecutorMap[r]=new n(t.functions[r],i)})}return Object.defineProperty(n.prototype,"weightIds",{get:function(){return this.parent?this.parent.weightIds:this._weightIds},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"functionExecutorMap",{get:function(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"weightMap",{get:function(){return this.parent?this.parent.weightMap:this._weightMap},set:function(t){var e=Object.keys(t).map(function(i){return t[i].map(function(r){return r.id})});this._weightIds=[].concat.apply([],e),this._weightMap=t},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"inputs",{get:function(){return this._inputs.map(function(t){return{name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}})},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"outputs",{get:function(){return this._outputs.map(function(t){return{name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}})},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"inputNodes",{get:function(){return this._inputs.map(function(t){return t.signatureKey||t.name})},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"outputNodes",{get:function(){return this._outputs.map(function(t){var e=t.signatureKey||t.name;return t.defaultOutput?e+":"+t.defaultOutput:e})},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"functions",{get:function(){var t=this;return Object.keys(this._functions).reduce(function(e,i){return e[i]=t._functions[i].signature,e},{})},enumerable:!0,configurable:!0}),n.prototype.getCompilationKey=function(t,e){var i=t.map(function(a){return a.name}).sort(),r=e.map(function(a){return a.name}).sort();return i.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)},n.prototype.compile=function(t,e){var i=rw(t,e,this.weightMap,this._initNodes),r=i.missingInputs,a=i.dynamicNode,s=i.syncInputs;if(a!=null)throw new Error("This execution contains the node '"+a.name+"', which has "+("the dynamic op '"+a.op+"'. Please use ")+"model.executeAsync() instead. Alternatively, to avoid the "+("dynamic ops, specify the inputs ["+s+"]"));if(r.length>0){var o=e.map(function(u){return u.name}),l=Object.keys(t);throw new Error("Cannot compute the outputs ["+o+"] from the provided inputs "+("["+l+"]. Missing the following inputs: ["+r+"]"))}return X4(this.graph,this.weightMap,i)},n.prototype.execute=function(t,e){var i=this;t=this.mapInputs(t);var r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);var a=r.map(function(d){return i.graph.nodes[Yt(d)[0]]}),s=e.map(function(d){return Yt(d)[0]}),o=s.map(function(d){return i.graph.nodes[d]});o.length===0&&(o=this._outputs);var l=this.getCompilationKey(a,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));var c={},h={};return B.tidy(function(){var d=new nw(i.weightMap,c,h,i.functionExecutorMap),p=Kb({},i.weightMap);Object.keys(t).forEach(function(w){var S=Yt(w),L=S[0],x=S[1],C=[];C[x]=t[w],p[L]=C});for(var f=i.getFrozenTensorIds(p),m={},g=0;g0?(w=this.processStack(s,f,e,m,b,v,o,g,c),[4,Promise.all(w)]):[3,3];case 2:return C.sent(),[3,1];case 3:if(d==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead."),S=l.filter(function(R){return!iw(R)&&!Pt(R.name,m,e)}).map(function(R){return R.name}),S.length>0)throw L="",d!=null&&(L="Alternatively, to avoid the dynamic ops, use model.execute() "+("and specify the inputs ["+p+"]")),new Error("Cannot compute the outputs ["+S+"] from the provided "+("inputs ["+a+"]. Consider providing the following inputs: ")+("["+h+"]. "+L));return[2,m]}})})},n.prototype.processStack=function(t,e,i,r,a,s,o,l,u){for(var c=this,h=[],d=function(){var f=e.pop();i.currentContext=f.contexts;var m="";if(f.node.op==="Enter"&&A("isConstant",f.node,r,i)&&(m=Kn(f.node.name,i)[0]),t.indexOf(f.node)===-1){var g=tw(f.node,r,i);m||(m=Kn(f.node.name,i)[0]);var v=i.currentContext;g instanceof Promise?h.push(g.then(function(b){return r[m]=b,i.currentContext=v,c.checkTensorForDisposal(m,f.node,r,i,s,o,l),c.processChildNodes(f.node,e,i,r,a,u),b})):(r[m]=g,p.checkTensorForDisposal(m,f.node,r,i,s,o,l),p.processChildNodes(f.node,e,i,r,a,u))}else p.processChildNodes(f.node,e,i,r,a,u)},p=this;e.length>0;)d();return h},n.prototype.processChildNodes=function(t,e,i,r,a,s){t.children.forEach(function(o){var l=Kn(o.name,i)[0];if(a[l]||!s.has(o.name))return;o.op==="Merge"?o.inputNames.some(function(u){return!!Pt(u,r,i)})&&(a[l]=!0,e.push({contexts:i.currentContext,node:o})):o.inputNames.every(function(u){return!!Pt(u,r,i)})&&(a[l]=!0,e.push({contexts:i.currentContext,node:o}))})},n.prototype.dispose=function(){var t=this;Object.keys(this.weightMap).forEach(function(e){return t.weightMap[e].forEach(function(i){return i.dispose()})})},n.prototype.checkInputShapeAndType=function(t){var e=this;Object.keys(t).forEach(function(i){var r=t[i],a=Yt(i)[0],s=e.graph.nodes[a];if(s.attrParams.shape&&s.attrParams.shape.value){var o=s.attrParams.shape.value,l=o.length===r.shape.length&&r.shape.every(function(u,c){return o[c]===-1||o[c]===u});B.util.assert(l,function(){return"The shape of dict['"+s.name+"'] provided in "+("model.execute(dict) must be ["+o+"], but was ")+("["+r.shape+"]")})}s.attrParams.dtype&&s.attrParams.dtype.value&&B.util.assert(r.dtype===s.attrParams.dtype.value,function(){return"The 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r=a.sent(),r.done?(this.iterator=null,[2,this.readFromChain(e)]):[2,r]}})})},t}(Tt),wi;(function(n){n[n.FAIL=0]="FAIL",n[n.SHORTEST=1]="SHORTEST",n[n.LONGEST=2]="LONGEST"})(wi||(wi={}));var wU=function(n){He(t,n);function t(e,i){i===void 0&&(i=wi.FAIL);var r=n.call(this)||this;return r.iterators=e,r.mismatchMode=i,r.count=0,r.currentPromise=null,r}return t.prototype.summary=function(){var e="TODO: fill in upstream of zip summaries";return"{"+e+"} -> Zip"},t.prototype.nextState=function(e){return se(this,void 0,void 0,function(){function i(o){if(o instanceof Tt){var l=o.next();return{value:l.then(function(u){return r++,u.done&&a++,u.value}),recurse:!1}}else return{value:null,recurse:!0}}var r,a,s;return oe(this,function(o){switch(o.label){case 0:return[4,e];case 1:return o.sent(),r=0,a=0,[4,pw(this.iterators,i)];case 2:if(s=o.sent(),r===a)return[2,{value:null,done:!0}];if(a>0)switch(this.mismatchMode){case wi.FAIL:throw new Error("Zipped streams should have the same length. "+("Mismatched at element "+this.count+"."));case wi.SHORTEST:return[2,{value:null,done:!0}];case wi.LONGEST:}return this.count++,[2,{value:s,done:!1}]}})})},t.prototype.next=function(){return se(this,void 0,void 0,function(){return oe(this,function(e){return this.currentPromise=this.nextState(this.currentPromise),[2,this.currentPromise]})})},t}(Tt),yw=function(n){He(t,n);function t(e,i){var r=n.call(this)||this;return r.upstream=e,r.bufferSize=i,r.buffer=new fw(i),r}return t.prototype.summary=function(){return this.upstream.summary()+" -> Prefetch"},t.prototype.refill=function(){for(;!this.buffer.isFull();){var e=this.upstream.next();this.buffer.push(e)}},t.prototype.next=function(){return this.refill(),this.buffer.shift()},t}(Tt),OU=function(n){He(t,n);function t(e,i,r){var a=n.call(this,e,i)||this;return a.upstream=e,a.windowSize=i,a.upstreamExhausted=!1,a.random=cw(r||Ne.util.now().toString()),a.lastRead=Promise.resolve({value:null,done:!1}),a}return t.prototype.next=function(){return se(this,void 0,void 0,function(){var e=this;return oe(this,function(i){return this.lastRead=this.lastRead.then(function(){return e.serialNext()}),[2,this.lastRead]})})},t.prototype.randomInt=function(e){return Math.floor(this.random()*e)},t.prototype.chooseIndex=function(){return this.randomInt(this.buffer.length())},t.prototype.serialNext=function(){return se(this,void 0,void 0,function(){var e,i;return oe(this,function(r){switch(r.label){case 0:this.upstreamExhausted||this.refill(),r.label=1;case 1:return this.buffer.isEmpty()?[3,3]:(e=this.chooseIndex(),[4,this.buffer.shuffleExcise(e)]);case 2:if(i=r.sent(),i.done)this.upstreamExhausted=!0;else return this.refill(),[2,i];return[3,1];case 3:return[2,{value:null,done:!0}]}})})},t}(yw);var Ga=function(){function n(){this.size=null}return n.prototype.batch=function(t,e){var i=this;e===void 0&&(e=!0);var r=this;Ne.util.assert(t>0,function(){return`batchSize needs to be positive, but it is + `+t});var a;return this.size===Infinity||this.size==null?a=this.size:e?a=Math.ceil(this.size/t):a=Math.floor(this.size/t),Kt(function(){return se(i,void 0,void 0,function(){return oe(this,function(s){switch(s.label){case 0:return[4,r.iterator()];case 1:return[2,s.sent().columnMajorBatch(t,e,EU)]}})})},a)},n.prototype.concatenate=function(t){var e=this,i=this,r;return this.size===Infinity||t.size===Infinity?r=Infinity:this.size!=null&&t.size!=null?r=this.size+t.size:r=null,Kt(function(){return se(e,void 0,void 0,function(){var a,s;return oe(this,function(o){switch(o.label){case 0:return[4,i.iterator()];case 1:return s=(a=o.sent()).concatenate,[4,t.iterator()];case 2:return[2,s.apply(a,[o.sent()])]}})})},r)},n.prototype.filter=function(t){var e=this,i=this,r;return this.size===Infinity?r=Infinity:r=null,Kt(function(){return se(e,void 0,void 0,function(){return oe(this,function(a){switch(a.label){case 0:return[4,i.iterator()];case 1:return[2,a.sent().filter(function(s){return Ne.tidy(function(){return t(s)})})]}})})},r)},n.prototype.forEachAsync=function(t){return se(this,void 0,void 0,function(){return oe(this,function(e){switch(e.label){case 0:return[4,this.iterator()];case 1:return[2,e.sent().forEachAsync(t)]}})})},n.prototype.map=function(t){var e=this,i=this;return Kt(function(){return se(e,void 0,void 0,function(){return oe(this,function(r){switch(r.label){case 0:return[4,i.iterator()];case 1:return[2,r.sent().map(function(a){return Ne.tidy(function(){return t(a)})})]}})})},this.size)},n.prototype.mapAsync=function(t){var e=this,i=this;return Kt(function(){return se(e,void 0,void 0,function(){return oe(this,function(r){switch(r.label){case 0:return[4,i.iterator()];case 1:return[2,r.sent().mapAsync(t)]}})})},this.size)},n.prototype.prefetch=function(t){var e=this;if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");var i=this;return Kt(function(){return se(e,void 0,void 0,function(){return oe(this,function(r){switch(r.label){case 0:return[4,i.iterator()];case 1:return[2,r.sent().prefetch(t)]}})})},this.size)},n.prototype.repeat=function(t){var e=this,i=this,r;return this.size!=null&&t>0?r=this.size*t:t===0?r=0:this.size!=null&&(t===void 0||t<0)?r=Infinity:r=null,Kt(function(){return se(e,void 0,void 0,function(){var a,s=this;return oe(this,function(o){return a=ld(function(){return se(s,void 0,void 0,function(){var l;return oe(this,function(u){switch(u.label){case 0:return l={},[4,i.iterator()];case 1:return[2,(l.value=u.sent(),l.done=!1,l)]}})})}),[2,bU(a.take(t))]})})},r)},n.prototype.skip=function(t){var e=this,i=this,r;return this.size!=null&&t>=0&&this.size>=t?r=this.size-t:this.size!=null&&(this.sizet?r=t:this.size!=null&&this.size<=t?r=this.size:r=null,Kt(function(){return se(e,void 0,void 0,function(){return oe(this,function(a){switch(a.label){case 0:return[4,i.iterator()];case 1:return[2,a.sent().take(t)]}})})},r)},n.prototype.toArray=function(){return se(this,void 0,void 0,function(){return oe(this,function(t){switch(t.label){case 0:if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return[4,this.iterator()];case 1:return[2,t.sent().toArray()]}})})},n.prototype.toArrayForTest=function(){return se(this,void 0,void 0,function(){return oe(this,function(t){switch(t.label){case 0:if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return[4,this.iterator()];case 1:return[2,t.sent().toArrayForTest()]}})})},n.MAX_BUFFER_SIZE=1e4,n}();function Kt(n,t){return t===void 0&&(t=null),new(function(e){He(i,e);function i(){var r=e!==null&&e.apply(this,arguments)||this;return r.size=t,r}return i.prototype.iterator=function(){return se(this,void 0,void 0,function(){return oe(this,function(r){return[2,n()]})})},i}(Ga))}function DU(n){var t=this;return Kt(function(){return se(t,void 0,void 0,function(){return oe(this,function(e){return[2,mw(n)]})})},n.length)}function kU(n){var t=this;if(!qr(n))throw new Error("The argument to zip() must be an object or array.");var e;if(Array.isArray(n))for(var i=0;i1}),Ne.util.assert(r.length===0,function(){return"Duplicate column names found: "+r.toString()}),this.columnConfigs){for(a=0,s=Object.keys(this.columnConfigs);a14||!Number.isInteger(r))throw new Error("Invalid fftSize: it must be a power of 2 between "+("2 to 4 and 2 to 14, but got "+i.fftSize));if(i.numFrames=e.numFramesPerSpectrogram||43,i.sampleRateHz=e.sampleRateHz,i.columnTruncateLength=e.columnTruncateLength||i.fftSize,i.audioTrackConstraints=e.audioTrackConstraints,i.smoothingTimeConstant=e.smoothingTimeConstant||0,i.includeSpectrogram=!(e.includeSpectrogram===!1),i.includeWaveform=e.includeWaveform===!0,!i.includeSpectrogram&&!i.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.");return i}return t.prototype.summary=function(){return"microphone"},t.create=function(e){return e===void 0&&(e={}),se(this,void 0,void 0,function(){var i;return oe(this,function(r){switch(r.label){case 0:if(Ne.env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");return i=new t(e),[4,i.start()];case 1:return r.sent(),[2,i]}})})},t.prototype.start=function(){return se(this,void 0,void 0,function(){var e,i,r,a;return oe(this,function(s){switch(s.label){case 0:return s.trys.push([0,2,,3]),e=this,[4,navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})];case 1:return e.stream=s.sent(),[3,3];case 2:throw i=s.sent(),new Error("Error thrown while initializing video stream: "+i.message);case 3:if(!this.stream)throw new Error("Could not obtain audio from microphone.");if(r=window.AudioContext||window.webkitAudioContext,this.audioContext=new r,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error("Mismatch in sampling rate: "+("Expected: "+this.sampleRateHz+"; ")+("Actual: "+this.audioContext.sampleRate));return a=this.audioContext.createMediaStreamSource(this.stream),this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,a.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize),[2]}})})},t.prototype.next=function(){return se(this,void 0,void 0,function(){var e,i,r,a,s;return oe(this,function(o){switch(o.label){case 0:return this.isClosed?[2,{value:null,done:!0}]:[4,this.getAudioData()];case 1:return r=o.sent(),this.includeSpectrogram&&(a=this.flattenQueue(r.freqDataQueue),e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])),this.includeWaveform&&(s=this.flattenQueue(r.timeDataQueue),i=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])),[2,{value:{spectrogram:e,waveform:i},done:!1}]}})})},t.prototype.capture=function(){return se(this,void 0,void 0,function(){return oe(this,function(e){switch(e.label){case 0:return[4,this.next()];case 1:return[2,e.sent().value]}})})},t.prototype.getAudioData=function(){return se(this,void 0,void 0,function(){var e,i,r,a=this;return oe(this,function(s){return e=[],i=[],r=0,[2,new Promise(function(o){var l=setInterval(function(){a.includeSpectrogram&&(a.analyser.getFloatFrequencyData(a.freqData),a.freqData[0]===-Infinity&&o({freqDataQueue:e,timeDataQueue:i}),e.push(a.freqData.slice(0,a.columnTruncateLength))),a.includeWaveform&&(a.analyser.getFloatTimeDomainData(a.timeData),i.push(a.timeData.slice())),++r===a.numFrames&&(clearInterval(l),o({freqDataQueue:e,timeDataQueue:i}))},a.fftSize/a.sampleRateHz*1e3)})]})})},t.prototype.stop=function(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())},t.prototype.toArray=function(){throw new Error("Can not convert infinite audio stream to array.")},t.prototype.getSampleRate=function(){return this.sampleRateHz},t.prototype.flattenQueue=function(e){var i=e[0].length,r=new Float32Array(e.length*i);return e.forEach(function(a,s){return r.set(a,s*i)}),r},t.prototype.getTensorFromAudioDataArray=function(e,i){var r=new Float32Array(Ne.util.sizeFromShape(i));return r.set(e,r.length-e.length),Ne.tensor(r,i)},t}(Tt);var UU=function(n){He(t,n);function t(e,i){var r=n.call(this)||this;if(r.webcamVideoElement=e,r.webcamConfig=i,r.isClosed=!0,r.resize=!1,r.needToResize())if(r.resize=!0,r.cropSize=[r.webcamConfig.resizeHeight,r.webcamConfig.resizeWidth],r.cropBoxInd=Ne.tensor1d([0],"int32"),r.webcamConfig.centerCrop){var a=r.webcamConfig.resizeWidth*1/r.webcamVideoElement.width,s=r.webcamConfig.resizeHeight*1/r.webcamVideoElement.height,o=(1-a)/2,l=(1-s)/2,u=o+a,c=s+l;r.cropBox=Ne.tensor2d([l,o,c,u],[1,4])}else r.cropBox=Ne.tensor2d([0,0,1,1],[1,4]);return r}return t.prototype.summary=function(){return"webcam"},t.create=function(e,i){return i===void 0&&(i={}),se(this,void 0,void 0,function(){var r;return oe(this,function(a){switch(a.label){case 0:if(Ne.env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!i.resizeWidth||!i.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=i.resizeWidth,e.height=i.resizeHeight}return r=new t(e,i),[4,r.start()];case 1:return a.sent(),[2,r]}})})},t.prototype.start=function(){return se(this,void 0,void 0,function(){var e,i,r=this;return oe(this,function(a){switch(a.label){case 0:this.webcamConfig.facingMode&&Ne.util.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",function(){return"Invalid webcam facing mode: "+r.webcamConfig.facingMode+". Please provide 'user' or 'environment'"}),a.label=1;case 1:return a.trys.push([1,3,,4]),e=this,[4,navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})];case 2:return e.stream=a.sent(),[3,4];case 3:throw i=a.sent(),i.message="Error thrown while initializing video stream: "+i.message,i;case 4:if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(s){console.log(s),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,[2,new Promise(function(s){r.webcamVideoElement.onloadedmetadata=function(){s()}})]}})})},t.prototype.next=function(){return se(this,void 0,void 0,function(){var e;return oe(this,function(i){if(this.isClosed)return[2,{value:null,done:!0}];try{e=Ne.browser.fromPixels(this.webcamVideoElement)}catch(r){throw new Error("Error thrown converting video to pixels: "+JSON.stringify(r))}if(this.resize)try{return[2,{value:this.cropAndResizeFrame(e),done:!1}]}catch(r){throw new Error("Error thrown cropping the video: "+r.message)}finally{e.dispose()}else return[2,{value:e,done:!1}];return[2]})})},t.prototype.needToResize=function(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))},t.prototype.cropAndResizeFrame=function(e){var i=this;return Ne.tidy(function(){var r=e.toFloat().expandDims(0),a;a=Ne.image.cropAndResize(r,i.cropBox,i.cropBoxInd,i.cropSize,"bilinear");var s=a.shape;return a.reshape(s.slice(1))})},t.prototype.capture=function(){return se(this,void 0,void 0,function(){return oe(this,function(e){switch(e.label){case 0:return[4,this.next()];case 1:return[2,e.sent().value]}})})},t.prototype.stop=function(){var e=this.stream.getTracks();e.forEach(function(i){return i.stop()});try{this.webcamVideoElement.srcObject=null}catch(i){console.log(i),this.webcamVideoElement.src=null}this.isClosed=!0},t.prototype.toArray=function(){throw new Error("Can not convert infinite video stream to array.")},t}(Tt);var Iw=function(){function n(){}return n}();var Aw=function(n){He(t,n);function t(){return n!==null&&n.apply(this,arguments)||this}return t.prototype.split=function(e){return new BU(this,e)},t}(Tt),BU=function(n){He(t,n);function t(e,i){var r=n.call(this)||this;return r.upstream=e,r.impl=new zU(e,i),r}return t.prototype.summary=function(){return this.impl.summary()},t.prototype.next=function(){return se(this,void 0,void 0,function(){return oe(this,function(e){return[2,this.impl.next()]})})},t}(Aw),zU=function(n){He(t,n);function t(e,i){var r=n.call(this)||this;return r.upstream=e,r.separator=i,r.carryover="",r}return t.prototype.summary=function(){return this.upstream.summary()+" -> Split('"+this.separator+"')"},t.prototype.pump=function(){return se(this,void 0,void 0,function(){var e,i,r,a,s;return oe(this,function(o){switch(o.label){case 0:return[4,this.upstream.next()];case 1:if(e=o.sent(),e.done)return this.carryover===""?[2,!1]:(this.outputQueue.push(this.carryover),this.carryover="",[2,!0]);for(i=e.value.split(this.separator),i[0]=this.carryover+i[0],r=0,a=i.slice(0,-1);r Utf8"},t.prototype.pump=function(){return se(this,void 0,void 0,function(){var e,i,r;return oe(this,function(a){switch(a.label){case 0:return[4,this.upstream.next()];case 1:return e=a.sent(),e.done?[2,!1]:(i=e.value,Ne.env().get("IS_BROWSER")?r=this.decoder.decode(i,{stream:!0}):r=this.decoder.write(Buffer.from(i.buffer)),this.outputQueue.push(r),[2,!0])}})})},t}(ud);var Tw=function(n){He(t,n);function t(e,i){i===void 0&&(i={});var r=n.call(this)||this;return r.file=e,r.options=i,Ne.util.assert(e instanceof Uint8Array||(Ne.env().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),function(){return"FileChunkIterator only supports File, Blob and Uint8Array right now."}),r.offset=i.offset||0,r.chunkSize=i.chunkSize||1024*1024,r}return t.prototype.summary=function(){return"FileChunks "+this.file},t.prototype.next=function(){return se(this,void 0,void 0,function(){var e,i,r=this;return oe(this,function(a){switch(a.label){case 0:return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?[2,{value:null,done:!0}]:(e=new Promise(function(s,o){var l=r.offset+r.chunkSize;if(r.file instanceof Uint8Array)s(new Uint8Array(r.file.slice(r.offset,l)));else{var u=new FileReader;u.onload=function(h){var d=u.result;if(d instanceof ArrayBuffer&&(d=new Uint8Array(d)),!(d instanceof Uint8Array))return o(new TypeError("FileReader returned unknown type."));s(d)},u.onabort=function(h){return o(new 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Float32Array(T.util.sizeFromShape([o,i,r,c])),p=[a&&i>1?l-1:l,a&&r>1?u-1:u],f=[a&&i>1?i-1:i,a&&r>1?r-1:r],m=0,g=p[0]/f[0],v=p[1]/f[1],b=0;b1?o-1:o,r&&d>1?l-1:l],m=[r&&h>1?h-1:h,r&&d>1?d-1:d],g=f[0]/m[0],v=f[1]/m[1],b=this.readSync(e.dataId),w=0,S=0;S1?l-1:l,a&&r>1?u-1:u],f=[a&&i>1?i-1:i,a&&r>1?r-1:r],m=p[0]/f[0],g=p[1]/f[1],v=0,b=0;b1?o-1:o,r&&d>1?l-1:l],g=[r&&h>1?h-1:h,r&&d>1?d-1:d],v=m[0]/g[0],b=m[1]/g[1],w=1/v,S=1/b,L=Math.ceil(w)*2+2,x=Math.ceil(S)*2+2,C=0;C=h)continue;var X=R+Z*e.strides[1],ee=Z*v,ne=Math.min(o-1,r?Math.round(ee):Math.floor(ee));if(D!==ne)continue;for(var ie=0;ie=d)continue;var re=X+te*e.strides[2],le=te*b,he=Math.min(l-1,r?Math.round(le):Math.floor(le));P===he&&(q+=f[re+j])}}p[H+j]=q}return T.tensor4d(p,i.shape,i.dtype)},t.prototype.localResponseNormalization4D=function(e,i,r,a,s){ae(e,"localResponseNormalization4D");var o=e.shape[3],l=o-1,u=this.readSync(e.dataId),c=e.size,h=new Float32Array(c);function d(g){for(var v=g%o,b=g-v+Math.max(0,v-i),w=g-v+Math.min(v+i,l),S=0;b<=w;b++){var L=u[b];S+=L*L}return S}for(var p=0;p=0&&o[l]1,function(){return"blockSize should be > 1 for depthToSpace, but was: "+i});for(var a=e.shape[0],s=e.shape[1],o=e.shape[2],l=e.shape[3],u=s*i,c=o*i,h=l/(i*i),d=this.readSync(e.dataId),p=new Float32Array(a*u*c*h),f=0,m=0;m=u)continue;for(var P=f>1?(k-R)*(c-1)/(f-1):0,H=m>1?(W-D)*(h-1)/(m-1):0,_=0;_1?R*(c-1)+_*P:.5*(R+k)*(c-1);if(K<0||K>c-1){for(var j=0;j1?D*(h-1)+j*H:.5*(D+W)*(h-1);if(ne<0||ne>h-1){for(var q=0;q1?D*(h-1)+j*H:.5*(D+W)*(h-1);if(ne<0||ne>h-1){for(var q=0;q=e.size/u)throw new Error("Invalid indices: "+m+" does not index into "+e.shape);for(var w=0;w=a/s)throw new Error("Invalid indices: "+v+" does not index into "+r);for(var L=0;Lo&&(o=u)}r[a]=o}return r}var i0=ir(function(n,t){return n*t}),xB=bd(function(n,t,e,i){return{real:n*e-t*i,imag:n*i+t*e}}),r0=Yr(T.Multiply,i0,xB),CB={kernelName:T.Multiply,backendName:"cpu",kernelFunc:r0};var a0=Kr(function(n){return 1/Math.sqrt(n)}),RB=jr(T.Rsqrt,a0),OB={kernelName:T.Rsqrt,backendName:"cpu",kernelFunc:RB};function s0(n,t,e,i,r){var a=T.slice_util.isSliceContinous(i,t,e),s=T.util.sizeFromShape(e),o=T.util.computeStrides(i);if(a){var l=T.slice_util.computeFlatOffset(t,o);return n.subarray(l,l+s)}for(var u=T.util.getTypedArrayFromDType(r,s),c=0;cj?j=ie:a==="avg"&&(q+=ie,G++)}if(isNaN(j))break}var te=F+P*w+C;g[te]=a==="avg"?q/G:j}return m}function c0(n,t,e,i,r,a){r===void 0&&(r=!1),a===void 0&&(a=!1);for(var s=T.buffer(i.outShape,"int32"),o=i.strideHeight,l=i.strideWidth,u=i.dilationHeight,c=i.dilationWidth,h=i.effectiveFilterHeight,d=i.effectiveFilterWidth,p=i.padInfo.top,f=i.padInfo.left,m=T.buffer(t,e,n),g=0;gk&&(k=K,r?W=a?((g*i.inHeight+F)*i.inWidth+H)*i.inChannels+v:(F*i.inWidth+H)*i.inChannels+v:W=P*d+_)}s.set(W,g,b,x,v)}}return s}function jB(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.x;ae(r,"avgPool");var a=i.filterSize,s=i.strides,o=i.pad,l=i.dimRoundingMode,u=1;T.util.assert(T.backend_util.eitherStridesOrDilationsAreOne(s,u),function(){return"Error in avgPool: Either strides or dilations must be 1. "+("Got strides "+s+" and dilations '"+u+"'")});var c=T.backend_util.computePool2DInfo(r.shape,a,s,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&T.util.arraysEqual(c.inShape,c.outShape))h=Gr({inputs:{x:r},backend:e});else{var d=e.data.get(r.dataId).values,p=T.util.computeStrides(r.shape),f=Ld(d,r.shape,r.dtype,p,c,"avg");h=e.makeTensorInfo(c.outShape,r.dtype,f.values)}return h}var $B={kernelName:T.AvgPool,backendName:"cpu",kernelFunc:jB};function XB(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.dy,a=t.input,s=a;ae([r,a],"avgPoolBackprop");for(var o=i.filterSize,l=i.strides,u=i.pad,c=T.backend_util.computePool2DInfo(s.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,v=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,S=v-1-c.padInfo.top,L=T.buffer(s.shape,"float32"),x=1/(p*f),C=e.data.get(r.dataId).values,R=T.buffer(r.shape,"float32",C),D=0;D=c.outHeight||Math.floor(j)!==j)continue;for(var q=0;q=c.outWidth||Math.floor(G)!==G)continue;var Z=R.get(D,j,G,k);_+=Z}}L.set(_*x,D,W,F,k)}return e.makeTensorInfo(L.shape,L.dtype,L.values)}var JB={kernelName:T.AvgPoolBackprop,backendName:"cpu",kernelFunc:XB};function ZB(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.x,a=t.scale,s=t.offset,o=t.mean,l=t.variance;T.util.assert(o.shape.length===l.shape.length,function(){return"Batch normalization gradient requires mean and variance to have equal ranks."}),T.util.assert(s==null||o.shape.length===s.shape.length,function(){return"Batch normalization gradient requires mean and offset to have equal ranks."}),T.util.assert(a==null||o.shape.length===a.shape.length,function(){return"Batch normalization gradient requires mean and scale to have equal ranks."}),ae([r,o,l,a,s],"batchNorm");var u=i.varianceEpsilon;u==null&&(u=.001);for(var c=e.data.get(r.dataId).values,h=e.data.get(o.dataId).values,d=e.data.get(l.dataId).values,p=a?e.data.get(a.dataId).values:new Float32Array([1]),f=s?e.data.get(s.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,v=p.length,b=d.length,w=h.length,S=0,L=0,x=0,C=0,R=0;R=g&&(S=0),L>=w&&(L=0),x>=v&&(x=0),C>=b&&(C=0);return e.makeTensorInfo(r.shape,r.dtype,m)}var QB={kernelName:T.FusedBatchNorm,backendName:"cpu",kernelFunc:ZB};var ez=je(T.ClipByValue,function(n,t){var e=t;return n>e.clipValueMax?e.clipValueMax:n0});if(o.length===1)return o[0];var l=o.map(function(S){return S.shape});if(T.backend_util.assertParamsConsistent(l,a),o[0].dtype==="complex64"){var u=o.map(function(S){return ja({inputs:{input:S},backend:e})}),c=o.map(function(S){return Do({inputs:{input:S},backend:e})}),h=Xa({inputs:u,backend:e,attrs:{axis:r}}),d=Xa({inputs:c,backend:e,attrs:{axis:r}}),p=bn({inputs:{real:h,imag:d},backend:e});return u.forEach(function(S){return e.disposeIntermediateTensorInfo(S)}),c.forEach(function(S){return e.disposeIntermediateTensorInfo(S)}),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(d),p}var f=o.map(function(S){var L=T.util.sizeFromShape(S.shape.slice(a)),x=[-1,L];return Si({inputs:{x:S},backend:e,attrs:{shape:x}})});s=T.backend_util.computeOutShape(f.map(function(S){return S.shape}),1);var m=T.util.getTypedArrayFromDType(o[0].dtype,T.util.sizeFromShape(s));if(f[0].shape[0]===1){var g=0;f.forEach(function(S){var L=e.data.get(S.dataId).values,x=T.util.sizeFromShape(S.shape);m.set(L,g),g+=x})}else{var v=0;f.forEach(function(S){for(var L=e.data.get(S.dataId).values,x=0,C=0;C=0&&re=0&&heie&&(ie=ke)}}}var Pe=T.util.locToIndex([q,G,X,ne],K,T.util.computeStrides(H));j[Pe]=ie}var _e=h.write(T.util.toTypedArray(j,a.dtype),H,a.dtype);return{dataId:_e,shape:H,dtype:a.dtype}}};var cz={kernelName:T.Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:function(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t,a=r.x,s=r.filter,o=r.dy,l=i,u=l.strides,c=l.pad,h=l.dilations,d=e,p=T.util.toNestedArray(a.shape,d.data.get(a.dataId).values),f=T.util.toNestedArray(s.shape,d.data.get(s.dataId).values),m=T.backend_util.computeDilation2DInfo(a.shape,s.shape,u,c,"NHWC",h),g=m.batchSize,v=m.inHeight,b=m.inWidth,w=m.inChannels,S=m.outHeight,L=m.outWidth,x=m.padInfo,C=m.strideHeight,R=m.strideWidth,D=m.filterHeight,k=m.filterWidth,W=m.dilationHeight,F=m.dilationWidth,P=m.outShape;T.util.assert(o.rank===P.length,function(){return"Error in "+T.Dilation2DBackpropFilter+", dy "+("must have the same rank as output "+P.length+", but got ")+(""+o.rank)});for(var H=T.util.toNestedArray(P,d.data.get(o.dataId).values),_=T.util.makeZerosNestedTypedArray(s.shape,s.dtype),K=0;K=0&&re=0&&heee&&(ee=be,ne=te,ie=le)}}}_[ne][ie][X]+=H[K][j][G][X]}var Oe=d.write(T.util.toTypedArray(_,a.dtype),s.shape,s.dtype);return{dataId:Oe,shape:s.shape,dtype:s.dtype}}};var hz={kernelName:T.Dilation2DBackpropInput,backendName:"cpu",kernelFunc:function(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t,a=r.x,s=r.filter,o=r.dy,l=i,u=l.strides,c=l.pad,h=l.dilations,d=e,p=T.util.toNestedArray(a.shape,d.data.get(a.dataId).values),f=T.util.toNestedArray(s.shape,d.data.get(s.dataId).values),m=T.backend_util.computeDilation2DInfo(a.shape,s.shape,u,c,"NHWC",h),g=m.batchSize,v=m.inHeight,b=m.inWidth,w=m.inChannels,S=m.outHeight,L=m.outWidth,x=m.padInfo,C=m.strideHeight,R=m.strideWidth,D=m.filterHeight,k=m.filterWidth,W=m.dilationHeight,F=m.dilationWidth,P=m.outShape;T.util.assert(o.rank===P.length,function(){return"Error in "+T.Dilation2DBackpropInput+", dy "+("must have the same rank as output "+P.length+", but got ")+(""+o.rank)});for(var H=T.util.toNestedArray(P,d.data.get(o.dataId).values),_=T.util.makeZerosNestedTypedArray(a.shape,a.dtype),K=0;K=0&&re=0&&heee&&(ee=be,ne=re,ie=he)}}}_[K][ne][ie][X]+=H[K][j][G][X]}var Oe=d.write(T.util.toTypedArray(_,a.dtype),a.shape,a.dtype);return{dataId:Oe,shape:a.shape,dtype:a.dtype}}};var dz=ir(function(n,t){return n/t}),pz=Yr(T.Div,dz),Id={kernelName:T.Div,backendName:"cpu",kernelFunc:pz};var fz=je(T.Elu,function(n){return n>=0?n:Math.exp(n)-1}),mz={kernelName:T.Elu,backendName:"cpu",kernelFunc:fz};var gz=T.backend_util.ERF_P,vz=T.backend_util.ERF_A1,yz=T.backend_util.ERF_A2,bz=T.backend_util.ERF_A3,wz=T.backend_util.ERF_A4,Sz=T.backend_util.ERF_A5,Lz=je(T.Erf,function(n){var t=Math.sign(n),e=Math.abs(n),i=1/(1+gz*e);return t*(1-((((Sz*i+wz)*i+bz)*i+yz)*i+vz)*i*Math.exp(-e*e))}),Iz={kernelName:T.Erf,backendName:"cpu",kernelFunc:Lz};function h0(n,t,e){for(var i=n.shape,r=i[0],a=i[1],s=e.data.get(n.dataId),o=s.complexTensorInfos.real,l=s.complexTensorInfos.imag,u=[r,a],c=T.util.sizeFromShape(u),h=T.util.getTypedArrayFromDType("float32",c),d=T.util.getTypedArrayFromDType("float32",c),p=0;p=0&&x=d.outHeight||Math.floor(q)!==q)continue;for(var G=0;G=d.outWidth||Math.floor(Z)!==Z)continue;var X=w*S-1-f.get(k,q,Z,W),ee=j*S+G,ne=X===ee?1:0;if(ne===0)continue;var ie=D.get(k,q,Z,W);K+=ie*ne}}C.set(K,k,F,P,W)}return e.makeTensorInfo(C.shape,C.dtype,C.values)}var Yz={kernelName:T.MaxPoolBackprop,backendName:"cpu",kernelFunc:Gz};function Kz(n,t,e,i,r){var a=T.util.computeStrides(t),s=Ld(n,t,e,a,r,"max"),o=c0(n,t,e,r,!0,i);return[s.values,o.values]}var jz={kernelName:T.MaxPoolWithArgmax,backendName:"cpu",kernelFunc:function(n){var t=n.inputs,e=n.attrs,i=n.backend,r=t.x,a=e,s=a.filterSize,o=a.strides,l=a.pad,u=a.includeBatchInIndex,c=i;ae(r,"MaxPoolWithArgmax");var h=c.data.get(r.dataId).values,d=T.backend_util.computePool2DInfo(r.shape,s,o,[1,1],l),p=Kz(h,r.shape,r.dtype,u,d),f=p[0],m=p[1],g=c.write(f,d.outShape,r.dtype),v=c.write(m,d.outShape,r.dtype);return[{dataId:g,shape:d.outShape,dtype:r.dtype},{dataId:v,shape:d.outShape,dtype:"int32"}]}};var $z=T.kernel_impls.nonMaxSuppressionV4Impl,Xz={kernelName:T.NonMaxSuppressionV4,backendName:"cpu",kernelFunc:function(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t,a=r.boxes,s=r.scores,o=i,l=o.maxOutputSize,u=o.iouThreshold,c=o.scoreThreshold,h=o.padToMaxOutputSize,d=e;ae(a,"NonMaxSuppressionPadded");var p=d.data.get(a.dataId).values,f=d.data.get(s.dataId).values,m=$z(p,f,l,u,c,h),g=m.selectedIndices,v=m.validOutputs;return[g,v]}};var Jz=T.kernel_impls.nonMaxSuppressionV5Impl,Zz={kernelName:T.NonMaxSuppressionV5,backendName:"cpu",kernelFunc:function(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t,a=r.boxes,s=r.scores,o=i,l=o.maxOutputSize,u=o.iouThreshold,c=o.scoreThreshold,h=o.softNmsSigma,d=e;ae(a,"NonMaxSuppressionWithScore");var p=d.data.get(a.dataId).values,f=d.data.get(s.dataId).values,m=l,g=u,v=c,b=h,w=Jz(p,f,m,g,v,b),S=w.selectedIndices,L=w.selectedScores;return[S,L]}};var Qz=ir(function(n,t){return n!==t?1:0}),eP=Yr(T.NotEqual,Qz,null,"bool"),tP={kernelName:T.NotEqual,backendName:"cpu",kernelFunc:eP};function nP(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.x,a=i.paddings,s=i.constantValue;ae(r,"pad");var o=a.map(function(x,C){return x[0]+r.shape[C]+x[1]}),l=a.map(function(x){return x[0]}),u=e.data.get(r.dataId).values,c=T.util.sizeFromShape(r.shape),h=r.shape.length,d=T.util.computeStrides(r.shape),p=T.util.sizeFromShape(o),f=o.length,m=T.util.computeStrides(o),g=T.util.getTypedArrayFromDType(r.dtype,p);s!==0&&g.fill(s);for(var v=0;v=0&&j=0&&q.5?Math.ceil(n):t%2===0?t:t+1}),oP={kernelName:T.Round,backendName:"cpu",kernelFunc:sP};var lP=T.backend_util.SELU_SCALEALPHA,uP=T.backend_util.SELU_SCALE,cP=je(T.Selu,function(n){return n>=0?uP*n:lP*(Math.exp(n)-1)}),hP={kernelName:T.Selu,backendName:"cpu",kernelFunc:cP};var dP=je(T.Sigmoid,function(n){return 1/(1+Math.exp(-n))}),pP={kernelName:T.Sigmoid,backendName:"cpu",kernelFunc:dP};var fP=je(T.Sign,function(n){return 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isnan(val.w)); + } + `,l=` + uniform float INFINITY; + + bool isinf(float val) { + return abs(val) == INFINITY; + } + bvec4 isinf(vec4 val) { + return equal(abs(val), vec4(INFINITY)); + } + `,u=` + int round(float value) { + return int(floor(value + 0.5)); + } + + ivec4 round(vec4 value) { + return ivec4(floor(value + vec4(0.5))); + } + `),{version:n,attribute:t,varyingVs:e,varyingFs:i,texture2D:r,output:a,defineOutput:s,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function or(n,t,e){e===void 0&&(e="index");var i=N.util.computeStrides(t);return i.map(function(r,a){var s="int "+n[a]+" = "+e+" / "+r,o=a===i.length-1?"int "+n[a+1]+" = "+e+" - "+n[a]+" * "+r:"index -= "+n[a]+" * "+r;return s+"; "+o+";"}).join("")}function Fd(n){var t=N.util.computeStrides(n).map(function(e){return e.toString()});return` + int getFlatIndex(ivec3 coords) { + return coords.x * `+t[0]+" + coords.y * "+t[1]+` + coords.z; + } +`}var q0=` + const float FLOAT_MAX = 1.70141184e38; + const float FLOAT_MIN = 1.17549435e-38; + + lowp vec4 encode_float(highp float v) { + if (isnan(v)) { + return vec4(255, 255, 255, 255); + } + + highp float av = abs(v); + + if(av < FLOAT_MIN) { + return vec4(0.0, 0.0, 0.0, 0.0); + } else if(v > FLOAT_MAX) { + return vec4(0.0, 0.0, 128.0, 127.0) / 255.0; + } else if(v < -FLOAT_MAX) { + return vec4(0.0, 0.0, 128.0, 255.0) / 255.0; + } + + highp vec4 c = vec4(0,0,0,0); + + highp float e = floor(log2(av)); + highp float m = exp2(fract(log2(av))) - 1.0; + + c[2] = floor(128.0 * m); + m -= c[2] / 128.0; + c[1] = floor(32768.0 * m); + m -= c[1] / 32768.0; + c[0] = floor(8388608.0 * m); + + highp float ebias = e + 127.0; + c[3] = floor(ebias / 2.0); + ebias -= c[3] * 2.0; + c[2] += floor(ebias) * 128.0; + + c[3] += 128.0 * step(0.0, -v); + + return c / 255.0; + } +`;var G0=N.backend_util.getBroadcastDims;function q_(n,t,e,i){var r=[];n.forEach(function(f){var m=N.util.sizeFromShape(f.shapeInfo.logicalShape);f.shapeInfo.isUniform?r.push("uniform float "+f.name+(m>1?"["+m+"]":"")+";"):(r.push("uniform sampler2D "+f.name+";"),r.push("uniform int offset"+f.name+";"))});var a=r.join(` +`),s=n.map(function(f){return U_(f,t,i)}).join(` +`),o=t.texShape,l=kt(),u=P_(l),c,h,d=H_(l);t.isPacked?(c=B_(t.logicalShape,o),h=M_(l)):(c=z_(t.logicalShape,o),h=__(l)),i&&(d+=V_);var p=[d,u,h,a,c,s,e].join(` +`);return p}function Zr(n){var t=n.shapeInfo.logicalShape;switch(t.length){case 0:return G_(n);case 1:return Y_(n);case 2:return K_(n);case 3:return j_(n);case 4:return $_(n);case 5:return X_(n);case 6:return J_(n);default:throw new Error(t.length+"-D input sampling is not yet supported")}}function Y0(n){var t=n.shapeInfo.logicalShape;switch(t.length){case 0:return Z_(n);case 1:return Q_(n);case 2:return eM(n);case 3:return tM(n);default:return nM(n)}}function U_(n,t,e){e===void 0&&(e=!1);var i="";e?i+=Y0(n):i+=Zr(n);var r=n.shapeInfo.logicalShape,a=t.logicalShape;return r.length<=a.length&&(e?i+=iM(n,t):i+=rM(n,t)),i}function B_(n,t){switch(n.length){case 0:return K0();case 1:return aM(n,t);case 2:return lM(n,t);case 3:return sM(n,t);default:return oM(n,t)}}function z_(n,t){switch(n.length){case 0:return K0();case 1:return uM(n,t);case 2:return fM(n,t);case 3:return cM(n,t);case 4:return hM(n,t);case 5:return dM(n,t);case 6:return pM(n,t);default:throw new Error(n.length+"-D output sampling is not yet supported")}}function P_(n){return` + float sampleTexture(sampler2D textureSampler, vec2 uv) { + return `+n.texture2D+`(textureSampler, uv).r; + } + `}function __(n){return` + void setOutput(float val) { + `+n.output+` = vec4(val, 0, 0, 0); + } + `}function M_(n){return` + void setOutput(vec4 val) { + `+n.output+` = val; + } + `}function H_(n){var t=n.version+` + precision highp float; + precision highp int; + precision highp sampler2D; + `+n.varyingFs+` vec2 resultUV; + `+n.defineOutput+` + const vec2 halfCR = vec2(0.5, 0.5); + + struct ivec5 + { + int x; + int y; + int z; + int w; + int u; + }; + + struct ivec6 + { + int x; + int y; + int z; + int w; + int u; + int v; + }; + + uniform float NAN; + `+n.defineSpecialNaN+` + `+n.defineSpecialInf+` + `+n.defineRound+` + + int imod(int x, int y) { + return x - y * (x / y); + } + + int idiv(int a, int b, float sign) { + int res = a / b; + int mod = imod(a, b); + if (sign < 0. && mod != 0) { + res -= 1; + } + return res; + } + + //Based on the work of Dave Hoskins + //https://www.shadertoy.com/view/4djSRW + #define HASHSCALE1 443.8975 + float random(float seed){ + vec2 p = resultUV * seed; + vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1); + p3 += dot(p3, p3.yzx + 19.19); + return fract((p3.x + p3.y) * p3.z); + } + + `+mM+` + `+gM+` + `+vM+` + `;return t}var mM=` +vec2 uvFromFlat(int texNumR, int texNumC, int index) { + int texR = index / texNumC; + int texC = index - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { + int texelIndex = index / 2; + int texR = texelIndex / texNumC; + int texC = texelIndex - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +`,gM=` +vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, + int texNumC, int row, int col) { + int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); + int texR = texelIndex / texNumC; + int texC = texelIndex - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +`,vM=` +vec2 packedUVfrom3D(int texNumR, int texNumC, + int texelsInBatch, int texelsInLogicalRow, int b, + int row, int col) { + int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2); + int texR = index / texNumC; + int texC = index - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +`,V_=` + float getChannel(vec4 frag, vec2 innerDims) { + vec2 modCoord = mod(innerDims, 2.); + return modCoord.x == 0. ? + (modCoord.y == 0. ? frag.r : frag.g) : + (modCoord.y == 0. ? frag.b : frag.a); + } + float getChannel(vec4 frag, int dim) { + float modCoord = mod(float(dim), 2.); + return modCoord == 0. ? frag.r : frag.g; + } +`;function K0(){return` + int getOutputCoords() { + return 0; + } + `}function aM(n,t){var e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return e[0]===1?` + int getOutputCoords() { + return 2 * int(resultUV.x * `+e[1]+`.0); + } + `:e[1]===1?` + int getOutputCoords() { + return 2 * int(resultUV.y * `+e[0]+`.0); + } + `:` + int getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(`+e[0]+", "+e[1]+`)); + return 2 * (resTexRC.x * `+e[1]+` + resTexRC.y); + } + `}function uM(n,t){return t[0]===1?` + int getOutputCoords() { + return int(resultUV.x * `+t[1]+`.0); + } + `:t[1]===1?` + int getOutputCoords() { + return int(resultUV.y * `+t[0]+`.0); + } + `:` + int getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(`+t[0]+", "+t[1]+`)); + return resTexRC.x * `+t[1]+` + resTexRC.y; + } + `}function sM(n,t){var e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],i=Math.ceil(n[2]/2),r=i*Math.ceil(n[1]/2);return` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(`+e[0]+", "+e[1]+`)); + int index = resTexRC.x * `+e[1]+` + resTexRC.y; + + int b = index / `+r+`; + index -= b * `+r+`; + + int r = 2 * (index / `+i+`); + int c = imod(index, `+i+`) * 2; + + return ivec3(b, r, c); + } + `}function cM(n,t){var e=or(["r","c","d"],n);return` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(`+t[0]+", "+t[1]+`)); + int index = resTexRC.x * `+t[1]+` + resTexRC.y; + `+e+` + return ivec3(r, c, d); + } + `}function oM(n,t){for(var e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],i=Math.ceil(n[n.length-1]/2),r=i*Math.ceil(n[n.length-2]/2),a=r,s="",o="b, r, c",l=2;l=1?c="coords = 0;":c=o.map(function(S){return"coords."+h[S+u]+" = 0;"}).join(` +`);var d="";s<2&&a>0?d="coords":d=n.shapeInfo.logicalShape.map(function(S,L){return"coords."+h[L+u]}).join(", ");var p="return outputValue;",f=N.util.sizeFromShape(n.shapeInfo.logicalShape),m=f===1,g=N.util.sizeFromShape(t.logicalShape),v=g===1;if(a===1&&!m&&!v)p=` + return vec4(outputValue.xy, outputValue.xy); + `;else if(m&&!v)s===1?p=` + return vec4(outputValue.x, outputValue.x, 0., 0.); + `:p=` + return vec4(outputValue.x); + `;else if(o.length){var b=a-2,w=a-1;o.indexOf(b)>-1&&o.indexOf(w)>-1?p="return vec4(outputValue.x);":o.indexOf(b)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(w)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return` + vec4 `+r+`() { + `+l+` coords = getOutputCoords(); + `+c+` + vec4 outputValue = get`+i+"("+d+`); + `+p+` + } + `}function rM(n,t){var e=n.name,i=e.charAt(0).toUpperCase()+e.slice(1),r="get"+i+"AtOutCoords",a=t.texShape,s=n.shapeInfo.texShape,o=n.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!n.shapeInfo.isUniform&&o===l&&n.shapeInfo.flatOffset==null&&N.util.arraysEqual(s,a))return` + float `+r+`() { + return sampleTexture(`+e+`, resultUV); + } + `;var u=Je(l),c=G0(n.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(function(m){return"coords."+p[m+h]+" = 0;"}).join(` +`);var f="";return l<2&&o>0?f="coords":f=n.shapeInfo.logicalShape.map(function(m,g){return"coords."+p[g+h]}).join(", "),` + float `+r+`() { + `+u+` coords = getOutputCoords(); + `+d+` + return get`+i+"("+f+`); + } + `}function Je(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error("GPU for rank "+n+" is not yet supported")}function ea(n,t){var e=JSON.parse(JSON.stringify(n));return e.shapeInfo.logicalShape=t,e}function ta(n,t){return t.map(function(e){return n[e]}).join(", ")}var yM=function(){function n(t,e,i,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,N.util.assert(t.length>2,function(){return"Packed arg"+(i.charAt(0).toUpperCase()+i.slice(1))+" supports only inputs with rank above 2."});var a=t[t.length-1],s=Math.ceil(a/e);this.outputShape=t.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");var o=this.outputShape,l=o.length,u=Je(l),c=jt("coords",l),h,d;if(s===1){d=l+1;var p=Je(d);h=` + `+p+" sourceLocR = "+p+"("+c.join()+`, 0); + ++`+c[l-1]+`; + `+p+" sourceLocG = "+p+"("+c.join()+`, 0); + ++`+c[l-2]+`; + `+p+" sourceLocA = "+p+"("+c.join()+`, 0); + --`+c[l-1]+`; + `+p+" sourceLocB = "+p+"("+c.join()+`, 0); + --`+c[l-2]+";"}else d=l,h=` + `+u+` sourceLocR = coords; + ++`+c[l-1]+`; + `+u+` sourceLocG = coords; + ++`+c[l-2]+`; + `+u+` sourceLocA = coords; + --`+c[l-1]+`; + `+u+` sourceLocB = coords; + --`+c[l-2]+";";var f=["x","y","z","w","u","v"].slice(0,d),m="."+f[d-1],g=f.map(function(D){return"int "+D}),v=jt("sourceLocR",d-1).concat("inIdx.r"),b=jt("sourceLocG",d-1).concat("inIdx.g"),w=jt("sourceLocB",d-1).concat("inIdx.b"),S=jt("sourceLocA",d-1).concat("inIdx.a"),L=i==="max"?"greaterThan":"lessThan",x=r?"":` + inIdx = round(vec4(getBestIndicesAChannel(`+v.join()+`), + getBestIndicesAChannel(`+b.join()+`), + getBestIndicesAChannel(`+w.join()+`), + getBestIndicesAChannel(`+S.join()+")));",C=`vec4( + getAChannel(`+v.join()+`), + hasNextCol ? getAChannel(`+b.join()+`) : 0., + hasNextRow ? getAChannel(`+w.join()+`) : 0., + hasNextRow && hasNextCol ? getAChannel(`+S.join()+") : 0.)",R=r?"":` + float getBestIndicesAChannel(`+g.join()+`) { + return getChannel(getBestIndicesA(`+f.join()+`), + vec2(`+f.slice(-2).join()+`)); + }`;this.userCode=` + float getAChannel(`+g.join()+`) { + return getChannel(getA(`+f.join()+`), + vec2(`+f.slice(-2).join()+`)); + } + `+R+` + void main() { + `+u+` coords = getOutputCoords(); + bool hasNextCol = `+c[l-1]+" < "+(o[l-1]-1)+`; + bool hasNextRow = `+c[l-2]+" < "+(o[l-2]-1)+`; + `+h+` + ivec4 srcIdx = ivec4(sourceLocR`+m+", sourceLocG"+m+`, + sourceLocB`+m+", sourceLocA"+m+") * "+e+`; + ivec4 inIdx = srcIdx; + vec4 bestIndex = vec4(inIdx); + vec4 bestValue = `+C+`; + + for (int i = 0; i < `+e+`; i++) { + inIdx = srcIdx; + `+x+` + vec4 candidate = `+C+`; + bvec4 nan = isnan(candidate); + bvec4 replace = bvec4( + vec4(`+L+`(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); + + bestValue = vec4(replace.x ? candidate.x : bestValue.x, + replace.y ? candidate.y : bestValue.y, + replace.z ? candidate.z : bestValue.z, + replace.w ? candidate.w : bestValue.w); + bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); + srcIdx++; + } + setOutput(bestIndex); + } + `}return n}();var bM=function(){function n(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,i=t.filterWidth,r=t.strideHeight,a=t.strideWidth,s=t.dilationHeight,o=t.dilationWidth,l=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=l-1-t.padInfo.top,h=u-1-t.padInfo.left,d=1/(e*i);this.userCode=` + const ivec2 pads = ivec2(`+c+", "+h+`); + const float avgMultiplier = float(`+d+`); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + + ivec2 dyRCCorner = coords.yz - pads; + int dyRCorner = dyRCCorner.x; + int dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < `+l+`; + wR += `+s+`) { + float dyR = float(dyRCorner + wR) / `+r+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < `+u+`; + wC+= `+o+`) { + float dyC = float(dyCCorner + wC) / `+a+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(b, idyR, idyC, d); + + dotProd += dyValue * avgMultiplier; + } + } + setOutput(dotProd); + } + `}return n}(),wM=function(){function n(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterDepth,i=t.filterHeight,r=t.filterWidth,a=t.strideDepth,s=t.strideHeight,o=t.strideWidth,l=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,h=t.effectiveFilterDepth,d=t.effectiveFilterHeight,p=t.effectiveFilterWidth,f=h-1-t.padInfo.front,m=d-1-t.padInfo.top,g=p-1-t.padInfo.left,v=1/(e*i*r);this.userCode=` + const ivec3 pads = ivec3(`+f+", "+m+", "+g+`); + const float avgMultiplier = float(`+v+`); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; + int dyDCorner = dyCorner.x; + int dyRCorner = dyCorner.y; + int dyCCorner = dyCorner.z; + + // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get + // dx(xD, xR, xC, ch). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + + for (int wD = 0; wD < `+h+`; + wD += `+l+`) { + float dyD = float(dyDCorner + wD) / `+a+`.0; + + if (dyD < 0.0 || dyD >= `+t.outDepth+`.0 || fract(dyD) > 0.0) { + continue; + } + int idyD = int(dyD); + + for (int wR = 0; wR < `+d+`; + wR += `+u+`) { + float dyR = float(dyRCorner + wR) / `+s+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || + fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < `+p+`; + wC += `+c+`) { + float dyC = float(dyCCorner + wC) / `+o+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(batch, idyD, idyR, idyC, ch); + + dotProd += dyValue * avgMultiplier; + } + } + } + setOutput(dotProd); + } + `}return n}();var j0={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},$0=function(){function n(t,e,i){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.backend_util.assertAndGetBroadcastShape(e,i),this.userCode=` + float binaryOpComplex( + float areal, float aimag, float breal, float bimag) { + `+t+` + } + + void main() { + float areal = getARealAtOutCoords(); + float aimag = getAImagAtOutCoords(); + float breal = getBRealAtOutCoords(); + float bimag = getBImagAtOutCoords(); + setOutput(binaryOpComplex(areal, aimag, breal, bimag)); + } + `}return n}();var X0=` + if (isnan(a)) return a; + if (isnan(b)) return b; +`,Wd="return a + b;",Ud="return a - b;",J0="return a * b;",SM=` + float s = sign(a) * sign(b); + int ia = round(a); + int ib = round(b); + if (ib != 0) { + // Windows (D3D) wants guaranteed non-zero int division at compile-time. + return float(idiv(ia, ib, s)); + } else { + return NAN; + } +`,LM=` +if(a < 0.0 && floor(b) < b){ + return NAN; +} +if (b == 0.0) { + return 1.0; +} +return (round(mod(b, 2.0)) != 1) ? + pow(abs(a), b) : sign(a) * pow(abs(a), b); +`,IM="return float(a == b);",AM="return float(a != b);",TM="return float(a < b);",NM="return float(a <= b);",xM="return float(a > b);",CM="return float(a >= b);",RM="return float(a >= 1.0 && b >= 1.0);",OM="return float(a >= 1.0 || b >= 1.0);",EM=X0+` + return max(a, b); +`,DM=X0+` + return min(a, b); +`,kM=`if (b == 0.0) return NAN; + return mod(a, b);`,FM="return (b >= 1.0) ? a : a * (b + 1.0);",Z0="return (a < 0.) ? b * a : a;",bt=function(){function n(t,e,i){this.variableNames=["A","B"],this.outputShape=N.backend_util.assertAndGetBroadcastShape(e,i),this.userCode=` + float binaryOperation(float a, float b) { + `+t+` + } + + void main() { + float a = getAAtOutCoords(); + float b = getBAtOutCoords(); + setOutput(binaryOperation(a, b)); + } + `}return n}();var Mo=` + result.r = isNaN.r > 0. ? NAN : result.r; + result.g = isNaN.g > 0. ? NAN : result.g; + result.b = isNaN.b > 0. ? NAN : result.b; + result.a = isNaN.a > 0. ? NAN : result.a; +`,WM=` + ivec4 ia = round(a); + ivec4 ib = round(b); + bvec4 cond = notEqual(ib, ivec4(0)); + ivec4 result = ivec4(0); + vec4 s = sign(a) * sign(b); + + // Windows (D3D) wants guaranteed non-zero int division at compile-time. + if (cond[0]) { + result[0] = idiv(ia[0], ib[0], s[0]); + } + if (cond[1]) { + result[1] = idiv(ia[1], ib[1], s[1]); + } + if (cond[2]) { + result[2] = idiv(ia[2], ib[2], s[2]); + } + if (cond[3]) { + result[3] = idiv(ia[3], ib[3], s[3]); + } + return vec4(result); +`,UM=` + // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. + vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); + vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); + vec4 result = multiplier * pow(abs(a), b); + + // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS + bvec4 isExpZero = equal(b, vec4(0.0)); + result.r = isExpZero.r ? 1.0 : result.r; + result.g = isExpZero.g ? 1.0 : result.g; + result.b = isExpZero.b ? 1.0 : result.b; + result.a = isExpZero.a ? 1.0 : result.a; + + vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); + `+Mo+` + return result; +`,Q0=` + vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); + return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); +`,BM=` + vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); + return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); +`,zM=` + return vec4(equal(a, b)); +`,PM=` + return vec4(notEqual(a, b)); +`,_M=` + return vec4(lessThan(a, b)); +`,MM=` + return vec4(lessThanEqual(a, b)); +`,HM=` + return vec4(greaterThan(a, b)); +`,VM=` + return vec4(greaterThanEqual(a, b)); +`,qM=` + return vec4( + vec4(greaterThanEqual(a, vec4(1.0))) * + vec4(greaterThanEqual(b, vec4(1.0)))); +`,GM=` + return min( + vec4(greaterThanEqual(a, vec4(1.0))) + + vec4(greaterThanEqual(b, vec4(1.0))), + vec4(1.0)); +`,YM=` + vec4 result = vec4(max(a, b)); + vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); + `+Mo+` + return result; +`,KM=` + vec4 result = vec4(min(a, b)); + vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); + `+Mo+` + return result; +`,jM=` + vec4 result = mod(a, b); + vec4 isNaN = vec4(equal(b, vec4(0.0))); + `+Mo+` + return result; +`,Li=function(){function n(t,e,i,r){r===void 0&&(r=!1),this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.backend_util.assertAndGetBroadcastShape(e,i);var a=this.outputShape.length,s="";if(r)if(a===0||N.util.sizeFromShape(this.outputShape)===1)s=` + result.y = 0.; + result.z = 0.; + result.w = 0.; + `;else{var o=Je(a);if(s=` + `+o+` coords = getOutputCoords(); + `,a===1)s+=` + result.y = (coords + 1) >= `+this.outputShape[0]+` ? 0. : result.y; + result.z = 0.; + result.w = 0.; + `;else{var l=jt("coords",a);s+=` + bool nextRowOutOfBounds = + (`+l[a-2]+" + 1) >= "+this.outputShape[a-2]+`; + bool nextColOutOfBounds = + (`+l[a-1]+" + 1) >= "+this.outputShape[a-1]+`; + result.y = nextColOutOfBounds ? 0. : result.y; + result.z = nextRowOutOfBounds ? 0. : result.z; + result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; + `}}this.userCode=` + vec4 binaryOperation(vec4 a, vec4 b) { + `+t+` + } + + void main() { + vec4 a = getAAtOutCoords(); + vec4 b = getBAtOutCoords(); + + vec4 result = binaryOperation(a, b); + `+s+` + + setOutput(result); + } + `}return n}();var $M=function(){function n(t){this.variableNames=["A"],this.outputShape=t,this.userCode=` + uniform float minVal; + uniform float maxVal; + + void main() { + float value = getAAtOutCoords(); + if (isnan(value)) { + setOutput(value); + return; + } + + setOutput(clamp(value, minVal, maxVal)); + } + `}return n.prototype.getCustomSetupFunc=function(t,e){var i=this;return function(r,a){i.minLoc==null&&(i.minLoc=r.getUniformLocationNoThrow(a,"minVal"),i.maxLoc=r.getUniformLocationNoThrow(a,"maxVal")),r.gl.uniform1f(i.minLoc,t),r.gl.uniform1f(i.maxLoc,e)}},n}();var XM=function(){function n(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=` + uniform float minVal; + uniform float maxVal; + + void main() { + vec4 value = getAAtOutCoords(); + + if (any(isnan(value))) { + setOutput(value); + return; + } + + setOutput(clamp(value, vec4(minVal), vec4(maxVal))); + } + `}return n.prototype.getCustomSetupFunc=function(t,e){var i=this;return function(r,a){i.minLoc==null&&(i.minLoc=r.getUniformLocationNoThrow(a,"minVal"),i.maxLoc=r.getUniformLocationNoThrow(a,"maxVal")),r.gl.uniform1f(i.minLoc,t),r.gl.uniform1f(i.maxLoc,e)}},n}();var JM=function(){function n(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` + void main() { + float re = abs(getRealAtOutCoords()); + float im = abs(getImagAtOutCoords()); + float mx = max(re, im); + + // sadly the length function in glsl is not underflow-safe + // (at least not on Intel GPUs). So the safe solution is + // to ensure underflow-safety in all cases. + setOutput( + mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) + ); + } + `}return n}();var ZM=function(){function n(t){this.outputShape=[],this.outputShape=N.backend_util.computeOutShape(t,1),this.variableNames=t.map(function(l,u){return"T"+u});var e=new Array(t.length-1);e[0]=t[0][1];for(var i=1;i= "+l[u-1]+`) { + return getChannel( + getT`+u+"("+Ho(o,c,f)+`), + vec2(`+Ho(h,c,f)+`)); + }`}var m=l.length,g=l[l.length-1];p+=` + return getChannel( + getT`+m+"("+Ho(o,c,g)+`), + vec2(`+Ho(h,c,g)+"));",this.userCode=` + float getValue(`+o.map(function(v){return"int "+v})+`) { + `+p+` + } + + void main() { + `+a+` coords = getOutputCoords(); + vec4 result = vec4(getValue(`+s+`), 0., 0., 0.); + + `+s[r-1]+" = "+s[r-1]+` + 1; + if (`+s[r-1]+" < "+i[r-1]+`) { + result.g = getValue(`+s+`); + } + + `+s[r-2]+" = "+s[r-2]+` + 1; + if (`+s[r-2]+" < "+i[r-2]+`) { + result.a = getValue(`+s+`); + } + + `+s[r-1]+" = "+s[r-1]+` - 1; + if (`+s[r-2]+" < "+i[r-2]+` && + `+s[r-1]+" < "+i[r-1]+`) { + result.b = getValue(`+s+`); + } + setOutput(result); + } + `}return n}();function Ho(n,t,e){var i=n.indexOf(t),r=n.map(function(a,s){return s===i?a+" - "+e:a});return r.join()}var eH=function(){function n(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,i=t.strideWidth,r=t.padInfo.top,a=t.padInfo.left,s=t.dataFormat==="channelsLast";this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int wR = coords.x; + int wC = coords.y; + int d1 = coords.z; + int d2 = coords.w; + + // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + + for (int b = 0; b < `+t.batchSize+`; b++) { + for (int yR = 0; yR < `+t.outHeight+`; yR++) { + int xR = wR + yR * `+e+" - "+r+`; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int yC = 0; yC < `+t.outWidth+`; yC++) { + int xC = wC + yC * `+i+" - "+a+`; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + if (`+s+`) { + float dyValue = getDy(b, yR, yC, d2); + float xValue = getX(b, xR, xC, d1); + dotProd += (xValue * dyValue); + } else { + float dyValue = getDy(b, d2, yR, yC); + float xValue = getX(b, d1, xR, xC); + dotProd += (xValue * dyValue); + } + + } + } + } + setOutput(dotProd); + } + `}return n}(),tH=function(){function n(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,i=t.filterWidth,r=t.strideHeight,a=t.strideWidth,s=t.dataFormat==="channelsLast",o=e-1-t.padInfo.top,l=i-1-t.padInfo.left,u=s?1:2,c=s?2:3,h=s?3:1;this.userCode=` + const ivec2 pads = ivec2(`+o+", "+l+`); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d1 = coords[`+h+`]; + + ivec2 dyCorner = ivec2(coords[`+u+"], coords["+c+`]) - pads; + int dyRCorner = dyCorner.x; + int dyCCorner = dyCorner.y; + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < `+e+`; wR++) { + float dyR = float(dyRCorner + wR) / `+r+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + int wRPerm = `+e+` - 1 - wR; + + for (int wC = 0; wC < `+i+`; wC++) { + float dyC = float(dyCCorner + wC) / `+a+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + int wCPerm = `+i+` - 1 - wC; + + for (int d2 = 0; d2 < `+t.outChannels+`; d2++) { + + if (`+s+`) { + float xValue = getDy(batch, idyR, idyC, d2); + float wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd += xValue * wValue; + } else { + float xValue = getDy(batch, d2, idyR, idyC); + float wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd += xValue * wValue; + } + + } + } + } + setOutput(dotProd); + } + `}return n}(),nH=function(){function n(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,i=t.strideHeight,r=t.strideWidth,a=t.padInfo.front,s=t.padInfo.top,o=t.padInfo.left;this.userCode=` + void main() { + ivec5 coords = getOutputCoords(); + int wF = coords.x; + int wR = coords.y; + int wC = coords.z; + int d1 = coords.w; + int d2 = coords.u; + + float dotProd = 0.0; + + for (int b = 0; b < `+t.batchSize+`; b++) { + for (int yF = 0; yF < `+t.outDepth+`; yF++) { + int xF = wF + yF * `+e+" - "+a+`; + + if (xF < 0 || xF >= `+t.inDepth+`) { + continue; + } + + for (int yR = 0; yR < `+t.outHeight+`; yR++) { + int xR = wR + yR * `+i+" - "+s+`; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int yC = 0; yC < `+t.outWidth+`; yC++) { + int xC = wC + yC * `+r+" - "+o+`; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + float dyValue = getDy(b, yF, yR, yC, d2); + float xValue = getX(b, xF, xR, xC, d1); + dotProd += (xValue * dyValue); + } + } + } + } + setOutput(dotProd); + } + `}return n}(),iH=function(){function n(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,i=t.filterHeight,r=t.filterWidth,a=t.strideDepth,s=t.strideHeight,o=t.strideWidth,l=e-1-t.padInfo.front,u=i-1-t.padInfo.top,c=r-1-t.padInfo.left;this.userCode=` + const ivec3 pads = ivec3(`+l+", "+u+", "+c+`); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int d1 = coords.u; + + + ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; + int dyFCorner = dyCorner.x; + int dyRCorner = dyCorner.y; + int dyCCorner = dyCorner.z; + + float dotProd = 0.0; + for (int wF = 0; wF < `+e+`; wF++) { + float dyF = float(dyFCorner + wF) / `+a+`.0; + + if (dyF < 0.0 || dyF >= `+t.outDepth+`.0 || fract(dyF) > 0.0) { + continue; + } + int idyF = int(dyF); + + int wFPerm = `+e+` - 1 - wF; + + for (int wR = 0; wR < `+i+`; wR++) { + float dyR = float(dyRCorner + wR) / `+s+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || + fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + int wRPerm = `+i+` - 1 - wR; + + for (int wC = 0; wC < `+r+`; wC++) { + float dyC = float(dyCCorner + wC) / `+o+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + int wCPerm = `+r+` - 1 - wC; + + for (int d2 = 0; d2 < `+t.outChannels+`; d2++) { + float xValue = getDy(batch, idyF, idyR, idyC, d2); + float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); + dotProd += xValue * wValue; + } + } + } + } + setOutput(dotProd); + } + `}return n}();var rH=function(){function n(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,i=t.strideWidth,r=t.padInfo.top,a=t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int wR = coords.x; + int wC = coords.y; + int d1 = coords.z; + int dm = coords.w; + int d2 = d1 * `+s+` + dm; + + float dotProd = 0.0; + + // TO DO: Vec4 over the batch size + for (int b = 0; b < `+t.batchSize+`; b++) { + for (int yR = 0; yR < `+t.outHeight+`; yR++) { + int xR = wR + yR * `+e+" - "+r+`; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int yC = 0; yC < `+t.outWidth+`; yC++) { + int xC = wC + yC * `+i+" - "+a+`; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + float dyValue = getDy(b, yR, yC, d2); + float xValue = getX(b, xR, xC, d1); + dotProd += (xValue * dyValue); + } + } + } + setOutput(dotProd); + } + `}return n}(),aH=function(){function n(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,i=t.filterWidth,r=t.strideHeight,a=t.strideWidth,s=e-1-t.padInfo.top,o=i-1-t.padInfo.left,l=t.outChannels/t.inChannels;this.userCode=` + const ivec2 pads = ivec2(`+s+", "+o+`); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d1 = coords[3]; + ivec2 dyCorner = coords.yz - pads; + int dyRCorner = dyCorner.x; + int dyCCorner = dyCorner.y; + + float dotProd = 0.0; + + for (int wR = 0; wR < `+e+`; wR++) { + float dyR = float(dyRCorner + wR) / `+r+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + int wRPerm = `+e+` - 1 - wR; + + for (int wC = 0; wC < `+i+`; wC++) { + float dyC = float(dyCCorner + wC) / `+a+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + int wCPerm = `+i+` - 1 - wC; + + // TO DO: Vec4 over the channelMul + for (int dm = 0; dm < `+l+`; dm++) { + int d2 = d1 * `+l+` + dm; + float xValue = getDy(batch, idyR, idyC, d2); + float wValue = getW(wRPerm, wCPerm, d1, dm); + dotProd += xValue * wValue; + } + } + } + setOutput(dotProd); + } + `}return n}();var eS=function(){function n(t,e,i,r){e===void 0&&(e=!1),i===void 0&&(i=null),r===void 0&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var a=t.padInfo.top,s=t.padInfo.left,o=t.strideHeight,l=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,h=t.filterHeight,d=t.filterWidth,p=Math.floor(t.inChannels/4)*4,f=t.inChannels%4,m=t.dataFormat==="channelsLast",g=m?1:2,v=m?2:3,b=m?3:1,w="",S="";i&&(r?w=`float activation(float a) { + float b = getPreluActivationWeightsAtOutCoords(); + `+i+` + }`:w=` + float activation(float x) { + `+i+` + } + `,S="result = activation(result);");var L=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode=` + `+w+` + + const ivec2 strides = ivec2(`+o+", "+l+`); + const ivec2 pads = ivec2(`+a+", "+s+`); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d2 = coords[`+b+`]; + + ivec2 xRCCorner = + ivec2(coords[`+g+"], coords["+v+`]) * strides - pads; + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < `+h+`; wR++) { + int xR = xRCorner + wR * `+u+`; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int wC = 0; wC < `+d+`; wC++) { + int xC = xCCorner + wC * `+c+`; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + for (int d1 = 0; d1 < `+p+`; d1 += 4) { + vec4 wValues = vec4( + getW(wR, wC, d1, d2), + getW(wR, wC, d1 + 1, d2), + getW(wR, wC, d1 + 2, d2), + getW(wR, wC, d1 + 3, d2) + ); + + if (`+m+`) { + vec4 xValues = vec4( + getX(batch, xR, xC, d1), + getX(batch, xR, xC, d1 + 1), + getX(batch, xR, xC, d1 + 2), + getX(batch, xR, xC, d1 + 3) + ); + dotProd += dot(xValues, wValues); + } else { + vec4 xValues = vec4( + getX(batch, d1, xR, xC), + getX(batch, d1 + 1, xR, xC), + getX(batch, d1 + 2, xR, xC), + getX(batch, d1 + 3, xR, xC) + ); + dotProd += dot(xValues, wValues); + } + } + + if (`+(f===1)+`) { + + if (`+m+`) { + dotProd += + getX(batch, xR, xC, `+p+`) * + getW(wR, wC, `+p+`, d2); + } else { + dotProd += + getX(batch, `+p+`, xR, xC) * + getW(wR, wC, `+p+`, d2); + } + + } else if (`+(f===2)+`) { + vec2 wValues = vec2( + getW(wR, wC, `+p+`, d2), + getW(wR, wC, `+p+` + 1, d2) + ); + + if (`+m+`) { + vec2 xValues = vec2( + getX(batch, xR, xC, `+p+`), + getX(batch, xR, xC, `+p+` + 1) + ); + dotProd += dot(xValues, wValues); + } else { + vec2 xValues = vec2( + getX(batch, `+p+`, xR, xC), + getX(batch, `+p+` + 1, xR, xC) + ); + dotProd += dot(xValues, wValues); + } + + } else if (`+(f===3)+`) { + vec3 wValues = vec3( + getW(wR, wC, `+p+`, d2), + getW(wR, wC, `+p+` + 1, d2), + getW(wR, wC, `+p+` + 2, d2) + ); + + if (`+m+`) { + vec3 xValues = vec3( + getX(batch, xR, xC, `+p+`), + getX(batch, xR, xC, `+p+` + 1), + getX(batch, xR, xC, `+p+` + 2) + ); + dotProd += dot(xValues, wValues); + } else { + vec3 xValues = vec3( + getX(batch, `+p+`, xR, xC), + getX(batch, `+p+` + 1, xR, xC), + getX(batch, `+p+` + 2, xR, xC) + ); + dotProd += dot(xValues, wValues); + } + + } + } + } + + float result = dotProd; + `+L+` + `+S+` + setOutput(result); + } + `}return n}(),sH=function(){function n(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,i=t.padInfo.top,r=t.padInfo.left,a=t.strideDepth,s=t.strideHeight,o=t.strideWidth,l=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,h=t.filterDepth,d=t.filterHeight,p=t.filterWidth,f=Math.floor(t.inChannels/4)*4,m=t.inChannels%4;this.userCode=` + const ivec3 strides = ivec3(`+a+", "+s+", "+o+`); + const ivec3 pads = ivec3(`+e+", "+i+", "+r+`); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int d2 = coords.u; + + ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; + int xFCorner = xFRCCorner.x; + int xRCorner = xFRCCorner.y; + int xCCorner = xFRCCorner.z; + + // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get + // y(yF, yR, yC, d2). ? = to be determined. : = across all + // values in that axis. + float dotProd = 0.0; + for (int wF = 0; wF < `+h+`; wF++) { + int xF = xFCorner + wF * `+l+`; + + if (xF < 0 || xF >= `+t.inDepth+`) { + continue; + } + + for (int wR = 0; wR < `+d+`; wR++) { + int xR = xRCorner + wR * `+u+`; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int wC = 0; wC < `+p+`; wC++) { + int xC = xCCorner + wC * `+c+`; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + for (int d1 = 0; d1 < `+f+`; d1 += 4) { + vec4 xValues = vec4( + getX(batch, xF, xR, xC, d1), + getX(batch, xF, xR, xC, d1 + 1), + getX(batch, xF, xR, xC, d1 + 2), + getX(batch, xF, xR, xC, d1 + 3) + ); + vec4 wValues = vec4( + getW(wF, wR, wC, d1, d2), + getW(wF, wR, wC, d1 + 1, d2), + getW(wF, wR, wC, d1 + 2, d2), + getW(wF, wR, wC, d1 + 3, d2) + ); + + dotProd += dot(xValues, wValues); + } + + if (`+(m===1)+`) { + dotProd += + getX(batch, xF, xR, xC, `+f+`) * + getW(wF, wR, wC, `+f+`, d2); + } else if (`+(m===2)+`) { + vec2 xValues = vec2( + getX(batch, xF, xR, xC, `+f+`), + getX(batch, xF, xR, xC, `+f+` + 1) + ); + vec2 wValues = vec2( + getW(wF, wR, wC, `+f+`, d2), + getW(wF, wR, wC, `+f+` + 1, d2) + ); + dotProd += dot(xValues, wValues); + } else if (`+(m===3)+`) { + vec3 xValues = vec3( + getX(batch, xF, xR, xC, `+f+`), + getX(batch, xF, xR, xC, `+f+` + 1), + getX(batch, xF, xR, xC, `+f+` + 2) + ); + vec3 wValues = vec3( + getW(wF, wR, wC, `+f+`, d2), + getW(wF, wR, wC, `+f+` + 1, d2), + getW(wF, wR, wC, `+f+` + 2, d2) + ); + dotProd += dot(xValues, wValues); + } + } + } + } + setOutput(dotProd); + } + `}return n}();var tS=function(){function n(t,e,i,r){e===void 0&&(e=!1),i===void 0&&(i=null),r===void 0&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var a=t.inHeight,s=t.inWidth,o=t.padInfo.top,l=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,h=t.dilationHeight,d=t.dilationWidth,p=t.filterHeight,f=t.filterWidth,m=t.outChannels/t.inChannels,g="",v="";i&&(r?g=`float activation(float a) { + float b = getPreluActivationWeightsAtOutCoords(); + `+i+` + }`:g=` + float activation(float x) { + `+i+` + } + `,v="result = activation(result);");var b=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode=` + `+g+` + + const ivec2 strides = ivec2(`+u+", "+c+`); + const ivec2 pads = ivec2(`+o+", "+l+`); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords.x; + ivec2 xRCCorner = coords.yz * strides - pads; + int d2 = coords.w; + int d1 = d2 / `+m+`; + int q = d2 - d1 * `+m+`; + + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. + for (int wR = 0; wR < `+p+`; wR++) { + int xR = xRCorner + wR * `+h+`; + + if (xR < 0 || xR >= `+a+`) { + continue; + } + + for (int wC = 0; wC < `+f+`; wC++) { + int xC = xCCorner + wC * `+d+`; + + if (xC < 0 || xC >= `+s+`) { + continue; + } + + float xVal = getX(batch, xR, xC, d1); + float wVal = getW(wR, wC, d1, q); + dotProd += xVal * wVal; + } + } + + float result = dotProd; + `+b+` + `+v+` + setOutput(result); + } + `}return n}();var nS=function(){function n(t,e,i,r){e===void 0&&(e=!1),i===void 0&&(i=null),r===void 0&&(r=!1),this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;for(var a=t.inHeight,s=t.inWidth,o=t.padInfo.top,l=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,h=t.dilationHeight,d=t.dilationWidth,p=t.filterHeight,f=t.filterWidth,m=f,g="int xR; int xC; int xCOffset;",v=0;v= 0 && xR < `+a+" && xCOffset >= 0 && xCOffset < "+s+`) { + xTexelR`+v+"C"+b+` = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if(xCOffset + 1 >= `+s+`) { + xTexelR`+v+"C"+b+`.zw = vec2(0.); + } + } else { + xTexelR`+v+"C"+b+` = vec4(0.); + } + + xCOffset = xC + 1 - 2; + if(xR >= 0 && xR < `+a+" && xCOffset >= 0 && xCOffset < "+s+`) { + vec4 previous = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if(xCOffset + 1 >= `+s+`) { + previous.zw = vec2(0.); + } + + xR`+v+"C"+b+" = vec4(previous.zw, xTexelR"+v+"C"+b+`.xy); + } else { + xR`+v+"C"+b+" = vec4(0, 0, xTexelR"+v+"C"+b+`.xy); + } + `:g+=` + if(xR >= 0 && xR < `+a+" && xC >= 0 && xC < "+s+`) { + xTexelR`+v+"C"+b+` = getX(batch, xR, xC, d1); + } else { + xTexelR`+v+"C"+b+` = vec4(0.); + } + + xR`+v+"C"+b+" = xTexelR"+v+"C"+b+`; + `,b+1= 0 && xR < `+a+` && + xCOffset >= 0 && xCOffset < `+s+`) { + xTexelR`+v+"C"+(b+2)+` = getX(batch, xR, xCOffset, d1); + } + `,d>1&&(g+=` + xCOffset -= 2; + if(xR >= 0 && xR < `+a+` && + xCOffset >= 0 && xCOffset < `+s+`) { + xTexelR`+v+"C"+b+` = getX(batch, xR, xCOffset, d1); + } else { + xTexelR`+v+"C"+b+` = vec4(0.); + } + `),g+=` + xR`+v+"C"+(b+1)+` = vec4( + xTexelR`+v+"C"+b+".zw, xTexelR"+v+"C"+(b+2)+`.xy); + `):g+=` + xCOffset = xC + `+S+`; + + if(xR >= 0 && xR < `+a+` && + xCOffset >= 0 && xCOffset < `+s+`) { + xTexelR`+v+"C"+(b+2)+` = getX(batch, xR, xCOffset, d1); + } + + xR`+v+"C"+(b+1)+" = xTexelR"+v+"C"+(b+2)+`; + `}}else b= 0 && xR < `+a+`) { + `,l%2===1?(g+=` + xCOffset = xC + 1 - `+c+`; + if(xCOffset >= 0 && xCOffset < `+s+`) { + xTexelR`+v+"C"+b+` = getX(batch, xR, xCOffset, d1); + } else { + xTexelR`+v+"C"+b+` = vec4(0.); + } + + if(xC + 1 >= 0 && xC + 1 < `+s+`) { + xTexelR`+v+"C"+(b+2)+` = getX(batch, xR, xC + 1, d1); + } else { + xTexelR`+v+"C"+(b+2)+` = vec4(0.); + } + + xR`+v+"C"+b+` = vec4( + xTexelR`+v+"C"+b+".zw, xTexelR"+v+"C"+(b+2)+`.zw); + `,b+1= 0 && xCOffset < `+s+`) { + final = getX(batch, xR, xCOffset, d1); + } + xR`+v+"C"+(b+1)+" = vec4(xTexelR"+v+"C"+(b+2)+`.xy, final.xy); + `)):(g+=` + if(xC >= 0 && xC < `+s+`) { + xTexelR`+v+"C"+b+` = getX(batch, xR, xC, d1); + } else { + xTexelR`+v+"C"+b+` = vec4(0.); + } + + xCOffset = xC + `+c+`; + if(xCOffset >= 0 && xCOffset < `+s+`) { + xTexelR`+v+"C"+(b+2)+` = getX(batch, xR, xCOffset, d1); + } else { + xTexelR`+v+"C"+(b+2)+` = vec4(0.); + } + + xR`+v+"C"+b+` = vec4( + xTexelR`+v+"C"+b+".xy, xTexelR"+v+"C"+(b+2)+`.xy); + `,b+11?[""+(o-1)/(h-1),"(y2-y1) * height_ratio","y1*"+m+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+m],b=v[0],w=v[1],S=v[2],L=d>1?[""+(l-1)/(d-1),"(x2-x1) * width_ratio","x1*"+g+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+g],x=L[0],C=L[1],R=L[2];this.userCode=` + const float height_ratio = float(`+b+`); + const float width_ratio = float(`+x+`); + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int y = coords[1]; + int x = coords[2]; + int d = coords[3]; + + // get box vals + float y1 = getBoxes(b,0); + float x1 = getBoxes(b,1); + float y2 = getBoxes(b,2); + float x2 = getBoxes(b,3); + + // get image in batch index + int bInd = round(getBoxInd(b)); + if(bInd < 0 || bInd >= `+s+`) { + return; + } + + float height_scale = `+w+`; + float width_scale = `+C+`; + + float in_y = `+S+`; + if( in_y < 0.0 || in_y > `+m+` ) { + setOutput(float(`+a+`)); + return; + } + float in_x = `+R+`; + if( in_x < 0.0 || in_x > `+g+` ) { + setOutput(float(`+a+`)); + return; + } + + vec2 sourceFracIndexCR = vec2(in_x,in_y); + if(`+p+` == 1) { + // Compute the four integer indices. + ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); + ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); + + float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); + float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); + float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); + float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); + + vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); + + float top = topLeft + (topRight - topLeft) * fracCR.x; + float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; + float newValue = top + (bottom - top) * fracCR.y; + setOutput(newValue); + } else { + // Compute the coordinators of nearest neighbor point. + ivec2 sourceNearestCR = ivec2(floor( + sourceFracIndexCR + vec2(0.5,0.5))); + float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); + setOutput(newValue); + } + } + `}return n}(),aS=function(){function n(t,e,i){this.variableNames=["x"],this.outputShape=t;var r=t.length,a=e?"0.0":"getX("+iS(r,"coords")+")",s=t[t.length-1],o="",l="";e?(o=i?"end != "+(s-1):"end != 0",l=i?"end + 1":"end - 1"):(o=i?"end + pow2 < "+s:"end >= pow2",l=i?"end + pow2":"end - pow2"),this.userCode=` + uniform float index; + void main() { + `+Je(r)+` coords = getOutputCoords(); + int end = `+rS(r,"coords")+`; + float val = `+a+`; + int pow2 = int(pow(2.0, index)); + if (`+o+`) { + int idx = `+l+`; + `+rS(r,"coords")+` = idx; + val += getX(`+iS(r,"coords")+`); + } + setOutput(val); + } + `}return n.prototype.getCustomSetupFunc=function(t){var e=this;return function(i,r){e.index==null&&(e.index=i.getUniformLocation(r,"index")),i.gl.uniform1f(e.index,t)}},n}();function iS(n,t){if(n===1)return""+t;if(n===2)return t+".x, "+t+".y";if(n===3)return t+".x, "+t+".y, "+t+".z";if(n===4)return t+".x, "+t+".y, "+t+".z, "+t+".w";throw Error("Cumulative sum for rank "+n+" is not yet supported")}function rS(n,t){if(n===1)return""+t;if(n===2)return t+".y";if(n===3)return t+".z";if(n===4)return t+".w";throw Error("Cumulative sum for rank "+n+" is not yet supported")}var lH=function(){function n(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Ja.DENSE;var e=Qa(t),i=kt();this.outputShape=t,this.userCode=` + ivec3 outCoordsFromFlatIndex(int index) { + `+or(["r","c","d"],t)+` + return ivec3(r, c, d); + } + + void main() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(`+e[0]+", "+e[1]+`)); + int index = 4 * (resTexRC.x * `+e[1]+` + resTexRC.y); + + vec4 result = vec4(0.); + + for (int i=0; i<4; i++) { + int flatIndex = index + i; + ivec3 rc = outCoordsFromFlatIndex(flatIndex); + result[i] = getA(rc.x, rc.y, rc.z); + } + + `+i.output+` = result; + } + `}return n}();var uH=function(){function n(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ja.DENSE;var e=Qa(t),i=kt();this.outputShape=t,this.userCode=` + ivec3 outCoordsFromFlatIndex(int index) { + `+or(["r","c","d"],t)+` + return ivec3(r, c, d); + } + + void main() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(`+e[0]+", "+e[1]+`)); + int index = 4 * (resTexRC.x * `+e[1]+` + resTexRC.y); + + vec4 result = vec4(0.); + + for (int i=0; i<4; i++) { + int flatIndex = index + i; + ivec3 rc = outCoordsFromFlatIndex(flatIndex); + result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z)); + } + + `+i.output+` = result; + } + `}return n}();var cH=function(){function n(t,e,i){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=i,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int h = `+this.getHeightCoordString()+`; + int w = `+this.getWidthCoordString()+`; + int d = `+this.getDepthCoordString()+`; + + int in_h = h / `+e+`; + int offset_h = imod(h, `+e+`); + int in_w = w / `+e+`; + int offset_w = imod(w, `+e+`); + int offset_d = (offset_h * `+e+` + offset_w) * + `+this.getOutputDepthSize()+`; + int in_d = d + offset_d; + + float result = `+this.getInputSamplingString()+`; + setOutput(result); + } + `}return n.prototype.getHeightCoordString=function(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"},n.prototype.getWidthCoordString=function(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"},n.prototype.getDepthCoordString=function(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"},n.prototype.getOutputDepthSize=function(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]},n.prototype.getInputSamplingString=function(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},n}();var hH=function(){function n(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; + setOutput(val); + } + `}return n}();var dH=function(){function n(t){this.variableNames=["A"],this.outTexUsage=en.DOWNLOAD;var e=kt();this.outputShape=t,this.userCode=` + `+q0+` + + void main() { + float x = getAAtOutCoords(); + `+e.output+` = encode_float(x); + } + `}return n}();var pH=function(){function n(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=en.DOWNLOAD;var e=kt();this.outputShape=t,this.userCode=` + `+q0+` + + void main() { + ivec3 coords = getOutputCoords(); + float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); + `+e.output+` = encode_float(x); + } + `}return n}();var fH=function(){function n(t,e,i){i===void 0&&(i=!1),this.variableNames=["A"];var r=kt(),a=e[0],s=e[1];this.outputShape=t;var o="result";i&&(o="floor(result * 255. + 0.5)"),this.userCode=` + `+Fd(t)+` + + void main() { + ivec3 coords = getOutputCoords(); + + int flatIndex = getFlatIndex(coords); + int offset = imod(flatIndex, 4); + + flatIndex = idiv(flatIndex, 4, 1.); + + int r = flatIndex / `+s+`; + int c = imod(flatIndex, `+s+`); + vec2 uv = (vec2(c, r) + halfCR) / vec2(`+s+".0, "+a+`.0); + vec4 values = `+r.texture2D+`(A, uv); + + float result; + + if(offset == 0) { + result = values[0]; + } else if(offset == 1) { + result = values[1]; + } else if(offset == 2) { + result = values[2]; + } else { + result = values[3]; + } + + `+r.output+" = vec4("+o+`, 0., 0., 0.); + } + `}return n}();var mH=function(){function n(t,e,i){i===void 0&&(i=!1),this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;var r=kt(),a=e[0],s=e[1];this.outputShape=t;var o="",l="result";i&&(l="floor(result * 255. + 0.5)");for(var u=0;u<=1;u++)for(var c=0;c<=1;c++){var h=u*2+c;o+=` + localCoords = coords; + if(localCoords[2] + `+c+" < "+t[2]+`) { + localCoords[2] += `+c+`; + if(localCoords[1] + `+u+" < "+t[1]+`) { + localCoords[1] += `+u+`; + + flatIndex = getFlatIndex(localCoords); + offset = imod(flatIndex, 4); + + flatIndex = idiv(flatIndex, 4, 1.); + + r = flatIndex / `+s+`; + c = imod(flatIndex, `+s+`); + uv = (vec2(c, r) + halfCR) / vec2(`+s+".0, "+a+`.0); + values = `+r.texture2D+`(A, uv); + + if(offset == 0) { + result[`+h+`] = values[0]; + } else if(offset == 1) { + result[`+h+`] = values[1]; + } else if(offset == 2) { + result[`+h+`] = values[2]; + } else { + result[`+h+`] = values[3]; + } + } + } + `}this.userCode=` + `+Fd(t)+` + + void main() { + ivec3 coords = getOutputCoords(); + + vec4 result = vec4(0.); + int flatIndex, r, c, offset; + ivec3 localCoords; + vec2 uv; + vec4 values; + + `+o+` + + `+r.output+" = "+l+`; + } + `}return n}();var sS={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"},oS=function(){function n(t,e,i){this.variableNames=["real","imag"];var r=e[1];this.outputShape=e;var a=i?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,s=i?r+".0":"1.0";this.userCode=` + const float exponentMultiplier = `+a+`; + + float unaryOpComplex(float real, float expR, float imag, float expI) { + `+t+` + } + + float mulMatDFT(int batch, int index) { + float indexRatio = float(index) / float(`+r+`); + float exponentMultiplierTimesIndexRatio = + exponentMultiplier * indexRatio; + + float result = 0.0; + + for (int i = 0; i < `+r+`; i++) { + // x = (-2|2 * PI / N) * index * i; + float x = exponentMultiplierTimesIndexRatio * float(i); + float expR = cos(x); + float expI = sin(x); + float real = getReal(batch, i); + float imag = getImag(batch, i); + + result += + unaryOpComplex(real, expR, imag, expI) / `+s+`; + } + + return result; + } + + void main() { + ivec2 coords = getOutputCoords(); + setOutput(mulMatDFT(coords[0], coords[1])); + } + `}return n}();var gH=function(){function n(t,e){this.outputShape=[],this.variableNames=["x"],this.outputShape=t,this.userCode=` + uniform float value; + void main() { + // Input can be obtained from uniform value. + setOutput(value); + } + `}return n.prototype.getCustomSetupFunc=function(t){var e=this;return function(i,r){e.valueLoc==null&&(e.valueLoc=i.getUniformLocationNoThrow(r,"value")),i.gl.uniform1f(e.valueLoc,t)}},n}();var yH=function(){function n(t,e,i){this.variableNames=["A","indices"];var r=t.slice();r[i]=e,this.outputShape=r,this.rank=r.length;var a=Je(this.rank),s=vH(t,i);this.userCode=` + void main() { + `+a+` resRC = getOutputCoords(); + setOutput(getA(`+s+`)); + } + `}return n}();function vH(n,t){var e=n.length;if(e>4)throw Error("Gather for rank "+e+" is not yet supported");if(e===1)return"int(getIndices(resRC))";for(var i=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=0;a1?"strides[j]":"strides";this.userCode=` + `+r+" strides = "+r+"("+this.strides+`); + void main() { + `+a+` coords = getOutputCoords(); + int flattenIndex = 0; + for (int j = 0; j < `+this.sliceDim+`; j++) { + int index = round(getIndices(coords[0], j)); + flattenIndex += index * `+s+`; + } + setOutput(getX(flattenIndex, coords[1])); + } + `}return n}();function lS(n){var t=kt(),e=t.version+` + precision highp float; + `+t.attribute+` vec3 clipSpacePos; + `+t.attribute+` vec2 uv; + `+t.varyingVs+` vec2 resultUV; + + void main() { + gl_Position = vec4(clipSpacePos, 1); + resultUV = uv; + }`;return w0(n,e)}function uS(n){var t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return A0(n,t)}function cS(n){var t=new Uint16Array([0,1,2,2,1,3]);return T0(n,t)}function is(n,t,e,i,r,a){x0(t,e);var s=N0(n),o=n.TEXTURE_2D;return ce(n,function(){return n.bindTexture(o,s)}),ce(n,function(){return n.texParameteri(o,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE)}),ce(n,function(){return n.texParameteri(o,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE)}),ce(n,function(){return n.texParameteri(o,n.TEXTURE_MIN_FILTER,n.NEAREST)}),ce(n,function(){return n.texParameteri(o,n.TEXTURE_MAG_FILTER,n.NEAREST)}),ce(n,function(){return n.texImage2D(o,0,i,t,e,0,r,a,null)}),ce(n,function(){return n.bindTexture(n.TEXTURE_2D,null)}),s}function Bd(n){return n.internalFormatFloat}function hS(n,t,e,i){var r=Za(t,e),a=r[0],s=r[1];return is(n,a,s,Bd(i),i.textureFormatFloat,n.FLOAT)}function zd(n){return n.internalFormatHalfFloat}function dS(n,t,e,i){var r=Za(t,e),a=r[0],s=r[1];return is(n,a,s,zd(i),i.textureFormatFloat,i.textureTypeHalfFloat)}function Pd(n){return n.downloadTextureFormat}function pS(n,t,e,i){var r=Za(t,e),a=r[0],s=r[1];return is(n,a,s,Pd(i),n.RGBA,n.UNSIGNED_BYTE)}function _d(n){return n.internalFormatPackedFloat}function fS(n,t,e,i){var r=$r(t,e),a=r[0],s=r[1];return is(n,a,s,_d(i),n.RGBA,n.FLOAT)}function Md(n){return n.internalFormatPackedHalfFloat}function mS(n,t,e,i){var r=$r(t,e),a=r[0],s=r[1];return is(n,a,s,Md(i),n.RGBA,i.textureTypeHalfFloat)}function gS(n,t,e){var i=0,r=3*4,a=3*4+2*4;ce(n,function(){return n.bindBuffer(n.ARRAY_BUFFER,e)});var s=Rd(n,t,"clipSpacePos",e,3,a,i);return s&&Rd(n,t,"uv",e,2,a,r)}function vS(n,t,e,i,r,a){ce(n,function(){return n.bindTexture(n.TEXTURE_2D,t)});var s,o,l;r instanceof Uint8Array?(s=new Uint8Array(e*i*4),o=n.UNSIGNED_BYTE,l=n.RGBA):(s=new Float32Array(e*i*4),o=n.FLOAT,l=a.internalFormatPackedFloat),s.set(r),ce(n,function(){return n.texImage2D(n.TEXTURE_2D,0,l,e,i,0,n.RGBA,o,s)}),ce(n,function(){return n.bindTexture(n.TEXTURE_2D,null)})}function yS(n,t,e){ce(n,function(){return n.bindTexture(n.TEXTURE_2D,t)}),e.data instanceof Uint8Array?ce(n,function(){return n.texImage2D(n.TEXTURE_2D,0,n.RGBA,e.width,e.height,0,n.RGBA,n.UNSIGNED_BYTE,e.data)}):ce(n,function(){return n.texImage2D(n.TEXTURE_2D,0,n.RGBA,n.RGBA,n.UNSIGNED_BYTE,e)}),ce(n,function(){return n.bindTexture(n.TEXTURE_2D,null)})}function bS(n,t,e,i){var r=n.createBuffer();ce(n,function(){return n.bindBuffer(n.PIXEL_PACK_BUFFER,r)});var a=4,s=4,o=a*s*t*e;return ce(n,function(){return n.bufferData(n.PIXEL_PACK_BUFFER,o,n.STREAM_READ)}),ce(n,function(){return n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,0)}),ce(n,function(){return n.bindBuffer(n.PIXEL_PACK_BUFFER,null)}),r}function wS(n,t,e){var i=n,r=new Float32Array(e);return i.bindBuffer(i.PIXEL_PACK_BUFFER,t),i.getBufferSubData(i.PIXEL_PACK_BUFFER,0,r),i.bindBuffer(i.PIXEL_PACK_BUFFER,null),r}function SS(n,t,e,i){var r=Za(t,e),a=r[0],s=r[1],o=4,l=new Uint8Array(GP(t*e,o));return ce(n,function(){return n.readPixels(0,0,a,s,i.downloadTextureFormat,n.UNSIGNED_BYTE,l)}),new Float32Array(l.buffer)}function LS(n,t,e,i,r,a,s,o){var l=n,u=new Float32Array(YP(a,s));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function IS(n,t,e){var i=new Float32Array(t*e*4);return ce(n,function(){return n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,i)}),i}var wH={__proto__:null,createVertexShader:lS,createVertexBuffer:uS,createIndexBuffer:cS,getInternalFormatForFloat32MatrixTexture:Bd,createFloat32MatrixTexture:hS,getInternalFormatForFloat16MatrixTexture:zd,createFloat16MatrixTexture:dS,getInternalFormatForUnsignedBytesMatrixTexture:Pd,createUnsignedBytesMatrixTexture:pS,getInternalFormatForPackedMatrixTexture:_d,createPackedMatrixTexture:fS,getInternalFormatForFloat16PackedMatrixTexture:Md,createFloat16PackedMatrixTexture:mS,bindVertexProgramAttributeStreams:gS,uploadDenseMatrixToTexture:vS,uploadPixelDataToTexture:yS,createBufferFromOutputTexture:bS,downloadFloat32MatrixFromBuffer:wS,downloadByteEncodedFloatMatrixFromOutputTexture:SS,downloadPackedMatrixFromBuffer:LS,downloadMatrixFromPackedOutputTexture:IS};var AS=function(){function n(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];var e=N.env().getNumber("WEBGL_VERSION");t!=null?(this.gl=t,v0(e,t)):this.gl=Wn(e);var i="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(N.env().getNumber("WEBGL_VERSION")===1){var a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=es(this.gl,a),tn(this.gl,s))this.textureHalfFloatExtension=es(this.gl,s);else if(N.env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(i),tn(this.gl,r))this.colorBufferHalfFloatExtension=es(this.gl,r);else if(N.env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(i="EXT_color_buffer_float",tn(this.gl,i))this.colorBufferFloatExtension=this.gl.getExtension(i);else if(tn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=uS(this.gl),this.indexBuffer=cS(this.gl),this.framebuffer=C0(this.gl),this.textureConfig=Cd(this.gl,this.textureHalfFloatExtension)}return Object.defineProperty(n.prototype,"debug",{get:function(){return N.env().getBool("DEBUG")},enumerable:!0,configurable:!0}),n.prototype.dispose=function(){var t=this;if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var e=this.gl;ce(e,function(){return e.finish()}),ce(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),ce(e,function(){return e.deleteFramebuffer(t.framebuffer)}),ce(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,null)}),ce(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)}),ce(e,function(){return e.deleteBuffer(t.indexBuffer)}),this.disposed=!0},n.prototype.createFloat32MatrixTexture=function(t,e){return this.throwIfDisposed(),hS(this.gl,t,e,this.textureConfig)},n.prototype.createFloat16MatrixTexture=function(t,e){return this.throwIfDisposed(),dS(this.gl,t,e,this.textureConfig)},n.prototype.createUnsignedBytesMatrixTexture=function(t,e){return this.throwIfDisposed(),pS(this.gl,t,e,this.textureConfig)},n.prototype.uploadPixelDataToTexture=function(t,e){this.throwIfDisposed(),yS(this.gl,t,e)},n.prototype.uploadDenseMatrixToTexture=function(t,e,i,r){this.throwIfDisposed(),vS(this.gl,t,e,i,r,this.textureConfig)},n.prototype.createFloat16PackedMatrixTexture=function(t,e){return this.throwIfDisposed(),mS(this.gl,t,e,this.textureConfig)},n.prototype.createPackedMatrixTexture=function(t,e){return this.throwIfDisposed(),fS(this.gl,t,e,this.textureConfig)},n.prototype.deleteMatrixTexture=function(t){var e=this;this.throwIfDisposed(),this.outputTexture===t&&(Od(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,function(){return e.gl.deleteTexture(t)})},n.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(t,e,i){var r=this;return this.downloadMatrixDriver(t,function(){return SS(r.gl,e,i,r.textureConfig)})},n.prototype.downloadPackedMatrixFromBuffer=function(t,e,i,r,a,s){return LS(this.gl,t,e,i,r,a,s,this.textureConfig)},n.prototype.downloadFloat32MatrixFromBuffer=function(t,e){return wS(this.gl,t,e)},n.prototype.createBufferFromTexture=function(t,e,i){this.bindTextureToFrameBuffer(t);var r=bS(this.gl,e,i,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},n.prototype.createAndWaitForFence=function(){var t=this.createFence(this.gl);return this.pollFence(t)},n.prototype.createFence=function(t){var e=this,i,r;if(N.env().getBool("WEBGL_FENCE_API_ENABLED")){var a=t,s=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),r=function(){var o=a.clientWaitSync(s,0,0);return o===a.ALREADY_SIGNALED||o===a.CONDITION_SATISFIED},i=s}else N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(i=this.beginQuery(),this.endQuery(),r=function(){return e.isQueryAvailable(i,N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):r=function(){return!0};return{query:i,isFencePassed:r}},n.prototype.downloadMatrixFromPackedTexture=function(t,e,i){var r=this;return this.downloadMatrixDriver(t,function(){return IS(r.gl,e,i)})},n.prototype.createProgram=function(t){this.throwIfDisposed();var e=this.gl,i=S0(e,t),r=lS(e),a=L0(e);return ce(e,function(){return e.attachShader(a,r)}),ce(e,function(){return e.attachShader(a,i)}),I0(e,a),this.debug&&Wo(e,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=gS(e,this.program,this.vertexBuffer)),a},n.prototype.deleteProgram=function(t){var e=this;this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&ce(this.gl,function(){return e.gl.deleteProgram(t)})},n.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&Wo(this.gl,this.program),ce(this.gl,function(){return e.gl.useProgram(t)})},n.prototype.getUniformLocation=function(t,e,i){return i===void 0&&(i=!0),this.throwIfDisposed(),i?E0(this.gl,t,e):D0(this.gl,t,e)},n.prototype.getAttributeLocation=function(t,e){var i=this;return this.throwIfDisposed(),ce(this.gl,function(){return i.gl.getAttribLocation(t,e)})},n.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},n.prototype.setInputMatrixTexture=function(t,e,i){this.throwIfDisposed(),this.throwIfNoProgram(),k0(this.gl,t,e,i)},n.prototype.setOutputMatrixTexture=function(t,e,i){this.setOutputMatrixTextureDriver(t,i,e)},n.prototype.setOutputPackedMatrixTexture=function(t,e,i){this.throwIfDisposed();var r=$r(e,i),a=r[0],s=r[1];this.setOutputMatrixTextureDriver(t,a,s)},n.prototype.setOutputMatrixWriteRegion=function(t,e,i,r){this.setOutputMatrixWriteRegionDriver(i,t,r,e)},n.prototype.setOutputPackedMatrixWriteRegion=function(t,e,i,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},n.prototype.debugValidate=function(){this.program!=null&&Wo(this.gl,this.program),ts(this.gl)},n.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.debug&&this.debugValidate(),ce(t,function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)})},n.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),ce(this.gl,function(){return t.gl.finish()})},n.prototype.getQueryTimerExtension=function(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=es(this.gl,N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},n.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},n.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},n.prototype.beginQuery=function(){if(N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),i=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,i),i}var r=this.getQueryTimerExtensionWebGL1(),a=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,a),a},n.prototype.endQuery=function(){if(N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){var t=this.gl,e=this.getQueryTimerExtensionWebGL2();t.endQuery(e.TIME_ELAPSED_EXT);return}var i=this.getQueryTimerExtensionWebGL1();i.endQueryEXT(i.TIME_ELAPSED_EXT)},n.prototype.waitForQueryAndGetTime=function(t){return ko(this,void 0,void 0,function(){var e=this;return Fo(this,function(i){switch(i.label){case 0:return[4,N.util.repeatedTry(function(){return e.disposed||e.isQueryAvailable(t,N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return i.sent(),[2,this.getQueryTime(t,N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},n.prototype.getQueryTime=function(t,e){if(e===0)return null;if(e===2){var i=this.gl,r=i.getQueryParameter(t,i.QUERY_RESULT);return r/1e6}else{var a=this.getQueryTimerExtensionWebGL1(),r=a.getQueryObjectEXT(t,a.QUERY_RESULT_EXT);return r/1e6}},n.prototype.isQueryAvailable=function(t,e){if(e===0)return!0;if(e===2){var i=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=i.getQueryParameter(t,i.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{var r=this.getQueryTimerExtensionWebGL1(),a=r.getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}},n.prototype.pollFence=function(t){var e=this;return new Promise(function(i){e.addItemToPoll(function(){return t.isFencePassed()},function(){return i()})})},n.prototype.pollItems=function(){for(var t=SH(this.itemsToPoll.map(function(r){return r.isDoneFn})),e=0;e<=t;++e){var i=this.itemsToPoll[e].resolveFn;i()}this.itemsToPoll=this.itemsToPoll.slice(t+1)},n.prototype.addItemToPoll=function(t,e){var i=this;if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;N.util.repeatedTry(function(){return i.pollItems(),i.itemsToPoll.length===0})},n.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),Uo(this.gl,t,this.framebuffer),this.debug&&ts(this.gl)},n.prototype.unbindTextureToFrameBuffer=function(){this.outputTexture!=null?(Uo(this.gl,this.outputTexture,this.framebuffer),this.debug&&ts(this.gl)):Od(this.gl,this.framebuffer)},n.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var i=e();return this.unbindTextureToFrameBuffer(),i},n.prototype.setOutputMatrixTextureDriver=function(t,e,i){this.throwIfDisposed();var r=this.gl;Uo(r,t,this.framebuffer),this.debug&&ts(r),this.outputTexture=t,ce(r,function(){return r.viewport(0,0,e,i)}),ce(r,function(){return r.scissor(0,0,e,i)})},n.prototype.setOutputMatrixWriteRegionDriver=function(t,e,i,r){var a=this;this.throwIfDisposed(),ce(this.gl,function(){return a.gl.scissor(t,e,i,r)})},n.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},n.prototype.throwIfNoProgram=function(){if(this.program==null)throw new Error("No GPU program is currently set.")},n}();function SH(n){for(var t=0;t0&&(b.flatOffset=g.texData.slice.flatOffset),{name:t.variableNames[v],shapeInfo:b}}),s=a.map(function(g){return g.shapeInfo}),o={logicalShape:i.shape,texShape:i.texData.texShape,isUniform:!1,isPacked:i.texData.isPacked,flatOffset:null},l=q_(a,o,r,t.packedInputs),u=n.createProgram(l),c=null,h=n.getUniformLocation(u,"NAN",!1);N.env().getNumber("WEBGL_VERSION")===1&&(c=n.getUniformLocation(u,"INFINITY",!1));for(var d={},p=0;p0,l=s.isUniform?"uniform":s.texData.texShape;i+=s.shape+"_"+l+"_"+o});var r=n.userCode,a=n.constructor.name;return a+="_"+i+"_"+r,a}var TH=function(){function n(t,e,i){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var r=i.filterWidth,a=i.inChannels,s=i.strideWidth,o=i.strideHeight,l=i.padInfo,u=i.outWidth,c=i.dilationWidth,h=i.dilationHeight,d=i.dataFormat,p=l.left,f=l.top,m=a*r,g=kt(),v=d==="channelsLast",b=v?0:1,w=v?1:2,S="",L=0;L<=1;L++)for(var x=0;x<=1;x++)S+=` + blockIndex = rc.y + `+x+`; + pos = rc.x + `+L+`; + + if(blockIndex < `+t[1]+" && pos < "+t[0]+`) { + offsetY = int(blockIndex / (`+u+")) * "+o+" - "+f+`; + d0 = offsetY + `+h+" * (pos / "+m+`); + + if(d0 < `+e[b]+` && d0 >= 0) { + + offsetX = int(mod(float(blockIndex), `+u+".) * "+s+". - "+p+`.); + d1 = offsetX + `+c+" * (int(mod(float(pos), "+m+".) / "+a+`.)); + + if(d1 < `+e[w]+` && d1 >= 0) { + + ch = int(mod(float(pos), `+a+`.)); + + if (`+v+`) { + innerDims = vec2(d1, ch); + result[`+(L*2+x)+`] = getChannel( + getA(d0, int(innerDims.x), + int(innerDims.y)), innerDims); + } else { + innerDims = vec2(d0, d1); + result[`+(L*2+x)+`] = getChannel( + getA(ch, int(innerDims.x), + int(innerDims.y)), innerDims); + } + } + } + } + `;this.userCode=` + void main() { + ivec2 rc = getOutputCoords(); + + vec4 result = vec4(0); + + int blockIndex, pos, offsetY, d0, offsetX, d1, ch; + vec2 innerDims; + + `+S+` + + `+g.output+` = result; + } + `}return n}();var NH=function(){function n(t,e,i,r,a){this.variableNames=["x"],this.outputShape=[];var s=e,o=t[3]-1;this.outputShape=t;var l,u="float("+i+") + float("+r+") * sum";a===.5?l="inversesqrt("+u+")":a===1?l="1.0/("+u+")":l="exp(log("+u+") * float(-"+a+"));",this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int r = coords[1]; + int c = coords[2]; + int d = coords[3]; + float x = getX(b, r, c, d); + float sum = 0.0; + for (int j = -`+s+"; j <= "+s+`; j++) { + int idx = d + j; + if (idx >= 0 && idx <= `+o+`) { + float z = getX(b, r, c, idx); + sum += z * z; + } + } + float val = x * `+l+`; + setOutput(val); + } + `}return n}();var xH=function(){function n(t,e,i,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=i,this.alpha=r,this.beta=a,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int r = coords[1]; + int c = coords[2]; + + float result = 0.0; + for (int d = 0; d < `+this.depth+`; ++d) { + int depthBegin = int(max(0.0, float(d - `+e+`))); + int depthEnd = int(min(float(`+this.depth+`), + float(d + `+e+` + 1))); + + const int MIN_DEPTH_BEGIN = 0; + const int MAX_DEPTH_END = `+this.depth+`; + + float norm = 0.0; + for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { + if (k < depthBegin){ + continue; + } + else if (k >= depthBegin && k < depthEnd) { + norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); + } + else { + break; + } + } + + norm = float(`+r+") * norm + float("+i+`); + + for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ + if (k < depthBegin){ + continue; + } + else if (k >= depthBegin && k < depthEnd){ + float dyi = -2.0 * float(`+r+`) + * float(`+a+`) + * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) + / norm; + if (k == d) { + dyi += pow(norm, -1.0 * `+a+`); + } + if (k == coords[3]) { + dyi *= getDy(b, r, c, d); + result += dyi; + } + } + else { + break; + } + } + } + setOutput(result); + } + `}return n}();var CH=function(){function n(t,e,i,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;var s=e,o=t[3]-1;this.outputShape=t;var l,u="float("+i+") + float("+r+") * sum";a===.5?l="inversesqrt("+u+")":a===1?l="1.0/("+u+")":l="exp(log("+u+") * float(-"+a+"));",this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords.x; + int r = coords.y; + int c = coords.z; + int d = coords.w; + + bool hasNextCol = d < `+this.outputShape[3]+`; + bool hasNextRow = c < `+this.outputShape[2]+`; + + vec4 sum = vec4(0.); + vec4 xFragAtOutputCoords = getX(b, r, c, d); + + vec4 xAtOutputCoords = vec4( + getChannel(xFragAtOutputCoords, vec2(c, d)), + hasNextCol ? + getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, + hasNextRow ? + getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, + (hasNextRow && hasNextCol) ? + getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 + ); + + int firstChannel = d - `+s+`; + vec2 cache = vec2(0.); + if(firstChannel >= 0){ + vec4 firstChannelFrag = getX(b, r, c, firstChannel); + cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); + if(hasNextRow){ + cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); + } + } + + ivec2 depth = ivec2(d, d + 1); + for (int j = - `+s+"; j <= "+s+`; j++) { + ivec2 idx = depth + j; + bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); + bvec2 belowUpperBound = lessThanEqual(idx, ivec2(`+o+`)); + + bool depthInRange = aboveLowerBound.x && belowUpperBound.x; + bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; + + if(depthInRange || depthPlusOneInRange){ + vec4 z = vec4(0.); + vec4 xFragAtCurrentDepth; + z.xz = cache.xy; + if(depthPlusOneInRange && hasNextCol){ + xFragAtCurrentDepth = idx.y != d ? + getX(b, r, c, idx.y) : xFragAtOutputCoords; + z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); + if(hasNextRow){ + z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); + } + } + cache.xy = z.yw; + sum += z * z; + } + } + vec4 result = xAtOutputCoords * `+l+`; + setOutput(result); + } + `}return n}();var RH=function(){function n(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,i=t.strideWidth,r=t.dilationHeight,a=t.effectiveFilterHeight,s=t.effectiveFilterWidth,o=a-1-t.padInfo.top,l=s-1-t.padInfo.left,u=a*s-1;this.userCode=` + const ivec2 pads = ivec2(`+o+", "+l+`); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + + ivec2 dyRCCorner = coords.yz - pads; + int dyRCorner = dyRCCorner.x; + int dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < `+a+`; + wR += `+r+`) { + float dyR = float(dyRCorner + wR) / `+e+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < `+s+`; wC++) { + float dyC = float(dyCCorner + wC) / `+i+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(b, idyR, idyC, d); + int maxPosValue = `+u+` - int(getMaxPos(b, idyR, idyC, d)); + + // Get the current value, check it against the value from the + // position matrix. + int curPosValue = wR * `+s+` + wC; + float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); + + dotProd += dyValue * mask; + } + } + setOutput(dotProd); + } + `}return n}(),OH=function(){function n(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideDepth,i=t.strideHeight,r=t.strideWidth,a=t.dilationDepth,s=t.dilationHeight,o=t.dilationWidth,l=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,h=l-1-t.padInfo.front,d=u-1-t.padInfo.top,p=c-1-t.padInfo.left,f=l*u*c-1;this.userCode=` + const ivec3 pads = ivec3(`+h+", "+d+", "+p+`); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; + int dyDCorner = dyCorner.x; + int dyRCorner = dyCorner.y; + int dyCCorner = dyCorner.z; + + // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get + // dx(xD, xR, xC, ch). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + + for (int wD = 0; wD < `+l+`; + wD += `+a+`) { + float dyD = float(dyDCorner + wD) / `+e+`.0; + + if (dyD < 0.0 || dyD >= `+t.outDepth+`.0 || fract(dyD) > 0.0) { + continue; + } + int idyD = int(dyD); + + for (int wR = 0; wR < `+u+`; + wR += `+s+`) { + float dyR = float(dyRCorner + wR) / `+i+`.0; + + if (dyR < 0.0 || dyR >= `+t.outHeight+`.0 || + fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < `+c+`; + wC += `+o+`) { + float dyC = float(dyCCorner + wC) / `+r+`.0; + + if (dyC < 0.0 || dyC >= `+t.outWidth+`.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(batch, idyD, idyR, idyC, ch); + int maxPosValue = `+f+` - + int(getMaxPos(batch, idyD, idyR, idyC, ch)); + + // Get the current value, check it against the value from the + // position matrix. + int curPosValue = + wD * `+u+" * "+c+` + + wR * `+c+` + wC; + float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); + + dotProd += dyValue * mask; + } + } + } + setOutput(dotProd); + } + `}return n}();var Hd=function(){function n(t,e,i,r,a,s,o){i===void 0&&(i=!1),r===void 0&&(r=!1),a===void 0&&(a=!1),s===void 0&&(s=null),o===void 0&&(o=!1),this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;var l=i?t[1]:t[2],u=Math.ceil(l/2),c=i?"i * 2, rc.y":"rc.y, i * 2",h=r?"rc.z, i * 2":"i * 2, rc.z",d=i?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],p=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",m="";s&&(o?f=`vec4 activation(vec4 a) { + vec4 b = getPreluActivationWeightsAtOutCoords(); + `+s+` + }`:f=`vec4 activation(vec4 x) { + `+s+` + }`,m="result = activation(result);");var g=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=` + `+f+` + + const float sharedDimension = `+u+`.0; + + vec4 dot2x2ARowBCol(ivec3 rc) { + vec4 result = vec4(0); + for (int i = 0; i < `+u+`; i++) { + vec4 a = getMatrixA(rc.x, `+c+`); + vec4 b = getMatrixB(rc.x, `+h+`); + + // These swizzled products need to be separately added. + // See: https://github.com/tensorflow/tfjs/issues/1735 + result += (`+d[0]+" * "+p[0]+`); + result += (`+d[1]+" * "+p[1]+`); + } + return result; + } + + void main() { + ivec3 rc = getOutputCoords(); + vec4 result = dot2x2ARowBCol(rc); + + `+g+` + + `+m+` + + setOutput(result); + } + `}return n}();var EH=function(){function n(t,e,i){this.variableNames=["probs"],this.outputShape=[t,i],this.userCode=` + uniform float seed; + + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + + float r = random(seed); + float cdf = 0.0; + + for (int i = 0; i < `+(e-1)+`; i++) { + cdf += getProbs(batch, i); + + if (r < cdf) { + setOutput(float(i)); + return; + } + } + + // If no other event happened, last event happened. + setOutput(float(`+(e-1)+`)); + } + `}return n.prototype.getCustomSetupFunc=function(t){var e=this;return function(i,r){e.seedLoc==null&&(e.seedLoc=i.getUniformLocation(r,"seed")),i.gl.uniform1f(e.seedLoc,t)}},n}();var DH=function(){function n(t,e,i,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + int index = round(getIndices(coords.x)); + setOutput(mix(float(`+r+"), float("+i+`), + float(index == coords.y))); + } + `}return n}();var UH=function(){function n(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;var e=t.length;if(e===0)this.userCode=` + void main() { + setOutput(vec4(getA(), 0., 0., 0.)); + } + `;else{var i=jt("rc",e),r=Je(e),a=kH(e,t,i),s=FH(e,t[t.length-1],t[t.length-2],i),o=WH(t,i);this.userCode=` + void main() { + `+r+` rc = getOutputCoords(); + + if(`+a+`) { + setOutput(vec4(0)); + } else { + `+s+` + + setOutput(vec4(`+o+`)); + } + } + `}}return n}();function BH(n,t){for(var e=[],i=0;i<=1;i++)for(var r=0;r<=1;r++){for(var a=(i===0?"r":"rp1")+", "+(r===0?"c":"cp1"),s=2;s "+t[0];for(var i="",r=n-2;r= "+t[r],r= `+t+`; + bool rEdge = rp1 >= `+e+`; + `}function WH(n,t){var e=n.length,i=BH(e,t);return e===1?`getA(rc), + rc + 1 >= `+n[0]+` ? 0. : getA(rc + 1), + 0, 0`:"getA("+i[0]+`), + cEdge ? 0. : getA(`+i[1]+`), + rEdge ? 0. : getA(`+i[2]+`), + rEdge || cEdge ? 0. : getA(`+i[3]+")"}var zH=function(){function n(t,e,i){this.variableNames=["x"],this.outputShape=e.map(function(u,c){return u[0]+t[c]+u[1]});var r=t.length,a=Je(r),s=e.map(function(u){return u[0]}).join(","),o=e.map(function(u,c){return u[0]+t[c]}).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` + int start = `+s+`; + int end = `+o+`; + + void main() { + int outC = getOutputCoords(); + if (outC < start || outC >= end) { + setOutput(float(`+i+`)); + } else { + setOutput(getX(outC - start)); + } + } + `;return}this.userCode=` + `+a+" start = "+a+"("+s+`); + `+a+" end = "+a+"("+o+`); + + void main() { + `+a+` outC = getOutputCoords(); + if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { + setOutput(float(`+i+`)); + } else { + `+a+` coords = outC - start; + setOutput(getX(`+l+`)); + } + } + `}return n}();var PH=function(){function n(t,e,i){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map(function(v,b){return v[0]+t[b]+v[1]});for(var r=t.length,a=Je(r),s=e.map(function(v){return v[0]}).join(","),o=e.map(function(v,b){return v[0]+t[b]}).join(","),l=jt("rc",r),u=jt("source",r),c=l[r-1]+" < "+this.outputShape[r-1],h=r===1?"source":"vec2("+u.slice(-2).join()+")",d=[a+" rc = outputLoc;",l[r-1]+` += 1; + if(`+c+`) { + `,r===1?"":`} + rc = outputLoc; + `+l[r-2]+` += 1; + if(`+l[r-2]+" < "+this.outputShape[r-2]+") {",r===1?"":" "+l[r-1]+` += 1; + if(`+c+") {"],p=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",f="",m=0,g=r===1?2:4;m= `+t.inHeight+`) { + continue; + } + + for (int wC = 0; wC < `+d+`; + wC += `+c+`) { + int xC = xCCorner + wC; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + float value = getX(batch, xR, xC, d); + + // If a min / max value has already been found, use it. If not, + // use the current value. + float currMinMaxValue = mix( + value, minMaxValue, minMaxValueFound); + if (value `+w+` currMinMaxValue) { + minMaxValue = value; + minMaxValueFound = 1.0; + minMaxPosition = `+(r?a?g:v:"wR * "+d+" + wC")+`; + } + } + } + setOutput(float(minMaxPosition)); + } + `;return}var S="max",L=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";e==="avg"&&(L="avgValue / count");var x=Math.floor(s/4)*4,C=s%4,R=` + if (`+m+`) { + avgValue += dot(values, ones); + } else { + minMaxValue = `+S+`(values, minMaxValue); + } + `;this.userCode=` + const ivec2 strides = ivec2(`+o+", "+l+`); + const ivec2 pads = ivec2(`+p+", "+f+`); + const float initializationValue = `+b+`; + const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); + + float count = 0.0; + + float getValue(int batch, int xR, int xC, int d) { + if (xC < 0 || xC >= `+t.inWidth+`) { + return initializationValue; + } + count += 1.0; + return getX(batch, xR, xC, d); + } + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d = coords[3]; + + ivec2 xRCCorner = coords.yz * strides - pads; + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // max/min x(?, ?, d) to get y(yR, yC, d). + // ? = to be determined + vec4 minMaxValue = vec4(`+b+`); + float avgValue = 0.0; + count = 0.0; + + for (int wR = 0; wR < `+h+`; + wR += `+u+`) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int wC = 0; wC < `+x+`; wC += 4) { + int xC = xCCorner + wC * `+c+`; + + vec4 values = vec4( + getValue(batch, xR, xC, d), + getValue(batch, xR, xC + `+c+`, d), + getValue(batch, xR, xC + 2 * `+c+`, d), + getValue(batch, xR, xC + 3 * `+c+`, d) + ); + + `+R+` + } + + int xC = xCCorner + `+x+`; + if (`+(C===1)+`) { + vec4 values = vec4( + getValue(batch, xR, xC, d), + initializationValue, + initializationValue, + initializationValue + ); + + `+R+` + } else if (`+(C===2)+`) { + vec4 values = vec4( + getValue(batch, xR, xC, d), + getValue(batch, xR, xC + `+c+`, d), + initializationValue, + initializationValue + ); + + `+R+` + } else if (`+(C===3)+`) { + vec4 values = vec4( + getValue(batch, xR, xC, d), + getValue(batch, xR, xC + `+c+`, d), + getValue(batch, xR, xC + 2 * `+c+`, d), + initializationValue + ); + + `+R+` + } + } + setOutput(`+L+`); + } + `}return n}(),Vd=function(){function n(t,e,i,r,a){if(r===void 0&&(r=!1),a===void 0&&(a=!1),this.variableNames=["x"],e==="avg"&&i)throw new Error("Cannot compute positions for average pool.");var s=t.filterWidth,o=t.strideDepth,l=t.strideHeight,u=t.strideWidth,c=t.dilationDepth,h=t.dilationHeight,d=t.dilationWidth,p=t.effectiveFilterDepth,f=t.effectiveFilterHeight,m=t.effectiveFilterWidth,g=t.padInfo.front,v=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;var w=e==="avg",S="0.0";if(w||(S="-1.0 / 1e-20"),i){var L=">=";this.userCode=` + const ivec3 strides = + ivec3(`+o+", "+l+", "+u+`); + const ivec3 pads = ivec3(`+g+", "+v+", "+b+`); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; + int xDCorner = xCorner.x; + int xRCorner = xCorner.y; + int xCCorner = xCorner.z; + + // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). + // ? = to be determined + float minMaxValue = 0.0; + float minMaxValueFound = 0.0; + int minMaxPosition = 0; + + for (int wD = 0; wD < `+p+`; + wD += `+c+`) { + int xD = xDCorner + wD; + + if (xD < 0 || xD >= `+t.inDepth+`) { + continue; + } + + for (int wR = 0; wR < `+f+`; + wR += `+h+`) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int wC = 0; wC < `+m+`; + wC += `+d+`) { + int xC = xCCorner + wC; + + if (xC < 0 || xC >= `+t.inWidth+`) { + continue; + } + + float value = getX(batch, xD, xR, xC, ch); + + // If a min / max value has already been found, use it. If not, + // use the current value. + float currMinMaxValue = mix( + value, minMaxValue, minMaxValueFound); + if (value `+L+` currMinMaxValue) { + minMaxValue = value; + minMaxValueFound = 1.0; + minMaxPosition = `+(r?a?"(((batch * "+t.inDepth+" + xD) * "+t.inHeight+" + xR) * "+t.inWidth+" + xC) * "+t.inChannels+" + ch":"((xD * "+t.inHeight+" + xR) * "+t.inWidth+" + xC) * "+t.inChannels+" + ch":"wD * "+f+" * "+m+` + + wR * `+m+" + wC")+`; + } + } + } + } + setOutput(float(minMaxPosition)); + } + `;return}var x="max",C=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";e==="avg"&&(C="avgValue / count");var R=Math.floor(s/4)*4,D=s%4,k=` + if (`+w+`) { + avgValue += dot(values, ones); + } else { + minMaxValue = `+x+`(values, minMaxValue); + } + `;this.userCode=` + const ivec3 strides = + ivec3(`+o+", "+l+", "+u+`); + const ivec3 pads = ivec3(`+g+", "+v+", "+b+`); + const float initializationValue = `+S+`; + const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); + + float count = 0.0; + + float getValue(int batch, int xD, int xR, int xC, int ch) { + if (xC < 0 || xC >= `+t.inWidth+`) { + return initializationValue; + } + count += 1.0; + return getX(batch, xD, xR, xC, ch); + } + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; + int xDCorner = xCorner.x; + int xRCorner = xCorner.y; + int xCCorner = xCorner.z; + + // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). + // ? = to be determined + vec4 minMaxValue = vec4(`+S+`); + float avgValue = 0.0; + count = 0.0; + + for (int wD = 0; wD < `+p+`; + wD += `+c+`) { + int xD = xDCorner + wD; + + if (xD < 0 || xD >= `+t.inDepth+`) { + continue; + } + + for (int wR = 0; wR < `+f+`; + wR += `+h+`) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= `+t.inHeight+`) { + continue; + } + + for (int wC = 0; wC < `+R+`; wC += 4) { + int xC = xCCorner + wC * `+d+`; + + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + getValue(batch, xD, xR, xC + `+d+`, ch), + getValue(batch, xD, xR, xC + 2 * `+d+`, ch), + getValue(batch, xD, xR, xC + 3 * `+d+`, ch) + ); + + `+k+` + } + + int xC = xCCorner + `+R+`; + if (`+(D===1)+`) { + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + initializationValue, + initializationValue, + initializationValue + ); + + `+k+` + } else if (`+(D===2)+`) { + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + getValue(batch, xD, xR, xC + `+d+`, ch), + initializationValue, + initializationValue + ); + + `+k+` + } else if (`+(D===3)+`) { + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + getValue(batch, xD, xR, xC + `+d+`, ch), + getValue(batch, xD, xR, xC + 2 * `+d+`, ch), + initializationValue + ); + + `+k+` + } + } + setOutput(`+C+`); + } + } + `}return n}();var NS=function(){function n(t,e){this.variableNames=["x"];var i=t.windowSize,r=t.batchSize,a=t.inSize,s=t.outSize;this.outputShape=[r,s];var o="0.0",l="";e==="prod"?o="1.0":e==="min"?(o="1.0 / 1e-20",l="min"):e==="max"&&(o="-1.0 / 1e-20",l="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";e==="sum"?u="sumValue":e==="prod"?u="prodValue":e==="all"?u="allValue":e==="any"&&(u="anyValue");var c=Math.floor(i/4)*4,h=i%4,d=` + if (`+(e==="sum")+`) { + sumValue += dot(values, ones); + } else if (`+(e==="prod")+`) { + vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); + prodValue *= tmp[0] * tmp[1]; + } else { + minMaxValue = `+l+`(values, minMaxValue); + } + `,p="vec4";e==="all"?(o="1.0",d=` + bool reducedAllValue = all(values); + float floatedReducedAllValue = float(reducedAllValue); + allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); + `,p="bvec4"):e==="any"&&(o="0.0",d=` + bool reducedAnyValue = any(values); + float floatedReducedAnyValue = float(reducedAnyValue); + anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); + `,p="bvec4");var f="";a%i>0&&(f=` + if (inIdx < 0 || inIdx >= `+a+`) { + return initializationValue; + } + `),this.userCode=` + const float initializationValue = `+o+`; + const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); + + float getValue(int batch, int inIdx) { + `+f+` + return getX(batch, inIdx); + } + + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int outIdx = coords[1]; + int inOffset = outIdx * `+i+`; + + vec4 minMaxValue = vec4(`+o+`); + float prodValue = 1.0; + float sumValue = 0.0; + float allValue = 1.0; + float anyValue = 0.0; + + for (int i = 0; i < `+c+`; i += 4) { + int inIdx = inOffset + i; + `+p+" values = "+p+`( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + getValue(batch, inIdx + 3) + ); + + `+d+` + } + + int inIdx = inOffset + `+c+`; + if (`+(h===1)+`) { + `+p+" values = "+p+`( + getValue(batch, inIdx), + initializationValue, + initializationValue, + initializationValue + ); + + `+d+` + } else if (`+(h===2)+`) { + `+p+" values = "+p+`( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + initializationValue, + initializationValue + ); + + `+d+` + } else if (`+(h===3)+`) { + `+p+" values = "+p+`( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + initializationValue + ); + + `+d+` + } + setOutput(`+u+`); + } + `}return n}();var xS=function(){function n(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var i="",r=0;r<4;r++){var a="thisRC = rc;";r%2===1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),i+=` + `+a+` + `+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+` + int flatIndex = getFlatIndex(thisRC); + + ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); + vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); + + result[`+r+`] = + getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); + `+(r>0?"}":"")+` + `}this.userCode=` + `+_H(e)+` + `+Fd(t)+` + + void main() { + ivec3 rc = getOutputCoords(); + + vec4 result = vec4(0.); + + ivec3 thisRC; + int rows = `+t[1]+`; + int cols = `+t[2]+`; + + `+i+` + + setOutput(result); + } + `}return n}();function _H(n){var t=or(["r","c","d"],n);return` + ivec3 inputCoordsFromReshapedOutCoords(int index) { + `+t+` + return ivec3(r, c, d); + } + `}var MH=function(){function n(t,e,i){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,a=r[1],s=r[2],o=t.shape,l=o[1],u=o[2],c=[i&&l>1?a-1:a,i&&u>1?s-1:s],h=[i&&l>1?l-1:l,i&&u>1?u-1:u],d=c[0]/h[0],p=c[1]/h[1],f=1/d,m=1/p,g=Math.ceil(f)*2+2,v=Math.ceil(m)*2+2;this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + int r = coords[1]; + int c = coords[2]; + + float accumulator = 0.0; + + const float heightScale = float(`+d+`); + const float widthScale = float(`+p+`); + + const float invHeightScale = float(`+f+`); + const float invWidthScale = float(`+m+`); + + const int winHeight = int(`+g+`); + const int winWidth = int(`+v+`); + + // Compute bounds for where in dy we will look + float startRLerp = floor(float(r) * invHeightScale); + int startDyR = int(startRLerp - float(winHeight / 2)); + + float startCLerp = floor(float(c) * invWidthScale); + int startDyC = int(startCLerp - float(winWidth / 2)); + + // Loop over dy + for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { + int dyR = dyROffset + startDyR; + + // Guard against the window exceeding the bounds of dy + if (dyR < 0 || dyR >= `+l+`) { + continue; + } + + for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { + int dyC = dyCOffset + startDyC; + + // Guard against the window exceeding the bounds of dy + if (dyC < 0 || dyC >= `+u+`) { + continue; + } + + float dxR = float(dyR) * heightScale; + int topDxRIndex = int(floor(dxR)); + int bottomDxRIndex = int(min(ceil(dxR), `+(a-1)+`.0)); + float dxRLerp = dxR - float(topDxRIndex); + float inverseDxRLerp = 1.0 - dxRLerp; + + float dxC = float(dyC) * widthScale; + int leftDxCIndex = int(floor(dxC)); + int rightDxCIndex = int(min(ceil(dxC), `+(s-1)+`.0)); + float dxCLerp = dxC - float(leftDxCIndex); + float inverseDxCLerp = 1.0 - dxCLerp; + + if (r == topDxRIndex && c == leftDxCIndex) { + // topLeft + accumulator += + getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; + } + + if (r == topDxRIndex && c == rightDxCIndex) { + // topRight + accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; + } + + if (r == bottomDxRIndex && c == leftDxCIndex) { + // bottomLeft + accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; + } + + if (r == bottomDxRIndex && c == rightDxCIndex) { + // bottomRight + accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; + } + } + } + // End loop over dy + + setOutput(accumulator); + } + `}return n}();var HH=function(){function n(t,e,i,r){this.variableNames=["A"],this.outputShape=[];var a=t[0],s=t[1],o=t[2],l=t[3];this.outputShape=[a,e,i,l];var u=[r&&e>1?s-1:s,r&&i>1?o-1:o],c=[r&&e>1?e-1:e,r&&i>1?i-1:i];this.userCode=` + const vec2 effectiveInputOverOutputRatioRC = vec2( + `+u[0]/c[0]+`, + `+u[1]/c[1]+`); + const vec2 inputShapeRC = vec2(`+s+".0, "+o+`.0); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + ivec2 yRC = coords.yz; + + // Fractional source index. + vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC; + + // Compute the four integer indices. + ivec2 sourceFloorRC = ivec2(sourceFracIndexRC); + ivec2 sourceCeilRC = ivec2( + min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); + + float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); + float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); + float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); + float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); + + vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); + + float top = topLeft + (topRight - topLeft) * fracRC.y; + float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; + float newValue = top + (bottom - top) * fracRC.x; + + setOutput(newValue); + } + `}return n}();var VH=function(){function n(t,e,i,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];var a=t[0],s=t[1],o=t[2],l=t[3];this.outputShape=[a,e,i,l];var u=[r&&e>1?s-1:s,r&&i>1?o-1:o],c=[r&&e>1?e-1:e,r&&i>1?i-1:i];this.userCode=` + const vec3 effectiveInputOverOutputRatioRC = vec3( + `+u[0]/c[0]+`, + `+u[1]/c[1]+`, + `+u[1]/c[1]+`); + const vec3 inputShapeRC = vec3(`+s+".0, "+o+`.0, + `+o+`.0); + + float getAValue(int b, int r, int c, int d) { + return getChannel(getA(b, r, c, d), vec2(c, d)); + } + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + // Calculate values for next column in yRC.z. + ivec3 yRC = coords.yzz + ivec3(0, 0, 1); + + // Fractional source index. + vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC; + + // Compute the four integer indices. + ivec3 sourceFloorRC = ivec3(sourceFracIndexRC); + ivec3 sourceCeilRC = ivec3( + min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); + + // Should we calculate next column and row elements in 2x2 packed cell. + bool hasNextCol = d < `+(l-1)+`; + bool hasNextRow = coords.z < `+(i-1)+`; + + // In parallel, construct four corners for all four components in + // packed 2x2 cell. + vec4 topLeft = vec4( + getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), + hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); + + vec4 bottomLeft = vec4( + getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), + hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); + + vec4 topRight = vec4( + getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), + hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); + + vec4 bottomRight = vec4( + getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), + hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); + + vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); + + vec4 top = mix(topLeft, topRight, fracRC.yyzz); + vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); + vec4 newValue = mix(top, bottom, fracRC.x); + + setOutput(newValue); + } + `}return n}();var qH=function(){function n(t,e,i){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,a=r[1],s=r[2],o=t.shape,l=o[1],u=o[2],c=[i&&l>1?a-1:a,i&&u>1?s-1:s],h=[i&&l>1?l-1:l,i&&u>1?u-1:u],d=c[0]/h[0],p=c[1]/h[1],f=1/d,m=1/p,g=Math.ceil(f)*2+2,v=Math.ceil(m)*2+2;this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + int r = coords[1]; + int c = coords[2]; + + float accumulator = 0.0; + + const float heightScale = float(`+d+`); + const float widthScale = float(`+p+`); + + const float invHeightScale = float(`+f+`); + const float invWidthScale = float(`+m+`); + + const int winHeight = int(`+g+`); + const int winWidth = int(`+v+`); + + // Compute bounds for where in dy we will look + float startRLerp = floor(float(r) * invHeightScale); + int startDyR = int(floor(startRLerp - float(winHeight / 2))); + + float startCLerp = floor(float(c) * invWidthScale); + int startDyC = int(floor(startCLerp - float(winWidth / 2))); + + // Loop over dy + for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { + int dyR = dyROffset + startDyR; + + // Guard against the window exceeding the bounds of dy + if (dyR < 0 || dyR >= `+l+`) { + continue; + } + + for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { + int dyC = dyCOffset + startDyC; + + // Guard against the window exceeding the bounds of dy + if (dyC < 0 || dyC >= `+u+`) { + continue; + } + + float sourceFracRow = + float(`+c[0]+`) * + (float(dyR) / float(`+h[0]+`)); + + float sourceFracCol = + float(`+c[1]+`) * + (float(dyC) / float(`+h[1]+`)); + + int sourceNearestRow = int(min( + float(int(`+a+`) - 1), + `+i+` ? float(round(sourceFracRow)) : + float(floor(sourceFracRow)))); + + int sourceNearestCol = int(min( + float(int(`+s+`) - 1), + `+i+` ? float(round(sourceFracCol)) : + float(floor(sourceFracCol)))); + + if (r == sourceNearestRow && c == sourceNearestCol) { + accumulator += getDy(b, dyR, dyC, d); + } + } + } + // End loop over dy + + setOutput(accumulator); + } + `}return n}();var GH=function(){function n(t,e,i,r){this.variableNames=["A"],this.outputShape=[];var a=t[0],s=t[1],o=t[2],l=t[3];this.outputShape=[a,e,i,l];var u=[r&&e>1?s-1:s,r&&i>1?o-1:o],c=[r&&e>1?e-1:e,r&&i>1?i-1:i],h=r?"0.5":"0.0";this.userCode=` + const vec2 effectiveInputOverOutputRatioRC = vec2( + `+u[0]/c[0]+`, + `+u[1]/c[1]+`); + const vec2 inputShapeRC = vec2(`+s+".0, "+o+`.0); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + ivec2 yRC = coords.yz; + + // Fractional source index. + vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC; + + // Compute the coordinators of nearest neighbor point. + ivec2 sourceNearestRC = ivec2( + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + `+h+`))); + + float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); + + setOutput(newValue); + } + `}return n}();var YH=function(){function n(t,e){this.variableNames=["x"];var i=t.length;if(i>4)throw new Error("WebGL backend: Reverse of rank-"+i+" tensor is not yet supported");if(this.outputShape=t,i===1){this.userCode=` + void main() { + int coord = getOutputCoords(); + setOutput(getX(`+t[0]+` - coord - 1)); + } + `;return}var r=function(o){return e.indexOf(o)!==-1&&t[o]!==1?t[o]+" - coords["+o+"] - 1":"coords["+o+"]"},a=t.map(function(o,l){return r(l)}).join(","),s=Je(i);this.userCode=` + void main() { + `+s+` coords = getOutputCoords(); + setOutput(getX(`+a+`)); + } + `}return n}();var KH=function(){function n(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;var i=t.length;if(i>4)throw new Error("WebGL backend: Reverse of rank-"+i+" tensor is not yet supported");this.outputShape=t;var r=jt("rc",i),a=r[i-1]+" + 1 < "+this.outputShape[i-1],s=r[i-2]+" + 1 < "+this.outputShape[i-2],o=Je(i);i===1?this.userCode=` + void main(){ + int rc = getOutputCoords(); + vec4 result = vec4(0.); + result.r = getChannel(getX(`+t[0]+` - rc - 1), + `+t[0]+` - rc - 1); + if(`+a+`){ + result.g = getChannel(getX(`+t[0]+` - (rc + 1) - 1), + `+t[0]+` - (rc + 1) - 1); + } + setOutput(result); + } + `:this.userCode=` + void main() { + `+o+` rc = getOutputCoords(); + vec4 result = vec4(0.); + result.r = `+l(r.slice())+`; + if(`+a+`){ + result.g = `+u(r.slice())+`; + } + if(`+s+`) { + result.b = `+c(r.slice())+`; + if(`+a+`) { + result.a = `+h(r.slice())+`; + } + } + setOutput(result); + } + `;function l(f){return d(f)}function u(f){return f[i-1]="("+f[i-1]+" + 1)",d(f)}function c(f){return f[i-2]="("+f[i-2]+" + 1)",d(f)}function h(f){return f[i-1]="("+f[i-1]+" + 1)",f[i-2]="("+f[i-2]+" + 1)",d(f)}function d(f){var m=t.map(function(b,w){return p(w,f)}),g=m.join(","),v=m.slice(-2).join(",");return"getChannel(getX("+g+"), vec2("+v+"))"}function p(f,m){return e.indexOf(f)!==-1&&t[f]!==1?t[f]+" - "+m[f]+" - 1":""+m[f]}}return n}();var CS=function(){function n(t,e,i,r,a,s,o){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;var l=Je(a.length),u=Je(s.length),c="";i===1?c="i":i===2&&(c="i, j");var h="getIndices("+c+")",d="";r===1?d="i":r===2&&(d="i, coords[1]");var p="getUpdates("+d+")",f=e>1?"strides[j]":"strides";this.userCode=` + `+l+" strides = "+l+"("+a+`); + + void main() { + `+u+` coords = getOutputCoords(); + float sum = 0.0; + bool found = false; + for (int i = 0; i < `+t+`; i++) { + int flattenedIndex = 0; + for (int j = 0; j < `+e+`; j++) { + int index = round(`+h+`); + flattenedIndex += index * `+f+`; + } + if (flattenedIndex == coords[0]) { + sum += `+p+`; + found = true; + } + } + setOutput(mix(getDefaultValue(), sum, float(found))); + } + `}return n}();var jH=function(){function n(t,e){this.variableNames=["x","segmentIds"];var i=t.windowSize,r=t.batchSize,a=t.inSize,s=t.numSegments,o=s*Math.ceil(a/i);this.outputShape=[r,o];var l="0.0",u="sumValue",c=Math.floor(i/4)*4,h=i%4,d=` + sumValue += dot(values, segFilter); + `,p="";a%i>0&&(p=` + if (inIdx < 0 || inIdx >= `+a+`) { + return initializationValue; + } + `);var f="";a%i>0&&(f=` + if (inIdx < 0 || inIdx >= `+a+`) { + return -1.0; + } + `),this.userCode=` + const float initializationValue = `+l+`; + + float getValue(int batch, int inIdx) { + `+p+` + return getX(batch, inIdx); + } + + float getSegmentIdAtIndex(int inIdx) { + `+f+` + return getSegmentIds(inIdx); + } + + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int outIdx = coords[1]; + int inOffset = int(floor(float(outIdx) / float( + `+s+")) * float("+i+`)); + int currentSeg = int(mod(float(outIdx), float(`+s+`))); + + float sumValue = 0.0; + + for (int i = 0; i < `+c+`; i += 4) { + int inIdx = inOffset + i; + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + getValue(batch, inIdx + 3) + ); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 + ); + + `+d+` + } + + int inIdx = inOffset + `+c+`; + if (`+(h===1)+`) { + vec4 values = vec4( + getValue(batch, inIdx), + initializationValue, + initializationValue, + initializationValue + ); + + int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + 0, + 0, + 0 + ); + + `+d+` + } else if (`+(h===2)+`) { + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + initializationValue, + initializationValue + ); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, + 0, + 0 + ); + + `+d+` + } else if (`+(h===3)+`) { + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + initializationValue + ); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, + 0 + ); + + `+d+` + } + setOutput(`+u+`); + } + `}return n}();var $H=function(){function n(t,e,i){this.variableNames=["c","a","b"],this.outputShape=e;var r,a;if(i>4)throw Error("Where for rank "+i+" is not yet supported");if(i===1)a="resRC",r="resRC";else{for(var s=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[],u=0;u= 1.0) { + setOutput(getA(`+a+`)); + } else { + setOutput(getB(`+a+`)); + } + } + `}return n}();var JH=function(){function n(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e=Je(this.rank),i="uniform int start["+this.rank+"];",r=XH(this.rank),a,s=t.map(function(o,l){return"sourceLoc."+qd[l]+" = start["+l+"] + coords."+qd[l]+";"});a=` + `+e+` sourceLoc; + `+e+` coords = getOutputCoords(); + `+s.join(` +`)+` + `,this.userCode=` + `+i+` + void main() { + `+a+` + setOutput(getSource(`+r+`)); + } + `}return n.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the "+("length of start ("+t.length+")"));return function(i,r){if(e.startLoc==null&&(e.startLoc=i.getUniformLocationNoThrow(r,"start"),e.startLoc==null))return;i.gl.uniform1iv(e.startLoc,t)}},n}(),qd=["x","y","z","w","u","v"];function XH(n){if(n===1)return"sourceLoc";if(n<=6)return qd.slice(0,n).map(function(t){return"sourceLoc."+t}).join(",");throw Error("Slicing for rank "+n+" is not yet supported")}var ZH=function(){function n(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;var e=Je(this.rank),i=jt("coords",this.rank),r=jt("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":"vec2("+r.slice(-2).join()+")",s="getChannel(getSource("+r.join()+"), "+a+")",o=` + result.x = `+s+`; + if (++`+i[this.rank-1]+" < "+t[this.rank-1]+`) { + ++`+r[this.rank-1]+`; + result.y = `+s+`; + --`+r[this.rank-1]+`; + } + `,l=this.rank===1?"":` + --`+i[this.rank-1]+`; + if (++`+i[this.rank-2]+" < "+t[this.rank-2]+`) { + ++`+r[this.rank-2]+`; + result.z = `+s+`; + if (++`+i[this.rank-1]+" < "+t[this.rank-1]+`) { + ++`+r[this.rank-1]+`; + result.w = `+s+`; + } + } + `,u=this.rank<=4?`sourceLoc = coords + + `+e+"("+t.map(function(c,h){return"start["+h+"]"}).join()+");":t.map(function(c,h){return r[h]+" = "+i[h]+" + start["+h+"];"}).join(` +`);this.userCode=` + uniform int start[`+this.rank+`]; + void main() { + `+e+` coords = getOutputCoords(); + `+e+` sourceLoc; + `+u+` + vec4 result = vec4(0.); + `+o+` + `+l+` + setOutput(result); + } + `}return n.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the "+("length of start ("+t.length+")"));return function(i,r){if(e.startLoc==null&&(e.startLoc=i.getUniformLocationNoThrow(r,"start"),e.startLoc==null))return;i.gl.uniform1iv(e.startLoc,t)}},n}();var QH=function(){function n(t,e,i){this.variableNames=["x"],this.outputShape=i;var r=i.length,a=Je(i.length),s=Je(i.length),o="";if(r===1)o="coords * strides + begin";else{var l=0;o=i.map(function(u,c){return l++,i.length===1?"coords * strides["+c+"] + begin["+c+"]":"coords["+(l-1)+"] * strides["+c+"] + begin["+c+"]"}).join(",")}this.userCode=` + `+a+" begin = "+a+"("+t+`); + `+a+" strides = "+a+"("+e+`); + + void main() { + `+s+` coords = getOutputCoords(); + setOutput(getX(`+o+`)); + } + `}return n}();var e9=function(){function n(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return n.prototype.acquireTexture=function(t,e,i){var r=OS(e,i),a=ES(t,r,i);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);var s=RS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,i);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();var o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}var l;return r===xt.PACKED_2X2_FLOAT32?l=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):r===xt.PACKED_2X2_FLOAT16?l=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):r===xt.UNPACKED_FLOAT32?l=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):r===xt.UNPACKED_FLOAT16?l=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):r===xt.PACKED_4X1_UNSIGNED_BYTE&&(l=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[a].push(l),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),l},n.prototype.releaseTexture=function(t,e,i,r){if(this.freeTextures==null)return;var a=OS(i,r),s=ES(e,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);var o=RS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),l=N.env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(t),this._numBytesAllocated-=o):(this.freeTextures[s].push(t),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;var u=this.usedTextures[s],c=u.indexOf(t);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()},n.prototype.log=function(){if(!this.logEnabled)return;var t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+t+")");var e=this._numBytesFree/this._numBytesAllocated;console.log("Bytes allocated: "+this._numBytesAllocated),console.log("Bytes unused: "+this._numBytesFree+" ("+Math.round(100*e)+"%)")},Object.defineProperty(n.prototype,"numBytesAllocated",{get:function(){return this._numBytesAllocated},enumerable:!0,configurable:!0}),Object.defineProperty(n.prototype,"numBytesFree",{get:function(){return this._numBytesFree},enumerable:!0,configurable:!0}),n.prototype.getNumUsedTextures=function(){return this.numUsedTextures},n.prototype.getNumFreeTextures=function(){return this.numFreeTextures},n.prototype.dispose=function(){var t=this;if(this.freeTextures==null)return;for(var e in this.freeTextures)this.freeTextures[e].forEach(function(i){t.gpgpu.deleteMatrixTexture(i)});for(var e in this.usedTextures)this.usedTextures[e].forEach(function(r){t.gpgpu.deleteMatrixTexture(r)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0},n}();function t9(n,t){var e=n;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===n.RGBA)return 16;if(t===e.RGBA16F)return 8;throw new Error("Unknown internal format "+t)}function RS(n,t,e,i,r){var a=n9(t,i),s;if(r){var o=$r(n[0],n[1]),l=o[0],u=o[1];s=l*u}else{var c=Za(n[0],n[1]),h=c[0],d=c[1];s=h*d}var p=t9(e,a);return s*p}function n9(n,t){switch(n){case xt.PACKED_2X2_FLOAT32:return _d(t);case xt.PACKED_2X2_FLOAT16:return Md(t);case xt.UNPACKED_FLOAT32:return Bd(t);case xt.UNPACKED_FLOAT16:return zd(t);case xt.PACKED_4X1_UNSIGNED_BYTE:return Pd(t);default:throw new Error("Unknown physical texture type "+n)}}function i9(n){return N.env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?xt.PACKED_2X2_FLOAT32:xt.UNPACKED_FLOAT32:n?xt.PACKED_2X2_FLOAT16:xt.UNPACKED_FLOAT16}function OS(n,t){if(n===en.UPLOAD)return xt.PACKED_2X2_FLOAT32;if(n===en.RENDER||n==null)return i9(t);if(n===en.DOWNLOAD||n===en.PIXELS)return xt.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+n)}function ES(n,t,e){return n[0]+"_"+n[1]+"_"+t+"_"+e}var a9=function(){function n(t,e){this.variableNames=["A"];for(var i=new Array(t.length),r=0;r5)throw Error("Tile for rank "+t+" is not yet supported");if(t===1)return"imod(resRC, "+n[0]+")";for(var e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],i=[],r=0;r= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); +`;function l9(n){return n===void 0&&(n=0),Xn+(` + return x > 0.0 ? 1.0 : float(`+n+`); + `)}var US="return -x;",BS="return ceil(x);",zS="return floor(x);",u9=` + if (isnan(x)) { return 0.0; } + return sign(x); +`,c9="return float(isnan(x));",h9="return float(isinf(x));",d9="return float(!isnan(x) && !isinf(x));",p9=` + // OpenGL ES does not support round function. + // The algorithm is based on banker's rounding. + float base = floor(x); + if ((x - base) < 0.5) { + return floor(x); + } else if ((x - base) > 0.5) { + return ceil(x); + } else { + if (mod(base, 2.0) == 0.0) { + return base; + } else { + return base + 1.0; + } + } +`,PS="return exp(x);",_S="return exp(x) - 1.0;",f9=`if (x < 0.0) return NAN; + return log(x);`,m9="return log(1.0 + x);",g9="return sqrt(x);",v9="return inversesqrt(x);",y9="return 1.0 / (1.0 + exp(-1.0 * x));",b9=` + float epsilon = 1.1920928955078125e-7; + float threshold = log(epsilon) + 2.0; + + bool too_large = x > -threshold; + bool too_small = x < threshold; + + float result; + float exp_x = exp(x); + + if (too_large){ + result = x; + } + else if (too_small){ + result = exp_x; + } + else{ + result = log(exp_x + 1.0); + } + return result; +`,w9=Xn+` + if (abs(x) > 1.) { + return NAN; + } + return asin(x); +`,S9=Xn+` + if (abs(x) > 1.) { + return NAN; + } + return acos(x); +`,L9=Xn+` + return atan(x); +`,I9=` + float e2x = exp(x); + return (e2x - 1.0 / e2x) / 2.0; +`,A9=` + float e2x = exp(-x); + return (e2x + 1.0 / e2x) / 2.0; +`,T9=` + float e2x = exp(-2.0 * abs(x)); + return sign(x) * (1.0 - e2x) / (1.0 + e2x); +`,N9=Xn+"return log(x + sqrt(x * x + 1.0));",x9=Xn+` + if (x < 1.0) return NAN; + return log(x + sqrt(x * x - 1.0));`,C9=Xn+` + if ((x < -1.0) || (x > 1.0)) return NAN; + return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,R9=` + // Error function is calculated approximately with elementary function. + // See "Handbook of Mathematical Functions with Formulas, + // Graphs, and Mathematical Tables", Abramowitz and Stegun. + float p = `+N.backend_util.ERF_P+`; + float a1 = `+N.backend_util.ERF_A1+`; + float a2 = `+N.backend_util.ERF_A2+`; + float a3 = `+N.backend_util.ERF_A3+`; + float a4 = `+N.backend_util.ERF_A4+`; + float a5 = `+N.backend_util.ERF_A5+`; + + float sign = sign(x); + x = abs(x); + float t = 1.0 / (1.0 + p * x); + return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); +`,O9="return 1.0 / x;",E9="return float(!(x >= 1.0));",D9="return float(int(x));",Vo="return x;";var k9="return x;",F9=` + vec4 result = log(x); + vec4 isNaN = vec4(lessThan(x, vec4(0.0))); + result.r = isNaN.r == 1.0 ? NAN : result.r; + result.g = isNaN.g == 1.0 ? NAN : result.g; + result.b = isNaN.b == 1.0 ? NAN : result.b; + result.a = isNaN.a == 1.0 ? NAN : result.a; + + return result; +`,MS=` + vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,HS=` + vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,VS=` + vec4 result; + + result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); + result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); + result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); + result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); + + return result; +`,as=function(){function n(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=` + vec4 unaryOperation(vec4 x) { + `+e+` + } + + void main() { + vec4 x = getAAtOutCoords(); + vec4 y = unaryOperation(x); + + setOutput(y); + } + `}return n}();var W9=function(){function n(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;var e=t.length,i=jt("rc",e),r=Je(e),a=W_(e,i),s=i.slice(-2),o=e<=1?"rc":"vec2("+s.join(",")+")";this.userCode=` + void main() { + `+r+` rc = getOutputCoords(); + vec4 packedInput = getA(`+a+`); + + setOutput(getChannel(packedInput, `+o+`)); + } + `}return n}();var qS=N.backend_util.segment_util,U9=N.kernel_impls.split,B9=N.kernel_impls.tile,z9=N.kernel_impls.topkImpl,P9=N.kernel_impls.whereImpl,_9=1e-7,M9=1e-4,qo={};function H9(n){return n in qo||(qo[n]={}),qo[n]}function Go(n,t){if(t===void 0&&(t=!1),n==="linear")return t?k9:s9;if(n==="relu")return t?MS:kS;if(n==="elu")return t?VS:WS;if(n==="relu6")return t?HS:FS;if(n==="prelu")return t?Q0:Z0;throw new Error("Activation "+n+" has not been implemented for the WebGL backend.")}var V9=128,q9=600;function G9(){return N.env().global.screen==null?1024:N.env().global.screen.height*N.env().global.screen.width*window.devicePixelRatio*q9/1024/1024}var GS=1e3,YS=function(n){HP(t,n);function t(e){var i=n.call(this)||this;if(i.pendingRead=new WeakMap,i.pendingDisposal=new WeakSet,i.dataRefCount=new WeakMap,i.numBytesInGPU=0,i.uploadWaitMs=0,i.downloadWaitMs=0,i.warnedAboutMemory=!1,i.warnedAboutCPUBackend=!1,i.pendingDeletes=0,i.disposed=!1,!N.env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){var r=Wn(N.env().getNumber("WEBGL_VERSION"));i.binaryCache=H9(N.env().getNumber("WEBGL_VERSION")),i.gpgpu=new AS(r),i.canvas=r.canvas,i.gpgpuCreatedLocally=!0}else i.gpgpu=e,i.binaryCache={},i.gpgpuCreatedLocally=!1,i.canvas=e.gl.canvas;return i.textureManager=new e9(i.gpgpu),i.numMBBeforeWarning=G9(),i.texData=new N.DataStorage(i,N.engine()),i}return t.prototype.numDataIds=function(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes},t.prototype.write=function(e,i,r){if((N.env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||N.env().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");var a={};return this.texData.set(a,{shape:i,dtype:r,values:e,usage:en.UPLOAD,refCount:1}),a},t.prototype.incRef=function(e){var i=this.texData.get(e);i.refCount++},t.prototype.decRef=function(e){if(this.texData.has(e)){var i=this.texData.get(e);i.refCount--}},t.prototype.move=function(e,i,r,a){if(N.env().getBool("DEBUG")&&this.checkNumericalProblems(i),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:a,values:i,usage:en.UPLOAD,refCount:1})},t.prototype.disposeIntermediateTensorInfo=function(e){var i=e.dataId;if(this.texData.has(i)){var r=this.texData.get(i);r.refCount--,r.refCount<1&&this.disposeData(i)}},t.prototype.readSync=function(e){var i=this.texData.get(e),r=i.values,a=i.dtype,s=i.complexTensors,o=i.slice,l=i.shape,u=i.isPacked;if(o!=null){var c=void 0;u?c=new as(l,Vo):c=new Re(l,Vo);var h=this.runWebGLProgram(c,[{dataId:e,shape:l,dtype:a}],a),d=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),d}if(r!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return r;var p=this.activeTimers!=null,f;p&&(f=N.util.now());var m;if(a==="complex64"){var g=s.real.dataSync(),v=s.imag.dataSync();m=N.backend_util.mergeRealAndImagArrays(g,v)}else m=this.getValuesFromTexture(e);return p&&(this.downloadWaitMs+=N.util.now()-f),this.convertAndCacheOnCPU(e,m)},t.prototype.read=function(e){return ko(this,void 0,void 0,function(){var i,r,a,s,o,l,u,c,h,d,p,f,m,g,v,b,w,S,L,x,C,R;return Fo(this,function(D){switch(D.label){case 0:if(this.pendingRead.has(e))return i=this.pendingRead.get(e),[2,new Promise(function(k){return i.push(k)})];if(r=this.texData.get(e),a=r.values,s=r.shape,o=r.slice,l=r.dtype,u=r.complexTensors,c=r.isPacked,o!=null)return h=void 0,c?h=new as(s,Vo):h=new Re(s,Vo),d=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:l}],l),p=this.read(d.dataId),this.disposeIntermediateTensorInfo(d),[2,p];if(a!=null)return[2,this.convertAndCacheOnCPU(e)];if(!N.env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&N.env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return f=null,l!=="complex64"&&N.env().get("WEBGL_BUFFER_SUPPORTED")&&(m=this.decode(e),g=this.texData.get(m.dataId),f=(R=this.gpgpu).createBufferFromTexture.apply(R,[g.texture].concat(Qa(s)))),this.pendingRead.set(e,[]),l!=="complex64"?[4,this.gpgpu.createAndWaitForFence()]:[3,2];case 1:D.sent(),D.label=2;case 2:return l==="complex64"?[4,Promise.all([u.real.data(),u.imag.data()])]:[3,4];case 3:return b=D.sent(),w=b[0],S=b[1],v=N.backend_util.mergeRealAndImagArrays(w,S),[3,5];case 4:f==null?v=this.getValuesFromTexture(e):(L=N.util.sizeFromShape(s),v=this.gpgpu.downloadFloat32MatrixFromBuffer(f,L)),D.label=5;case 5:return m!=null&&this.disposeIntermediateTensorInfo(m),x=this.convertAndCacheOnCPU(e,v),C=this.pendingRead.get(e),this.pendingRead.delete(e),C.forEach(function(k){return k(x)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),[2,x]}})})},t.prototype.checkNumericalProblems=function(e){if(e==null)return;for(var i=0;i0?[4,Promise.all(s)]:[3,2];case 1:return u=c.sent(),l.kernelMs=N.util.sum(u),l.getExtraProfileInfo=function(){return u.map(function(h,d){return{name:o[d],ms:h}}).map(function(h){return h.name+": "+h.ms}).join(", ")},[3,3];case 2:l.kernelMs={error:"WebGL query timers are not supported in this environment."},c.label=3;case 3:return this.uploadWaitMs=0,this.downloadWaitMs=0,[2,l]}})})},t.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}},t.prototype.startTimer=function(){return N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:N.util.now(),endMs:null}},t.prototype.endTimer=function(e){return N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=N.util.now(),e)},t.prototype.getQueryTime=function(e){return ko(this,void 0,void 0,function(){var i;return Fo(this,function(r){return N.env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:(i=e,[2,i.endMs-i.startMs])})})},t.prototype.disposeData=function(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);var i=this.texData.get(e).complexTensors;i!=null&&(i.real.dispose(),i.imag.dispose()),this.texData.delete(e)},t.prototype.releaseGPUData=function(e){var i=this.texData.get(e),r=i.texture,a=i.dtype,s=i.texShape,o=i.usage,l=i.isPacked,u=i.slice,c=u&&u.origDataId||e,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),r!=null&&(this.numBytesInGPU-=this.computeBytes(s,a),this.textureManager.releaseTexture(r,s,o,l)));var d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null},t.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},t.prototype.getDataInfo=function(e){return this.texData.get(e)},t.prototype.getCPUBackend=function(){return N.env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=N.engine().findBackend("cpu")),this.cpuBackend):null},t.prototype.shouldExecuteOnCPU=function(e,i){var r=this;i===void 0&&(i=V9);var a=this.getCPUBackend();return!this.warnedAboutCPUBackend&&a==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),a!=null&&e.every(function(s){return r.texData.get(s.dataId).texture==null&&N.util.sizeFromShape(s.shape)N.env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){var s=Math.floor(e.length/2),o=this.concat(e.slice(0,s),i),l=this.concat(e.slice(s),i);return this.concat([o,l],i)}if(N.env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){var u=new QM(e.map(function(f){return f.shape}),i);return this.compileAndRun(u,e)}var c=N.backend_util.computeOutShape(e.map(function(f){return f.shape}),i),h=e.map(function(f){return f.as2D(-1,N.util.sizeFromShape(f.shape.slice(i)))}),d=new ZM(h.map(function(f){return f.shape})),p=this.compileAndRun(d,h);return p.reshape(c)},t.prototype.neg=function(e){var i=this,r=this.tryRunOnCpuOrThrow([e],function(){return i.cpuBackend.neg(e)});if(r)return r;if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,US,e.dtype);var a=new Re(e.shape,US);return this.compileAndRun(a,[e])},t.prototype.batchMatMul=function(e,i,r,a){var s=r?e.shape[2]:e.shape[1],o=a?i.shape[1]:i.shape[2],l=r?e.shape[1]:e.shape[2],u=e.shape,c=u[0];if((s===1||o===1)&&l>GS){r&&(e=N.transpose(e,[0,2,1])),a&&(i=N.transpose(i,[0,2,1]));var h=o===1?e:e.as3D(c,l,1),d=o===1?2:1,p=o===1?i.as3D(c,1,l):i;return this.multiply(h,p).sum(d,!0)}var f=N.upcastType(e.dtype,i.dtype),m=new Hd(e.shape,[c,s,o],r,a);return this.compileAndRun(m,[e,i],f)},t.prototype.fusedBatchMatMul=function(e){var i=e.a,r=e.b,a=e.transposeA,s=e.transposeB,o=e.bias,l=e.activation,u=e.preluActivationWeights,c=a?i.shape[2]:i.shape[1],h=s?r.shape[1]:r.shape[2],d=i.shape,p=d[0],f=N.upcastType(i.dtype,r.dtype),m=o!=null,g=u!=null,v=l?Go(l,!0):null,b=new Hd(i.shape,[p,c,h],a,s,m,v,g),w=[i,r];return o&&w.push(o),u&&w.push(u),this.compileAndRun(b,w,f)},t.prototype.multiply=function(e,i){if(e.dtype==="complex64"){var r=this.texData.get(e.dataId),a=this.texData.get(i.dataId),s=new $0(j0.REAL,e.shape,i.shape),o=new $0(j0.IMAG,e.shape,i.shape),l=[this.makeComplexComponentTensorInfo(e,r.complexTensors.real),this.makeComplexComponentTensorInfo(e,r.complexTensors.imag),this.makeComplexComponentTensorInfo(i,a.complexTensors.real),this.makeComplexComponentTensorInfo(i,a.complexTensors.imag)],u=this.compileAndRun(s,l),c=this.compileAndRun(o,l),h=this.complex(u,c);return u.dispose(),c.dispose(),h}var d=N.upcastType(e.dtype,i.dtype);if(this.shouldExecuteOnCPU([e,i])){var r=this.texData.get(e.dataId),a=this.texData.get(i.dataId),p=x_(e.shape,i.shape,r.values,a.values,d),f=p[0],m=p[1];return this.makeOutput(m,d,f)}if(N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,i,J0,e.dtype);var g=new bt(J0,e.shape,i.shape);return this.compileAndRun(g,[e,i],e.dtype)},t.prototype.localResponseNormalization4D=function(e,i,r,a,s){var o=N.env().getBool("WEBGL_PACK_NORMALIZATION")?new CH(e.shape,i,r,a,s):new NH(e.shape,i,r,a,s);return this.compileAndRun(o,[e])},t.prototype.LRNGrad=function(e,i,r,a,s,o,l){var u=new xH(i.shape,a,s,o,l);return this.compileAndRun(u,[i,r,e])},t.prototype.tile=function(e,i){if(e.dtype==="string"){var r=this.readSync(e.dataId),a=r.map(function(l){return N.util.decodeString(l)}),s=N.buffer(e.shape,e.dtype,a);return B9(s,i)}var o=new a9(e.shape,i);return this.compileAndRun(o,[e])},t.prototype.pad=function(e,i,r){var a=N.env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PH(e.shape,i,r):new zH(e.shape,i,r);return this.compileAndRun(a,[e])},t.prototype.gather=function(e,i,r){var a=this,s=this.tryRunOnCpuOrThrow([e,i],function(){return a.cpuBackend.gather(e,i,r)});if(s)return s;var o=new yH(e.shape,i.size,r);return this.compileAndRun(o,[e,i])},t.prototype.batchToSpaceND=function(e,i,r){N.util.assert(e.rank<=4,function(){return"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"});var a=i.reduce(function(h,d){return h*d}),s=N.backend_util.getReshaped(e.shape,i,a),o=N.backend_util.getPermuted(s.length,i.length),l=N.backend_util.getReshapedPermuted(e.shape,i,a),u=N.backend_util.getSliceBeginCoords(r,i.length),c=N.backend_util.getSliceSize(l,r,i.length);return N.transpose(e.reshape(s),o).reshape(l).slice(u,c)},t.prototype.spaceToBatchND=function(e,i,r){N.util.assert(e.rank<=4,function(){return"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"});var a=i.reduce(function(p,f){return p*f}),s=[[0,0]];s.push.apply(s,r);for(var o=1+i.length;oN.env().get("WEBGL_MAX_TEXTURES_IN_SHADER")){var i=Math.floor(e.length/2),r=this.addN(e.slice(0,i)),a=this.addN(e.slice(i));return this.addN([r,a])}var s=e.map(function(c){return c.dtype}).reduce(function(c,h){return N.upcastType(c,h)}),o=e.map(function(c){return c.shape}),l=N.env().getBool("WEBGL_PACK"),u=l?new k_(e[0].shape,o):new D_(e[0].shape,o);return this.compileAndRun(u,e,s)},t.prototype.subtract=function(e,i){if(e.dtype==="complex64"&&i.dtype==="complex64")return this.complexSeparableBinaryOp(e,i,Ud);var r=N.upcastType(e.dtype,i.dtype);if(this.shouldExecuteOnCPU([e,i])){var a=this.texData.get(e.dataId),s=this.texData.get(i.dataId),o=O_(e.shape,i.shape,a.values,s.values,r),l=o[0],u=o[1];return this.makeOutput(u,r,l)}if(N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,i,Ud,e.dtype);var c=new bt(Ud,e.shape,i.shape);return this.compileAndRun(c,[e,i],r)},t.prototype.pow=function(e,i){var r=N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS"),a=r?new Li(UM,e.shape,i.shape):new bt(LM,e.shape,i.shape),s=N.upcastType(e.dtype,i.dtype);return this.compileAndRun(a,[e,i],s)},t.prototype.ceil=function(e){if(this.shouldExecuteOnCPU([e])){var i=S_(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,i)}if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,BS,e.dtype);var r=new Re(e.shape,BS);return this.compileAndRun(r,[e])},t.prototype.floor=function(e){if(this.shouldExecuteOnCPU([e])){var i=A_(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,i)}if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,zS,e.dtype);var r=new Re(e.shape,zS);return this.compileAndRun(r,[e])},t.prototype.sign=function(e){var i=new Re(e.shape,u9);return this.compileAndRun(i,[e])},t.prototype.isNaN=function(e){var i=new Re(e.shape,c9);return this.compileAndRun(i,[e],"bool")},t.prototype.isInf=function(e){var i=new Re(e.shape,h9);return this.compileAndRun(i,[e],"bool")},t.prototype.isFinite=function(e){var i=new Re(e.shape,d9);return this.compileAndRun(i,[e],"bool")},t.prototype.round=function(e){var i=new Re(e.shape,p9);return this.compileAndRun(i,[e])},t.prototype.exp=function(e){if(this.shouldExecuteOnCPU([e])){var i=L_(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,i)}if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,PS,e.dtype);var r=new Re(e.shape,PS);return this.compileAndRun(r,[e])},t.prototype.expm1=function(e){if(this.shouldExecuteOnCPU([e])){var i=I_(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,i)}if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,_S,e.dtype);var r=new Re(e.shape,_S);return this.compileAndRun(r,[e])},t.prototype.softmax=function(e,i){var r=N.util.parseAxisParam([i],e.shape),a=N.max(e,r),s=N.backend_util.expandShapeToKeepDim(a.shape,r),o=this.subtract(e,a.reshape(s)),l=this.exp(o),u=this.sum(l,r).reshape(s);return N.div(l,u)},t.prototype.log=function(e){if(this.shouldExecuteOnCPU([e])){var i=T_(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,i)}if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,F9,e.dtype);var r=new Re(e.shape,f9);return this.compileAndRun(r,[e])},t.prototype.log1p=function(e){var i=new Re(e.shape,m9);return this.compileAndRun(i,[e])},t.prototype.sqrt=function(e){var i=new Re(e.shape,g9);return this.compileAndRun(i,[e])},t.prototype.rsqrt=function(e){if(this.shouldExecuteOnCPU([e])){var i=C_(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,i)}var r=new Re(e.shape,v9);return this.compileAndRun(r,[e])},t.prototype.reciprocal=function(e){var i=new Re(e.shape,O9);return this.compileAndRun(i,[e])},t.prototype.relu=function(e){var i;return N.env().getBool("WEBGL_PACK")?i=new as(e.shape,MS):i=new Re(e.shape,kS),this.compileAndRun(i,[e])},t.prototype.relu6=function(e){var i;return N.env().getBool("WEBGL_PACK")?i=new as(e.shape,HS):i=new Re(e.shape,FS),this.compileAndRun(i,[e])},t.prototype.prelu=function(e,i){var r=N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Li(Q0,e.shape,i.shape):new bt(Z0,e.shape,i.shape);return this.compileAndRun(r,[e,i])},t.prototype.elu=function(e){if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,VS,e.dtype);var i=new Re(e.shape,WS);return this.compileAndRun(i,[e])},t.prototype.eluDer=function(e,i){var r=N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Li(BM,e.shape,i.shape):new bt(FM,e.shape,i.shape);return this.compileAndRun(r,[e,i])},t.prototype.selu=function(e){var i=new Re(e.shape,o9);return this.compileAndRun(i,[e])},t.prototype.int=function(e){var i=new Re(e.shape,D9);return this.compileAndRun(i,[e],"int32")},t.prototype.clip=function(e,i,r){var a;N.env().getBool("WEBGL_PACK_CLIP")?a=new XM(e.shape):a=new $M(e.shape);var s=a.getCustomSetupFunc(i,r);return this.compileAndRun(a,[e],null,s)},t.prototype.abs=function(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){var i=b_(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,i)}if(N.env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,DS,e.dtype);var r=new Re(e.shape,DS);return this.compileAndRun(r,[e])},t.prototype.complexAbs=function(e){var i=this.texData.get(e.dataId),r=new JM(e.shape),a=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag)];return this.compileAndRun(r,a)},t.prototype.sigmoid=function(e){var i=new Re(e.shape,y9);return this.compileAndRun(i,[e])},t.prototype.softplus=function(e){var i=new Re(e.shape,b9);return this.compileAndRun(i,[e])},t.prototype.asin=function(e){var i=new Re(e.shape,w9);return this.compileAndRun(i,[e])},t.prototype.acos=function(e){var i=new Re(e.shape,S9);return this.compileAndRun(i,[e])},t.prototype.atan=function(e){var i=new Re(e.shape,L9);return this.compileAndRun(i,[e])},t.prototype.sinh=function(e){var i=new Re(e.shape,I9);return this.compileAndRun(i,[e])},t.prototype.cosh=function(e){var i=new Re(e.shape,A9);return this.compileAndRun(i,[e])},t.prototype.tanh=function(e){var i=new Re(e.shape,T9);return this.compileAndRun(i,[e])},t.prototype.asinh=function(e){var i=new Re(e.shape,N9);return this.compileAndRun(i,[e])},t.prototype.acosh=function(e){var i=new Re(e.shape,x9);return this.compileAndRun(i,[e])},t.prototype.atanh=function(e){var i=new Re(e.shape,C9);return this.compileAndRun(i,[e])},t.prototype.erf=function(e){var i=new Re(e.shape,R9);return this.compileAndRun(i,[e])},t.prototype.step=function(e,i){var r=new Re(e.shape,l9(i));return this.compileAndRun(r,[e])},t.prototype.conv2dByMatMul=function(e,i,r,a,s,o){var l=e.shape,u=this.texData.get(e.dataId),c=r.inChannels,h=l[0]*l[1]*l[2],d=r.outChannels,p=r.dataFormat==="channelsLast",f=!1,m=!1,g=(h===1||d===1)&&c>GS,v=l[2]%2!==0&&!!u.isPacked;if(g||!N.env().getBool("WEBGL_LAZILY_UNPACK")||!N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){var b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=N.reshape(e,[1,b,r.inChannels]),S=N.reshape(i,[1,r.inChannels,r.outChannels]),L=this.fusedBatchMatMul({a:w,b:S,transposeA:f,transposeB:m,bias:a,activation:s,preluActivationWeights:o});return N.reshape(L,r.outShape)}var x=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),C={dataId:e.dataId,shape:[1,x,r.inChannels],dtype:e.dtype},R=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,N.util.assert(ns(u.shape,C.shape),function(){return"packed reshape "+u.shape+" to "+C.shape+" isn't free"});var D=N.reshape(i,[1,r.inChannels,r.outChannels]),k=this.fusedBatchMatMul({a:C,b:D,transposeA:f,transposeB:m,bias:a,activation:s,preluActivationWeights:o}),W=this.texData.get(k.dataId);return N.util.assert(W.isPacked,function(){return"batchMatMul result is expected to be packed"}),u.shape=R,W.shape=r.outShape,N.engine().makeTensorFromDataId(k.dataId,r.outShape,k.dtype)},t.prototype.conv2dWithIm2Row=function(e,i,r,a,s,o){var l=r.filterWidth,u=r.filterHeight,c=r.inChannels,h=r.outWidth,d=r.outHeight,p=r.dataFormat,f=p==="channelsLast",m=l*u*c,g=d*h,v=[m,g],b=!0,w=!1,S=e.squeeze([0]),L=i.reshape([1,m,-1]),x=new TH(v,S.shape,r),C=this.compileAndRun(x,[S]).reshape([1,v[0],v[1]]),R=a!=null,D=o!=null,k=s?Go(s,!0):null,W=new Hd(C.shape,[1,g,r.outChannels],b,w,R,k,D),F=[C,L];a&&F.push(a),D&&F.push(o);var P=this.compileAndRun(W,F);return f?P.reshape([1,d,h,r.outChannels]):P.reshape([1,r.outChannels,d,h])},t.prototype.fusedConv2d=function(e){var i=e.input,r=e.filter,a=e.convInfo,s=e.bias,o=e.activation,l=e.preluActivationWeights;if(a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID"))return this.conv2dByMatMul(i,r,a,s,o,l);if(N.env().getBool("WEBGL_CONV_IM2COL")&&i.shape[0]===1)return this.conv2dWithIm2Row(i,r,a,s,o,l);var u=s!=null,c=l!=null,h=o?Go(o,!1):null,d=new eS(a,u,h,c),p=[i,r];return s&&p.push(s),l&&p.push(l),this.compileAndRun(d,p)},t.prototype.conv2d=function(e,i,r){if(r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return this.conv2dByMatMul(e,i,r);if(N.env().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,i,r);var a=new eS(r);return this.compileAndRun(a,[e,i])},t.prototype.conv2dDerInput=function(e,i,r){var a=new tH(r);return this.compileAndRun(a,[e,i])},t.prototype.conv2dDerFilter=function(e,i,r){var a=new eH(r);return this.compileAndRun(a,[e,i])},t.prototype.fusedDepthwiseConv2D=function(e){var i=e.input,r=e.filter,a=e.convInfo,s=e.bias,o=e.activation,l=e.preluActivationWeights,u=N.env().getBool("WEBGL_PACK_DEPTHWISECONV")&&a.strideWidth<=2&&a.outChannels/a.inChannels===1,c=o?Go(o,u):null,h=[i,r],d=s!=null,p=l!=null;d&&h.push(s),p&&h.push(l);var f;return u?(f=new nS(a,d,c,p),this.compileAndRun(f,h)):(f=new tS(a,d,c,p),this.compileAndRun(f,h))},t.prototype.depthwiseConv2D=function(e,i,r){var a;return N.env().getBool("WEBGL_PACK_DEPTHWISECONV")&&r.strideWidth<=2&&r.outChannels/r.inChannels===1?(a=new nS(r),this.compileAndRun(a,[e,i])):(a=new tS(r),this.compileAndRun(a,[e,i]))},t.prototype.depthwiseConv2DDerInput=function(e,i,r){var a=new aH(r);return this.compileAndRun(a,[e,i])},t.prototype.depthwiseConv2DDerFilter=function(e,i,r){var a=new rH(r);return this.compileAndRun(a,[e,i])},t.prototype.conv3d=function(e,i,r){var a=new sH(r);return this.compileAndRun(a,[e,i])},t.prototype.conv3dDerInput=function(e,i,r){var a=new iH(r);return this.compileAndRun(a,[e,i])},t.prototype.conv3dDerFilter=function(e,i,r){var a=new nH(r);return this.compileAndRun(a,[e,i])},t.prototype.cast=function(e,i){return N.backend_util.castTensor(e,i,this)},t.prototype.unstack=function(e,i){for(var r=e.shape[i],a=new Array(e.rank-1),s=0,o=0;o1,function(){return"blockSize should be > 1 for depthToSpace, but was: "+i});var a=e.shape[0],s=r==="NHWC"?e.shape[1]:e.shape[2],o=r==="NHWC"?e.shape[2]:e.shape[3],l=r==="NHWC"?e.shape[3]:e.shape[1],u=s*i,c=o*i,h=l/(i*i),d=r==="NHWC"?[a,u,c,h]:[a,h,u,c],p=new cH(d,i,r);return this.compileAndRun(p,[e])},t.prototype.split=function(e,i,r){return U9(e,i,r)},t.prototype.scatterND=function(e,i,r){var a=N.backend_util.calculateShapes(i,e,r),s=a.sliceRank,o=a.numUpdates,l=a.sliceSize,u=a.strides,c=a.outputSize,h=[c/l,l],d=e.reshape([o,s]),p=i.reshape([o,l]);if(c===0)return N.backend_util.reshapeTensor(N.tensor([]),r);var f=N.scalar(0),m=new CS(o,s,d.rank,p.rank,u,h),g=this.compileAndRun(m,[p,d,f]);return g.reshape(r)},t.prototype.sparseToDense=function(e,i,r,a){var s=N.backend_util.calculateShapes(i,e,r),o=s.sliceRank,l=s.numUpdates,u=s.strides,c=s.outputSize,h=!1,d=new CS(l,o,e.rank,i.rank,u,[c,1],h),p=this.compileAndRun(d,[i,e,a]);return p.reshape(r)},t.prototype.fft=function(e){var i=!1;return this.fftImpl(e,i)},t.prototype.ifft=function(e){var i=!0;return this.fftImpl(e,i)},t.prototype.fftImpl=function(e,i){var r=this.texData.get(e.dataId),a=new oS(sS.REAL,e.shape,i),s=new oS(sS.IMAG,e.shape,i),o=[this.makeComplexComponentTensorInfo(e,r.complexTensors.real),this.makeComplexComponentTensorInfo(e,r.complexTensors.imag)],l=this.compileAndRun(a,o),u=this.compileAndRun(s,o),c=this.complex(l,u).as2D(e.shape[0],e.shape[1]);return l.dispose(),u.dispose(),c},t.prototype.gatherND=function(e,i){var r=i.shape,a=r[r.length-1],s=N.backend_util.prepareAndValidate(e,i),o=s[0],l=s[1],u=s[2],c=s[3],h=i.reshape([l,a]),d=e.reshape([e.size/u,u]),p=new bH(a,c,[l,u]),f=this.compileAndRun(p,[d,h]);return f.reshape(o)},t.prototype.fill=function(e,i,r){if(r=r||N.util.inferDtype(i),r==="string"){var a=N.util.getArrayFromDType(r,N.util.sizeFromShape(e));return a.fill(i),N.engine().makeTensor(a,e,r,this)}else{var s=new gH(e,i),o=s.getCustomSetupFunc(i);return this.compileAndRun(s,[],r,o)}},t.prototype.onesLike=function(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)},t.prototype.zerosLike=function(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)},t.prototype.linspace=function(e,i,r){return N.backend_util.linspaceImpl(e,i,r)},t.prototype.makeTensorInfo=function(e,i,r){var a=this.write(r,e,i);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:i}},t.prototype.makeOutput=function(e,i,r){var a=this.makeTensorInfo(e,i,r).dataId;return N.engine().makeTensorFromDataId(a,e,i,this)},t.prototype.unpackTensor=function(e){var i=new W9(e.shape);return this.runWebGLProgram(i,[e],e.dtype)},t.prototype.packTensor=function(e){var i=new UH(e.shape),r=!0;return this.runWebGLProgram(i,[e],e.dtype,null,r)},t.prototype.packedReshape=function(e,i){var r=[ar(e.shape)].concat(sr(e.shape)),a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[ar(i)].concat(sr(i)),o=new xS(s,r),l=!0,u=this.runWebGLProgram(o,[a],e.dtype,null,l);return{dataId:u.dataId,shape:i,dtype:u.dtype}},t.prototype.decode=function(e){var i=this.texData.get(e),r=i.isPacked,a=i.shape,s=i.dtype,o=Bo(a),l;r?l=new uH(o):l=new lH(o);var u=!0,c=this.runWebGLProgram(l,[{shape:o,dtype:s,dataId:e}],s,null,u);return{dtype:s,shape:a,dataId:c.dataId}},t.prototype.runWebGLProgram=function(e,i,r,a,s){var o=this;s===void 0&&(s=!1);var l=this.makeTensorInfo(e.outputShape,r),u=this.texData.get(l.dataId);if(e.packedOutput&&(u.isPacked=!0),e.outPackingScheme===Ja.DENSE){var c=Qa(e.outputShape);u.texShape=c.map(function(w){return w*2})}if(e.outTexUsage!=null&&(u.usage=e.outTexUsage),N.util.sizeFromShape(l.shape)===0)return u.values=N.util.getTypedArrayFromDType(l.dtype,0),l;var h=[],d=i.map(function(w){if(w.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var S=o.texData.get(w.dataId);if(S.texture==null){if(!e.packedInputs&&N.util.sizeFromShape(w.shape)<=N.env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:w.shape,texData:null,isUniform:!0,uniformValues:S.values};e.packedInputs&&(S.isPacked=!0,S.shape=w.shape)}else if(!!S.isPacked!==!!e.packedInputs)w=S.isPacked?o.unpackTensor(w):o.packTensor(w),h.push(w),S=o.texData.get(w.dataId);else if(S.isPacked&&!ns(S.shape,w.shape)){var L=w,x=w.shape;w.shape=S.shape,w=o.packedReshape(w,x),h.push(w),S=o.texData.get(w.dataId),L.shape=x}return o.uploadToGPU(w.dataId),{shape:w.shape,texData:S,isUniform:!1}});this.uploadToGPU(l.dataId);var p={shape:l.shape,texData:u,isUniform:!1},f=AH(e,d,p),m=this.getAndSaveBinary(f,function(){return LH(o.gpgpu,e,d,p)}),g=this.activeTimers!=null,v;if(g&&(v=this.startTimer()),IH(this.gpgpu,m,d,p,a),h.forEach(function(w){return o.disposeIntermediateTensorInfo(w)}),g&&(v=this.endTimer(v),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(v)})),!N.env().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){var b=this.unpackTensor(l);return this.disposeIntermediateTensorInfo(l),b}return l},t.prototype.compileAndRun=function(e,i,r,a,s){s===void 0&&(s=!1),r=r||i[0].dtype;var o=this.runWebGLProgram(e,i,r,a,s);return N.engine().makeTensorFromDataId(o.dataId,o.shape,o.dtype)},t.prototype.getAndSaveBinary=function(e,i){return e in this.binaryCache||(this.binaryCache[e]=i()),this.binaryCache[e]},t.prototype.getTextureManager=function(){return this.textureManager},t.prototype.dispose=function(){var e=this;if(this.disposed)return;if(!N.env().getBool("IS_TEST")){var i=Object.keys(this.binaryCache);i.forEach(function(r){e.gpgpu.deleteProgram(e.binaryCache[r].webGLProgram),delete e.binaryCache[r]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0},t.prototype.floatPrecision=function(){var e=this;return this.floatPrecisionValue==null&&(this.floatPrecisionValue=N.tidy(function(){if(!N.env().get("WEBGL_RENDER_FLOAT32_ENABLED")){var i=N.env().getBool("DEBUG");N.env().set("DEBUG",!1);var r=e.abs(N.scalar(1e-8)).dataSync()[0];if(N.env().set("DEBUG",i),r>0)return 32}return 16})),this.floatPrecisionValue},t.prototype.epsilon=function(){return this.floatPrecision()===32?_9:M9},t.prototype.uploadToGPU=function(e){var i,r=this.texData.get(e),a=r.shape,s=r.dtype,o=r.values,l=r.texture,u=r.usage,c=r.isPacked;if(l!=null)return;var h=this.activeTimers!=null,d;h&&(d=N.util.now());var p=r.texShape;if(p==null&&(p=W0(a,c),r.texShape=p),o!=null){var f=Bo(a),m=void 0,g=p[1],v=p[0],b=o instanceof Uint8Array;c?(i=$r(p[0],p[1]),g=i[0],v=i[1],m=new mH(f,[v,g],b)):m=new fH(f,[v,g],b);var w=this.makeTensorInfo([v,g],s);b?this.texData.get(w.dataId).usage=en.PIXELS:this.texData.get(w.dataId).usage=en.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(w.dataId),g,v,o);var S=!0,L=this.runWebGLProgram(m,[w],s,null,S),x=this.texData.get(L.dataId);r.texture=x.texture,r.texShape=x.texShape,r.isPacked=x.isPacked,r.usage=x.usage,this.disposeIntermediateTensorInfo(w),this.texData.delete(L.dataId),r.values=null,h&&(this.uploadWaitMs+=N.util.now()-d)}else{var C=this.acquireTexture(p,u,s,c);r.texture=C}},t.prototype.convertAndCacheOnCPU=function(e,i){var r=this.texData.get(e),a=r.dtype;return this.releaseGPUData(e),i!=null&&(r.values=Y9(i,a)),r.values},t.prototype.acquireTexture=function(e,i,r,a){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){var s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn("High memory usage in GPU: "+s+" MB, most likely due to a memory leak")}return this.textureManager.acquireTexture(e,i,a)},t.prototype.computeBytes=function(e,i){return e[0]*e[1]*N.util.bytesPerElement(i)},t.prototype.tryRunOnCpuOrThrow=function(e,i){if(this.shouldExecuteOnCPU(e))try{return i()}catch(r){if(N.env().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null},t}(N.KernelBackend);function Y9(n,t){if(t==="float32"||t==="complex64")return n;if(t==="int32"||t==="bool"){for(var e=t==="int32"?new Int32Array(n.length):new Uint8Array(n.length),i=0;i 0. ? NAN : result.r; + result.g = isNaN.g > 0. ? NAN : result.g; + result.b = isNaN.b > 0. ? NAN : result.b; + result.a = isNaN.a > 0. ? NAN : result.a; +`;function Yo(n){return function(t){var e=t.inputs,i=t.backend,r=e.x,a=i,s=new Re(r.shape,n);return a.runWebGLProgram(s,[r],r.dtype)}}function Gd(n,t,e,i){return function(r){var a=r.inputs,s=r.backend,o=a,l=o.a,u=o.b,c=s,h=N.env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Li(t,l.shape,u.shape,!!e):new bt(n,l.shape,u.shape),d=i||l.dtype,p=c.runWebGLProgram(h,[l,u],d);return p}}var J9=$9+` + return atan(a, b); +`,Z9=` + vec4 result = atan(a, b); + vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); + `+X9+` + return result; +`,Q9=Gd(J9,Z9),e5={kernelName:N.Atan2,backendName:"webgl",kernelFunc:Q9};function Yd(n){var t=n.inputs,e=n.backend,i=t.x;return e.incRef(i.dataId),{dataId:i.dataId,shape:i.shape,dtype:i.dtype}}var t5={kernelName:N.Identity,backendName:"webgl",kernelFunc:Yd};function n5(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.x;Xr(r,"avgPool");var a=i.filterSize,s=i.strides,o=i.pad,l=i.dimRoundingMode,u=1;N.util.assert(N.backend_util.eitherStridesOrDilationsAreOne(s,u),function(){return"Error in avgPool: Either strides or dilations must be 1. "+("Got strides "+s+" and dilations '"+u+"'")});var c=N.backend_util.computePool2DInfo(r.shape,a,s,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&N.util.arraysEqual(c.inShape,c.outShape))return Yd({inputs:{x:r},backend:e});var h=new rs(c,"avg",!1);return e.runWebGLProgram(h,[r],"float32")}var i5={kernelName:N.AvgPool,backendName:"webgl",kernelFunc:n5};function r5(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.dy,a=t.input,s=a;Xr([r,a],"avgPoolBackprop");var o=i.filterSize,l=i.strides,u=i.pad,c=N.backend_util.computePool2DInfo(s.shape,o,l,1,u),h=new bM(c);return e.runWebGLProgram(h,[r],s.dtype)}var a5={kernelName:N.AvgPoolBackprop,backendName:"webgl",kernelFunc:r5};var s5=function(){function n(t,e,i,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.backend_util.assertAndGetBroadcastShape(t,e),N.backend_util.assertAndGetBroadcastShape(t,i);var o="0.0";r!=null&&(N.backend_util.assertAndGetBroadcastShape(t,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");var l="1.0";a!=null&&(N.backend_util.assertAndGetBroadcastShape(t,a),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + void main() { + float x = getXAtOutCoords(); + float mean = getMeanAtOutCoords(); + float variance = getVarianceAtOutCoords(); + float offset = `+o+`; + float scale = `+l+`; + float inv = scale * inversesqrt(variance + float(`+s+`)); + setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); + } + `}return n}();var o5=function(){function n(t,e,i,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.backend_util.assertAndGetBroadcastShape(t,e),N.backend_util.assertAndGetBroadcastShape(t,i);var o="vec4(0.0)";r!=null&&(N.backend_util.assertAndGetBroadcastShape(t,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");var l="vec4(1.0)";a!=null&&(N.backend_util.assertAndGetBroadcastShape(t,a),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + void main() { + vec4 offset = `+o+`; + vec4 scale = `+l+`; + + vec4 x = getXAtOutCoords(); + vec4 mean = getMeanAtOutCoords(); + vec4 variance = getVarianceAtOutCoords(); + + vec4 inv = scale * inversesqrt(variance + vec4(`+s+`)); + + setOutput((x - mean) * inv + offset); + } + `}return n}();var l5=function(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.x,a=t.mean,s=t.variance,o=t.offset,l=t.scale;N.util.assert(a.shape.length===s.shape.length,function(){return"Batch normalization gradient requires mean and variance to have equal ranks."}),N.util.assert(o==null||a.shape.length===o.shape.length,function(){return"Batch normalization gradient requires mean and offset to have equal ranks."}),N.util.assert(l==null||a.shape.length===l.shape.length,function(){return"Batch normalization gradient requires mean and scale to have equal ranks."});var u=i.varianceEpsilon;u==null&&(u=.001);var c=[r,a,s],h=null;o!=null&&(h=o.shape,c.push(o));var d=null;l!=null&&(d=l.shape,c.push(l));var p=N.env().getBool("WEBGL_PACK_NORMALIZATION")?new o5(r.shape,a.shape,s.shape,h,d,u):new s5(r.shape,a.shape,s.shape,h,d,u),f=e.runWebGLProgram(p,c,c[0].dtype);return f},u5={kernelName:N.FusedBatchNorm,backendName:"webgl",kernelFunc:l5};var c5=jS+` + return cos(x); +`,h5=Yo(c5),d5={kernelName:N.Cos,backendName:"webgl",kernelFunc:h5};var p5=` +if (a == b) { + return 1.0; +}; +return a / b;`,f5=` + // vec4 one = vec4(equal(a, b)); + // return one + (vec4(1.0) - one) * a / b; + vec4 result = a / b; + if(a.x == b.x) { + result.x = 1.; + } + if(a.y == b.y) { + result.y = 1.; + } + if(a.z == b.z) { + result.z = 1.; + } + if(a.w == b.w) { + result.w = 1.; + } + + return result; +`,m5=Gd(p5,f5,!0),g5={kernelName:N.Div,backendName:"webgl",kernelFunc:m5};var v5=function(){function n(t){this.variableNames=["Image"],this.outputShape=[];var e=t[2];this.outputShape=t,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int x = coords[2]; + + int coordX = `+e+` - x; + float outputValue; + if(coordX >= 0 && coordX < `+e+`) { + outputValue = getImage(coords[0], coords[1], coordX, coords[3]); + } else { + outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); + } + setOutput(outputValue); + } + `}return n}();var y5={kernelName:N.FlipLeftRight,backendName:"webgl",kernelFunc:function(n){var t=n.inputs,e=n.backend,i=t.image,r=e,a=new v5(i.shape),s=r.runWebGLProgram(a,[i],i.dtype);return s}};var b5=function(){function n(t){this.variableNames=["A"];var e=kt(),i=t[0],r=t[1];this.outputShape=t,this.userCode=` + void main() { + ivec3 coords = getOutputCoords(); + int texR = coords[0]; + int texC = coords[1]; + int depth = coords[2]; + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(`+r+".0, "+i+`.0); + + vec4 values = `+e.texture2D+`(A, uv); + float value; + if (depth == 0) { + value = values.r; + } else if (depth == 1) { + value = values.g; + } else if (depth == 2) { + value = values.b; + } else if (depth == 3) { + value = values.a; + } + + setOutput(floor(value * 255.0 + 0.5)); + } + `}return n}();var w5=function(){function n(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;var e=kt(),i=t[0],r=t[1];this.outputShape=t,this.userCode=` + void main() { + ivec3 coords = getOutputCoords(); + int texR = coords[0]; + int texC = coords[1]; + int depth = coords[2]; + + vec4 result = vec4(0.); + + for(int row=0; row<=1; row++) { + for(int col=0; col<=1; col++) { + texC = coords[1] + row; + depth = coords[2] + col; + + vec2 uv = (vec2(texC, texR) + halfCR) / + vec2(`+r+".0, "+i+`.0); + vec4 values = `+e.texture2D+`(A, uv); + float value; + if (depth == 0) { + value = values.r; + } else if (depth == 1) { + value = values.g; + } else if (depth == 2) { + value = values.b; + } else if (depth == 3) { + value = values.a; + } + + result[row * 2 + col] = floor(value * 255.0 + 0.5); + } + } + + `+e.output+` = result; + } + `}return n}();var L5={kernelName:N.FromPixels,backendName:"webgl",kernelFunc:S5},na;function S5(n){var t=n.inputs,e=n.backend,i=n.attrs,r=t.pixels,a=i.numChannels,s=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=s?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=l[0],c=l[1],h=[c,u],d=[c,u,a];(o||s)&&(na==null&&(na=document.createElement("canvas").getContext("2d")),na.canvas.width=u,na.canvas.height=c,na.drawImage(r,0,0,u,c),r=na.canvas);var p=e.makeTensorInfo(h,"int32");e.texData.get(p.dataId).usage=en.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(p.dataId),r);var f=N.env().getBool("WEBGL_PACK")?new w5(d):new b5(d),m=e.runWebGLProgram(f,[p],"int32");return e.disposeData(p.dataId),m}function I5(n){for(var t=[];t.length===0||t[t.length-1].outSize!==1;){var e=t.length?t[t.length-1].outSize:n[1],i=N.backend_util.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:i,outSize:Math.ceil(e/i)})}return t}function A5(n,t,e,i){for(var r=I5(n.shape),a=n,s=0;s6)throw Error("Transpose for rank "+t+" is not yet supported");for(var e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],i=new Array(t),r=0;r6)throw Error("Packed transpose for rank "+this.rank+" is not yet supported.");for(var a=Je(this.rank),s=V0("rc",this.rank),o=new Array(this.rank),r=0;r= 0 && coordX < `+s+" && coordY >= 0 && coordY < "+a+`) { + outputValue = getImage(coords[0], coordY, coordX, coords[3]); + } + setOutput(outputValue); + } + `}return n}();var q5={kernelName:N.RotateWithOffset,backendName:"webgl",kernelFunc:function(n){var t=n.inputs,e=n.attrs,i=n.backend,r=t.image,a=e,s=a.radians,o=a.fillValue,l=a.center,u=i,c=new V5(r.shape,s,o,l),h=u.runWebGLProgram(c,[r],r.dtype);return h}};var G5=jS+` + return sin(x); +`,Y5=Yo(G5),K5={kernelName:N.Sin,backendName:"webgl",kernelFunc:Y5};var j5="return x * x;",$5=Yo(j5),X5={kernelName:N.Square,backendName:"webgl",kernelFunc:$5};var XS="return (a - b) * (a - b);",J5=Gd(XS,XS),Z5={kernelName:N.SquaredDifference,backendName:"webgl",kernelFunc:J5};var Q5="return tan(x);",eV=Yo(Q5),tV={kernelName:N.Tan,backendName:"webgl",kernelFunc:eV};var nV={kernelName:N.Transpose,backendName:"webgl",kernelFunc:function(n){for(var t=n.inputs,e=n.attrs,i=n.backend,r=t.x,a=e.perm,s=i,o=r.shape.length,l=new Array(o),u=0;u{"use strict";Object.defineProperty(ur,"__esModule",{value:!0});var $d=Gi(),Xd=Yb(),Jd=ow(),QS=Rw(),oV=g0(),lV=ZS();var uV="2.6.0";var cV={"tfjs-core":$d.version_core,"tfjs-backend-cpu":oV.version_cpu,"tfjs-backend-webgl":lV.version_webgl,"tfjs-data":QS.version_data,"tfjs-layers":Xd.version_layers,"tfjs-converter":Jd.version_converter,tfjs:uV};Object.keys($d).forEach(function(n){n!=="default"&&Object.defineProperty(ur,n,{enumerable:!0,get:function(){return $d[n]}})});Object.keys(Xd).forEach(function(n){n!=="default"&&Object.defineProperty(ur,n,{enumerable:!0,get:function(){return Xd[n]}})});Object.keys(Jd).forEach(function(n){n!=="default"&&Object.defineProperty(ur,n,{enumerable:!0,get:function(){return Jd[n]}})});ur.data=QS;ur.version=cV});var eL=ge(ss=>{const os=Ot();ss.disposeBox=n=>{n.startEndTensor.dispose(),n.startPoint.dispose(),n.endPoint.dispose()};ss.createBox=n=>({startEndTensor:n,startPoint:os.slice(n,[0,0],[-1,2]),endPoint:os.slice(n,[0,2],[-1,2])});ss.scaleBox=(n,t)=>{const e=os.mul(n.startPoint,t),i=os.mul(n.endPoint,t),r=os.concat2d([e,i],1);return ss.createBox(r)}});var Zd=ge(tL=>{const Ve=Ot(),Ko=eL(),hV={strides:[8,16],anchors:[2,6]},nL=6;function dV(n,t,e){const i=[];for(let r=0;r{const e=n.box?n.box:n;return Ko.scaleBox(e,t).startEndTensor.squeeze()})}class fV{constructor(n,t){this.blazeFaceModel=n,this.width=t.detector.inputSize,this.height=t.detector.inputSize,this.maxFaces=t.detector.maxFaces,this.anchorsData=dV(t.detector.inputSize,t.detector.inputSize,hV),this.anchors=Ve.tensor2d(this.anchorsData),this.inputSizeData=[t.detector.inputSize,t.detector.inputSize],this.inputSize=Ve.tensor1d([t.detector.inputSize,t.detector.inputSize]),this.iouThreshold=t.detector.iouThreshold,this.scoreThreshold=t.detector.scoreThreshold}async getBoundingBoxes(n,t,e=!0){const[i,r,a]=Ve.tidy(()=>{const p=n.resizeBilinear([this.width,this.height]),f=Ve.mul(Ve.sub(p.div(255),.5),2),m=this.blazeFaceModel.predict(f),g=m.squeeze(),v=pV(g,this.anchors,this.inputSize),b=Ve.slice(g,[0,0],[-1,1]),w=Ve.sigmoid(b).squeeze();return[g,v,w]}),s=await Ve.image.nonMaxSuppressionAsync(r,a,this.maxFaces,this.iouThreshold,this.scoreThreshold),o=await s.array();s.dispose();let l=o.map(p=>Ve.slice(r,[p,0],[1,-1]));t||(l=await Promise.all(l.map(async p=>{const f=await p.array();return p.dispose(),f})));const u=n.shape[1],c=n.shape[2];let h;t?h=Ve.div([c,u],this.inputSize):h=[c/this.inputSizeData[0],u/this.inputSizeData[1]];const d=[];for(let p=0;p{const g=f instanceof Ve.Tensor?Ko.createBox(f):Ko.createBox(Ve.tensor2d(f));if(!e)return g;const v=o[p];let b;t?b=this.anchors.slice([v,0],[1,2]):b=this.anchorsData[v];const w=Ve.slice(i,[v,nL-1],[1,-1]).squeeze().reshape([nL,-1]),S=Ve.slice(a,[v],[1]);return{box:g,landmarks:w,probability:S,anchor:b}});d.push(m)}return r.dispose(),a.dispose(),i.dispose(),{boxes:d,scaleFactor:h}}async estimateFaces(n,t=!1,e=!0){const i=Ve.tidy(()=>(n instanceof Ve.Tensor||(n=Ve.browser.fromPixels(n)),n.toFloat().expandDims(0))),{boxes:r,scaleFactor:a}=await this.getBoundingBoxes(i,t,e);return i.dispose(),t?r.map(s=>{const o=iL(s,a),l={topLeft:o.slice([0],[2]),bottomRight:o.slice([2],[2])};if(e){const{landmarks:u,probability:c,anchor:h}=s,d=u.add(h).mul(a);l.landmarks=d,l.probability=c}return l}):Promise.all(r.map(async s=>{const o=iL(s,a);let l;if(e){const[u,c,h]=await Promise.all([s.landmarks,o,s.probability].map(async g=>g.array())),d=s.anchor,[p,f]=a,m=u.map(g=>[(g[0]+d[0])*p,(g[1]+d[1])*f]);l={topLeft:c.slice(0,2),bottomRight:c.slice(2),landmarks:m,probability:h},Ko.disposeBox(s.box),s.landmarks.dispose(),s.probability.dispose()}else{const u=await o.array();l={topLeft:u.slice(0,2),bottomRight:u.slice(2)}}return o.dispose(),l}))}}tL.BlazeFaceModel=fV});var rL=ge(Qd=>{const mV=Ot(),gV=Zd();async function vV(n){const t=await mV.loadGraphModel(n.detector.modelPath,{fromTFHub:n.detector.modelPath.includes("tfhub.dev")}),e=new gV.BlazeFaceModel(t,n);return e}Qd.load=vV;const yV=Zd();Object.defineProperty(Qd,"BlazeFaceModel",{enumerable:!0,get(){return yV.BlazeFaceModel}})});var ep=ge(aL=>{aL.MESH_ANNOTATIONS={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],rightEyeIris:[473,474,475,476,477],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],leftEyeIris:[468,469,470,471,472],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]}});var sL=ge(cr=>{const bV=Ot();function wV(n,t){const e=[n.startPoint[0]*t[0],n.startPoint[1]*t[1]],i=[n.endPoint[0]*t[0],n.endPoint[1]*t[1]];return{startPoint:e,endPoint:i}}cr.scaleBoxCoordinates=wV;function tp(n){return[Math.abs(n.endPoint[0]-n.startPoint[0]),Math.abs(n.endPoint[1]-n.startPoint[1])]}cr.getBoxSize=tp;function np(n){return[n.startPoint[0]+(n.endPoint[0]-n.startPoint[0])/2,n.startPoint[1]+(n.endPoint[1]-n.startPoint[1])/2]}cr.getBoxCenter=np;function SV(n,t,e){const i=t.shape[1],r=t.shape[2],a=[[n.startPoint[1]/i,n.startPoint[0]/r,n.endPoint[1]/i,n.endPoint[0]/r]];return bV.image.cropAndResize(t,a,[0],e)}cr.cutBoxFromImageAndResize=SV;function LV(n,t=1.5){const e=np(n),i=tp(n),r=[t*i[0]/2,t*i[1]/2],a=[e[0]-r[0],e[1]-r[1]],s=[e[0]+r[0],e[1]+r[1]];return{startPoint:a,endPoint:s,landmarks:n.landmarks}}cr.enlargeBox=LV;function IV(n){const t=np(n),e=tp(n),i=Math.max(...e),r=i/2,a=[t[0]-r,t[1]-r],s=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:s,landmarks:n.landmarks}}cr.squarifyBox=IV});var hL=ge(wn=>{wn.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function oL(n){return n-2*Math.PI*Math.floor((n+Math.PI)/(2*Math.PI))}wn.normalizeRadians=oL;function AV(n,t){const e=Math.PI/2-Math.atan2(-(t[1]-n[1]),t[0]-n[0]);return oL(e)}wn.computeRotation=AV;function TV(n){return n*180/Math.PI}wn.radToDegrees=TV;function lL(n,t){return[[1,0,n],[0,1,t],[0,0,1]]}function ia(n,t){let e=0;for(let i=0;i{const Ii=Ot(),Un=sL(),ra=ep(),Ai=hL(),OV=468,EV=.25,DV=13,kV=[DV,ra.MESH_ANNOTATIONS.midwayBetweenEyes[0]],FV=3,WV=2,UV=[FV,WV],ip=ra.MESH_ANNOTATIONS.leftEyeLower0,rp=[ip[0],ip[ip.length-1]],ap=ra.MESH_ANNOTATIONS.rightEyeLower0,sp=[ap[0],ap[ap.length-1]],BV=3,zV=4,PV=71,op=76,_V=2.3,ls=64,pL=[{key:"EyeUpper0",indices:[9,10,11,12,13,14,15]},{key:"EyeUpper1",indices:[25,26,27,28,29,30,31]},{key:"EyeUpper2",indices:[41,42,43,44,45,46,47]},{key:"EyeLower0",indices:[0,1,2,3,4,5,6,7,8]},{key:"EyeLower1",indices:[16,17,18,19,20,21,22,23,24]},{key:"EyeLower2",indices:[32,33,34,35,36,37,38,39,40]},{key:"EyeLower3",indices:[54,55,56,57,58,59,60,61,62]},{key:"EyebrowUpper",indices:[63,64,65,66,67,68,69,70]},{key:"EyebrowLower",indices:[48,49,50,51,52,53]}];function jo(n,t,e,i){for(let r=0;r[a[0]*(d[0]-this.meshWidth/2),a[1]*(d[1]-this.meshHeight/2),d[2]]),o=Ai.buildRotationMatrix(e,[0,0]),l=s.map(d=>[...Ai.rotatePoint(d,o),d[2]]),u=Ai.invertTransformMatrix(i),c=[...Un.getBoxCenter({startPoint:t.startPoint,endPoint:t.endPoint}),1],h=[Ai.dot(c,u[0]),Ai.dot(c,u[1])];return l.map(d=>[d[0]+h[0],d[1]+h[1],d[2]])}getLeftToRightEyeDepthDifference(n){const t=n[rp[0]][2],e=n[sp[0]][2];return t-e}getEyeBox(n,t,e,i,r=!1){const a=Un.squarifyBox(Un.enlargeBox(this.calculateLandmarksBoundingBox([n[e],n[i]]),_V)),s=Un.getBoxSize(a);let o=Ii.image.cropAndResize(t,[[a.startPoint[1]/this.meshHeight,a.startPoint[0]/this.meshWidth,a.endPoint[1]/this.meshHeight,a.endPoint[0]/this.meshWidth]],[0],[ls,ls]);return r&&(o=Ii.image.flipLeftRight(o)),{box:a,boxSize:s,crop:o}}getEyeCoords(n,t,e,i=!1){const r=[];for(let a=0;a{let l=a;return o===2?l=i:o===4&&(l=r),[s[0],s[1],l]})}async predict(n,t,e){if(this.shouldUpdateRegionsOfInterest()){const i=!1,r=!0,{boxes:a,scaleFactor:s}=await this.boundingBoxDetector.getBoundingBoxes(n,i,r);if(a.length===0)return this.regionsOfInterest=[],null;const o=a.map(l=>{const u={startPoint:l.box.startPoint.squeeze().arraySync(),endPoint:l.box.endPoint.squeeze().arraySync()},c=Un.scaleBoxCoordinates(u,s),h=Un.enlargeBox(c);return{...h,landmarks:l.landmarks.arraySync()}});a.forEach(l=>{l!=null&&l.startPoint!=null&&(l.startEndTensor.dispose(),l.startPoint.dispose(),l.endPoint.dispose())}),this.updateRegionsOfInterest(o),this.runsWithoutFaceDetector=0}else this.runsWithoutFaceDetector++;return Ii.tidy(()=>this.regionsOfInterest.map((i,r)=>{let a=0;const s=i.landmarks.length>=OV;let[o,l]=kV;s===!1&&([o,l]=UV),a=Ai.computeRotation(i.landmarks[o],i.landmarks[l]);const u=Un.getBoxCenter({startPoint:i.startPoint,endPoint:i.endPoint}),c=[u[0]/n.shape[2],u[1]/n.shape[1]];let h=n,d=Ai.IDENTITY_MATRIX;a!==0&&(h=Ii.image.rotateWithOffset(n,a,0,c),d=Ai.buildRotationMatrix(-a,u));const p={startPoint:i.startPoint,endPoint:i.endPoint},f=Un.cutBoxFromImageAndResize(p,h,[this.meshHeight,this.meshWidth]).div(255),[,m,g]=this.meshDetector.predict(f),v=Ii.reshape(g,[-1,3]);let b=v.arraySync();if(t){const{box:x,boxSize:C,crop:R}=this.getEyeBox(b,f,rp[0],rp[1],!0),{box:D,boxSize:k,crop:W}=this.getEyeBox(b,f,sp[0],sp[1]),F=this.irisModel.predict(Ii.concat([R,W])),P=F.dataSync(),H=P.slice(0,op*3),{rawCoords:_,iris:K}=this.getEyeCoords(H,x,C,!0),j=P.slice(op*3),{rawCoords:q,iris:G}=this.getEyeCoords(j,D,k),Z=this.getLeftToRightEyeDepthDifference(b);Math.abs(Z)<30?(jo(b,_,"left"),jo(b,q,"right")):Z<1?jo(b,_,"left",["EyeUpper0","EyeLower0"]):jo(b,q,"right",["EyeUpper0","EyeLower0"]);const X=this.getAdjustedIrisCoords(b,K,"left"),ee=this.getAdjustedIrisCoords(b,G,"right");b=b.concat(X).concat(ee)}const w=this.transformRawCoords(b,i,a,d);Ii.dispose(b);const S=Un.enlargeBox(this.calculateLandmarksBoundingBox(w));if(e){const 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o={confidence:s||0,box:a.box?[a.box.startPoint[0],a.box.startPoint[1],a.box.endPoint[0]-a.box.startPoint[0],a.box.endPoint[1]-a.box.startPoint[1]]:0,mesh:a.coords?a.coords.arraySync():null,image:a.image?Jn.clone(a.image):null},l={};if(o.mesh&&o.mesh.length>0)for(const u in vL.MESH_ANNOTATIONS)(this.config.iris.enabled||u.includes("Iris")===!1)&&(l[u]=vL.MESH_ANNOTATIONS[u].map(c=>o.mesh[c]));o.annotations=l,i.push(o)}Jn.dispose(a.confidence),Jn.dispose(a.image),Jn.dispose(a.coords)}return i}}$o.MediaPipeFaceMesh=yL});var LL=ge(wL=>{const $t=Ot(),aa={};let SL={age:0,gender:""},lp=0;async function $V(n,t){const e=$t.tidy(()=>{const i=$t.browser.fromPixels(n),r=$t.image.resizeBilinear(i,[t,t]),a=$t.cast($t.expandDims(r,0),"float32");return a});return e}async function XV(n,t){if(lp+=1,lp>=t.face.age.skipFrames)return lp=0,SL;!aa.age&&t.face.age.enabled&&(aa.age=await $t.loadGraphModel(t.face.age.modelPath)),!aa.gender&&t.face.gender.enabled&&(aa.gender=await $t.loadGraphModel(t.face.gender.modelPath));let e;if(n instanceof $t.Tensor){const r=$t.image.resizeBilinear(n,[t.face.age.inputSize,t.face.age.inputSize],!1);e=$t.mul(r,[255]),$t.dispose(r)}else e=await $V(n,t.face.age.inputSize);const i={};if(t.face.age.enabled){const r=await aa.age.predict(e);i.age=Math.trunc(10*r.dataSync()[0])/10,$t.dispose(r)}if(t.face.gender.enabled){const r=await aa.gender.predict(e);i.gender=Math.trunc(100*r.dataSync()[0])<50?"female":"male",$t.dispose(r)}return $t.dispose(e),SL=i,i}wL.predict=XV});var TL=ge(IL=>{const AL=Ot();class JV{constructor(n,t){this.model=n,this.outputStride=t;const e=this.model.inputs[0].shape;AL.util.assert(e[1]===-1&&e[2]===-1,()=>`Input shape [${e[1]}, ${e[2]}] must both be equal to or -1`)}predict(n){return AL.tidy(()=>{const t=this.preprocessInput(n.toFloat()),e=t.expandDims(0),i=this.model.predict(e),r=i.map(s=>s.squeeze([0])),a=this.nameOutputResults(r);return{heatmapScores:a.heatmap.sigmoid(),offsets:a.offsets,displacementFwd:a.displacementFwd,displacementBwd:a.displacementBwd}})}dispose(){this.model.dispose()}}IL.BaseModel=JV});var up=ge(NL=>{const xL=Ot(),ZV=TL();class QV extends ZV.BaseModel{preprocessInput(n){return xL.tidy(()=>xL.div(n,127.5).sub(1))}nameOutputResults(n){const[t,e,i,r]=n;return{offsets:t,heatmap:e,displacementFwd:i,displacementBwd:r}}}NL.MobileNet=QV});var RL=ge(CL=>{function cp(n){return Math.floor(n/2)}class e8{constructor(n,t){this.priorityQueue=new Array(n),this.numberOfElements=-1,this.getElementValue=t}enqueue(n){this.priorityQueue[++this.numberOfElements]=n,this.swim(this.numberOfElements)}dequeue(){const n=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,n}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(n){for(;n>0&&this.less(cp(n),n);)this.exchange(n,cp(n)),n=cp(n)}sink(n){for(;2*n<=this.numberOfElements;){let t=2*n;if(t{const t8=RL();function n8(n,t,e,i,r,a){const[s,o]=a.shape;let l=!0;const u=Math.max(e-r,0),c=Math.min(e+r+1,s);for(let h=u;ht){l=!1;break}if(!l)break}return l}function i8(n,t,e){const[i,r,a]=e.shape,s=new t8.MaxHeap(i*r*a,({score:o})=>o);for(let o=0;o{Sn.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];Sn.NUM_KEYPOINTS=Sn.partNames.length;Sn.partIds=Sn.partNames.reduce((n,t,e)=>(n[t]=e,n),{});const r8=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];Sn.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];Sn.connectedPartIndices=r8.map(([n,t])=>[Sn.partIds[n],Sn.partIds[t]]);Sn.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var dp=ge(Ti=>{const a8=hr();function DL(n,t,e,i){return{y:i.get(n,t,e),x:i.get(n,t,e+a8.NUM_KEYPOINTS)}}Ti.getOffsetPoint=DL;function s8(n,t,e){const{heatmapY:i,heatmapX:r,id:a}=n,{y:s,x:o}=DL(i,r,a,e);return{x:n.heatmapX*t+o,y:n.heatmapY*t+s}}Ti.getImageCoords=s8;function o8(n,t){const e=new Array(t);for(let i=0;ie?e:n}Ti.clamp=hp;function l8(n,t,e,i){const r=e-n,a=i-t;return r*r+a*a}Ti.squaredDistance=l8;function u8(n,t){return{x:n.x+t.x,y:n.y+t.y}}Ti.addVectors=u8;function c8(n,t,e){return{y:hp(n.y,t,e),x:hp(n.x,t,e)}}Ti.clampVector=c8});var BL=ge(kL=>{const us=hr(),sa=dp(),FL=us.poseChain.map(([n,t])=>[us.partIds[n],us.partIds[t]]),pp=FL.map(([,n])=>n),WL=FL.map(([n])=>n);function h8(n,t,e){const i=e.shape[2]/2;return{y:e.get(t.y,t.x,n),x:e.get(t.y,t.x,i+n)}}function fp(n,t,e,i){return{y:sa.clamp(Math.round(n.y/t),0,e-1),x:sa.clamp(Math.round(n.x/t),0,i-1)}}function UL(n,t,e,i,r,a,s,o=2){const[l,u]=i.shape,c=fp(t.position,a,l,u),h=h8(n,c,s),d=sa.addVectors(t.position,h);let p=d;for(let g=0;g=0;--d){const p=pp[d],f=WL[d];l[p]&&!l[f]&&(l[f]=UL(d,l[p],f,t,e,i,a))}for(let d=0;d{const p8=EL(),f8=BL(),PL=dp();function _L(n,t,{x:e,y:i},r){return n.some(({keypoints:a})=>{const s=a[r].position;return PL.squaredDistance(i,e,s.y,s.x)<=t})}function m8(n,t,e){const i=e.reduce((r,{position:a,score:s},o)=>(_L(n,t,a,o)||(r+=s),r),0);return i/e.length}const g8=1;function v8(n,t,e,i,r,a,s=.5,o=20){const l=[],u=p8.buildPartWithScoreQueue(s,g8,n),c=o*o;for(;l.length{const Zn=Ot(),gp=hr();function y8(n,t){const e=t.shape[0],i=new Float32Array(e);for(let r=0;r{const i=ML(n,e);return n.toTensor().mul(Zn.scalar(t,"int32")).toFloat().add(i)})}cs.getOffsetPoints=w8;function S8(n,t){return Zn.tidy(()=>{const e=n.div(Zn.scalar(t,"int32"));return n.sub(e.mul(Zn.scalar(t,"int32")))})}function L8(n){const[t,e,i]=n.shape;return Zn.tidy(()=>{const r=n.reshape([t*e,i]),a=r.argMax(0),s=a.div(Zn.scalar(e,"int32")).expandDims(1),o=S8(a,e).expandDims(1);return Zn.concat([s,o],1)})}cs.argmax2d=L8});var yp=ge(VL=>{const I8=hr(),vp=HL();async function A8(n,t,e){let i=0;const r=vp.argmax2d(n),a=await Promise.all([n.buffer(),t.buffer(),r.buffer()]),s=a[0],o=a[1],l=a[2],u=vp.getOffsetPoints(l,e,o),c=await u.buffer(),h=Array.from(vp.getPointsConfidence(s,l)),d=h.map((p,f)=>(i+=p,{position:{y:c.get(f,0),x:c.get(f,1)},part:I8.partNames[f],score:p}));return r.dispose(),u.dispose(),{keypoints:d,score:i/d.length}}VL.decodeSinglePose=A8});var wp=ge(ln=>{const oa=Ot(),T8=hr();function N8(n,t,e){return n(N8(n[i].score,n[r].score,t)||e.push([n[i],n[r]]),e),[])}ln.getAdjacentKeyPoints=x8;const{NEGATIVE_INFINITY:qL,POSITIVE_INFINITY:GL}=Number;function YL(n){return n.reduce(({maxX:t,maxY:e,minX:i,minY:r},{position:{x:a,y:s}})=>({maxX:Math.max(t,a),maxY:Math.max(e,s),minX:Math.min(i,a),minY:Math.min(r,s)}),{maxX:qL,maxY:qL,minX:GL,minY:GL})}ln.getBoundingBox=YL;function C8(n){const{minX:t,minY:e,maxX:i,maxY:r}=YL(n);return[{x:t,y:e},{x:i,y:e},{x:i,y:r},{x:t,y:r}]}ln.getBoundingBoxPoints=C8;async function R8(n){return Promise.all(n.map(t=>t.buffer()))}ln.toTensorBuffers3D=R8;function KL(n,t,e,i=0,r=0){return{score:n.score,keypoints:n.keypoints.map(({score:a,part:s,position:o})=>({score:a,part:s,position:{x:o.x*e+r,y:o.y*t+i}}))}}ln.scalePose=KL;function jL(n,t,e,i=0,r=0){return e===1&&t===1&&i===0&&r===0?n:n.map(a=>KL(a,t,e,i,r))}ln.scalePoses=jL;function $L(n){return n instanceof oa.Tensor?[n.shape[0],n.shape[1]]:[n.height,n.width]}ln.getInputTensorDimensions=$L;function bp(n){return n instanceof oa.Tensor?n:oa.browser.fromPixels(n)}ln.toInputTensor=bp;function O8(n,t,e){return oa.tidy(()=>{const i=bp(n);return i.resizeBilinear([t,e])})}ln.toResizedInputTensor=O8;function E8(n,[t,e]){const[i,r]=$L(n),a=e/t,s=r/i;let[o,l,u,c]=[0,0,0,0];s{let d=bp(n);return d=oa.pad3d(d,[[o,l],[u,c],[0,0]]),d.resizeBilinear([t,e])});return{resized:h,padding:{top:o,left:u,right:c,bottom:l}}}ln.padAndResizeTo=E8;function D8(n,[t,e],[i,r],a){const s=(t+a.top+a.bottom)/i,o=(e+a.left+a.right)/r,l=jL(n,s,o,-a.top,-a.left);return l}ln.scaleAndFlipPoses=D8});var JL=ge(Sp=>{const k8=Ot(),F8=up(),W8=mp(),U8=yp(),dr=wp();class XL{constructor(n,t){this.baseModel=n,this.inputResolution=t}async estimateMultiplePoses(n,t){const e=this.baseModel.outputStride,i=this.inputResolution,[r,a]=dr.getInputTensorDimensions(n),{resized:s,padding:o}=dr.padAndResizeTo(n,[i,i]),{heatmapScores:l,offsets:u,displacementFwd:c,displacementBwd:h}=this.baseModel.predict(s),d=await dr.toTensorBuffers3D([l,u,c,h]),p=d[0],f=d[1],m=d[2],g=d[3],v=await W8.decodeMultiplePoses(p,f,m,g,e,t.maxDetections,t.scoreThreshold,t.nmsRadius),b=dr.scaleAndFlipPoses(v,[r,a],[i,i],o);return l.dispose(),u.dispose(),c.dispose(),h.dispose(),s.dispose(),b}async estimateSinglePose(n){const t=this.baseModel.outputStride,e=this.inputResolution,[i,r]=dr.getInputTensorDimensions(n),{resized:a,padding:s}=dr.padAndResizeTo(n,e),{heatmapScores:o,offsets:l,displacementFwd:u,displacementBwd:c}=this.baseModel.predict(a),h=await U8.decodeSinglePose(o,l,t),d=[h],p=dr.scaleAndFlipPoses(d,[i,r],[e,e],s);return o.dispose(),l.dispose(),u.dispose(),c.dispose(),a.dispose(),p[0]}dispose(){this.baseModel.dispose()}}Sp.PoseNet=XL;async function B8(n){const t=n.outputStride,e=await k8.loadGraphModel(n.modelPath),i=new F8.MobileNet(e,t);return new XL(i,n.inputResolution)}async function z8(n){return B8(n)}Sp.load=z8});var QL=ge(Mt=>{const P8=up(),ZL=JL(),_8=mp(),M8=yp(),Xo=hr(),hs=wp();Mt.load=ZL.load;Mt.PoseNet=ZL.PoseNet;Mt.MobileNet=P8.MobileNet;Mt.decodeMultiplePoses=_8.decodeMultiplePoses;Mt.decodeSinglePose=M8.decodeSinglePose;Mt.partChannels=Xo.partChannels;Mt.partIds=Xo.partIds;Mt.partNames=Xo.partNames;Mt.poseChain=Xo.poseChain;Mt.getAdjacentKeyPoints=hs.getAdjacentKeyPoints;Mt.getBoundingBox=hs.getBoundingBox;Mt.getBoundingBoxPoints=hs.getBoundingBoxPoints;Mt.scaleAndFlipPoses=hs.scaleAndFlipPoses;Mt.scalePose=hs.scalePose});var Ap=ge(Ni=>{const H8=Ot();function Lp(n){return[Math.abs(n.endPoint[0]-n.startPoint[0]),Math.abs(n.endPoint[1]-n.startPoint[1])]}Ni.getBoxSize=Lp;function Ip(n){return[n.startPoint[0]+(n.endPoint[0]-n.startPoint[0])/2,n.startPoint[1]+(n.endPoint[1]-n.startPoint[1])/2]}Ni.getBoxCenter=Ip;function V8(n,t,e){const i=t.shape[1],r=t.shape[2],a=[[n.startPoint[1]/i,n.startPoint[0]/r,n.endPoint[1]/i,n.endPoint[0]/r]];return H8.image.cropAndResize(t,a,[0],e)}Ni.cutBoxFromImageAndResize=V8;function q8(n,t){const e=[n.startPoint[0]*t[0],n.startPoint[1]*t[1]],i=[n.endPoint[0]*t[0],n.endPoint[1]*t[1]],r=n.palmLandmarks.map(a=>{const s=[a[0]*t[0],a[1]*t[1]];return s});return{startPoint:e,endPoint:i,palmLandmarks:r}}Ni.scaleBoxCoordinates=q8;function G8(n,t=1.5){const e=Ip(n),i=Lp(n),r=[t*i[0]/2,t*i[1]/2],a=[e[0]-r[0],e[1]-r[1]],s=[e[0]+r[0],e[1]+r[1]];return{startPoint:a,endPoint:s,palmLandmarks:n.palmLandmarks}}Ni.enlargeBox=G8;function Y8(n){const t=Ip(n),e=Lp(n),i=Math.max(...e),r=i/2,a=[t[0]-r,t[1]-r],s=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:s,palmLandmarks:n.palmLandmarks}}Ni.squarifyBox=Y8;function K8(n,t){const e=[n.endPoint[0]-n.startPoint[0],n.endPoint[1]-n.startPoint[1]],i=[e[0]*t[0],e[1]*t[1]],r=[n.startPoint[0]+i[0],n.startPoint[1]+i[1]],a=[n.endPoint[0]+i[0],n.endPoint[1]+i[1]];return{startPoint:r,endPoint:a,palmLandmarks:n.palmLandmarks}}Ni.shiftBox=K8});var tI=ge(eI=>{const Ue=Ot(),j8=Ap();class $8{constructor(n,t,e,i,r,a){this.model=n,this.width=t,this.height=e,this.iouThreshold=r,this.scoreThreshold=a,this.anchors=i.map(s=>[s.x_center,s.y_center]),this.anchorsTensor=Ue.tensor2d(this.anchors),this.inputSizeTensor=Ue.tensor1d([t,e]),this.doubleInputSizeTensor=Ue.tensor1d([t*2,e*2])}normalizeBoxes(n){return Ue.tidy(()=>{const t=Ue.slice(n,[0,0],[-1,2]),e=Ue.slice(n,[0,2],[-1,2]),i=Ue.add(Ue.div(t,this.inputSizeTensor),this.anchorsTensor),r=Ue.div(e,this.doubleInputSizeTensor),a=Ue.mul(Ue.sub(i,r),this.inputSizeTensor),s=Ue.mul(Ue.add(i,r),this.inputSizeTensor);return Ue.concat2d([a,s],1)})}normalizeLandmarks(n,t){return Ue.tidy(()=>{const e=Ue.add(Ue.div(n.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[t]);return Ue.mul(e,this.inputSizeTensor)})}async getBoundingBoxes(n){const t=Ue.tidy(()=>Ue.mul(Ue.sub(n,.5),2));let e;if(Ue.getBackend()==="webgl"){const f=Ue.env().get("WEBGL_PACK_DEPTHWISECONV");Ue.env().set("WEBGL_PACK_DEPTHWISECONV",!0),e=this.model.predict(t),Ue.env().set("WEBGL_PACK_DEPTHWISECONV",f)}else e=this.model.predict(t);const i=e.squeeze(),r=Ue.tidy(()=>Ue.sigmoid(Ue.slice(i,[0,0],[-1,1])).squeeze()),a=Ue.slice(i,[0,1],[-1,4]),s=this.normalizeBoxes(a),o=await Ue.image.nonMaxSuppressionAsync(s,r,1,this.iouThreshold,this.scoreThreshold),l=await o.array(),u=[t,e,o,i,s,a,r];if(l.length===0)return u.forEach(f=>f.dispose()),null;const c=l[0],h=Ue.slice(s,[c,0],[1,-1]),d=Ue.slice(i,[c,5],[1,14]),p=Ue.tidy(()=>this.normalizeLandmarks(d,c).reshape([-1,2]));return u.push(d),u.forEach(f=>f.dispose()),{boxes:h,palmLandmarks:p}}async estimateHandBounds(n){const t=n.shape[1],e=n.shape[2],i=Ue.tidy(()=>n.resizeBilinear([this.width,this.height]).div(255)),r=await this.getBoundingBoxes(i);if(r===null)return i.dispose(),null;const a=r.boxes.arraySync(),s=a[0].slice(0,2),o=a[0].slice(2,4),l=r.palmLandmarks.arraySync();return i.dispose(),r.boxes.dispose(),r.palmLandmarks.dispose(),j8.scaleBoxCoordinates({startPoint:s,endPoint:o,palmLandmarks:l},[e/this.width,t/this.height])}}eI.HandDetector=$8});var iI=ge(nI=>{nI.MESH_ANNOTATIONS={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]}});var lI=ge(xi=>{function rI(n){return n-2*Math.PI*Math.floor((n+Math.PI)/(2*Math.PI))}xi.normalizeRadians=rI;function X8(n,t){const e=Math.PI/2-Math.atan2(-(t[1]-n[1]),t[0]-n[0]);return rI(e)}xi.computeRotation=X8;const aI=(n,t)=>[[1,0,n],[0,1,t],[0,0,1]];function la(n,t){let e=0;for(let i=0;i{const ua=Ot(),Bn=Ap(),Ci=lI(),e6=.8,t6=[0,-.4],n6=3,i6=[0,-.1],r6=1.65,cI=[0,5,9,13,17,1,2],a6=0,s6=2;class o6{constructor(n,t,e,i,r,a){this.regionsOfInterest=[],this.runsWithoutHandDetector=0,this.boundingBoxDetector=n,this.meshDetector=t,this.maxContinuousChecks=r,this.detectionConfidence=a,this.meshWidth=e,this.meshHeight=i,this.maxHandsNumber=1}getBoxForPalmLandmarks(n,t){const e=n.map(r=>{const a=[...r,1];return Ci.rotatePoint(a,t)}),i=this.calculateLandmarksBoundingBox(e);return Bn.enlargeBox(Bn.squarifyBox(Bn.shiftBox(i,t6)),n6)}getBoxForHandLandmarks(n){const t=this.calculateLandmarksBoundingBox(n),e=Bn.enlargeBox(Bn.squarifyBox(Bn.shiftBox(t,i6)),r6),i=[];for(let r=0;r[a[0]*(d[0]-this.meshWidth/2),a[1]*(d[1]-this.meshHeight/2),d[2]]),o=Ci.buildRotationMatrix(e,[0,0]),l=s.map(d=>{const p=Ci.rotatePoint(d,o);return[...p,d[2]]}),u=Ci.invertTransformMatrix(i),c=[...Bn.getBoxCenter(t),1],h=[Ci.dot(c,u[0]),Ci.dot(c,u[1])];return l.map(d=>[d[0]+h[0],d[1]+h[1],d[2]])}async estimateHand(n,t){const e=this.shouldUpdateRegionsOfInterest();if(e===!0){const L=await this.boundingBoxDetector.estimateHandBounds(n);if(L===null)return n.dispose(),this.regionsOfInterest=[],null;this.updateRegionsOfInterest(L,!0),this.runsWithoutHandDetector=0}else this.runsWithoutHandDetector++;const i=this.regionsOfInterest[0],r=Ci.computeRotation(i.palmLandmarks[a6],i.palmLandmarks[s6]),a=Bn.getBoxCenter(i),s=[a[0]/n.shape[2],a[1]/n.shape[1]],o=ua.image.rotateWithOffset(n,r,0,s),l=Ci.buildRotationMatrix(-r,a),u=e?this.getBoxForPalmLandmarks(i.palmLandmarks,l):i,c=Bn.cutBoxFromImageAndResize(u,o,[this.meshWidth,this.meshHeight]),h=c.div(255);c.dispose(),o.dispose();let d;if(ua.getBackend()==="webgl"){const L=ua.env().get("WEBGL_PACK_DEPTHWISECONV");ua.env().set("WEBGL_PACK_DEPTHWISECONV",!0),d=this.meshDetector.predict(h),ua.env().set("WEBGL_PACK_DEPTHWISECONV",L)}else d=this.meshDetector.predict(h);const[p,f]=d;h.dispose();const m=p.dataSync()[0];if(p.dispose(),ma[0]),e=n.map(a=>a[1]),i=[Math.min(...t),Math.min(...e)],r=[Math.max(...t),Math.max(...e)];return{startPoint:i,endPoint:r}}updateRegionsOfInterest(n,t){if(t)this.regionsOfInterest=[n];else{const e=this.regionsOfInterest[0];let i=0;if(e!=null&&e.startPoint!=null){const[r,a]=n.startPoint,[s,o]=n.endPoint,[l,u]=e.startPoint,[c,h]=e.endPoint,d=Math.max(r,l),p=Math.max(a,u),f=Math.min(s,c),m=Math.min(o,h),g=(f-d)*(m-p),v=(s-r)*(o-a),b=(c-l)*(h-a);i=g/(v+b-g)}this.regionsOfInterest[0]=i>e6?e:n}}shouldUpdateRegionsOfInterest(){const n=this.regionsOfInterest.length;return n!==this.maxHandsNumber||this.runsWithoutHandDetector>=this.maxContinuousChecks}}uI.HandPipeline=o6});var pI=ge(Tp=>{const ca=Ot(),l6=tI(),Np=iI(),u6=hI();async function c6(n){return ca.loadGraphModel(n,{fromTFHub:n.includes("tfhub.dev")})}async function h6(n){return ca.loadGraphModel(n,{fromTFHub:n.includes("tfhub.dev")})}async function d6(n){return ca.util.fetch(n).then(t=>t.json())}async function p6(n){const[t,e,i]=await Promise.all([d6(n.detector.anchors),c6(n.detector.modelPath),h6(n.skeleton.modelPath)]),r=new l6.HandDetector(e,n.inputSize,n.inputSize,t,n.iouThreshold,n.scoreThreshold),a=new u6.HandPipeline(r,i,n.inputSize,n.inputSize,n.skipFrames,n.minConfidence),s=new dI(a);return s}Tp.load=p6;class dI{constructor(n){this.pipeline=n}static getAnnotations(){return Np.MESH_ANNOTATIONS}async estimateHands(n,t){const e=ca.tidy(()=>(n instanceof ca.Tensor||(n=ca.browser.fromPixels(n)),n.toFloat().expandDims(0))),i=await this.pipeline.estimateHand(e,t);if(e.dispose(),!i)return[];const r={};for(const a of Object.keys(Np.MESH_ANNOTATIONS))r[a]=Np.MESH_ANNOTATIONS[a].map(s=>i.landmarks[s]);return[{confidence:i.confidence||0,box:i.box?[i.box.topLeft[0],i.box.topLeft[1],i.box.bottomRight[0]-i.box.topLeft[0],i.box.bottomRight[1]-i.box.topLeft[1]]:0,landmarks:i.landmarks,annotations:r}]}}Tp.HandPose=dI});var fI=ge(f6=>{AI(f6,{default:()=>m6});var m6={face:{enabled:!0,detector:{modelPath:"/models/blazeface/model.json",inputSize:128,maxFaces:10,skipFrames:5,minConfidence:.8,iouThreshold:.3,scoreThreshold:.75},mesh:{enabled:!0,modelPath:"/models/facemesh/model.json",inputSize:192},iris:{enabled:!0,modelPath:"/models/iris/model.json",inputSize:192},age:{enabled:!0,modelPath:"/models/ssrnet-imdb-age/model.json",inputSize:64,skipFrames:5},gender:{enabled:!0,modelPath:"/models/ssrnet-imdb-gender/model.json"}},body:{enabled:!0,modelPath:"/models/posenet/model.json",inputResolution:257,outputStride:16,maxDetections:5,scoreThreshold:.75,nmsRadius:20},hand:{enabled:!0,inputSize:256,skipFrames:5,minConfidence:.8,iouThreshold:.3,scoreThreshold:.75,detector:{anchors:"/models/handdetect/anchors.json",modelPath:"/models/handdetect/model.json"},skeleton:{modelPath:"/models/handskeleton/model.json"}}}});var vI=ge(Jo=>{const g6=bL(),v6=LL(),y6=QL(),b6=pI(),mI=fI().default,zn={facemesh:null,blazeface:null,ssrnet:null,iris:null};function gI(...n){const t=e=>e&&typeof e=="object";return n.reduce((e,i)=>(Object.keys(i).forEach(r=>{const a=e[r],s=i[r];Array.isArray(a)&&Array.isArray(s)?e[r]=a.concat(...s):t(a)&&t(s)?e[r]=gI(a,s):e[r]=s}),e),{})}async function w6(n,t){const e=gI(mI,t);let i=[];e.body.enabled&&(zn.posenet||(zn.posenet=await y6.load(e.body)),i=await zn.posenet.estimateMultiplePoses(n,e.body));let r=[];e.hand.enabled&&(zn.handpose||(zn.handpose=await b6.load(e.hand)),r=await zn.handpose.estimateHands(n,e.hand));const a=[];if(e.face.enabled){zn.facemesh||(zn.facemesh=await g6.load(e.face));const s=await zn.facemesh.estimateFaces(n,e.face);for(const o of s){const l=e.face.age.enabled||e.face.gender.enabled?await v6.predict(o.image,e):{},u=o.annotations.leftEyeIris&&o.annotations.rightEyeIris?Math.max(o.annotations.leftEyeIris[3][0]-o.annotations.leftEyeIris[1][0],o.annotations.rightEyeIris[3][0]-o.annotations.rightEyeIris[1][0]):0;a.push({confidence:o.confidence,box:o.box,mesh:o.mesh,annotations:o.annotations,age:l.age,gender:l.gender,iris:u!==0?Math.trunc(100*11.7/u)/100:0})}}return{face:a,body:i,hand:r}}Jo.detect=w6;Jo.defaults=mI;Jo.models=zn});return vI();})(); +/*! ***************************************************************************** +Copyright (c) Microsoft Corporation. All rights reserved. +Licensed under the Apache License, Version 2.0 (the "License"); you may not use +this file except in compliance with the License. You may obtain a copy of the +License at http://www.apache.org/licenses/LICENSE-2.0 + +THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED +WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, +MERCHANTABLITY OR NON-INFRINGEMENT. + +See the Apache Version 2.0 License for specific language governing permissions +and limitations under the License. +***************************************************************************** */ +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2018 Google LLC + * + * Use of this source code is governed by an MIT-style + * license that can be found in the LICENSE file or at + * https://opensource.org/licenses/MIT. + * ============================================================================= + */ +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + * ============================================================================= + */ +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2019 Google LLC + * + * Use of this source code is governed by an MIT-style + * license that can be found in the LICENSE file or at + * https://opensource.org/licenses/MIT. + * ============================================================================= + */ +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + * ============================================================================= + */ +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2020 Google Inc. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2020 Google LLC + * + * Use of this source code is governed by an MIT-style + * license that can be found in the LICENSE file or at + * https://opensource.org/licenses/MIT. + * ============================================================================= + */ +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** +* @license +* Copyright 2018 Google LLC. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================================= +*/ +/** @license See the LICENSE file. */ +//# sourceMappingURL=human.js.map diff --git a/dist/human.js.map b/dist/human.js.map new file mode 100644 index 00000000..3c55fc68 --- /dev/null +++ b/dist/human.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["empty:/home/vlado/dev/human/node_modules/node-fetch/browser.js", "empty:util", 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"src/facemesh/keypoints.js", "src/facemesh/box.js", "src/facemesh/util.js", "src/facemesh/pipeline.js", "src/facemesh/uvcoords.js", "src/facemesh/index.js", "src/ssrnet/index.js", "src/posenet/modelBase.js", "src/posenet/modelMobileNet.js", "src/posenet/heapSort.js", "src/posenet/buildParts.js", "src/posenet/keypoints.js", "src/posenet/vectors.js", "src/posenet/decodePose.js", "src/posenet/decodeMultiple.js", "src/posenet/decoders.js", "src/posenet/decodeSingle.js", "src/posenet/util.js", "src/posenet/modelPoseNet.js", "src/posenet/index.js", "src/handpose/box.js", "src/handpose/hand.js", "src/handpose/keypoints.js", "src/handpose/util.js", "src/handpose/pipeline.js", "src/handpose/index.js", "src/config.js", "src/index.js"], + "sourcesContent": ["", "", "", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {FusedBatchMatMulConfig, FusedConv2DConfig} from '../ops/fused_types';\nimport {Backend, DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {BackendValues, DataType, Rank, ShapeMap} from '../types';\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n kernelMs: number|{error: string};\n getExtraProfileInfo?(): string; // a field for additional timing information\n // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n read(dataId: DataId): Promise;\n readSync(dataId: DataId): BackendValues;\n disposeData(dataId: DataId): void;\n write(values: BackendValues, shape: number[], dtype: DataType): DataId;\n move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType):\n void;\n memory(): {unreliable: boolean;}; // Backend-specific information.\n /** Returns number of data ids currently in the storage. */\n numDataIds(): number;\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n private data = new WeakMap();\n private dataIdsCount = 0;\n\n constructor(private backend: KernelBackend, private dataMover: DataMover) {}\n\n get(dataId: DataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(this.backend, dataId);\n }\n return this.data.get(dataId);\n }\n\n set(dataId: DataId, value: T): void {\n this.dataIdsCount++;\n this.data.set(dataId, value);\n }\n\n has(dataId: DataId): boolean {\n return this.data.has(dataId);\n }\n\n delete(dataId: DataId): boolean {\n this.dataIdsCount--;\n return this.data.delete(dataId);\n }\n\n numDataIds(): number {\n return this.dataIdsCount;\n }\n}\n\nexport interface DataMover {\n /**\n * To be called by backends whenever they see a dataId that they don't own.\n * Upon calling this method, the mover will fetch the tensor from another\n * backend and register it with the current active backend.\n */\n moveData(backend: KernelBackend, dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n time(f: () => void): Promise;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, Backend, BackendTimer {\n time(f: () => void): Promise {\n return notYetImplemented('time');\n }\n read(dataId: object): Promise {\n return notYetImplemented('read');\n }\n readSync(dataId: object): BackendValues {\n return notYetImplemented('readSync');\n }\n numDataIds(): number {\n return notYetImplemented('numDataIds');\n }\n disposeData(dataId: object): void {\n return notYetImplemented('disposeData');\n }\n write(values: BackendValues, shape: number[], dtype: DataType): DataId {\n return notYetImplemented('write');\n }\n move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType):\n void {\n return notYetImplemented('move');\n }\n memory(): {unreliable: boolean; reasons?: string[]} {\n return notYetImplemented('memory');\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision(): 16|32 {\n return notYetImplemented('floatPrecision');\n }\n /** Returns the smallest representable number. */\n epsilon(): number {\n return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n }\n\n batchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean,\n transposeB: boolean): Tensor3D {\n return notYetImplemented('batchMatMul');\n }\n\n fusedBatchMatMul(\n {a, b, transposeA, transposeB, bias, activation, preluActivationWeights}:\n FusedBatchMatMulConfig): Tensor3D {\n return notYetImplemented('fusedBatchMatMul');\n }\n\n slice(x: T, begin: number[], size: number[]): T {\n return notYetImplemented('slice');\n }\n stridedSlice(\n x: T, begin: number[], end: number[], strides: number[]): T {\n return notYetImplemented('stridedSlice');\n }\n unstack(x: Tensor, axis: number): Tensor[] {\n return notYetImplemented('unstack');\n }\n reverse(a: T, axis: number[]): T {\n return notYetImplemented('reverse');\n }\n\n concat(tensors: Tensor[], axis: number): Tensor {\n return notYetImplemented('concat');\n }\n\n neg(a: T): T {\n return notYetImplemented('neg');\n }\n\n add(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('add');\n }\n addN(tensors: T[]): T {\n return notYetImplemented('addN');\n }\n subtract(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('subtract');\n }\n multiply(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('multiply');\n }\n realDivide(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('realDivide');\n }\n floorDiv(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('floorDiv');\n }\n\n sum(x: Tensor, axes: number[]): Tensor {\n return notYetImplemented('sum');\n }\n prod(x: Tensor, axes: number[]): Tensor {\n return notYetImplemented('prod');\n }\n\n unsortedSegmentSum(\n x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n return notYetImplemented('unsortedSegmentSum');\n }\n\n argMin(x: Tensor, axis: number): Tensor {\n return notYetImplemented('argMin');\n }\n argMax(x: Tensor, axis: number): Tensor {\n return notYetImplemented('argMax');\n }\n\n equal(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('equal');\n }\n notEqual(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('notEqual');\n }\n\n less(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('less');\n }\n lessEqual(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('lessEqual');\n }\n\n greater(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('greater');\n }\n greaterEqual(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('greaterEqual');\n }\n\n logicalNot(a: T): T {\n return notYetImplemented('logicalNot');\n }\n logicalAnd(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('logicalAnd');\n }\n logicalOr(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('logicalOr');\n }\n\n where(condition: Tensor): Tensor2D {\n return notYetImplemented('where');\n }\n select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('select');\n }\n\n topk(x: T, k: number, sorted: boolean): [T, T] {\n return notYetImplemented('topk');\n }\n\n min(x: Tensor, axes: number[]): Tensor {\n return notYetImplemented('min');\n }\n minimum(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('minimum');\n }\n\n mod(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('mod');\n }\n\n max(x: Tensor, axes: number[]): Tensor {\n return notYetImplemented('max');\n }\n maximum(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('maximum');\n }\n\n all(x: Tensor, axes: number[]): Tensor {\n return notYetImplemented('all');\n }\n any(x: Tensor, axes: number[]): Tensor {\n return notYetImplemented('any');\n }\n\n squaredDifference(a: Tensor, b: Tensor): Tensor {\n return notYetImplemented('squaredDifference');\n }\n\n ceil(x: T): T {\n return notYetImplemented('ceil');\n }\n floor(x: T): T {\n return notYetImplemented('floor');\n }\n round(x: T): T {\n return notYetImplemented('round');\n }\n\n sign(x: T): T {\n return notYetImplemented('sign');\n }\n\n isNaN(x: T): T {\n return notYetImplemented('isNaN');\n }\n isInf(x: T): T {\n return notYetImplemented('isInf');\n }\n isFinite(x: T): T {\n return notYetImplemented('isFinite');\n }\n\n pow(a: T, b: Tensor): T {\n return notYetImplemented('pow');\n }\n exp(x: T): T {\n return notYetImplemented('exp');\n }\n expm1(x: T): T {\n return notYetImplemented('expm1');\n }\n softmax(x: T, dim: number): T {\n return notYetImplemented('softmax');\n }\n log(x: T): T {\n return notYetImplemented('log');\n }\n log1p(x: T): T {\n return notYetImplemented('log1p');\n }\n sqrt(x: T): T {\n return notYetImplemented('sqrt');\n }\n rsqrt(x: T): T {\n return notYetImplemented('rsqrt');\n }\n square(x: T): T {\n return notYetImplemented('square');\n }\n reciprocal(x: T): T {\n return notYetImplemented('reciprocal');\n }\n relu(x: T): T {\n return notYetImplemented('relu');\n }\n relu6(x: T): T {\n return notYetImplemented('relu6');\n }\n prelu(x: T, a: T): T {\n return notYetImplemented('prelu');\n }\n elu(x: T): T {\n return notYetImplemented('elu');\n }\n eluDer(dy: T, y: T): T {\n return notYetImplemented('eluDer');\n }\n selu(x: T): T {\n return notYetImplemented('selu');\n }\n int(x: T): T {\n return notYetImplemented('int');\n }\n\n clip(x: T, min: number, max: number): T {\n return notYetImplemented('clip');\n }\n\n abs(x: T): T {\n return notYetImplemented('abs');\n }\n complexAbs(x: T): T {\n return notYetImplemented('complexAbs');\n }\n\n sigmoid(x: T): T {\n return notYetImplemented('sigmoid');\n }\n\n softplus(x: T): T {\n return notYetImplemented('softplus');\n }\n\n sin(x: T): T {\n return notYetImplemented('sin');\n }\n cos(x: T): T {\n return notYetImplemented('cos');\n }\n tan(x: T): T {\n return notYetImplemented('tan');\n }\n\n asin(x: T): T {\n return notYetImplemented('asin');\n }\n acos(x: T): T {\n return notYetImplemented('acos');\n }\n atan(x: T): T {\n return notYetImplemented('atan');\n }\n atan2(a: T, b: T): T {\n return notYetImplemented('atan2');\n }\n\n sinh(x: T): T {\n return notYetImplemented('sinh');\n }\n cosh(x: T): T {\n return notYetImplemented('cosh');\n }\n tanh(x: T): T {\n return notYetImplemented('tanh');\n }\n\n asinh(x: T): T {\n return notYetImplemented('asinh');\n }\n acosh(x: T): T {\n return notYetImplemented('acosh');\n }\n atanh(x: T): T {\n return notYetImplemented('atanh');\n }\n\n erf(x: T): T {\n return notYetImplemented('erf');\n }\n\n step(x: T, alpha: number): T {\n return notYetImplemented('step');\n }\n\n fusedConv2d(\n {input, filter, convInfo, bias, activation, preluActivationWeights}:\n FusedConv2DConfig): Tensor4D {\n return notYetImplemented('fusedConv2d');\n }\n\n conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n return notYetImplemented('conv2d');\n }\n conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n return notYetImplemented('conv2dDerInput');\n }\n conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n return notYetImplemented('conv2dDerFilter');\n }\n\n fusedDepthwiseConv2D(\n {input, filter, convInfo, bias, activation, preluActivationWeights}:\n FusedConv2DConfig): Tensor4D {\n return notYetImplemented('fusedDepthwiseConv2D');\n }\n\n depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n return notYetImplemented('depthwiseConv2D');\n }\n depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n return notYetImplemented('depthwiseConv2DDerInput');\n }\n depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n return notYetImplemented('depthwiseConv2DDerFilter');\n }\n conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n return notYetImplemented('conv3d');\n }\n conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n Tensor5D {\n return notYetImplemented('conv3dDerInput');\n }\n conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n return notYetImplemented('conv3dDerFilter');\n }\n maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n return notYetImplemented('maxPool');\n }\n maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n return notYetImplemented('maxPoolBackprop');\n }\n avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n return notYetImplemented('avgPool');\n }\n avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n return notYetImplemented('avgPoolBackprop');\n }\n avgPool3d(x: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n return notYetImplemented('avgPool3d');\n }\n avgPool3dBackprop(dy: Tensor5D, x: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n return notYetImplemented('avgPool3dBackprop');\n }\n maxPool3d(x: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n return notYetImplemented('maxPool3d');\n }\n maxPool3dBackprop(\n dy: Tensor5D, x: Tensor5D, y: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n return notYetImplemented('maxPool3dBackprop');\n }\n\n reshape(x: T, shape: ShapeMap[R]):\n Tensor {\n return notYetImplemented('reshape');\n }\n cast(x: T, dtype: DataType): T {\n return notYetImplemented('cast');\n }\n\n tile(x: T, reps: number[]): T {\n return notYetImplemented('tile');\n }\n\n pad(\n x: T, paddings: Array<[number, number]>, constantValue: number): T {\n return notYetImplemented('pad');\n }\n\n transpose(x: T, perm: number[]): T {\n return notYetImplemented('transpose');\n }\n\n gather(x: T, indices: Tensor1D, axis: number): T {\n return notYetImplemented('gather');\n }\n\n gatherND(x: Tensor, indices: Tensor): Tensor {\n return notYetImplemented('gatherND');\n }\n\n scatterND(\n indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor {\n return notYetImplemented('scatterND');\n }\n\n batchToSpaceND(\n x: T, blockShape: number[], crops: number[][]): T {\n return notYetImplemented('batchToSpaceND');\n }\n\n spaceToBatchND(\n x: T, blockShape: number[], paddings: number[][]): T {\n return notYetImplemented('spaceToBatchND');\n }\n\n resizeBilinear(\n x: Tensor4D, newHeight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n return notYetImplemented('resizeBilinear');\n }\n\n resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n Tensor4D {\n return notYetImplemented('resizeBilinearBackprop');\n }\n\n resizeNearestNeighbor(\n x: Tensor4D, newHEight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n return notYetImplemented('resizeNearestNeighbor');\n }\n\n resizeNearestNeighborBackprop(\n dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n return notYetImplemented('resizeNearestNeighborBackprop');\n }\n\n batchNorm(\n x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n offset?: Tensor4D|Tensor1D, scale?: Tensor4D|Tensor1D,\n varianceEpsilon?: number): Tensor4D {\n return notYetImplemented('batchNorm');\n }\n\n localResponseNormalization4D(\n x: Tensor4D, radius: number, bias: number, alpha: number,\n beta: number): Tensor4D {\n return notYetImplemented('localResponseNormalization4D');\n }\n\n LRNGrad(\n dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,\n bias: number, alpha: number, beta: number): Tensor4D {\n return notYetImplemented('LRNGrad');\n }\n\n multinomial(\n logits: Tensor2D, normalized: boolean, numSamples: number,\n seed: number): Tensor2D {\n return notYetImplemented('multinomial');\n }\n\n oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n Tensor2D {\n return notYetImplemented('oneHot');\n }\n\n cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n Tensor {\n return notYetImplemented('cumsum');\n }\n\n nonMaxSuppression(\n boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n iouThreshold: number, scoreThreshold?: number): Tensor1D {\n return notYetImplemented('nonMaxSuppression');\n }\n\n fft(x: Tensor2D): Tensor2D {\n return notYetImplemented('fft');\n }\n ifft(x: Tensor2D): Tensor2D {\n return notYetImplemented('ifft');\n }\n complex(real: T, imag: T): T {\n return notYetImplemented('complex');\n }\n real(input: T): T {\n return notYetImplemented('real');\n }\n imag(input: T): T {\n return notYetImplemented('imag');\n }\n\n cropAndResize(\n image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n cropSize: [number, number], method: 'bilinear'|'nearest',\n extrapolationValue: number): Tensor4D {\n return notYetImplemented('cropAndResize');\n }\n\n depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D {\n return notYetImplemented('depthToSpace');\n }\n\n // Aligns with the \"SplitV\" kernel in TensorFlow.\n split(value: T, sizeSplits: number[], axis: number): T[] {\n return notYetImplemented('split');\n }\n\n sparseToDense(\n sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n defaultValue: Scalar): Tensor {\n return notYetImplemented('sparseToDense');\n }\n\n diag(x: Tensor): Tensor {\n return notYetImplemented('diag');\n }\n\n fill(\n shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor {\n return notYetImplemented('fill');\n }\n\n onesLike(x: Tensor): Tensor {\n return notYetImplemented('onesLike');\n }\n\n zerosLike(x: Tensor): Tensor {\n return notYetImplemented('zerosLike');\n }\n\n linspace(start: number, stop: number, num: number): Tensor1D {\n return notYetImplemented('linspace');\n }\n\n dispose(): void {\n return notYetImplemented('dispose');\n }\n}\n\nfunction notYetImplemented(kernelName: string): never {\n throw new Error(\n `'${kernelName}' not yet implemented or not found in the registry. ` +\n `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Platform} from './platforms/platform';\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n\ntype FlagValue = number|boolean;\ntype FlagEvaluationFn = (() => FlagValue)|(() => Promise);\nexport type Flags = {\n [featureName: string]: FlagValue\n};\nexport type FlagRegistryEntry = {\n evaluationFn: FlagEvaluationFn;\n setHook?: (value: FlagValue) => void;\n};\n\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n private flags: Flags = {};\n private flagRegistry: {[flagName: string]: FlagRegistryEntry} = {};\n\n private urlFlags: Flags = {};\n\n platformName: string;\n platform: Platform;\n\n // tslint:disable-next-line: no-any\n constructor(public global: any) {\n this.populateURLFlags();\n }\n\n setPlatform(platformName: string, platform: Platform) {\n if (this.platform != null) {\n console.warn(\n `Platform ${this.platformName} has already been set. ` +\n `Overwriting the platform with ${platform}.`);\n }\n this.platformName = platformName;\n this.platform = platform;\n }\n\n registerFlag(\n flagName: string, evaluationFn: FlagEvaluationFn,\n setHook?: (value: FlagValue) => void) {\n this.flagRegistry[flagName] = {evaluationFn, setHook};\n\n // Override the flag value from the URL. This has to happen here because the\n // environment is initialized before flags get registered.\n if (this.urlFlags[flagName] != null) {\n const flagValue = this.urlFlags[flagName];\n console.warn(\n `Setting feature override from URL ${flagName}: ${flagValue}.`);\n this.set(flagName, flagValue);\n }\n }\n\n async getAsync(flagName: string): Promise {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n\n this.flags[flagName] = await this.evaluateFlag(flagName);\n return this.flags[flagName];\n }\n\n get(flagName: string): FlagValue {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n\n const flagValue = this.evaluateFlag(flagName);\n if (flagValue instanceof Promise) {\n throw new Error(\n `Flag ${flagName} cannot be synchronously evaluated. ` +\n `Please use getAsync() instead.`);\n }\n\n this.flags[flagName] = flagValue;\n\n return this.flags[flagName];\n }\n\n getNumber(flagName: string): number {\n return this.get(flagName) as number;\n }\n\n getBool(flagName: string): boolean {\n return this.get(flagName) as boolean;\n }\n\n getFlags(): Flags {\n return this.flags;\n }\n // For backwards compatibility.\n get features(): Flags {\n return this.flags;\n }\n\n set(flagName: string, value: FlagValue): void {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(\n `Cannot set flag ${flagName} as it has not been registered.`);\n }\n this.flags[flagName] = value;\n if (this.flagRegistry[flagName].setHook != null) {\n this.flagRegistry[flagName].setHook(value);\n }\n }\n\n private evaluateFlag(flagName: string): FlagValue|Promise {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(\n `Cannot evaluate flag '${flagName}': no evaluation function found.`);\n }\n return this.flagRegistry[flagName].evaluationFn();\n }\n\n setFlags(flags: Flags) {\n this.flags = Object.assign({}, flags);\n }\n\n reset() {\n this.flags = {};\n this.urlFlags = {};\n this.populateURLFlags();\n }\n\n private populateURLFlags(): void {\n if (typeof this.global === 'undefined' ||\n typeof this.global.location === 'undefined' ||\n typeof this.global.location.search === 'undefined') {\n return;\n }\n\n const urlParams = getQueryParams(this.global.location.search);\n if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n keyValues.forEach(keyValue => {\n const [key, value] = keyValue.split(':') as [string, string];\n this.urlFlags[key] = parseValue(key, value);\n });\n }\n }\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n const params = {};\n queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n decodeParam(params, t[0], t[1]);\n return t.join('=');\n });\n return params;\n}\n\nfunction decodeParam(\n params: {[key: string]: string}, name: string, value?: string) {\n params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n\nfunction parseValue(flagName: string, value: string): FlagValue {\n value = value.toLowerCase();\n if (value === 'true' || value === 'false') {\n return value === 'true';\n } else if (`${+ value}` === value) {\n return +value;\n }\n throw new Error(\n `Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n return ENV;\n}\n\nexport let ENV: Environment = null;\nexport function setEnvironmentGlobal(environment: Environment) {\n ENV = environment;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace: {_tfGlobals: Map};\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace(): {_tfGlobals: Map} {\n if (globalNameSpace == null) {\n // tslint:disable-next-line:no-any\n let ns: any;\n if (typeof (window) !== 'undefined') {\n ns = window;\n } else if (typeof (global) !== 'undefined') {\n ns = global;\n } else if (typeof (process) !== 'undefined') {\n ns = process;\n } else if (typeof (self) !== 'undefined') {\n ns = self;\n } else {\n throw new Error('Could not find a global object');\n }\n globalNameSpace = ns;\n }\n return globalNameSpace;\n}\n\n// tslint:disable-next-line:no-any\nfunction getGlobalMap(): Map {\n const ns = getGlobalNamespace();\n if (ns._tfGlobals == null) {\n ns._tfGlobals = new Map();\n }\n return ns._tfGlobals;\n}\n\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n * the first time it is fetched.\n */\nexport function getGlobal(key: string, init: () => T): T {\n const globalMap = getGlobalMap();\n if (globalMap.has(key)) {\n return globalMap.get(key);\n } else {\n const singleton = init();\n globalMap.set(key, singleton);\n return globalMap.get(key);\n }\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Allow UpperCamelCase variable names\n// tslint:disable: variable-name\n// Unfortunately just enabling PascalCase per file (tslint:enable:\n// allow-pascal-case) doesn't work.\nimport {NamedTensorInfoMap, TensorInfo} from './kernel_registry';\nimport {ExplicitPadding} from './ops/conv_util';\nimport {Activation} from './ops/fused_types';\nimport {DataType, PixelData} from './types';\n\nexport const Abs = 'Abs';\nexport type AbsInputs = UnaryInputs;\n\nexport const Acos = 'Acos';\nexport type AcosInputs = UnaryInputs;\n\nexport const Acosh = 'Acosh';\nexport type AcoshInputs = UnaryInputs;\n\nexport const Add = 'Add';\nexport type AddInputs = BinaryInputs;\n\nexport const AddN = 'AddN';\nexport type AddNInputs = TensorInfo[];\n\nexport const All = 'All';\nexport type AllInputs = Pick;\nexport interface AllAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Any = 'Any';\nexport type AnyInputs = Pick;\nexport interface AnyAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const ArgMax = 'ArgMax';\nexport type ArgMaxInputs = Pick;\nexport interface ArgMaxAttrs {\n axis: number;\n}\n\nexport const ArgMin = 'ArgMin';\nexport type ArgMinInputs = Pick;\nexport interface ArgMinAttrs {\n axis: number;\n}\n\nexport const Asin = 'Asin';\nexport type AsinInputs = UnaryInputs;\n\nexport const Asinh = 'Asinh';\nexport type AsinhInputs = UnaryInputs;\n\nexport const Atan = 'Atan';\nexport type AtanInputs = UnaryInputs;\n\nexport const Atanh = 'Atanh';\nexport type AtanhInputs = UnaryInputs;\n\nexport const Atan2 = 'Atan2';\nexport type Atan2Inputs = BinaryInputs;\n\nexport const AvgPool = 'AvgPool';\nexport type AvgPoolInputs = Pick;\nexport interface AvgPoolAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport type AvgPoolBackpropInputs = Pick;\nexport interface AvgPoolBackpropAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n}\n\nexport const AvgPool3D = 'AvgPool3D';\nexport type AvgPool3DInputs = Pick;\nexport interface AvgPool3DAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n dataFormat: 'NDHWC'|'NCDHW';\n dilations?: [number, number, number]|number;\n}\n\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport type AvgPool3DBackpropInputs = Pick;\nexport interface AvgPool3DBackpropAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dilations: [number, number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const BatchMatMul = 'BatchMatMul';\nexport type BatchMatMulInputs = Pick;\nexport interface BatchMatMulAttrs {\n transposeA: boolean;\n transposeB: boolean;\n}\n\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport type BatchToSpaceNDInputs = Pick;\nexport interface BatchToSpaceNDAttrs {\n blockShape: number[];\n crops: number[][];\n}\n\nexport type BinaryInputs = Pick;\n\nexport const BroadcastTo = 'BroadcastTo';\nexport type BroadcastToInputs = Pick;\nexport interface BroadCastToAttrs {\n shape: number[];\n inputShape: number[]; // for gradient\n}\n\nexport const Cast = 'Cast';\nexport type CastInputs = UnaryInputs;\nexport interface CastAttrs {\n dtype: DataType;\n}\n\nexport const Ceil = 'Ceil';\nexport type CeilInputs = UnaryInputs;\n\nexport const ClipByValue = 'ClipByValue';\nexport type ClipByValueInputs = UnaryInputs;\nexport interface ClipByValueAttrs {\n clipValueMin: number;\n clipValueMax: number;\n}\n\nexport const Complex = 'Complex';\nexport type ComplexInputs = Pick;\n\nexport const Concat = 'Concat';\nexport type ConcatInputs = TensorInfo[];\nexport interface ConcatAttrs {\n axis: number;\n}\n\nexport const Conv2D = 'Conv2D';\nexport type Conv2DInputs = Pick;\nexport interface Conv2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport type Conv2DBackpropFilterInputs = Pick;\nexport interface Conv2DBackpropFilterAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport type Conv2DBackpropInputInputs = Pick;\nexport interface Conv2DBackpropInputAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dimRoundingMode?: 'floor'|'round'|'ceil';\n inputShape: [number, number, number, number];\n}\n\nexport const Conv3D = 'Conv3D';\nexport type Conv3DInputs = Pick;\nexport interface Conv3DAttrs {\n strides: [number, number, number]|number;\n pad: 'valid'|'same';\n dataFormat: 'NDHWC'|'NCDHW';\n dilations: [number, number, number]|number;\n}\n\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport type Conv3DBackpropFilterInputs = Pick;\n\nexport interface Conv3DBackpropFilterAttrs {\n strides: [number, number, number]|number;\n pad: 'valid'|'same';\n}\n\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport type Conv3DBackpropInputInputs = Pick;\nexport interface Conv3DBackpropInputAttrs {\n pad: 'valid'|'same';\n}\n\nexport const Cos = 'Cos';\nexport type CosInputs = UnaryInputs;\n\nexport const Cosh = 'Cosh';\nexport type CoshInputs = UnaryInputs;\n\nexport const Cumsum = 'Cumsum';\nexport type CumsumInputs = Pick;\nexport interface CumsumAttrs {\n axis: number;\n exclusive: boolean;\n reverse: boolean;\n}\n\nexport const CropAndResize = 'CropAndResize';\nexport type CropAndResizeInputs =\n Pick;\nexport interface CropAndResizeAttrs {\n cropSize: [number, number];\n method: 'bilinear'|'nearest';\n extrapolationValue: number;\n}\n\nexport const DepthToSpace = 'DepthToSpace';\nexport type DepthToSpaceInputs = Pick;\nexport interface DepthToSpaceAttrs {\n blockSize: number;\n dataFormat: 'NHWC'|'NCHW';\n}\n\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport type DepthwiseConv2dNativeInputs =\n Pick;\nexport interface DepthwiseConv2dNativeAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const DepthwiseConv2dNativeBackpropFilter =\n 'DepthwiseConv2dNativeBackpropFilter';\nexport type DepthwiseConv2dNativeBackpropFilterInputs =\n Pick;\n\nexport const DepthwiseConv2dNativeBackpropInput =\n 'DepthwiseConv2dNativeBackpropInput';\nexport type DepthwiseConv2dNativeBackpropInputInputs =\n Pick;\n\nexport const Diag = 'Diag';\nexport type DiagInputs = Pick;\n\nexport const Dilation2D = 'Dilation2D';\nexport type Dilation2DInputs = Pick;\nexport interface Dilation2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dilations: [number, number]|number;\n}\n\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport type Dilation2DBackpropInputInputs =\n Pick;\n\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport type Dilation2DBackpropFilterInputs =\n Pick;\n\nexport const Div = 'Div';\nexport type DivInputs = BinaryInputs;\n\nexport const Elu = 'Elu';\nexport type EluInputs = Pick;\n\nexport const EluGrad = 'EluGrad';\nexport type EluGradInputs = Pick;\n\nexport const Erf = 'Erf';\nexport type ErfInputs = UnaryInputs;\n\nexport const Equal = 'Equal';\nexport type EqualInputs = BinaryInputs;\n\nexport const Exp = 'Exp';\nexport type ExpInputs = UnaryInputs;\n\nexport const Expm1 = 'Expm1';\nexport type Expm1Inputs = UnaryInputs;\n\nexport const FFT = 'FFT';\nexport type FFTInputs = Pick;\n\nexport const Fill = 'Fill';\nexport interface FillAttrs {\n shape: number[];\n value: number|string;\n dtype: DataType;\n}\n\nexport const FlipLeftRight = 'FlipLeftRight';\nexport type FlipLeftRightInputs = Pick;\n\nexport const Floor = 'Floor';\nexport type FloorInputs = UnaryInputs;\n\nexport const FloorDiv = 'FloorDiv';\nexport type FloorDivInputs = BinaryInputs;\n\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport type FusedBatchNormInputs =\n Pick;\nexport interface FusedBatchNormAttrs {\n varianceEpsilon: number;\n}\n\nexport const GatherV2 = 'GatherV2';\nexport type GatherV2Inputs = Pick;\nexport interface GatherV2Attrs {\n axis: number;\n}\n\nexport const GatherNd = 'GatherNd';\nexport type GatherNdInputs = Pick;\n\nexport const Greater = 'Greater';\nexport type GreaterInputs = BinaryInputs;\n\nexport const GreaterEqual = 'GreaterEqual';\nexport type GreaterEqualInputs = BinaryInputs;\n\nexport const Identity = 'Identity';\nexport type IdentityInputs = Pick;\n\nexport const IFFT = 'IFFT';\nexport type IFFTInputs = Pick;\n\nexport const Imag = 'Imag';\nexport type ImagInputs = Pick;\n\nexport const IsFinite = 'IsFinite';\nexport type IsFiniteInputs = UnaryInputs;\n\nexport const IsInf = 'IsInf';\nexport type IsInfInputs = UnaryInputs;\n\nexport const IsNan = 'IsNan';\nexport type IsNanInputs = UnaryInputs;\n\nexport const Less = 'Less';\nexport type LessInputs = BinaryInputs;\n\nexport const LessEqual = 'LessEqual';\nexport type LessEqualInputs = BinaryInputs;\n\nexport const LinSpace = 'LinSpace';\nexport interface LinSpaceAttrs {\n start: number;\n stop: number;\n num: number;\n}\nexport const Log = 'Log';\nexport type LogInputs = UnaryInputs;\n\nexport const Log1p = 'Log1p';\nexport type Log1pInputs = UnaryInputs;\n\nexport const LogicalAnd = 'LogicalAnd';\nexport type LogicalAndInputs = BinaryInputs;\n\nexport const LogicalNot = 'LogicalNot';\nexport type LogicalNotInputs = Pick;\n\nexport const LogicalOr = 'LogicalOr';\nexport type LogicalOrInputs = BinaryInputs;\n\nexport const LogSoftmax = 'LogSoftmax';\nexport type LogSoftmaxInputs = Pick;\nexport interface LogSoftmaxAttrs {\n axis: number;\n}\n\nexport const LRN = 'LRN';\nexport type LRNInputs = Pick;\nexport interface LRNAttrs {\n depthRadius: number;\n bias: number;\n alpha: number;\n beta: number;\n}\n\nexport const LRNBackprop = 'LRNBackprop';\nexport type LRNBackpropInputs = Pick;\nexport interface LRNBackpropAttrs {\n depthRadius: number;\n bias: number;\n alpha: number;\n beta: number;\n}\n\nexport const Max = 'Max';\nexport type MaxInputs = Pick;\nexport interface MaxAttrs {\n reductionIndices: number|number[];\n keepDims: boolean;\n}\n\nexport const Maximum = 'Maximum';\nexport type MaximumInputs = BinaryInputs;\n\nexport const MaxPool = 'MaxPool';\nexport type MaxPoolInputs = Pick;\nexport interface MaxPoolAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport type MaxPoolBackpropInputs =\n Pick;\nexport interface MaxPoolBackpropAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3D = 'MaxPool3D';\nexport type MaxPool3DInputs = Pick;\nexport interface MaxPool3DAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dataFormat: 'NDHWC'|'NCDHW';\n dilations?: [number, number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport type MaxPool3DBackpropInputs =\n Pick;\nexport interface MaxPool3DBackpropAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dilations?: [number, number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport type MaxPoolWithArgmaxInputs = Pick;\nexport interface MaxPoolWithArgmaxAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n includeBatchInIndex: boolean;\n}\n\nexport const Mean = 'Mean';\nexport type MeanInputs = Pick;\nexport interface MeanAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Min = 'Min';\nexport type MinInputs = Pick;\nexport interface MinAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Minimum = 'Minimum';\nexport type MinimumInputs = BinaryInputs;\n\nexport const Mod = 'Mod';\nexport type ModInputs = BinaryInputs;\n\nexport const Multiply = 'Multiply';\nexport type MultiplyInputs = BinaryInputs;\n\nexport const Negate = 'Negate';\nexport type NegateInputs = UnaryInputs;\n\nexport const NotEqual = 'NotEqual';\nexport type NotEqualInputs = BinaryInputs;\n\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport type NonMaxSuppressionV3Inputs =\n Pick;\nexport interface NonMaxSuppressionV3Attrs {\n maxOutputSize: number;\n iouThreshold: number;\n scoreThreshold: number;\n}\n\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport type NonMaxSuppressionV4Inputs =\n Pick;\nexport interface NonMaxSuppressionV4Attrs {\n maxOutputSize: number;\n iouThreshold: number;\n scoreThreshold: number;\n padToMaxOutputSize: boolean;\n}\n\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport type NonMaxSuppressionV5Inputs =\n Pick;\nexport interface NonMaxSuppressionV5Attrs {\n maxOutputSize: number;\n iouThreshold: number;\n scoreThreshold: number;\n softNmsSigma: number;\n}\n\nexport const OnesLike = 'OnesLike';\nexport type OnesLikeInputs = UnaryInputs;\n\nexport const OneHot = 'OneHot';\nexport type OneHotInputs = Pick;\nexport interface OneHotAttrs {\n depth: number;\n onValue: number;\n offValue: number;\n}\n\nexport const PadV2 = 'PadV2';\nexport type PadV2Inputs = Pick;\nexport interface PadV2Attrs {\n paddings: Array<[number, number]>;\n constantValue: number;\n}\n\nexport const Pool = 'Pool';\nexport type PoolInputs = Pick;\n\nexport const Pow = 'Pow';\nexport type PowInputs = BinaryInputs;\n\nexport const Prelu = 'Prelu';\nexport type PreluInputs = Pick;\n\nexport const Prod = 'Prod';\nexport type ProdInputs = Pick;\nexport interface ProdAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Range = 'Range';\nexport interface RangeAttrs {\n start: number;\n stop: number;\n step: number;\n dtype: 'float32'|'int32';\n}\n\nexport const Real = 'Real';\nexport type RealInputs = Pick;\n\nexport const Reciprocal = 'Reciprocal';\nexport type ReciprocalInputs = UnaryInputs;\n\nexport const Relu = 'Relu';\nexport type ReluInputs = Pick;\n\nexport const Reshape = 'Reshape';\nexport type ReshapeInputs = Pick;\nexport interface ReshapeAttrs {\n shape: number[];\n}\n\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport type ResizeNearestNeighborInputs = Pick;\nexport interface ResizeNearestNeighborAttrs {\n alignCorners: boolean;\n size: [number, number];\n}\n\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport type ResizeNearestNeighborGradInputs =\n Pick;\n\nexport const ResizeBilinear = 'ResizeBilinear';\nexport type ResizeBilinearInputs = Pick;\nexport interface ResizeBilinearAttrs {\n alignCorners: boolean;\n size: [number, number];\n}\n\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport type ResizeBilinearGradInputs = Pick;\n\nexport const Relu6 = 'Relu6';\nexport type Relu6Inputs = Pick;\n\nexport const Reverse = 'Reverse';\nexport type ReverseInputs = Pick;\nexport interface ReverseAttrs {\n dims: number|number[];\n}\n\nexport const Round = 'Round';\nexport type RoundInputs = UnaryInputs;\n\nexport const Rsqrt = 'Rsqrt';\nexport type RsqrtInputs = UnaryInputs;\n\nexport const ScatterNd = 'ScatterNd';\nexport type ScatterNdInputs = Pick;\nexport interface ScatterNdAttrs {\n shape: number[];\n}\n\nexport const SelectV2 = 'SelectV2';\nexport type SelectV2Inputs = Pick;\n\nexport const Selu = 'Selu';\nexport type SeluInputs = Pick;\n\nexport const Slice = 'Slice';\nexport type SliceInputs = Pick;\nexport interface SliceAttrs {\n begin: number|number[];\n size: number|number[];\n}\nexport const Sin = 'Sin';\nexport type SinInputs = UnaryInputs;\n\nexport const Sinh = 'Sinh';\nexport type SinhInputs = UnaryInputs;\n\nexport const Sign = 'Sign';\nexport type SignInputs = UnaryInputs;\n\nexport const Sigmoid = 'Sigmoid';\nexport type SigmoidInputs = UnaryInputs;\n\nexport const Softplus = 'Softplus';\nexport type SoftplusInputs = UnaryInputs;\n\nexport const Sqrt = 'Sqrt';\nexport type SqrtInputs = UnaryInputs;\n\nexport const Sum = 'Sum';\nexport type SumInputs = Pick;\nexport interface SumAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport type SpaceToBatchNDInputs = Pick;\nexport interface SpaceToBatchNDAttrs {\n blockShape: number[];\n paddings: number[][];\n}\n\nexport const SplitV = 'SplitV';\nexport type SplitVInputs = Pick;\nexport interface SplitVAttrs {\n numOrSizeSplits: number[]|number;\n axis: number;\n}\n\nexport const Softmax = 'Softmax';\nexport type SoftmaxInputs = Pick;\nexport interface SoftmaxAttrs {\n dim: number;\n}\n\nexport const SquaredDifference = 'SquaredDifference';\nexport type SquaredDifferenceInputs = BinaryInputs;\n\nexport const Square = 'Square';\nexport type SquareInputs = Pick;\n\nexport const Sub = 'Sub';\nexport type SubInputs = BinaryInputs;\n\nexport const SparseToDense = 'SparseToDense';\nexport type SparseToDenseInputs =\n Pick;\nexport interface SparseToDenseAttrs {\n outputShape: number[];\n}\n\nexport const StridedSlice = 'StridedSlice';\nexport type StridedSliceInputs = Pick;\nexport interface StridedSliceAttrs {\n begin: number[];\n end: number[];\n strides: number[];\n beginMask: number;\n endMask: number;\n ellipsisMask: number;\n newAxisMask: number;\n shrinkAxisMask: number;\n}\n\nexport const Tan = 'Tan';\nexport type TanInputs = UnaryInputs;\n\nexport const Tanh = 'Tanh';\nexport type TanhInputs = UnaryInputs;\n\nexport const Tile = 'Tile';\nexport type TileInputs = Pick;\nexport interface TileAttrs {\n reps: number[];\n}\n\nexport const TopK = 'TopK';\nexport type TopKInputs = Pick;\nexport interface TopKAttrs {\n k: number;\n sorted: boolean;\n}\n\nexport const Transpose = 'Transpose';\nexport type TransposeInputs = Pick;\nexport interface TransposeAttrs {\n perm: number[];\n}\n\nexport const Unique = 'Unique';\nexport type UniqueInputs = Pick;\nexport interface UniqueAttrs {\n axis: number;\n}\n\nexport type UnaryInputs = Pick;\n\nexport const Unpack = 'Unpack';\nexport type UnpackInputs = Pick;\nexport interface UnpackAttrs {\n axis: number;\n}\n\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport type UnsortedSegmentSumInputs =\n Pick;\nexport interface UnsortedSegmentSumAttrs {\n numSegments: number;\n}\n\nexport const ZerosLike = 'ZerosLike';\nexport type ZerosLikeInputs = UnaryInputs;\n\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport type StepInputs = UnaryInputs;\nexport interface StepAttrs {\n alpha: number;\n}\n\nexport const FromPixels = 'FromPixels';\nexport interface FromPixelsInputs {\n pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n HTMLVideoElement;\n}\nexport interface FromPixelsAttrs {\n numChannels: number;\n}\n\nexport const RotateWithOffset = 'RotateWithOffset';\nexport type RotateWithOffsetInputs = Pick;\nexport interface RotateWithOffsetAttrs {\n radians: number;\n fillValue: number|[number, number, number];\n center: number|[number, number];\n}\n\nexport const _FusedMatMul = '_FusedMatMul';\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulInputs extends NamedTensorInfoMap {\n a: TensorInfo;\n b: TensorInfo;\n bias?: TensorInfo;\n preluActivationWeights?: TensorInfo;\n}\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulAttrs {\n transposeA: boolean;\n transposeB: boolean;\n activation: Activation;\n}\n\nexport const FusedConv2D = 'FusedConv2D';\nexport interface FusedConv2DInputs extends NamedTensorInfoMap {\n x: TensorInfo;\n filter: TensorInfo;\n bias?: TensorInfo;\n preluActivationWeights?: TensorInfo;\n}\nexport interface FusedConv2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode: 'floor'|'round'|'ceil';\n activation: Activation;\n}\n\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\nexport interface FusedDepthwiseConv2DInputs extends NamedTensorInfoMap {\n x: TensorInfo;\n filter: TensorInfo;\n bias?: TensorInfo;\n preluActivationWeights?: TensorInfo;\n}\nexport interface FusedDepthwiseConv2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode: 'floor'|'round'|'ceil';\n activation: Activation;\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from './environment';\n\nimport {getGlobal} from './global_util';\nimport {NamedGradientMap} from './tape';\nimport {Tensor} from './tensor';\nimport {DataType, RecursiveArray} from './types';\n\nconst kernelRegistry =\n getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry =\n getGlobal('gradRegistry', () => new Map());\n\nexport type DataId = object;\n\ntype AttributeValue =\n number|number[]|boolean|boolean[]|string|string[]|NamedAttrMap;\n\n/** These are extra non-tensor/primitive params passed to kernel functions. */\nexport type Attribute = AttributeValue|RecursiveArray;\n\n/** Specifies the code to run when executing a kernel. */\nexport type KernelFunc = (params: {\n inputs: NamedTensorInfoMap,\n backend: {},\n attrs?: NamedAttrMap,\n}) => TensorInfo|TensorInfo[];\n\n/** The function to run when computing a gradient during backprop. */\nexport type GradFunc =\n (dy: Tensor|Tensor[], saved: Tensor[], attrs: NamedAttrMap) =>\n NamedGradientMap;\n\n/** Function that gets called after the backend initializes. */\nexport type KernelSetupFunc = (backend: {}) => void;\n/** Function that gets called right before the backend is disposed. */\nexport type KernelDisposeFunc = KernelSetupFunc;\n\n/** Config object for registering a kernel in the global registry. */\nexport interface KernelConfig {\n kernelName: string;\n backendName: string;\n kernelFunc: KernelFunc;\n setupFunc?: KernelSetupFunc;\n disposeFunc?: KernelDisposeFunc;\n}\n\n/** Config object for registering a gradient in the global registry. */\nexport interface GradConfig {\n kernelName: string;\n inputsToSave?: string[];\n // When saveAllInputs is true, all inputs will be saved. Only use this flag\n // if inputs is an array of Tensors.\n saveAllInputs?: boolean;\n outputsToSave?: boolean[];\n gradFunc: GradFunc;\n}\n\n/** Holds metadata for a given tensor. */\nexport interface TensorInfo {\n dataId: DataId;\n shape: number[];\n dtype: DataType;\n}\n\nexport interface NamedTensorInfoMap {\n [name: string]: TensorInfo;\n}\n\nexport interface NamedAttrMap {\n [name: string]: Attribute;\n}\n\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(\n kernelName: string, backendName: string): KernelConfig {\n const key = makeKey(kernelName, backendName);\n return kernelRegistry.get(key);\n}\n\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName: string): GradConfig {\n return gradRegistry.get(kernelName);\n}\n\nexport function getKernelsForBackend(backendName: string): KernelConfig[] {\n const it = kernelRegistry.entries();\n const result: KernelConfig[] = [];\n\n while (true) {\n const {done, value} = it.next();\n if (done) {\n break;\n }\n const [key, config] = value;\n const [backend, ] = key.split('_');\n if (backend === backendName) {\n result.push(config);\n }\n }\n return result;\n}\n\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config: KernelConfig) {\n const {kernelName, backendName} = config;\n const key = makeKey(kernelName, backendName);\n if (kernelRegistry.has(key)) {\n console.warn(\n `The kernel '${kernelName}' for backend ` +\n `'${backendName}' is already registered`);\n }\n kernelRegistry.set(key, config);\n}\n\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config: GradConfig) {\n const {kernelName} = config;\n\n if (gradRegistry.has(kernelName)) {\n // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n // to debug mode.\n if (env().getBool('DEBUG')) {\n console.warn(`Overriding the gradient for '${kernelName}'`);\n }\n }\n gradRegistry.set(kernelName, config);\n}\n\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(\n kernelName: string, backendName: string): void {\n const key = makeKey(kernelName, backendName);\n if (!kernelRegistry.has(key)) {\n throw new Error(\n `The kernel '${kernelName}' for backend ` +\n `'${backendName}' is not registered`);\n }\n kernelRegistry.delete(key);\n}\n\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName: string): void {\n if (!gradRegistry.has(kernelName)) {\n throw new Error(\n `The gradient '${kernelName}' for backend is not registered`);\n }\n gradRegistry.delete(kernelName);\n}\n\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(\n registeredBackendName: string, newBackendName: string): void {\n const kernels = getKernelsForBackend(registeredBackendName);\n kernels.forEach(kernelConfig => {\n const newKernelConfig =\n Object.assign({}, kernelConfig, {backendName: newBackendName});\n registerKernel(newKernelConfig);\n });\n}\n\nfunction makeKey(kernelName: string, backendName: string) {\n return `${backendName}_${kernelName}`;\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\nimport {BackendValues, DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n Float32Array): void {\n let counter = array.length;\n let temp = 0;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element with it\n temp = array[counter];\n array[counter] = array[index];\n array[index] = temp;\n }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n let sum = 0;\n for (let i = 0; i < arr.length; i++) {\n sum += arr[i];\n }\n return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n const r = Math.random();\n return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n let result = 0;\n for (let i = 0; i < a.length; i++) {\n const diff = Number(a[i]) - Number(b[i]);\n result += diff * diff;\n }\n return result;\n}\n\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n * error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr: boolean, msg: () => string) {\n if (!expr) {\n throw new Error(typeof msg === 'string' ? msg : msg());\n }\n}\n\nexport function assertShapesMatch(\n shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n assert(\n arraysEqual(shapeA, shapeB),\n () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n assert(\n a != null,\n () => `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten().\n/**\n * Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n * @param arr The nested array to flatten.\n * @param result The destination array which holds the elements.\n * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n * to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function\nflatten|TypedArray>(\n arr: T|RecursiveArray, result: T[] = [], skipTypedArray = false): T[] {\n if (result == null) {\n result = [];\n }\n if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n for (let i = 0; i < arr.length; ++i) {\n flatten(arr[i], result, skipTypedArray);\n }\n } else {\n result.push(arr as T);\n }\n return result;\n}\n\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape: number[]): number {\n if (shape.length === 0) {\n // Scalar.\n return 1;\n }\n let size = shape[0];\n for (let i = 1; i < shape.length; i++) {\n size *= shape[i];\n }\n return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\n\nexport function isInt(a: number): boolean {\n return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n // tslint:disable-next-line:no-any\n if ((Math as any).tanh != null) {\n // tslint:disable-next-line:no-any\n return (Math as any).tanh(x);\n }\n if (x === Infinity) {\n return 1;\n } else if (x === -Infinity) {\n return -1;\n } else {\n const e2x = Math.exp(2 * x);\n return (e2x - 1) / (e2x + 1);\n }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n const width = Math.ceil(Math.sqrt(size));\n return [width, Math.ceil(size / width)];\n}\n\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n: number): Uint32Array {\n const shuffledIndices = new Uint32Array(n);\n for (let i = 0; i < n; ++i) {\n shuffledIndices[i] = i;\n }\n shuffle(shuffledIndices);\n return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n if (size <= a.length) {\n return a;\n }\n return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n checkFn: () => boolean, delayFn = (counter: number) => 0,\n maxCounter?: number): Promise {\n return new Promise((resolve, reject) => {\n let tryCount = 0;\n\n const tryFn = () => {\n if (checkFn()) {\n resolve();\n return;\n }\n\n tryCount++;\n\n const nextBackoff = delayFn(tryCount);\n\n if (maxCounter != null && tryCount >= maxCounter) {\n reject();\n return;\n }\n setTimeout(tryFn, nextBackoff);\n };\n\n tryFn();\n });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n shape: number[], size: number): number[] {\n let shapeProd = 1;\n let implicitIdx = -1;\n\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] >= 0) {\n shapeProd *= shape[i];\n } else if (shape[i] === -1) {\n if (implicitIdx !== -1) {\n throw Error(\n `Shapes can only have 1 implicit size. ` +\n `Found -1 at dim ${implicitIdx} and dim ${i}`);\n }\n implicitIdx = i;\n } else if (shape[i] < 0) {\n throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n }\n }\n\n if (implicitIdx === -1) {\n if (size > 0 && size !== shapeProd) {\n throw Error(`Size(${size}) must match the product of shape ${shape}`);\n }\n return shape;\n }\n\n if (shapeProd === 0) {\n throw Error(\n `Cannot infer the missing size in [${shape}] when ` +\n `there are 0 elements`);\n }\n if (size % shapeProd !== 0) {\n throw Error(\n `The implicit shape can't be a fractional number. ` +\n `Got ${size} / ${shapeProd}`);\n }\n\n const newShape = shape.slice();\n newShape[implicitIdx] = size / shapeProd;\n return newShape;\n}\n\nexport function parseAxisParam(\n axis: number|number[], shape: number[]): number[] {\n const rank = shape.length;\n\n // Normalize input\n axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n // Check for valid range\n assert(\n axis.every(ax => ax >= -rank && ax < rank),\n () =>\n `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n `got axis ${axis}`);\n\n // Check for only integers\n assert(\n axis.every(ax => isInt(ax)),\n () => `All values in axis param must be integers but ` +\n `got axis ${axis}`);\n\n // Handle negative axis.\n return axis.map(a => a < 0 ? rank + a : a);\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n {newShape: number[], keptDims: number[]} {\n const newShape: number[] = [];\n const keptDims: number[] = [];\n const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n const axes = (axis == null || isEmptyArray) ?\n null :\n parseAxisParam(axis, shape).sort();\n let j = 0;\n for (let i = 0; i < shape.length; ++i) {\n if (axes != null) {\n if (axes[j] === i && shape[i] !== 1) {\n throw new Error(\n `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n }\n if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n if (axes[j] <= i) {\n j++;\n }\n }\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n }\n return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType(\n dtype: D, size: number): DataTypeMap[D] {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values as DataTypeMap[D];\n}\n\nexport function getArrayFromDType(\n dtype: D, size: number): DataTypeMap[D] {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else if (dtype === 'string') {\n values = new Array<'string'>(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values as DataTypeMap[D];\n}\n\nexport function checkConversionForErrors(\n vals: DataTypeMap[D]|number[], dtype: D): void {\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i] as number;\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n }\n }\n}\n\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype: DataType): boolean {\n return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n dtype === 'int32' || dtype === 'string';\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n if (newType === 'complex64') {\n return false;\n }\n if (newType === 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'bool' && oldType === 'bool') {\n return false;\n }\n return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n return a instanceof Float32Array || a instanceof Int32Array ||\n a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n if (dtype === 'float32' || dtype === 'int32') {\n return 4;\n } else if (dtype === 'complex64') {\n return 8;\n } else if (dtype === 'bool') {\n return 1;\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: Uint8Array[]): number {\n if (arr == null) {\n return 0;\n }\n let bytes = 0;\n arr.forEach(x => bytes += x.length);\n return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n if (Array.isArray(values)) {\n return inferDtype(values[0]);\n }\n if (values instanceof Float32Array) {\n return 'float32';\n } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n return 'int32';\n } else if (isNumber(values)) {\n return 'float32';\n } else if (isString(values)) {\n return 'string';\n } else if (isBoolean(values)) {\n return 'bool';\n }\n return 'float32';\n}\n\nexport function isFunction(f: Function) {\n return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n for (let i = start; i < size; ++i) {\n if (size % i === 0) {\n return i;\n }\n }\n return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n const rank = shape.length;\n if (rank < 2) {\n return [];\n }\n\n // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n // strides.\n const strides = new Array(rank - 1);\n strides[rank - 2] = shape[rank - 1];\n for (let i = rank - 3; i >= 0; --i) {\n strides[i] = strides[i + 1] * shape[i + 1];\n }\n return strides;\n}\n\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(\n value: DataType, dtype: DataType): BackendValues {\n if (dtype === 'string') {\n return encodeString(value);\n }\n\n return toTypedArray([value], dtype);\n}\n\nexport function toTypedArray(a: TensorLike, dtype: DataType): TypedArray {\n if (dtype === 'string') {\n throw new Error('Cannot convert a string[] to a TypedArray');\n }\n if (Array.isArray(a)) {\n a = flatten(a);\n }\n\n if (env().getBool('DEBUG')) {\n checkConversionForErrors(a as number[], dtype);\n }\n if (noConversionNeeded(a, dtype)) {\n return a as TypedArray;\n }\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(a as number[]);\n } else if (dtype === 'int32') {\n return new Int32Array(a as number[]);\n } else if (dtype === 'bool') {\n const bool = new Uint8Array((a as number[]).length);\n for (let i = 0; i < bool.length; ++i) {\n if (Math.round((a as number[])[i]) !== 0) {\n bool[i] = 1;\n }\n }\n return bool;\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\nfunction createNestedArray(offset: number, shape: number[], a: TypedArray) {\n const ret = new Array();\n if (shape.length === 1) {\n const d = shape[0];\n for (let i = 0; i < d; i++) {\n ret[i] = a[offset + i];\n }\n } else {\n const d = shape[0];\n const rest = shape.slice(1);\n const len = rest.reduce((acc, c) => acc * c);\n for (let i = 0; i < d; i++) {\n ret[i] = createNestedArray(offset + i * len, rest, a);\n }\n }\n return ret;\n}\n\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape: number[], a: TypedArray) {\n if (shape.length === 0) {\n // Scalar type should return a single number.\n return a[0];\n }\n const size = shape.reduce((acc, c) => acc * c);\n if (size === 0) {\n // A tensor with shape zero should be turned into empty list.\n return [];\n }\n if (size !== a.length) {\n throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n }\n\n return createNestedArray(0, shape, a);\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n return (a instanceof Float32Array && dtype === 'float32') ||\n (a instanceof Int32Array && dtype === 'int32') ||\n (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function makeOnesTypedArray(\n size: number, dtype: D): DataTypeMap[D] {\n const array = makeZerosTypedArray(size, dtype);\n for (let i = 0; i < array.length; i++) {\n array[i] = 1;\n }\n return array;\n}\n\nexport function makeZerosTypedArray(\n size: number, dtype: D): DataTypeMap[D] {\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(size) as DataTypeMap[D];\n } else if (dtype === 'int32') {\n return new Int32Array(size) as DataTypeMap[D];\n } else if (dtype === 'bool') {\n return new Uint8Array(size) as DataTypeMap[D];\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(\n shape: number[], dtype: D) {\n const size = shape.reduce((prev, curr) => prev * curr, 1);\n if (dtype == null || dtype === 'float32') {\n return toNestedArray(shape, new Float32Array(size));\n } else if (dtype === 'int32') {\n return toNestedArray(shape, new Int32Array(size));\n } else if (dtype === 'bool') {\n return toNestedArray(shape, new Uint8Array(size));\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now(): number {\n return env().platform.now();\n}\n\nexport function assertNonNegativeIntegerDimensions(shape: number[]) {\n shape.forEach(dimSize => {\n assert(\n Number.isInteger(dimSize) && dimSize >= 0,\n () =>\n `Tensor must have a shape comprised of positive integers but got ` +\n `shape [${shape}].`);\n });\n}\n\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(\n path: string, requestInits?: RequestInit): Promise {\n return env().platform.fetch(path, requestInits);\n}\n\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s: string, encoding = 'utf-8'): Uint8Array {\n encoding = encoding || 'utf-8';\n return env().platform.encode(s, encoding);\n}\n\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes: Uint8Array, encoding = 'utf-8'): string {\n encoding = encoding || 'utf-8';\n return env().platform.decode(bytes, encoding);\n}\n\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(\n locs: number[], rank: number, strides: number[]): number {\n if (rank === 0) {\n return 0;\n } else if (rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += strides[i] * locs[i];\n }\n return index;\n}\n\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(\n index: number, rank: number, strides: number[]): number[] {\n if (rank === 0) {\n return [];\n } else if (rank === 1) {\n return [index];\n }\n const locs: number[] = new Array(rank);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / strides[i]);\n index -= locs[i] * strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer} from './backends/backend';\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport {DataType, DataTypeMap, TypedArray} from './types';\nimport * as util from './util';\n\nexport type KernelProfile = {\n kernelName: string,\n outputs: Tensor[],\n inputs: NamedTensorMap,\n timeMs: Promise,\n extraInfo: Promise\n};\n\nexport class Profiler {\n constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n if (logger == null) {\n this.logger = new Logger();\n }\n }\n\n profileKernel(kernelName: string, inputs: NamedTensorMap, f: () => Tensor[]):\n KernelProfile {\n let outputs: Tensor[];\n const holdResultWrapperFn = () => {\n outputs = f();\n };\n const timer = this.backendTimer.time(holdResultWrapperFn);\n\n for (let i = 0; i < outputs.length; i++) {\n const output = outputs[i];\n // Dangling promise here because we don't want to propagate up\n // asynchronicity.\n output.data().then(tensorVals => {\n checkComputationForErrors(tensorVals, output.dtype, kernelName);\n });\n }\n\n const kernelProfile = {\n kernelName,\n outputs,\n inputs,\n timeMs: timer.then(timing => timing.kernelMs),\n extraInfo: timer.then(\n timing => timing.getExtraProfileInfo != null ?\n timing.getExtraProfileInfo() :\n '')\n };\n return kernelProfile;\n }\n\n logKernelProfile(kernelProfile: KernelProfile): void {\n const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;\n\n outputs.forEach(result => {\n Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n this.logger.logKernelProfile(\n kernelName, result, valueContainer[0], valueContainer[1], inputs,\n valueContainer[2]);\n });\n });\n }\n}\n\nexport function checkComputationForErrors(\n vals: DataTypeMap[D], dtype: D, kernelName: string): boolean {\n if (dtype !== 'float32') {\n // Only floating point computations will generate NaN values\n return false;\n }\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i] as number;\n if (isNaN(num) || !isFinite(num)) {\n // Throwing custom exception so behavior is testable.\n console.warn(`Found ${num} in the result of '${kernelName}'`);\n return true;\n }\n }\n return false;\n}\n\nexport class Logger {\n logKernelProfile(\n name: string, result: Tensor, vals: TypedArray,\n timeMs: number|{error: string}, inputs: NamedTensorMap,\n extraInfo?: string) {\n const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n timeMs['error'];\n const paddedName = util.rightPad(name, 25);\n const rank = result.rank;\n const size = result.size;\n const shape = util.rightPad(result.shape.toString(), 14);\n let inputShapesDescription = '';\n\n for (const name in inputs) {\n const input = inputs[name];\n if (input != null) {\n // The input might be a non-tensor (e.g HTMLImageElement), in which case\n // we claim the output shape as input shape.\n const inputShape = input.shape || result.shape;\n const inputRank = inputShape.length;\n inputShapesDescription +=\n `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n }\n }\n\n console.log(\n `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n inputShapesDescription}\\t%c${extraInfo}`,\n 'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n 'color: green', 'color: steelblue');\n }\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n id: number;\n kernelName: string;\n outputs: Tensor[];\n inputs: NamedTensorMap;\n // Optional params, defined only for ops with gradient impl.\n gradient?: (dys: Tensor[]) => NamedGradientMap;\n saved?: Tensor[];\n}\n\nexport type NamedGradientMap = {\n [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n // Forward pass to compute all the nodes and Tensors that are transitively a\n // function of x.\n const tensorsFromX: {[tensorId: number]: boolean} = {};\n const nodesFromX: {[nodeId: number]: boolean} = {};\n for (let i = 0; i < xs.length; i++) {\n tensorsFromX[xs[i].id] = true;\n }\n\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (const inputName in nodeInputs) {\n const input = nodeInputs[inputName];\n\n let anyInputFromX = false;\n for (let j = 0; j < xs.length; j++) {\n if (tensorsFromX[input.id]) {\n node.outputs.forEach(output => tensorsFromX[output.id] = true);\n anyInputFromX = true;\n nodesFromX[node.id] = true;\n break;\n }\n }\n\n if (anyInputFromX) {\n break;\n }\n }\n }\n\n // Backward pass to find all of the nodes and Tensors that lead to y.\n const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n tensorsLeadToY[y.id] = true;\n const nodesToY: {[nodeId: number]: boolean} = {};\n\n for (let i = tape.length - 1; i >= 0; i--) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n\n // If any of the outputs lead to y, mark all of the inputs as leading to y.\n for (let j = 0; j < node.outputs.length; j++) {\n if (tensorsLeadToY[node.outputs[j].id]) {\n for (const inputName in nodeInputs) {\n tensorsLeadToY[nodeInputs[inputName].id] = true;\n nodesToY[node.id] = true;\n }\n break;\n }\n }\n }\n\n // Return the paths that come from x and lead to y.\n const filteredTape: TapeNode[] = [];\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n\n if (nodesFromX[node.id] && nodesToY[node.id]) {\n // Prune the inputs from the node that aren't a function of x.\n const prunedInputs: {[inputName: string]: Tensor} = {};\n for (const inputName in node.inputs) {\n const nodeInput = node.inputs[inputName];\n if (tensorsFromX[nodeInput.id]) {\n prunedInputs[inputName] = nodeInput;\n }\n }\n\n // Copy the node and overwrite inputsAndArgs to the pruned version.\n const prunedNode = Object.assign({}, node);\n prunedNode.inputs = prunedInputs;\n prunedNode.outputs = node.outputs;\n\n filteredTape.push(prunedNode);\n }\n }\n\n return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n filteredTape: TapeNode[], tidy: (f: Function) => Tensor,\n add: (a: Tensor, b: Tensor) => Tensor) {\n // Walk the tape backward and keep a map of Tensor to its gradient.\n for (let i = filteredTape.length - 1; i >= 0; i--) {\n const node = filteredTape[i];\n\n const dys: Tensor[] = [];\n node.outputs.forEach(o => {\n const gradTensor = tensorAccumulatedGradientMap[o.id];\n if (gradTensor != null) {\n dys.push(gradTensor);\n } else {\n // This particular output is not in the back-propagation subgraph, so it\n // does not affect the final output, thus we put null for its dy.\n dys.push(null);\n }\n });\n\n if (node.gradient == null) {\n throw new Error(\n `Cannot compute gradient: gradient function not found ` +\n `for ${node.kernelName}.`);\n }\n\n // Backprop dy through this node and accumulate gradients over the inputs.\n const inputGradients = node.gradient(dys);\n\n for (const inputName in node.inputs) {\n if (!(inputName in inputGradients)) {\n throw new Error(\n `Cannot backprop through input ${inputName}. ` +\n `Available gradients found: ${Object.keys(inputGradients)}.`);\n }\n\n // Call the gradient function.\n const dx = tidy(() => inputGradients[inputName]());\n if (dx.dtype !== 'float32') {\n throw new Error(\n `Error in gradient for op ${\n node.kernelName}. The gradient of input ` +\n `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n }\n const x = node.inputs[inputName];\n if (!util.arraysEqual(dx.shape, x.shape)) {\n throw new Error(\n `Error in gradient for op ${\n node.kernelName}. The gradient of input ` +\n `'${inputName}' has shape '${dx.shape}', which does not match ` +\n `the shape of the input '${x.shape}'`);\n }\n\n if (tensorAccumulatedGradientMap[x.id] == null) {\n tensorAccumulatedGradientMap[x.id] = dx;\n } else {\n const curGradient = tensorAccumulatedGradientMap[x.id];\n tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n curGradient.dispose();\n }\n }\n }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n verbose: boolean) {\n const strides = computeStrides(shape);\n const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n const rank = shape.length;\n const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n const lines = ['Tensor'];\n if (verbose) {\n lines.push(` dtype: ${dtype}`);\n lines.push(` rank: ${rank}`);\n lines.push(` shape: [${shape}]`);\n lines.push(` values:`);\n }\n lines.push(valsLines.map(l => ' ' + l).join('\\n'));\n return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n strides: number[]): number[] {\n const n = sizeFromShape(shape);\n const numCols = strides[strides.length - 1];\n const padPerCol = new Array(numCols).fill(0);\n const rank = shape.length;\n const valuesOrTuples =\n dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n if (rank > 1) {\n for (let row = 0; row < n / numCols; row++) {\n const offset = row * numCols;\n for (let j = 0; j < numCols; j++) {\n padPerCol[j] = Math.max(\n padPerCol[j],\n valToString(valuesOrTuples[offset + j], 0, dtype).length);\n }\n }\n }\n return padPerCol;\n}\n\nfunction valToString(\n val: number|string|[number, number], pad: number, dtype: DataType) {\n let valStr: string;\n if (Array.isArray(val)) {\n valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n } else if (isString(val)) {\n valStr = `'${val}'`;\n } else if (dtype === 'bool') {\n valStr = boolNumToString(val);\n } else {\n valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n }\n\n return rightPad(valStr, pad);\n}\n\nfunction boolNumToString(v: number): string {\n return v === 0 ? 'false' : 'true';\n}\n\nfunction subTensorToString(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n strides: number[], padPerCol: number[], isLast = true): string[] {\n const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n const size = shape[0];\n const rank = shape.length;\n if (rank === 0) {\n if (dtype === 'complex64') {\n const complexTuple = createComplexTuples(vals);\n return [valToString(complexTuple[0], 0, dtype)];\n }\n if (dtype === 'bool') {\n return [boolNumToString(vals[0] as number)];\n }\n return [vals[0].toString()];\n }\n\n if (rank === 1) {\n if (size > FORMAT_LIMIT_NUM_VALS) {\n const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n let firstVals = Array.from(\n vals.slice(0, firstValsSize));\n let lastVals = Array.from(vals.slice(\n (size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement,\n size * storagePerElement));\n if (dtype === 'complex64') {\n firstVals = createComplexTuples(firstVals);\n lastVals = createComplexTuples(lastVals);\n }\n return [\n '[' +\n firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ', ..., ' +\n lastVals\n .map(\n (x, i) => valToString(\n x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n .join(', ') +\n ']'\n ];\n }\n const displayVals: Array =\n dtype === 'complex64' ? createComplexTuples(vals) :\n Array.from(vals);\n\n return [\n '[' +\n displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ']'\n ];\n }\n\n // The array is rank 2 or more.\n const subshape = shape.slice(1);\n const substrides = strides.slice(1);\n const stride = strides[0] * storagePerElement;\n const lines: string[] = [];\n if (size > FORMAT_LIMIT_NUM_VALS) {\n for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n false /* isLast */));\n }\n lines.push('...');\n for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n i === size - 1 /* isLast */));\n }\n } else {\n for (let i = 0; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n i === size - 1 /* isLast */));\n }\n }\n const sep = rank === 2 ? ',' : '';\n lines[0] = '[' + lines[0] + sep;\n for (let i = 1; i < lines.length - 1; i++) {\n lines[i] = ' ' + lines[i] + sep;\n }\n let newLineSep = ',\\n';\n for (let i = 2; i < rank; i++) {\n newLineSep += '\\n';\n }\n lines[lines.length - 1] =\n ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n TypedArray): Array<[number, number]> {\n const complexTuples: Array<[number, number]> = [];\n for (let i = 0; i < vals.length; i += 2) {\n complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n }\n return complexTuples;\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensorToString} from './tensor_format';\nimport {ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides, toNestedArray} from './util';\n\nexport interface TensorData {\n dataId?: DataId;\n values?: DataTypeMap[D];\n}\n\n// This interface mimics KernelBackend (in backend.ts), which would create a\n// circular dependency if imported.\nexport interface Backend {}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n size: number;\n shape: ShapeMap[R];\n strides: number[];\n values: DataTypeMap[D];\n\n constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n this.shape = shape.slice() as ShapeMap[R];\n this.size = util.sizeFromShape(shape);\n\n if (values != null) {\n const n = values.length;\n util.assert(\n n === this.size,\n () => `Length of values '${n}' does not match the size ` +\n `inferred by the shape '${this.size}'.`);\n }\n if (dtype === 'complex64') {\n throw new Error(\n `complex64 dtype TensorBuffers are not supported. Please create ` +\n `a TensorBuffer for the real and imaginary parts separately and ` +\n `call tf.complex(real, imag).`);\n }\n this.values = values || util.getArrayFromDType(dtype, this.size);\n this.strides = computeStrides(shape);\n }\n\n /**\n * Sets a value in the buffer at a given location.\n *\n * @param value The value to set.\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n set(value: SingleValueMap[D], ...locs: number[]): void {\n if (locs.length === 0) {\n locs = [0];\n }\n util.assert(\n locs.length === this.rank,\n () => `The number of provided coordinates (${locs.length}) must ` +\n `match the rank (${this.rank})`);\n\n const index = this.locToIndex(locs);\n this.values[index] = value as number;\n }\n\n /**\n * Returns the value in the buffer at the provided location.\n *\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n get(...locs: number[]): SingleValueMap[D] {\n if (locs.length === 0) {\n locs = [0];\n }\n let i = 0;\n for (const loc of locs) {\n if (loc < 0 || loc >= this.shape[i]) {\n const msg = `Requested out of range element at ${locs}. ` +\n ` Buffer shape=${this.shape}`;\n throw new Error(msg);\n }\n i++;\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return this.values[index] as SingleValueMap[D];\n }\n\n locToIndex(locs: number[]): number {\n if (this.rank === 0) {\n return 0;\n } else if (this.rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return index;\n }\n\n indexToLoc(index: number): number[] {\n if (this.rank === 0) {\n return [];\n } else if (this.rank === 1) {\n return [index];\n }\n const locs: number[] = new Array(this.shape.length);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / this.strides[i]);\n index -= locs[i] * this.strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n }\n\n get rank() {\n return this.shape.length;\n }\n\n /**\n * Creates an immutable `tf.Tensor` object from the buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n toTensor(): Tensor {\n return trackerFn().makeTensor(this.values, this.shape, this.dtype) as\n Tensor;\n }\n}\n\nexport interface TensorTracker {\n makeTensor(\n values: DataValues, shape: number[], dtype: DataType,\n backend?: Backend): Tensor;\n makeVariable(\n initialValue: Tensor, trainable?: boolean, name?: string,\n dtype?: DataType): Variable;\n incRef(a: Tensor, backend: Backend): void;\n disposeTensor(t: Tensor): void;\n disposeVariable(v: Variable): void;\n read(dataId: DataId): Promise;\n readSync(dataId: DataId): BackendValues;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n cast(x: T, dtype: DataType): T;\n buffer(\n shape: ShapeMap[R], dtype: D,\n values?: DataTypeMap[D]): TensorBuffer;\n print(x: T, verbose: boolean): void;\n clone(x: T): T;\n // TODO(yassogba) bring reshape back?\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn: (msg: string) => void = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n opHandler = handler;\n}\n\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn: (msg: string) => void) {\n deprecationWarningFn = fn;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object; // object instead of {} to force non-primitive.\n\n// Declare this namespace to make Tensor class augmentation work in google3.\nexport declare namespace Tensor {}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n /** Unique id of this tensor. */\n readonly id: number;\n /**\n * Id of the bucket holding the data for this tensor. Multiple arrays can\n * point to the same bucket (e.g. when calling array.reshape()).\n */\n dataId: DataId;\n /** The shape of the tensor. */\n readonly shape: ShapeMap[R];\n /** Number of elements in the tensor. */\n readonly size: number;\n /** The data type for the array. */\n readonly dtype: DataType;\n /** The rank type for the array (see `Rank` enum). */\n readonly rankType: R;\n\n /** Whether this tensor has been globally kept. */\n kept = false;\n /** The id of the scope this tensor is being tracked in. */\n scopeId: number;\n\n /**\n * Number of elements to skip in each dimension when indexing. See\n * https://docs.scipy.org/doc/numpy/reference/generated/\\\n * numpy.ndarray.strides.html\n */\n readonly strides: number[];\n\n constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number) {\n this.shape = shape.slice() as ShapeMap[R];\n this.dtype = dtype || 'float32';\n this.size = util.sizeFromShape(shape);\n this.strides = computeStrides(shape);\n this.dataId = dataId;\n this.id = id;\n this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n }\n\n get rank(): number {\n return this.shape.length;\n }\n\n /**\n * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async buffer(): Promise> {\n const vals = await this.data();\n return opHandler.buffer(this.shape, this.dtype as D, vals);\n }\n\n /**\n * Returns a `tf.TensorBuffer` that holds the underlying data.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n bufferSync(): TensorBuffer {\n return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n }\n\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * asynchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async array(): Promise {\n const vals = await this.data();\n return toNestedArray(this.shape, vals) as ArrayMap[R];\n }\n\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * synchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n arraySync(): ArrayMap[R] {\n return toNestedArray(this.shape, this.dataSync()) as ArrayMap[R];\n }\n\n /**\n * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n * promise of `TypedArray` that resolves when the computation has finished.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async data(): Promise {\n this.throwIfDisposed();\n const data = trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n const bytes = await data as Uint8Array[];\n try {\n return bytes.map(b => util.decodeString(b)) as DataTypeMap[D];\n } catch {\n throw new Error(\n 'Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data as Promise;\n }\n\n /**\n * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n * UI thread until the values are ready, which can cause performance issues.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataSync(): DataTypeMap[D] {\n this.throwIfDisposed();\n const data = trackerFn().readSync(this.dataId);\n if (this.dtype === 'string') {\n try {\n return (data as Uint8Array[]).map(b => util.decodeString(b)) as\n DataTypeMap[D];\n } catch {\n throw new Error(\n 'Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data as DataTypeMap[D];\n }\n\n /** Returns the underlying bytes of the tensor's data. */\n async bytes(): Promise {\n this.throwIfDisposed();\n const data = await trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n return data as Uint8Array[];\n } else {\n return new Uint8Array((data as TypedArray).buffer);\n }\n }\n\n /**\n * Disposes `tf.Tensor` from memory.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dispose(): void {\n if (this.isDisposed) {\n return;\n }\n trackerFn().disposeTensor(this);\n this.isDisposedInternal = true;\n }\n\n protected isDisposedInternal = false;\n get isDisposed(): boolean {\n return this.isDisposedInternal;\n }\n\n throwIfDisposed() {\n if (this.isDisposed) {\n throw new Error(`Tensor is disposed.`);\n }\n }\n\n /**\n * Prints the `tf.Tensor`. See `tf.print` for details.\n *\n * @param verbose Whether to print verbose information about the tensor,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n print(verbose = false): void {\n return opHandler.print(this, verbose);\n }\n\n /**\n * Returns a copy of the tensor. See `tf.clone` for details.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n clone(this: T): T {\n this.throwIfDisposed();\n return opHandler.clone(this);\n }\n\n /**\n * Returns a human-readable description of the tensor. Useful for logging.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n toString(verbose = false): string {\n const vals = this.dataSync();\n return tensorToString(vals, this.shape, this.dtype, verbose);\n }\n\n cast(dtype: DataType): T {\n this.throwIfDisposed();\n return opHandler.cast(this as T, dtype);\n }\n variable(trainable = true, name?: string, dtype?: DataType): Variable {\n this.throwIfDisposed();\n return trackerFn().makeVariable(this, trainable, name, dtype) as\n Variable;\n }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: (instance: Tensor) => {\n // Implementation note: we should use properties of the object that will be\n // defined before the constructor body has finished executing (methods).\n // This is because when this code is transpiled by babel, babel will call\n // classCallCheck before the constructor body is run.\n // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n return !!instance && instance.data != null && instance.dataSync != null &&\n instance.throwIfDisposed != null;\n }\n});\n\nexport interface NumericTensor extends Tensor {\n dtype: NumericDataType;\n dataSync(): DataTypeMap[D];\n data(): Promise;\n}\n\nexport interface StringTensor extends Tensor {\n dtype: 'string';\n dataSync(): DataTypeMap[D];\n data(): Promise;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n name: string;\n\n constructor(\n initialValue: Tensor, public trainable: boolean, name: string,\n tensorId: number) {\n super(\n initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n this.name = name;\n }\n\n /**\n * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n * the same shape and dtype as the old `tf.Tensor`.\n *\n * @param newValue New tensor to be assigned to this variable.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n assign(newValue: Tensor): void {\n if (newValue.dtype !== this.dtype) {\n throw new Error(\n `dtype of the new value (${newValue.dtype}) and ` +\n `previous value (${this.dtype}) must match`);\n }\n if (!util.arraysEqual(newValue.shape, this.shape)) {\n throw new Error(\n `shape of the new value (${newValue.shape}) and ` +\n `previous value (${this.shape}) must match`);\n }\n trackerFn().disposeTensor(this);\n this.dataId = newValue.dataId;\n trackerFn().incRef(this, null /* backend */);\n }\n\n dispose(): void {\n trackerFn().disposeVariable(this);\n this.isDisposedInternal = true;\n }\n}\n\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: (instance: Variable) => {\n return instance instanceof Tensor && instance.assign != null &&\n instance.assign instanceof Function;\n }\n});\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n R0: number[];\n R1: [number];\n R2: [number, number];\n R3: [number, number, number];\n R4: [number, number, number, number];\n R5: [number, number, number, number, number];\n R6: [number, number, number, number, number, number];\n}\n\n/** @docalias number[] */\nexport interface ArrayMap {\n R0: number;\n R1: number[];\n R2: number[][];\n R3: number[][][];\n R4: number[][][][];\n R5: number[][][][][];\n R6: number[][][][][][];\n}\n\nexport interface DataTypeMap {\n float32: Float32Array;\n int32: Int32Array;\n bool: Uint8Array;\n complex64: Float32Array;\n string: string[];\n}\n\nexport interface SingleValueMap {\n bool: boolean;\n int32: number;\n float32: number;\n complex64: number;\n string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\n/** Tensor data used in tensor creation and user-facing API. */\nexport type DataValues = DataTypeMap[DataType];\n/** The underlying tensor data that gets stored in a backend. */\nexport type BackendValues = Float32Array|Int32Array|Uint8Array|Uint8Array[];\n\nexport enum Rank {\n R0 = 'R0',\n R1 = 'R1',\n R2 = 'R2',\n R3 = 'R3',\n R4 = 'R4',\n R5 = 'R5',\n R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray =\n T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray {\n [index: number]: T|RecursiveArray;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n 'float32' = 'float32',\n 'int32' = 'int32',\n 'bool' = 'int32',\n 'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n 'float32' = 'float32',\n 'int32' = 'int32',\n 'bool' = 'bool',\n 'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n 'float32' = 'float32',\n 'int32' = 'float32',\n 'bool' = 'float32',\n 'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n 'float32' = 'complex64',\n 'int32' = 'complex64',\n 'bool' = 'complex64',\n 'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n 'float32': UpcastFloat32AndMap,\n 'int32': UpcastInt32AndMap,\n 'bool': UpcastBoolAndMap,\n 'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n if (typeA === 'string' || typeB === 'string') {\n if (typeA === 'string' && typeB === 'string') {\n return 'string';\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n TypedArray|number|boolean|string|RecursiveArray|\n RecursiveArray|RecursiveArray|Uint8Array[];\nexport type ScalarLike = number|boolean|string|Uint8Array;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[]|Uint8Array[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D = TypedArray|number[]|number[][]|boolean[]|boolean[][]|\n string[]|string[][]|Uint8Array[]|Uint8Array[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n boolean[][][]|string[]|string[][][]|Uint8Array[]|Uint8Array[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n boolean[][][][]|string[]|string[][][][]|Uint8Array[]|Uint8Array[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D =\n TypedArray|number[]|number[][][][][]|boolean[]|boolean[][][][][]|string[]|\n string[][][][][]|Uint8Array[]|Uint8Array[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D =\n TypedArray|number[]|number[][][][][][]|boolean[]|boolean[][][][][][]|\n string[]|string[][][][][][]|Uint8Array[]|Uint8Array[][][][][];\n\n/** Type for representing image dat in Uint8Array type. */\nexport interface PixelData {\n width: number;\n height: number;\n data: Uint8Array;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch(a: T, b: T): [T, T] {\n if (a.dtype === b.dtype) {\n return [a, b];\n }\n const dtype = upcastType(a.dtype, b.dtype);\n return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n assert(\n a.dtype === b.dtype,\n () => `The dtypes of the first(${a.dtype}) and` +\n ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n return tensorList.some(x => x.id === tensor.id);\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n * is safe to pass any object here, except that `Promise`s are not\n * supported.\n * @returns An array of `Tensors` found within the passed object. If the\n * argument is simply a `Tensor', a list containing that `Tensor` is\n * returned. If the object is not a `Tensor` or does not\n * contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n const list: Tensor[] = [];\n const seen = new Set<{}|void>();\n walkTensorContainer(result, list, seen);\n return list;\n}\n\nfunction walkTensorContainer(\n container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n if (container == null) {\n return;\n }\n if (container instanceof Tensor) {\n list.push(container);\n return;\n }\n if (!isIterable(container)) {\n return;\n }\n // Iteration over keys works also for arrays.\n const iterable = container as TensorContainerArray;\n for (const k in iterable) {\n const val = iterable[k];\n if (!seen.has(val)) {\n seen.add(val);\n walkTensorContainer(val, list, seen);\n }\n }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n return Array.isArray(obj) || typeof obj === 'object';\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './backends/backend';\nimport {Environment, setEnvironmentGlobal} from './environment';\nimport {getGlobalNamespace} from './global_util';\nimport {Add, Cast} from './kernel_names';\nimport {getGradient, getKernel, getKernelsForBackend, GradFunc, NamedAttrMap, TensorInfo} from './kernel_registry';\nimport {KernelProfile, Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, TapeNode} from './tape';\nimport {DataId, setTensorTracker, Tensor, TensorTracker, Variable} from './tensor';\nimport {GradSaveFunc, NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\nimport {BackendValues, DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc = (backend: KernelBackend, save?: GradSaveFunc) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,..., save?: Function) => {\n * value: Tensor,\n * gradFunc: (dy: Tensor, saved?: NamedTensorMap) => Tensor | Tensor[]\n * }\n */\nexport type CustomGradientFunc =\n (...inputs: Array) => {\n value: T;\n gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[];\n };\n\nexport type MemoryInfo = {\n numTensors: number; numDataBuffers: number; numBytes: number;\n unreliable?: boolean; reasons: string[];\n};\n\ntype KernelInfo = {\n name: string; bytesAdded: number; totalBytesSnapshot: number;\n tensorsAdded: number;\n totalTensorsSnapshot: number;\n inputShapes: number[][];\n outputShapes: number[][];\n kernelTimeMs: number | {error: string} | Promise;\n extraInfo: string | Promise;\n};\n\nexport type ProfileInfo = {\n newBytes: number; newTensors: number; peakBytes: number;\n kernels: KernelInfo[];\n result: TensorContainer;\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn = () => T;\n\ninterface ScopeState {\n track: Tensor[];\n name: string;\n id: number;\n}\n\nclass EngineState {\n // Public since optimizers will use it.\n registeredVariables: NamedVariableMap = {};\n\n nextTapeNodeId = 0;\n numBytes = 0;\n numTensors = 0;\n numStringTensors = 0;\n numDataBuffers = 0;\n\n activeTape: TapeNode[];\n // Number of nested tf.grad() statements when computing higher-order\n // gradients. E.g. `1` for first-order gradients and `2` for second-order\n // gradients. Used to track if the tape should be removed after a backprop.\n gradientDepth = 0;\n // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n // off the tape.\n kernelDepth = 0;\n\n // Keep Tensors that parallel the tapes.\n activeScope: ScopeState;\n scopeStack: ScopeState[] = [];\n /**\n * Keeps track of the number of data moves during a kernel execution. We\n * maintain a stack since kernels can call other kernels, recursively.\n */\n numDataMovesStack: number[] = [];\n nextScopeId = 0;\n\n tensorInfo = new WeakMap();\n\n profiling = false;\n activeProfile: ProfileInfo =\n {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};\n\n dispose() {\n for (const variableName in this.registeredVariables) {\n this.registeredVariables[variableName].dispose();\n }\n }\n}\n\nexport class Engine implements TensorTracker, DataMover {\n state: EngineState;\n backendName: string;\n registry: {[id: string]: KernelBackend} = {};\n registryFactory: {\n [id: string]: {\n factory: () => KernelBackend | Promise,\n priority: number\n }\n } = {};\n\n private profiler: Profiler;\n private backendInstance: KernelBackend;\n private pendingBackendInit: Promise;\n private pendingBackendInitId = 0;\n\n constructor(public ENV: Environment) {\n this.state = new EngineState();\n }\n\n async ready(): Promise {\n if (this.pendingBackendInit != null) {\n return this.pendingBackendInit.then(() => {});\n }\n if (this.backendInstance != null) {\n return;\n }\n const sortedBackends = this.getSortedBackends();\n\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const success = await this.initializeBackend(backendName).success;\n if (success) {\n await this.setBackend(backendName);\n return;\n }\n }\n\n throw new Error(\n `Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n\n get backend(): KernelBackend {\n if (this.pendingBackendInit != null) {\n throw new Error(\n `Backend '${this.backendName}' has not yet been initialized. Make ` +\n `sure to await tf.ready() or await tf.setBackend() before calling ` +\n `other methods`);\n }\n if (this.backendInstance == null) {\n const {name, asyncInit} = this.initializeBackendsAndReturnBest();\n if (asyncInit) {\n throw new Error(\n `The highest priority backend '${name}' has not yet been ` +\n `initialized. Make sure to await tf.ready() or ` +\n `await tf.setBackend() before calling other methods`);\n }\n this.setBackend(name);\n }\n return this.backendInstance;\n }\n\n backendNames(): string[] {\n return Object.keys(this.registryFactory);\n }\n\n findBackend(backendName: string): KernelBackend {\n if (!(backendName in this.registry)) {\n // If the backend hasn't been initialized but we have a registry entry for\n // it, initialize it and return it.\n if (backendName in this.registryFactory) {\n const {asyncInit} = this.initializeBackend(backendName);\n if (asyncInit) {\n // Backend is not ready yet.\n return null;\n }\n } else {\n return null;\n }\n }\n return this.registry[backendName];\n }\n\n findBackendFactory(backendName: string):\n () => KernelBackend | Promise {\n if (!(backendName in this.registryFactory)) {\n return null;\n }\n return this.registryFactory[backendName].factory;\n }\n\n registerBackend(\n backendName: string,\n factory: () => KernelBackend | Promise,\n priority = 1): boolean {\n if (backendName in this.registryFactory) {\n console.warn(\n `${backendName} backend was already registered. ` +\n `Reusing existing backend factory.`);\n return false;\n }\n this.registryFactory[backendName] = {factory, priority};\n return true;\n }\n\n async setBackend(backendName: string): Promise {\n if (this.registryFactory[backendName] == null) {\n throw new Error(`Backend name '${backendName}' not found in registry`);\n }\n this.backendName = backendName;\n if (this.registry[backendName] == null) {\n this.backendInstance = null;\n const {success, asyncInit} = this.initializeBackend(backendName);\n const result = asyncInit ? await success : success;\n if (!result) {\n return false;\n }\n }\n this.backendInstance = this.registry[backendName];\n this.setupRegisteredKernels();\n // Reset the profiler.\n this.profiler = new Profiler(this.backendInstance);\n\n return true;\n }\n\n private setupRegisteredKernels(): void {\n const kernels = getKernelsForBackend(this.backendName);\n kernels.forEach(kernel => {\n if (kernel.setupFunc != null) {\n kernel.setupFunc(this.backendInstance);\n }\n });\n }\n\n private disposeRegisteredKernels(backendName: string): void {\n const kernels = getKernelsForBackend(backendName);\n kernels.forEach(kernel => {\n if (kernel.disposeFunc != null) {\n kernel.disposeFunc(this.registry[backendName]);\n }\n });\n }\n\n /**\n * Initializes a backend by looking up the backend name in the factory\n * registry and calling the factory method. Returns a boolean representing\n * whether the initialization of the backend suceeded. Throws an error if\n * there is no backend in the factory registry.\n */\n private initializeBackend(backendName: string):\n {success: boolean|Promise, asyncInit: boolean} {\n const registryFactoryEntry = this.registryFactory[backendName];\n if (registryFactoryEntry == null) {\n throw new Error(\n `Cannot initialize backend ${backendName}, no registration found.`);\n }\n\n try {\n const backend = registryFactoryEntry.factory();\n /* Test if the factory returns a promise.\n Done in a more liberal way than\n previous 'Promise.resolve(backend)===backend'\n as we needed to account for custom Promise\n implementations (e.g. Angular) */\n if (backend && !(backend instanceof KernelBackend)\n && typeof backend.then === 'function') {\n const promiseId = ++this.pendingBackendInitId;\n const success =\n backend\n .then(backendInstance => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.registry[backendName] = backendInstance;\n this.pendingBackendInit = null;\n return true;\n })\n .catch(err => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.pendingBackendInit = null;\n console.warn(\n `Initialization of backend ${backendName} failed`);\n console.warn(err.stack || err.message);\n return false;\n });\n this.pendingBackendInit = success;\n return {success, asyncInit: true};\n } else {\n this.registry[backendName] = backend as KernelBackend;\n return {success: true, asyncInit: false};\n }\n } catch (err) {\n console.warn(`Initialization of backend ${backendName} failed`);\n console.warn(err.stack || err.message);\n return {success: false, asyncInit: false};\n }\n }\n\n removeBackend(backendName: string): void {\n if (!(backendName in this.registryFactory)) {\n throw new Error(`${backendName} backend not found in registry`);\n }\n if (this.backendName === backendName && this.pendingBackendInit != null) {\n // There is a pending promise of the backend we want to remove. Make it\n // obsolete.\n this.pendingBackendInitId++;\n }\n\n if (backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n\n delete this.registryFactory[backendName];\n\n // Unset the backend if it is active.\n if (this.backendName === backendName) {\n this.pendingBackendInit = null;\n this.backendName = null;\n this.backendInstance = null;\n }\n }\n\n private getSortedBackends(): string[] {\n if (Object.keys(this.registryFactory).length === 0) {\n throw new Error('No backend found in registry.');\n }\n return Object.keys(this.registryFactory).sort((a: string, b: string) => {\n // Highest priority comes first.\n return this.registryFactory[b].priority -\n this.registryFactory[a].priority;\n });\n }\n\n private initializeBackendsAndReturnBest():\n {name: string, asyncInit: boolean} {\n const sortedBackends = this.getSortedBackends();\n\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const {success, asyncInit} = this.initializeBackend(backendName);\n if (asyncInit || success) {\n return {name: backendName, asyncInit};\n }\n }\n throw new Error(\n `Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n\n moveData(backend: KernelBackend, dataId: DataId) {\n const info = this.state.tensorInfo.get(dataId);\n const srcBackend = info.backend;\n const values = this.readSync(dataId);\n // Delete the tensor from the old backend and move it to the new\n // backend.\n srcBackend.disposeData(dataId);\n info.backend = backend;\n backend.move(dataId, values, info.shape, info.dtype);\n if (this.shouldCheckForMemLeaks()) {\n // Track the number of moves during a kernel execution to correctly\n // detect memory leaks.\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n }\n }\n\n tidy(nameOrFn: string|ScopeFn, fn?: ScopeFn):\n T {\n let name: string = null;\n if (fn == null) {\n // Called with only 1 argument.\n if (typeof nameOrFn !== 'function') {\n throw new Error('Please provide a function to tidy()');\n }\n fn = nameOrFn;\n } else {\n // Called with 2 arguments.\n if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n throw new Error(\n 'When calling with two arguments, the first argument ' +\n 'to tidy() must be a string');\n }\n if (typeof fn !== 'function') {\n throw new Error(\n 'When calling with two arguments, the 2nd argument ' +\n 'to tidy() must be a function');\n }\n name = nameOrFn as string;\n // TODO(nsthorat,smilkov): Do operation logging and performance\n // profiling.\n }\n let result: T;\n return this.scopedRun(\n () => this.startScope(name), () => this.endScope(result), () => {\n result = fn();\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n return result;\n });\n }\n\n private scopedRun(start: () => void, end: () => void, f: () => T): T {\n start();\n try {\n const res = f();\n end();\n return res;\n } catch (ex) {\n end();\n throw ex;\n }\n }\n\n private static nextTensorId = 0;\n private nextTensorId(): number {\n return Engine.nextTensorId++;\n }\n\n private static nextVariableId = 0;\n private nextVariableId(): number {\n return Engine.nextVariableId++;\n }\n\n /**\n * This method is called instead of the public-facing tensor.clone() when\n * saving a tensor for backwards pass. It makes sure to add the clone\n * operation to the tape regardless of being called inside a kernel\n * execution.\n *\n * This method will go away once all kernels are modularized since we won't\n * need to turn off the tape inside runKernel().\n */\n private clone(x: Tensor): Tensor {\n const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n const inputs = {x};\n const grad = (dy: Tensor) => ({\n x: () => {\n const dtype = 'float32';\n const gradInputs = {x: dy};\n const attrs = {dtype};\n\n return ENGINE.runKernelFunc(\n backend => backend.cast(dy, dtype),\n gradInputs as {} as NamedTensorMap, null /* grad */, Cast,\n attrs as {} as NamedAttrMap);\n }\n });\n const saved: Tensor[] = [];\n this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n return y;\n }\n\n /**\n * Execute a kernel with the given name and return the output tensor.\n *\n * @param kernelName The name of the kernel to execute.\n * @param inputs A map of input names to tensors.\n * @param attrs A map of attribute names to their values. An attribute is a\n * primitive (non-tensor) input to the kernel.\n * @param inputsToSave A list of tensors, inputs to save for the backprop\n * computation.\n * @param outputsToSave A list of booleans, specifying which output to save\n * for the backprop computation. These are booleans since the output\n * tensors are not visible to the user.\n */\n runKernel(\n kernelName: string, inputs: NamedTensorMap, attrs: NamedAttrMap,\n inputsToSave?: Tensor[], outputsToSave?: boolean[]): Tensor|Tensor[] {\n const forwardFunc: null = null;\n const backwardsFunc: null = null;\n // Call runKernel as a stop-gap until we modularize all kernels.\n // Once we modularize all kernels, we will remove the existing\n // `runKernelFunc`.\n return this.runKernelFunc(\n forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave,\n outputsToSave);\n }\n\n private shouldCheckForMemLeaks(): boolean {\n return this.ENV.getBool('IS_TEST');\n }\n\n private checkKernelForMemLeak(\n kernelName: string, numDataIdsBefore: number,\n outInfos: TensorInfo[]): void {\n const numDataIdsAfter = this.backend.numDataIds();\n\n // Count the number of data ids associated with the result of the kernel.\n let numOutputDataIds = 0;\n outInfos.forEach(info => {\n // Complex numbers allocate 3 data ids, one for 'real', one for\n // 'imaginary', and one for the container that holds the former two.\n numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n });\n\n // Account for the number of moves during kernel execution. A \"data move\"\n // can happen in the middle of a kernel execution, placing a new (key,value)\n // pair in the data storage. Since data moves have net zero effect (we\n // always remove the data from the old backend), we have to cancel them out\n // when detecting memory leaks.\n const numMoves =\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n const dataIdsLeaked =\n numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n if (dataIdsLeaked > 0) {\n throw new Error(\n `Backend '${this.backendName}' has an internal memory leak ` +\n `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n }\n }\n\n /**\n * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n * only for kernels that are not yet fully modularized.\n */\n runKernelFunc(\n forwardFunc: ForwardFunc, inputs: I,\n backwardsFunc?: (dy: T, saved: Tensor[]) => {[P in keyof I]: () => I[P]},\n kernelName?: string, attrs?: NamedAttrMap, inputsToSave?: Tensor[],\n outputsToSave?: boolean[]): T {\n let outputs: Tensor[];\n let saved: Tensor[] = [];\n const isTapeOn = this.isTapeOn();\n if (kernelName == null) {\n kernelName =\n this.state.activeScope != null ? this.state.activeScope.name : '';\n }\n\n const startingBytecount = this.state.numBytes;\n const startingNumTensors = this.state.numTensors;\n\n if (this.shouldCheckForMemLeaks()) {\n this.state.numDataMovesStack.push(0);\n }\n\n let kernelFunc: () => Tensor[];\n const kernel = getKernel(kernelName, this.backendName);\n let out: TensorInfo|TensorInfo[];\n if (kernel != null) {\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = kernel.kernelFunc({inputs, attrs, backend: this.backend});\n const outInfos = Array.isArray(out) ? out : [out];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n }\n const outTensors = outInfos.map(\n ({dataId, shape, dtype}) =>\n this.makeTensorFromDataId(dataId, shape, dtype));\n\n // Save the inputs and outputs.\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since we would never run backprop, which disposes\n // the kept tensors.\n if (isTapeOn) {\n let tensorsToSave =\n this.getTensorsForGradient(kernelName, inputs, outTensors);\n if (tensorsToSave == null) {\n // Fallback for ops that call runKernelFunc and pass in\n // inputsToSave and outputsToSave. Currently this is the set of ops\n // with kernel support in the WASM backend. Once those ops and\n // respective gradients are modularised we can remove this path.\n if (outputsToSave == null) {\n outputsToSave = [];\n }\n const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n }\n saved = this.saveTensorsForBackwardMode(tensorsToSave);\n }\n return outTensors;\n };\n } else {\n const saveFunc: GradSaveFunc = (tensors) => {\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since we would never run backprop, which disposes\n // the kept tensors.\n if (!isTapeOn) {\n return;\n }\n saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n };\n\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n const outs = (Array.isArray(out) ? out : [out]) as Tensor[];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n }\n return outs;\n };\n }\n\n // Stop recording to a tape when running a kernel.\n let kernelProfile: KernelProfile;\n this.scopedRun(\n () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n outputs = kernelFunc();\n } else {\n kernelProfile = this.profiler.profileKernel(\n kernelName, inputs, () => kernelFunc());\n if (this.ENV.getBool('DEBUG')) {\n this.profiler.logKernelProfile(kernelProfile);\n }\n outputs = kernelProfile.outputs;\n }\n });\n\n if (isTapeOn) {\n this.addTapeNode(\n kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n }\n\n if (this.state.profiling) {\n this.state.activeProfile.kernels.push({\n name: kernelName,\n bytesAdded: this.state.numBytes - startingBytecount,\n totalBytesSnapshot: this.state.numBytes,\n tensorsAdded: this.state.numTensors - startingNumTensors,\n totalTensorsSnapshot: this.state.numTensors,\n inputShapes: Object.keys(inputs).map(\n key => inputs[key] != null ? inputs[key].shape : null),\n outputShapes: outputs.map(item => item.shape),\n kernelTimeMs: kernelProfile.timeMs,\n extraInfo: kernelProfile.extraInfo\n });\n }\n return (Array.isArray(out) ? outputs : outputs[0]) as T;\n }\n\n /**\n * Saves tensors used in forward mode for use in backward mode.\n *\n * @param tensors the list of tensors to save.\n */\n private saveTensorsForBackwardMode(tensors: Tensor[]): Tensor[] {\n const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n return saved;\n }\n\n /**\n * Returns a list of tensors to save for a given gradient calculation.\n *\n * Returns undefined if their is no registered gradient for this kernel in the\n * gradient registry.\n *\n * @param kernelName name of kernel to look up gradient for.\n * @param inputs a map of input tensors.\n * @param outputs an array of output tensors from forward mode of kernel.\n */\n private getTensorsForGradient(\n kernelName: string, inputs: NamedTensorMap,\n outputs: Tensor[]): Tensor[]|null {\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n const inputsToSave: string[] = gradConfig.inputsToSave || [];\n const outputsToSave: boolean[] = gradConfig.outputsToSave || [];\n\n // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n // specified in inputsToSave will be saved.\n let inputTensorsToSave: Tensor[];\n if (gradConfig.saveAllInputs) {\n util.assert(\n Array.isArray(inputs),\n () => 'saveAllInputs is true, expected inputs to be an array.');\n\n inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n } else {\n inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n }\n\n const outputTensorsToSave: Tensor[] =\n outputs.filter((_, i) => outputsToSave[i]);\n\n return inputTensorsToSave.concat(outputTensorsToSave);\n }\n // TODO(yassogba) throw exception here once all runkernelFunc calls with\n // inputsToSave/outputsToSave are removed\n return null;\n }\n\n /**\n * Internal method used by public APIs for tensor creation. Makes a new\n * tensor with the provided shape, dtype and values. It always\n * creates a new data id and writes the values to the underlying backend.\n */\n makeTensor(\n values: DataValues, shape: number[], dtype: DataType,\n backend?: KernelBackend): Tensor {\n if (values == null) {\n throw new Error('Values passed to engine.makeTensor() are null');\n }\n dtype = dtype || 'float32';\n backend = backend || this.backend;\n let backendVals = values as BackendValues;\n if (dtype === 'string' && util.isString(values[0])) {\n backendVals = (values as string[]).map(d => util.encodeString(d));\n }\n const dataId = backend.write(backendVals, shape, dtype);\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.incRef(t, backend);\n\n // Count bytes for string tensors.\n if (dtype === 'string') {\n const info = this.state.tensorInfo.get(dataId);\n const newBytes = bytesFromStringArray(backendVals as Uint8Array[]);\n this.state.numBytes += newBytes - info.bytes;\n info.bytes = newBytes;\n }\n return t;\n }\n\n /**\n * Internal method used by backends. Makes a new tensor\n * that is a wrapper around an existing data id. It doesn't create\n * a new data id, only increments the ref count used in memory tracking.\n */\n makeTensorFromDataId(\n dataId: DataId, shape: number[], dtype: DataType,\n backend?: KernelBackend): Tensor {\n dtype = dtype || 'float32';\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.incRef(t, backend);\n return t;\n }\n\n makeVariable(\n initialValue: Tensor, trainable = true, name?: string,\n dtype?: DataType): Variable {\n name = name || this.nextVariableId().toString();\n if (dtype != null && dtype !== initialValue.dtype) {\n initialValue = initialValue.cast(dtype);\n }\n const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n if (this.state.registeredVariables[v.name] != null) {\n throw new Error(`Variable with name ${v.name} was already registered`);\n }\n this.state.registeredVariables[v.name] = v;\n this.incRef(v, this.backend);\n return v;\n }\n\n incRef(a: Tensor, backend: KernelBackend): void {\n const refCount = this.state.tensorInfo.has(a.dataId) ?\n this.state.tensorInfo.get(a.dataId).refCount :\n 0;\n this.state.numTensors++;\n if (a.dtype === 'string') {\n this.state.numStringTensors++;\n }\n if (refCount === 0) {\n this.state.numDataBuffers++;\n\n // Bytes for complex numbers are counted by their components. Bytes for\n // string tensors are counted when writing values.\n let bytes = 0;\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n bytes = a.size * util.bytesPerElement(a.dtype);\n }\n this.state.tensorInfo.set(a.dataId, {\n backend: backend || this.backend,\n dtype: a.dtype,\n shape: a.shape,\n bytes,\n refCount: 0\n });\n this.state.numBytes += bytes;\n }\n\n this.state.tensorInfo.get(a.dataId).refCount++;\n\n if (!(a instanceof Variable)) {\n this.track(a);\n }\n }\n\n disposeTensor(a: Tensor): void {\n if (!this.state.tensorInfo.has(a.dataId)) {\n return;\n }\n\n this.state.numTensors--;\n if (a.dtype === 'string') {\n this.state.numStringTensors--;\n }\n const info = this.state.tensorInfo.get(a.dataId);\n const refCount = info.refCount;\n\n if (refCount <= 1) {\n // Don't count bytes for complex numbers as they are counted by their\n // components.\n if (a.dtype !== 'complex64') {\n this.state.numBytes -= info.bytes;\n }\n this.state.numDataBuffers--;\n\n info.backend.disposeData(a.dataId);\n this.state.tensorInfo.delete(a.dataId);\n } else {\n this.state.tensorInfo.get(a.dataId).refCount--;\n }\n // TODO(nsthorat): Construct an error and save the stack trace for\n // debugging when in debug mode. Creating a stack trace is too expensive\n // to do unconditionally.\n }\n\n disposeVariables(): void {\n for (const varName in this.state.registeredVariables) {\n const v = this.state.registeredVariables[varName];\n this.disposeVariable(v);\n }\n }\n\n disposeVariable(v: Variable): void {\n this.disposeTensor(v);\n if (this.state.registeredVariables[v.name] != null) {\n delete this.state.registeredVariables[v.name];\n }\n }\n\n memory(): MemoryInfo {\n const info = this.backend.memory() as MemoryInfo;\n info.numTensors = this.state.numTensors;\n info.numDataBuffers = this.state.numDataBuffers;\n info.numBytes = this.state.numBytes;\n if (this.state.numStringTensors > 0) {\n info.unreliable = true;\n if (info.reasons == null) {\n info.reasons = [];\n }\n info.reasons.push(\n 'Memory usage by string tensors is approximate ' +\n '(2 bytes per character)');\n }\n return info;\n }\n\n async profile(query: () => (TensorContainer | Promise)):\n Promise {\n this.state.profiling = true;\n\n const startBytes = this.state.numBytes;\n const startNumTensors = this.state.numTensors;\n\n this.state.activeProfile.kernels = [];\n this.state.activeProfile.result = await query();\n\n this.state.profiling = false;\n\n this.state.activeProfile.peakBytes = Math.max(\n ...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n this.state.activeProfile.newTensors =\n this.state.numTensors - startNumTensors;\n for (const kernel of this.state.activeProfile.kernels) {\n kernel.kernelTimeMs = await kernel.kernelTimeMs;\n kernel.extraInfo = await kernel.extraInfo;\n }\n return this.state.activeProfile;\n }\n\n isTapeOn(): boolean {\n return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n }\n\n private addTapeNode(\n kernelName: string, inputs: NamedTensorMap, outputs: Tensor[],\n gradientsFunc: GradFunc, saved: Tensor[], attrs: NamedAttrMap): void {\n const tapeNode: TapeNode =\n {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};\n\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n gradientsFunc = gradConfig.gradFunc;\n }\n if (gradientsFunc != null) {\n tapeNode.gradient = (dys: Tensor[]) => {\n // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n // the backprop graph to the user as null instead of zeros\n dys = dys.map((dy, i) => {\n if (dy == null) {\n const output = outputs[i];\n const vals = util.makeZerosTypedArray(output.size, output.dtype);\n return this.makeTensor(vals, output.shape, output.dtype);\n }\n return dy;\n });\n // Grad functions of ops with single outputs expect a dy, while ops\n // with multiple outputs expect dys (array of dy).\n return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n };\n }\n this.state.activeTape.push(tapeNode);\n }\n\n keep(result: T): T {\n result.kept = true;\n return result;\n }\n\n private startTape() {\n if (this.state.gradientDepth === 0) {\n this.state.activeTape = [];\n }\n this.state.gradientDepth++;\n }\n\n private endTape() {\n this.state.gradientDepth--;\n }\n\n /**\n * Start a scope. Use this with endScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n startScope(name?: string) {\n const scopeInfo: ScopeState = {\n track: [],\n name: 'unnamed scope',\n id: this.state.nextScopeId++\n };\n if (name) {\n scopeInfo.name = name;\n }\n this.state.scopeStack.push(scopeInfo);\n this.state.activeScope = scopeInfo;\n }\n\n /**\n * End a scope. Use this with startScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n endScope(result?: TensorContainer) {\n const tensorsToTrackInParent = getTensorsInContainer(result);\n const tensorsToTrackInParentSet =\n new Set(tensorsToTrackInParent.map(t => t.id));\n\n // Dispose the arrays tracked in this scope.\n for (let i = 0; i < this.state.activeScope.track.length; i++) {\n const tensor = this.state.activeScope.track[i];\n if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n tensor.dispose();\n }\n }\n\n const oldScope = this.state.scopeStack.pop();\n this.state.activeScope = this.state.scopeStack.length === 0 ?\n null :\n this.state.scopeStack[this.state.scopeStack.length - 1];\n\n // Track the current result in the parent scope.\n tensorsToTrackInParent.forEach(tensor => {\n // Only track the tensor if was allocated in the inner scope and is not\n // globally kept.\n if (!tensor.kept && tensor.scopeId === oldScope.id) {\n this.track(tensor);\n }\n });\n }\n\n /**\n * Returns gradients of `f` with respect to each of the `xs`. The gradients\n * returned are of the same length as `xs`, but some might be null if `f`\n * was not a function of that `x`. It also takes optional dy to multiply the\n * gradient, which defaults to `1`.\n */\n gradients(\n f: () => T, xs: Tensor[], dy?: T,\n allowNoGradients = false): {value: T, grads: Tensor[]} {\n util.assert(\n xs.length > 0, () => 'gradients() received an empty list of xs.');\n if (dy != null && dy.dtype !== 'float32') {\n throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n }\n\n const y = this.scopedRun(\n () => this.startTape(), () => this.endTape(),\n () => this.tidy('forward', f));\n\n util.assert(\n y instanceof Tensor,\n () => 'The result y returned by f() must be a tensor.');\n // Filter out the nodes that don't connect x => y.\n const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n throw new Error(\n 'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n 'that the f you passed encloses all operations that lead from x ' +\n 'to y.');\n }\n\n return this.tidy('backward', () => {\n const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n // Backprop gradients through the filtered nodes.\n backpropagateGradients(\n accumulatedGradientMap, filteredTape,\n // Pass the tidy function to avoid circular dep with `tape.ts`.\n f => this.tidy(f as ScopeFn),\n // Pass an add function to avoide a circular dep with `tape.ts`.\n add);\n const grads = xs.map(x => accumulatedGradientMap[x.id]);\n\n if (this.state.gradientDepth === 0) {\n // This means that we are not computing higher-order gradients\n // and can clean up the tape.\n this.state.activeTape.forEach(node => {\n for (const tensor of node.saved) {\n tensor.dispose();\n }\n });\n this.state.activeTape = null;\n }\n return {value: y, grads};\n });\n }\n\n customGrad(f: CustomGradientFunc):\n (...args: Array) => T {\n util.assert(\n util.isFunction(f),\n () => 'The f passed in customGrad(f) must be a function.');\n return (...inputs: Tensor[]): T => {\n util.assert(\n inputs.every(t => t instanceof Tensor),\n () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n 'tensors');\n\n let res: {\n value: T,\n gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[],\n };\n const inputMap: NamedTensorMap = {};\n inputs.forEach((input, i) => {\n inputMap[i] = input;\n });\n return this.runKernelFunc(\n (_, save) => {\n res = f(...[...inputs, save]);\n util.assert(\n res.value instanceof Tensor,\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.value` is a tensor');\n util.assert(\n util.isFunction(res.gradFunc),\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function.');\n return res.value;\n },\n inputMap,\n (dy: T, saved: Tensor[]) => {\n const gradRes = res.gradFunc(dy, saved);\n const grads: Tensor[] =\n Array.isArray(gradRes) ? gradRes : [gradRes];\n util.assert(\n grads.length === inputs.length,\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'the same number of tensors as inputs passed to f(...).');\n util.assert(\n grads.every(t => t instanceof Tensor),\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'a list of only tensors.');\n const gradMap: {[key: string]: () => Tensor} = {};\n grads.forEach((grad, i) => {\n gradMap[i] = () => grad;\n });\n return gradMap;\n });\n };\n }\n\n readSync(dataId: DataId): BackendValues {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readSync(dataId);\n }\n read(dataId: DataId): Promise {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.read(dataId);\n }\n\n async time(query: () => void): Promise {\n const start = now();\n const timingInfo = await this.backend.time(query) as TimingInfo;\n timingInfo.wallMs = now() - start;\n return timingInfo;\n }\n\n /**\n * Tracks a Tensor in the current scope to be automatically cleaned up\n * when the current scope ends, and returns the value.\n *\n * @param result The Tensor to track in the current scope.\n */\n private track(result: T): T {\n if (this.state.activeScope != null) {\n result.scopeId = this.state.activeScope.id;\n this.state.activeScope.track.push(result);\n }\n\n return result;\n }\n\n get registeredVariables(): NamedVariableMap {\n return this.state.registeredVariables;\n }\n\n /**\n * Resets the engine state. Removes all backends but does not remove\n * registered backend factories.\n */\n reset(): void {\n // Make any pending promise obsolete.\n this.pendingBackendInitId++;\n\n this.state.dispose();\n this.ENV.reset();\n this.state = new EngineState();\n\n for (const backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n this.backendName = null;\n this.backendInstance = null;\n this.pendingBackendInit = null;\n }\n}\n\nfunction ones(shape: number[]): Tensor {\n const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n return ENGINE.makeTensor(values, shape, 'float32');\n}\n\nexport function getOrMakeEngine(): Engine {\n const ns = getGlobalNamespace() as {} as {_tfengine: Engine};\n if (ns._tfengine == null) {\n const environment = new Environment(ns);\n ns._tfengine = new Engine(environment);\n }\n setEnvironmentGlobal(ns._tfengine.ENV);\n\n // Tell the current tensor interface that the global engine is responsible\n // for tracking.\n setTensorTracker(() => ns._tfengine);\n return ns._tfengine;\n}\n\nexport const ENGINE = getOrMakeEngine();\n\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a: Tensor, b: Tensor): Tensor {\n // We duplicate Add here to avoid a circular dependency with add.ts.\n const inputs = {a, b};\n return ENGINE.runKernelFunc((backend, save) => {\n const res = backend.add(a, b);\n save([a, b]);\n return res;\n }, inputs as {} as NamedTensorMap, null /* gradient */, Add);\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined(): boolean {\n return typeof navigator !== 'undefined' && navigator != null;\n}\n\nexport function isMobile(): boolean {\n if (_isNavigatorDefined()) {\n // tslint:disable-next-line:no-any\n const a = navigator.userAgent || navigator.vendor || (window as any).opera;\n // tslint:disable-next-line:max-line-length\n return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n .test(a) ||\n // tslint:disable-next-line:max-line-length\n /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n .test(a.substr(0, 4));\n }\n return false;\n}\n\nexport function isBrowser(): boolean {\n return (typeof window !== 'undefined' && window.document != null) ||\n //@ts-ignore\n (typeof WorkerGlobalScope !== 'undefined');\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\n\nimport * as device_util from './device_util';\nimport {env} from './environment';\n\nconst ENV = env();\n\n/**\n * This file contains environment-related flag registrations.\n */\n\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n if (debugValue) {\n console.warn(\n 'Debugging mode is ON. The output of every math call will ' +\n 'be downloaded to CPU and checked for NaNs. ' +\n 'This significantly impacts performance.');\n }\n});\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag(\n 'IS_NODE',\n () => (typeof process !== 'undefined') &&\n (typeof process.versions !== 'undefined') &&\n (typeof process.versions.node !== 'undefined'));\n\n/** Whether this browser is Chrome. */\nENV.registerFlag(\n 'IS_CHROME',\n () => typeof navigator !== 'undefined' && navigator != null &&\n navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n /Google Inc/.test(navigator.vendor));\n\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag(\n 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {env} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike, dtype?: DataType): number[] {\n let firstElem: typeof val = val;\n\n if (isTypedArray(val)) {\n return dtype === 'string' ? [] : [val.length];\n }\n if (!Array.isArray(val)) {\n return []; // Scalar.\n }\n const shape: number[] = [];\n\n while (Array.isArray(firstElem) ||\n isTypedArray(firstElem) && dtype !== 'string') {\n shape.push(firstElem.length);\n firstElem = firstElem[0];\n }\n if (Array.isArray(val) &&\n env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n deepAssertShapeConsistency(val, shape, []);\n }\n\n return shape;\n}\n\nfunction deepAssertShapeConsistency(\n val: TensorLike, shape: number[], indices: number[]) {\n indices = indices || [];\n if (!(Array.isArray(val)) && !isTypedArray(val)) {\n assert(\n shape.length === 0,\n () => `Element arr[${indices.join('][')}] is a primitive, ` +\n `but should be an array/TypedArray of ${shape[0]} elements`);\n return;\n }\n assert(\n shape.length > 0,\n () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n `but is an array of ${val.length} elements`);\n assert(\n val.length === shape[0],\n () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n `elements, but has ${val.length} elements`);\n const subShape = shape.slice(1);\n for (let i = 0; i < val.length; ++i) {\n deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n }\n}\n\nfunction assertDtype(\n expectedDtype: DataType|'numeric', actualDType: DataType, argName: string,\n functionName: string) {\n if (expectedDtype == null) {\n return;\n }\n if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n expectedDtype === 'numeric' && actualDType === 'string') {\n throw new Error(\n `Argument '${argName}' passed to '${functionName}' must ` +\n `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n }\n}\n\nexport function convertToTensor(\n x: T|TensorLike, argName: string, functionName: string,\n parseAsDtype: DataType|'numeric' = 'numeric'): T {\n if (x instanceof Tensor) {\n assertDtype(parseAsDtype, x.dtype, argName, functionName);\n return x;\n }\n let inferredDtype = inferDtype(x);\n // If the user expects a bool/int/float, use that info to update the\n // inferredDtype when it is not a string.\n if (inferredDtype !== 'string' &&\n ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n inferredDtype = parseAsDtype as DataType;\n }\n assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n if ((x == null) ||\n (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n typeof x !== 'boolean' && typeof x !== 'string')) {\n const type = x == null ? 'null' : (x as {}).constructor.name;\n throw new Error(\n `Argument '${argName}' passed to '${functionName}' must be a ` +\n `Tensor or TensorLike, but got '${type}'`);\n }\n const inferredShape = inferShape(x, inferredDtype);\n if (!isTypedArray(x) && !Array.isArray(x)) {\n x = [x] as number[];\n }\n const skipTypedArray = true;\n const values = inferredDtype !== 'string' ?\n toTypedArray(x, inferredDtype as DataType) :\n flatten(x as string[], [], skipTypedArray) as string[];\n return ENGINE.makeTensor(values, inferredShape, inferredDtype) as T;\n}\n\nexport function convertToTensorArray(\n arg: Array, argName: string, functionName: string,\n parseAsDtype: DataType|'numeric' = 'numeric'): T[] {\n if (!Array.isArray(arg)) {\n throw new Error(\n `Argument ${argName} passed to ${functionName} must be a ` +\n '`Tensor[]` or `TensorLike[]`');\n }\n const tensors = arg as T[];\n return tensors.map(\n (t, i) => convertToTensor(t, `${argName}[${i}]`, functionName),\n parseAsDtype);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\n\nexport const OP_SCOPE_SUFFIX = '__op';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f: {[name: string]: T}): T {\n const keys = Object.keys(f);\n if (keys.length !== 1) {\n throw new Error(\n `Please provide an object with a single key ` +\n `(operation name) mapping to a function. Got an object with ` +\n `${keys.length} keys.`);\n }\n\n let opName = keys[0];\n const fn = f[opName];\n\n // Strip the underscore from the end of the function name.\n if (opName.endsWith('_')) {\n opName = opName.substring(0, opName.length - 1);\n }\n\n // add an __op suffix to distinguish ops from kernels in tf.profile\n opName = opName + OP_SCOPE_SUFFIX;\n\n // tslint:disable-next-line:no-any\n const f2 = (...args: any[]) => {\n ENGINE.startScope(opName);\n try {\n const result = fn(...args);\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n ENGINE.endScope(result);\n return result;\n } catch (ex) {\n ENGINE.endScope(null);\n throw ex;\n }\n };\n Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n // tslint:disable-next-line:no-any\n return f2 as any as T;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Complex, ComplexInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real: T|TensorLike, imag: T|TensorLike): T {\n const $real = convertToTensor(real, 'real', 'complex');\n const $imag = convertToTensor(imag, 'imag', 'complex');\n util.assertShapesMatch(\n $real.shape, $imag.shape,\n `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n `must match in call to tf.complex().`);\n\n const forward: ForwardFunc = (backend) => {\n return backend.complex($real, $imag);\n };\n const inputs: ComplexInputs = {real: $real, imag: $imag};\n return ENGINE.runKernelFunc(\n forward, inputs as {} as NamedTensorMap, null /* gradient */,\n Complex) as T;\n}\n\nexport const complex = op({complex_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {TensorLike, TypedArray} from '../types';\nimport {DataType} from '../types';\nimport {assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray} from '../util';\n\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(\n values: TensorLike, shape: number[], inferredShape: number[],\n dtype?: DataType): Tensor {\n if (dtype == null) {\n dtype = inferDtype(values);\n }\n if (dtype === 'complex64') {\n throw new Error(\n `Cannot construct a complex64 tensor directly. ` +\n `Please use tf.complex(real, imag).`);\n }\n if (!isTypedArray(values) && !Array.isArray(values) &&\n typeof values !== 'number' && typeof values !== 'boolean' &&\n typeof values !== 'string') {\n throw new Error(\n 'values passed to tensor(values) must be a number/boolean/string or ' +\n 'an array of numbers/booleans/strings, or a TypedArray');\n }\n if (shape != null) {\n assertNonNegativeIntegerDimensions(shape);\n\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(\n providedSize === inferredSize,\n () =>\n `Based on the provided shape, [${shape}], the tensor should have ` +\n `${providedSize} values but has ${inferredSize}`);\n\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ?\n inferred !== sizeFromShape(shape.slice(i)) :\n true;\n assert(\n inferredShape[i] === shape[i] || !flatDimsDontMatch,\n () => `Error creating a new Tensor. Inferred shape ` +\n `(${inferredShape}) does not match the provided ` +\n `shape (${shape}). `);\n }\n }\n\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values] as number[];\n }\n\n shape = shape || inferredShape;\n values = dtype !== 'string' ?\n toTypedArray(values, dtype) :\n flatten(values as string[], [], true) as string[];\n return ENGINE.makeTensor(values as TypedArray, shape, dtype);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`. If the values are strings,\n * they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(\n values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor {\n const inferredShape = inferShape(values, dtype);\n return makeTensor(values, shape, inferredShape, dtype) as Tensor;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n 'float32': 4,\n 'float16': 2,\n 'int32': 4,\n 'uint16': 2,\n 'uint8': 1,\n 'bool': 1,\n 'complex64': 8\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n /**\n * An ordered list of paths.\n *\n * Paths are intentionally abstract in order to be general. For example, they\n * can be relative URL paths or relative paths on the file system.\n */\n paths: string[];\n\n /**\n * Specifications of the weights stored in the paths.\n */\n weights: WeightsManifestEntry[];\n}\n\n/**\n * Group to which the weight belongs.\n *\n * - 'optimizer': Weight from a stateful optimizer.\n */\nexport type WeightGroup = 'model'|'optimizer';\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n /**\n * Name of the weight, e.g., 'Dense_1/bias'\n */\n name: string;\n\n /**\n * Shape of the weight.\n */\n shape: number[];\n\n /**\n * Data type of the weight.\n */\n dtype: 'float32'|'int32'|'bool'|'string'|'complex64';\n\n /**\n * Type of the weight.\n *\n * Optional.\n *\n * The value 'optimizer' indicates the weight belongs to an optimizer\n * (i.e., used only during model training and not during inference).\n */\n group?: WeightGroup;\n\n /**\n * Information for dequantization of the weight.\n */\n quantization?: {\n scale?: number, // The scaling constant to multiply by.\n min?: number, // The (possibly nudged) minimum weight to add.\n dtype: 'uint16'|'uint8'|'float16' // The dtype of the quantized weights.\n };\n}\n\n/**\n * Options for saving a model.\n * @innamespace io\n */\nexport interface SaveConfig {\n /**\n * Whether to save only the trainable weights of the model, ignoring the\n * non-trainable ones.\n */\n trainableOnly?: boolean;\n\n /**\n * Whether the optimizer will be saved (if exists).\n *\n * Default: `false`.\n */\n includeOptimizer?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n /**\n * Information about the model artifacts saved.\n */\n modelArtifactsInfo: ModelArtifactsInfo;\n\n /**\n * HTTP responses from the server that handled the model-saving request (if\n * any). This is applicable only to server-based saving routes.\n */\n responses?: Response[];\n\n /**\n * Error messages and related data (if any).\n */\n errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n /**\n * Timestamp for when the model is saved.\n */\n dateSaved: Date;\n\n /**\n * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now\n * come in a JSON format and none of our IOHandlers support a non json\n * format. We could conder replacing this with 'Binary' if we want to\n * allow future handlers to save to non json formats (though they will\n * probably want more information than 'Binary').\n * Type of the model topology\n *\n * Type of the model topology\n *\n * Possible values:\n * - JSON: JSON config (human-readable, e.g., Keras JSON).\n * - GraphDef: TensorFlow\n * [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n * protocol buffer (binary).\n */\n modelTopologyType: 'JSON'|'GraphDef';\n\n /**\n * Size of model topology (Keras JSON or GraphDef), in bytes.\n */\n modelTopologyBytes?: number;\n\n /**\n * Size of weight specification or manifest, in bytes.\n */\n weightSpecsBytes?: number;\n\n /**\n * Size of weight value data, in bytes.\n */\n weightDataBytes?: number;\n}\n\n/** Model training configuration. */\nexport declare interface TrainingConfig {\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n // See\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/training_config.ts\n /** Optimizer used for the model training. */\n optimizer_config: {};\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n /** Loss function(s) for the model's output(s). */\n loss: string|string[]|{[key: string]: string};\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n /** Metric function(s) for the model's output(s). */\n metrics?: string[]|{[key: string]: string};\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n weighted_metrics?: string[];\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n sample_weight_mode?: string;\n\n loss_weights?: number[]|{[key: string]: number};\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields\n * of this interface are optional, in order to support topology- or weights-only\n * saving and loading.\n *\n * Note this interface is used internally in IOHandlers. For the file format\n * written to disk as `model.json`, see `ModelJSON`.\n */\nexport declare interface ModelArtifacts {\n /**\n * Model topology.\n *\n * For Keras-style `tf.Model`s, this is a JSON object.\n * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n * encoding of the `GraphDef` protocol buffer.\n */\n modelTopology?: {}|ArrayBuffer;\n\n /**\n * Serialized configuration for the model's training.\n */\n trainingConfig?: TrainingConfig;\n\n /**\n * Weight specifications.\n *\n * This corresponds to the weightsData below.\n */\n weightSpecs?: WeightsManifestEntry[];\n\n /**\n * Binary buffer for all weight values concatenated in the order specified\n * by `weightSpecs`.\n */\n weightData?: ArrayBuffer;\n\n /**\n * Hard-coded format name for models saved from TensorFlow.js or converted\n * by TensorFlow.js Converter.\n */\n format?: string;\n\n /**\n * What library is responsible for originally generating this artifact.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n */\n generatedBy?: string;\n\n /**\n * What library or tool is responsible for converting the original model\n * to this format, applicable only if the model is output by a converter.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.\n *\n * A value of `null` means the model artifacts are generated without any\n * conversion process (e.g., saved directly from a TensorFlow.js\n * `tf.LayersModel` instance.)\n */\n convertedBy?: string|null;\n\n /**\n * User-defined metadata about the model.\n */\n userDefinedMetadata?: {};\n\n /**\n * Initializer for the model.\n */\n modelInitializer?: {};\n}\n\n/**\n * The on-disk format of the `model.json` file.\n *\n * TF.js 1.0 always populates the optional fields when writing model.json.\n * Prior versions did not provide those fields.\n */\nexport declare interface ModelJSON {\n /**\n * Model topology.\n *\n * For Keras-style `tf.Model`s, this is a JSON object.\n * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n * encoding of the `GraphDef` protocol buffer.\n */\n modelTopology: {};\n\n /** Model training configuration. */\n trainingConfig?: TrainingConfig;\n\n /**\n * Weights manifest.\n *\n * The weights manifest consists of an ordered list of weight-manifest\n * groups. Each weight-manifest group consists of a number of weight values\n * stored in a number of paths. See the documentation of\n * `WeightsManifestConfig` for more details.\n */\n weightsManifest: WeightsManifestConfig;\n\n /**\n * Hard-coded format name for models saved from TensorFlow.js or converted\n * by TensorFlow.js Converter.\n */\n format?: string;\n\n /**\n * What library is responsible for originally generating this artifact.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n */\n generatedBy?: string;\n\n /**\n * What library or tool is responsible for converting the original model\n * to this format, applicable only if the model is output by a converter.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.\n *\n * A value of `null` means the model artifacts are generated without any\n * conversion process (e.g., saved directly from a TensorFlow.js\n * `tf.LayersModel` instance.)\n */\n convertedBy?: string|null;\n\n /**\n * User-defined metadata about the model.\n */\n userDefinedMetadata?: {};\n\n /**\n * Initializer for the model.\n */\n modelInitializer?: {};\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n Promise;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n save?: SaveHandler;\n load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n /**\n * List all models in the model store.\n *\n * @returns A dictionary mapping paths of existing models to their\n * model artifacts info. Model artifacts info include type of the model's\n * topology, byte sizes of the topology, weights, etc.\n */\n listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n /**\n * Remove a model specified by `path`.\n *\n * @param path\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n removeModel(path: string): Promise;\n}\n\n/**\n * Callback for the progress of a long-running action such as an HTTP\n * request for a large binary object.\n *\n * `fraction` should be a number in the [0, 1] interval, indicating how\n * much of the action has completed.\n */\nexport type OnProgressCallback = (fraction: number) => void;\n\n/** @innamespace io */\nexport interface LoadOptions {\n /**\n * RequestInit (options) for HTTP requests.\n *\n * For detailed information on the supported fields, see\n * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)\n */\n requestInit?: RequestInit;\n\n /**\n * Progress callback.\n */\n onProgress?: OnProgressCallback;\n\n /**\n * A function used to override the `window.fetch` function.\n */\n fetchFunc?: Function;\n\n /**\n * Strict loading model: whether extraneous weights or missing\n * weights should trigger an `Error`.\n *\n * If `true`, require that the provided weights exactly match those\n * required by the layers. `false` means that both extra weights\n * and missing weights will be silently ignored.\n *\n * Default: `true`.\n */\n strict?: boolean;\n\n /**\n * Path prefix for weight files, by default this is calculated from the\n * path of the model JSON file.\n *\n * For instance, if the path to the model JSON file is\n * `http://localhost/foo/model.json`, then the default path prefix will be\n * `http://localhost/foo/`. If a weight file has the path value\n * `group1-shard1of2` in the weight manifest, then the weight file will be\n * loaded from `http://localhost/foo/group1-shard1of2` by default. However,\n * if you provide a `weightPathPrefix` value of\n * `http://localhost/foo/alt-weights`, then the weight file will be loaded\n * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.\n */\n weightPathPrefix?: string;\n\n /**\n * Whether the module or model is to be loaded from TF Hub.\n *\n * Setting this to `true` allows passing a TF-Hub module URL, omitting the\n * standard model file name and the query parameters.\n *\n * Default: `false`.\n */\n fromTFHub?: boolean;\n\n /**\n * An async function to convert weight file name to URL. The weight file\n * names are stored in model.json's weightsManifest.paths field. By default we\n * consider weight files are colocated with the model.json file. For example:\n * model.json URL: https://www.google.com/models/1/model.json\n * group1-shard1of1.bin url:\n * https://www.google.com/models/1/group1-shard1of1.bin\n *\n * With this func you can convert the weight file name to any URL.\n */\n weightUrlConverter?: (weightFileName: string) => Promise;\n}\n\n/**\n * Additional options for Platform.fetch\n */\nexport interface RequestDetails {\n /**\n * Is this request for a binary file (as opposed to a json file)\n */\n isBinary?: boolean;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {complex} from '../ops/complex';\n\nimport {tensor} from '../ops/tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\n\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightGroup, WeightsManifestEntry} from './types';\n\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n * - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n * concatenated.\n * - An `Array` of `WeightManifestEntry`s, carrying information including\n * tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(\n tensors: NamedTensorMap|NamedTensor[], group?: WeightGroup):\n Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n // TODO(adarob, cais): Support quantization.\n const specs: WeightsManifestEntry[] = [];\n const dataPromises: Array> = [];\n\n const names: string[] = Array.isArray(tensors) ?\n tensors.map(tensor => tensor.name) :\n Object.keys(tensors);\n\n for (let i = 0; i < names.length; ++i) {\n const name = names[i];\n const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n t.dtype !== 'string' && t.dtype !== 'complex64') {\n throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n }\n const spec: WeightsManifestEntry = {name, shape: t.shape, dtype: t.dtype};\n if (t.dtype === 'string') {\n const utf8bytes = new Promise(async resolve => {\n const vals = await t.bytes() as Uint8Array[];\n const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n NUM_BYTES_STRING_LENGTH * vals.length;\n const bytes = new Uint8Array(totalNumBytes);\n let offset = 0;\n for (let i = 0; i < vals.length; i++) {\n const val = vals[i];\n const bytesOfLength =\n new Uint8Array(new Uint32Array([val.length]).buffer);\n bytes.set(bytesOfLength, offset);\n offset += NUM_BYTES_STRING_LENGTH;\n bytes.set(val, offset);\n offset += val.length;\n }\n resolve(bytes);\n });\n dataPromises.push(utf8bytes);\n } else {\n dataPromises.push(t.data());\n }\n if (group != null) {\n spec.group = group;\n }\n specs.push(spec);\n }\n\n const tensorValues = await Promise.all(dataPromises);\n return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n * concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n * whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n * to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n // TODO(adarob, cais): Support quantization.\n const out: NamedTensorMap = {};\n let float16Decode: (buffer: Uint16Array) => Float32Array | undefined;\n let offset = 0;\n for (const spec of specs) {\n const name = spec.name;\n const dtype = spec.dtype;\n const shape = spec.shape;\n const size = sizeFromShape(shape);\n let values: TypedArray|string[]|Uint8Array[];\n\n if ('quantization' in spec) {\n const quantization = spec.quantization;\n if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n if (!('min' in quantization && 'scale' in quantization)) {\n throw new Error(\n `Weight ${spec.name} with quantization ${quantization.dtype} ` +\n `doesn't have corresponding metadata min and scale.`);\n }\n } else if (quantization.dtype === 'float16') {\n if (dtype !== 'float32') {\n throw new Error(\n `Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n `which only supports weights of type float32 not ${dtype}.`);\n }\n } else {\n throw new Error(\n `Weight ${spec.name} has unknown ` +\n `quantization dtype ${quantization.dtype}. ` +\n `Supported quantization dtypes are: ` +\n `'uint8', 'uint16', and 'float16'.`);\n }\n const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n const byteBuffer =\n buffer.slice(offset, offset + size * quantizationSizeFactor);\n const quantizedArray = (quantization.dtype === 'uint8') ?\n new Uint8Array(byteBuffer) :\n new Uint16Array(byteBuffer);\n if (dtype === 'float32') {\n if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n values = new Float32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = v * quantization.scale + quantization.min;\n }\n } else if (quantization.dtype === 'float16') {\n if (float16Decode === undefined) {\n float16Decode = getFloat16Decoder();\n }\n values = float16Decode(quantizedArray as Uint16Array);\n } else {\n throw new Error(\n `Unsupported quantization type ${quantization.dtype} ` +\n `for weight type float32.`);\n }\n } else if (dtype === 'int32') {\n if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n throw new Error(\n `Unsupported quantization type ${quantization.dtype} ` +\n `for weight type int32.`);\n }\n values = new Int32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = Math.round(v * quantization.scale + quantization.min);\n }\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * quantizationSizeFactor;\n } else if (dtype === 'string') {\n const size = sizeFromShape(spec.shape);\n values = [];\n for (let i = 0; i < size; i++) {\n const byteLength = new Uint32Array(\n buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n offset += NUM_BYTES_STRING_LENGTH;\n const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n (values as Uint8Array[]).push(bytes);\n offset += byteLength;\n }\n } else {\n const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n if (dtype === 'float32') {\n values = new Float32Array(byteBuffer);\n } else if (dtype === 'int32') {\n values = new Int32Array(byteBuffer);\n } else if (dtype === 'bool') {\n values = new Uint8Array(byteBuffer);\n } else if (dtype === 'complex64') {\n values = new Float32Array(byteBuffer);\n const real = new Float32Array(values.length / 2);\n const image = new Float32Array(values.length / 2);\n for (let i = 0; i < real.length; i++) {\n real[i] = values[i * 2];\n image[i] = values[i * 2 + 1];\n }\n const realTensor = tensor(real, shape, 'float32');\n const imageTensor = tensor(image, shape, 'float32');\n out[name] = complex(realTensor, imageTensor);\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * dtypeFactor;\n }\n if (dtype !== 'complex64') {\n out[name] = tensor(values, shape, dtype);\n }\n }\n return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n // TODO(adarob, cais): Support quantization.\n if (xs === null) {\n throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n }\n\n let totalByteLength = 0;\n\n // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n // can have a different byte length from that of the `TypedArray` itself,\n // for example, when the `TypedArray` is created from an offset in an\n // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n // the `TypedArray` in byte length. If an element of `xs` does not show\n // this property, a new `TypedArray` that satisfy this property will be\n // constructed and pushed into `normalizedXs`.\n const normalizedXs: TypedArray[] = [];\n xs.forEach((x: TypedArray) => {\n totalByteLength += x.byteLength;\n // tslint:disable:no-any\n normalizedXs.push(\n x.byteLength === x.buffer.byteLength ? x :\n new (x.constructor as any)(x));\n if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n x as any instanceof Uint8Array)) {\n throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n }\n // tslint:enable:no-any\n });\n\n const y = new Uint8Array(totalByteLength);\n let offset = 0;\n normalizedXs.forEach((x: TypedArray) => {\n y.set(new Uint8Array(x.buffer), offset);\n offset += x.byteLength;\n });\n\n return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n if (useNodeBuffer) {\n return Buffer.byteLength(str);\n }\n return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n if (useNodeBuffer) {\n return Buffer.from(buffer).toString('base64');\n }\n const buf = new Uint8Array(buffer);\n let s = '';\n for (let i = 0, l = buf.length; i < l; i++) {\n s += String.fromCharCode(buf[i]);\n }\n return btoa(s);\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n if (useNodeBuffer) {\n const buf = Buffer.from(str, 'base64');\n return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n }\n const s = atob(str);\n const buffer = new Uint8Array(s.length);\n for (let i = 0; i < s.length; ++i) {\n buffer.set([s.charCodeAt(i)], i);\n }\n return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n if (buffers.length === 1) {\n return buffers[0];\n }\n\n let totalByteLength = 0;\n buffers.forEach((buffer: ArrayBuffer) => {\n totalByteLength += buffer.byteLength;\n });\n\n const temp = new Uint8Array(totalByteLength);\n let offset = 0;\n buffers.forEach((buffer: ArrayBuffer) => {\n temp.set(new Uint8Array(buffer), offset);\n offset += buffer.byteLength;\n });\n return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n const SEPARATOR = '/';\n path = path.trim();\n while (path.endsWith(SEPARATOR)) {\n path = path.slice(0, path.length - 1);\n }\n const items = path.split(SEPARATOR);\n return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n ModelArtifactsInfo {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('Expected JSON model topology, received ArrayBuffer.');\n }\n\n return {\n dateSaved: new Date(),\n modelTopologyType: 'JSON',\n modelTopologyBytes: modelArtifacts.modelTopology == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n weightDataBytes: modelArtifacts.weightData == null ?\n 0 :\n modelArtifacts.weightData.byteLength,\n };\n}\n\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable(): Uint32Array {\n const convertMantissa = (i: number): number => {\n let m = i << 13;\n let e = 0;\n\n while ((m & 0x00800000) === 0) {\n e -= 0x00800000;\n m <<= 1;\n }\n m &= ~0x00800000;\n e += 0x38800000;\n\n return m | e;\n };\n\n const mantisaTable = new Uint32Array(2048);\n\n mantisaTable[0] = 0;\n for (let i = 1; i < 1024; i++) {\n mantisaTable[i] = convertMantissa(i);\n }\n for (let i = 1024; i < 2048; i++) {\n mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n }\n\n return mantisaTable;\n}\n\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable(): Uint32Array {\n const exponentTable = new Uint32Array(64);\n\n exponentTable[0] = 0;\n exponentTable[31] = 0x47800000;\n exponentTable[32] = 0x80000000;\n exponentTable[63] = 0xc7800000;\n for (let i = 1; i < 31; i++) {\n exponentTable[i] = i << 23;\n }\n for (let i = 33; i < 63; i++) {\n exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n }\n\n return exponentTable;\n}\n\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable(): Uint32Array {\n const offsetTable = new Uint32Array(64);\n\n for (let i = 0; i < 64; i++) {\n offsetTable[i] = 1024;\n }\n offsetTable[0] = offsetTable[32] = 0;\n\n return offsetTable;\n}\n\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n * the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder(): (buffer: Uint16Array) => Float32Array {\n // Algorithm is based off of\n // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n\n // Cache lookup tables\n const mantisaTable = computeFloat16MantisaTable();\n const exponentTable = computeFloat16ExponentTable();\n const offsetTable = computeFloat16OffsetTable();\n\n return (quantizedArray: Uint16Array) => {\n const buffer = new ArrayBuffer(4 * quantizedArray.length);\n const bufferUint32View = new Uint32Array(buffer);\n for (let index = 0; index < quantizedArray.length; index++) {\n const float16Bits = quantizedArray[index];\n const float32Bits =\n mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n exponentTable[float16Bits >> 10];\n bufferUint32View[index] = float32Bits;\n }\n return new Float32Array(buffer);\n };\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler, LoadOptions} from './types';\n\nexport type IORouter = (url: string|string[], loadOptions?: LoadOptions) =>\n IOHandler;\n\nexport class IORouterRegistry {\n // Singleton instance.\n private static instance: IORouterRegistry;\n\n private saveRouters: IORouter[];\n private loadRouters: IORouter[];\n\n private constructor() {\n this.saveRouters = [];\n this.loadRouters = [];\n }\n\n private static getInstance(): IORouterRegistry {\n if (IORouterRegistry.instance == null) {\n IORouterRegistry.instance = new IORouterRegistry();\n }\n return IORouterRegistry.instance;\n }\n\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerSaveRouter(saveRouter: IORouter) {\n IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n }\n\n /**\n * Register a load-handler router.\n *\n * @param loadRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `load` method defined or `null`.\n */\n static registerLoadRouter(loadRouter: IORouter) {\n IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n }\n\n /**\n * Look up IOHandler for saving, given a URL-like string.\n *\n * @param url\n * @returns If only one match is found, an instance of IOHandler with the\n * `save` method defined. If no match is found, `null`.\n * @throws Error, if more than one match is found.\n */\n static getSaveHandlers(url: string|string[]): IOHandler[] {\n return IORouterRegistry.getHandlers(url, 'save');\n }\n\n /**\n * Look up IOHandler for loading, given a URL-like string.\n *\n * @param url\n * @param loadOptions Optional, custom load options.\n * @returns All valid handlers for `url`, given the currently registered\n * handler routers.\n */\n static getLoadHandlers(url: string|string[], loadOptions?: LoadOptions):\n IOHandler[] {\n return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n }\n\n private static getHandlers(\n url: string|string[], handlerType: 'save'|'load',\n loadOptions?: LoadOptions): IOHandler[] {\n const validHandlers: IOHandler[] = [];\n const routers = handlerType === 'load' ?\n IORouterRegistry.getInstance().loadRouters :\n IORouterRegistry.getInstance().saveRouters;\n routers.forEach(router => {\n const handler = router(url, loadOptions);\n if (handler !== null) {\n validHandlers.push(handler);\n }\n });\n return validHandlers;\n }\n}\n\nexport const registerSaveRouter = (loudRouter: IORouter) =>\n IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter: IORouter) =>\n IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url: string|string[]) =>\n IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers =\n (url: string|string[], loadOptions?: LoadOptions) =>\n IORouterRegistry.getLoadHandlers(url, loadOptions);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\n\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n// as the type of topology (JSON vs binary), byte size of the topology, byte\n// size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise {\n const idbFactory = getIndexedDBFactory();\n\n return new Promise((resolve, reject) => {\n const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n deleteRequest.onsuccess = () => resolve();\n deleteRequest.onerror = error => reject(error);\n });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n if (!env().getBool('IS_BROWSER')) {\n // TODO(cais): Add more info about what IOHandler subtypes are available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error(\n 'Failed to obtain IndexedDB factory because the current environment' +\n 'is not a web browser.');\n }\n // tslint:disable-next-line:no-any\n const theWindow: any = typeof window === 'undefined' ? self : window;\n const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n theWindow.shimIndexedDB;\n if (factory == null) {\n throw new Error(\n 'The current browser does not appear to support IndexedDB.');\n }\n return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n const db = openRequest.result as IDBDatabase;\n db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n protected readonly indexedDB: IDBFactory;\n protected readonly modelPath: string;\n\n static readonly URL_SCHEME = 'indexeddb://';\n\n constructor(modelPath: string) {\n this.indexedDB = getIndexedDBFactory();\n\n if (modelPath == null || !modelPath) {\n throw new Error(\n 'For IndexedDB, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n // TODO(cais): Support saving GraphDef models.\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n\n return this.databaseAction(this.modelPath, modelArtifacts) as\n Promise;\n }\n\n async load(): Promise {\n return this.databaseAction(this.modelPath) as Promise;\n }\n\n /**\n * Perform database action to put model artifacts into or read model artifacts\n * from IndexedDB object store.\n *\n * Whether the action is put or get depends on whether `modelArtifacts` is\n * specified. If it is specified, the action will be put; otherwise the action\n * will be get.\n *\n * @param modelPath A unique string path for the model.\n * @param modelArtifacts If specified, it will be the model artifacts to be\n * stored in IndexedDB.\n * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n * of `ModelArtifacts`, if the action is get.\n */\n private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n Promise {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n\n if (modelArtifacts == null) {\n // Read model out from object store.\n const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const getRequest = modelStore.get(this.modelPath);\n getRequest.onsuccess = () => {\n if (getRequest.result == null) {\n db.close();\n return reject(new Error(\n `Cannot find model with path '${this.modelPath}' ` +\n `in IndexedDB.`));\n } else {\n resolve(getRequest.result.modelArtifacts);\n }\n };\n getRequest.onerror = error => {\n db.close();\n return reject(getRequest.error);\n };\n modelTx.oncomplete = () => db.close();\n } else {\n // Put model into object store.\n const modelArtifactsInfo: ModelArtifactsInfo =\n getModelArtifactsInfoForJSON(modelArtifacts);\n // First, put ModelArtifactsInfo into info store.\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const putInfoRequest =\n infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n let modelTx: IDBTransaction;\n putInfoRequest.onsuccess = () => {\n // Second, put model data into model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const putModelRequest = modelStore.put({\n modelPath: this.modelPath,\n modelArtifacts,\n modelArtifactsInfo\n });\n putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n putModelRequest.onerror = error => {\n // If the put-model request fails, roll back the info entry as\n // well.\n infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const deleteInfoRequest = infoStore.delete(this.modelPath);\n deleteInfoRequest.onsuccess = () => {\n db.close();\n return reject(putModelRequest.error);\n };\n deleteInfoRequest.onerror = error => {\n db.close();\n return reject(putModelRequest.error);\n };\n };\n };\n putInfoRequest.onerror = error => {\n db.close();\n return reject(putInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n }\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n * which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n private indexedDB: IDBFactory;\n\n constructor() {\n this.indexedDB = getIndexedDBFactory();\n }\n\n async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n return new Promise<{[path: string]: ModelArtifactsInfo}>(\n (resolve, reject) => {\n const openRequest =\n this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n const store = tx.objectStore(INFO_STORE_NAME);\n // tslint:disable:max-line-length\n // Need to cast `store` as `any` here because TypeScript's DOM\n // library does not have the `getAll()` method even though the\n // method is supported in the latest version of most mainstream\n // browsers:\n // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n // tslint:enable:max-line-length\n // tslint:disable-next-line:no-any\n const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n getAllInfoRequest.onsuccess = () => {\n const out: {[path: string]: ModelArtifactsInfo} = {};\n for (const item of getAllInfoRequest.result) {\n out[item.modelPath] = item.modelArtifactsInfo;\n }\n resolve(out);\n };\n getAllInfoRequest.onerror = error => {\n db.close();\n return reject(getAllInfoRequest.error);\n };\n tx.oncomplete = () => db.close();\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n\n async removeModel(path: string): Promise {\n path = maybeStripScheme(path);\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n const getInfoRequest = infoStore.get(path);\n let modelTx: IDBTransaction;\n getInfoRequest.onsuccess = () => {\n if (getInfoRequest.result == null) {\n db.close();\n return reject(new Error(\n `Cannot find model with path '${path}' ` +\n `in IndexedDB.`));\n } else {\n // First, delete the entry in the info store.\n const deleteInfoRequest = infoStore.delete(path);\n const deleteModelData = () => {\n // Second, delete the entry in the model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const deleteModelRequest = modelStore.delete(path);\n deleteModelRequest.onsuccess = () =>\n resolve(getInfoRequest.result.modelArtifactsInfo);\n deleteModelRequest.onerror = error =>\n reject(getInfoRequest.error);\n };\n // Proceed with deleting model data regardless of whether deletion\n // of info data succeeds or not.\n deleteInfoRequest.onsuccess = deleteModelData;\n deleteInfoRequest.onerror = error => {\n deleteModelData();\n db.close();\n return reject(getInfoRequest.error);\n };\n }\n };\n getInfoRequest.onerror = error => {\n db.close();\n return reject(getInfoRequest.error);\n };\n\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n typeof window.localStorage === 'undefined') {\n throw new Error(\n 'purgeLocalStorageModels() cannot proceed because local storage is ' +\n 'unavailable in the current environment.');\n }\n const LS = window.localStorage;\n const purgedModelPaths: string[] = [];\n for (let i = 0; i < LS.length; ++i) {\n const key = LS.key(i);\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n if (key.startsWith(prefix) && key.length > prefix.length) {\n LS.removeItem(key);\n const modelName = getModelPathFromKey(key);\n if (purgedModelPaths.indexOf(modelName) === -1) {\n purgedModelPaths.push(modelName);\n }\n }\n }\n return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string): {\n info: string,\n topology: string,\n weightSpecs: string,\n weightData: string,\n modelMetadata: string\n} {\n return {\n info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n modelMetadata:\n [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n const items = key.split(PATH_SEPARATOR);\n if (items.length < 3) {\n throw new Error(`Invalid key format: ${key}`);\n }\n return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n key;\n}\n\ndeclare type LocalStorageKeys = {\n info: string,\n topology: string,\n weightSpecs: string,\n weightData: string,\n modelMetadata: string\n};\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n protected readonly LS: Storage;\n protected readonly modelPath: string;\n protected readonly keys: LocalStorageKeys;\n\n static readonly URL_SCHEME = 'localstorage://';\n\n constructor(modelPath: string) {\n if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n typeof window.localStorage === 'undefined') {\n // TODO(cais): Add more info about what IOHandler subtypes are\n // available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error(\n 'The current environment does not support local storage.');\n }\n this.LS = window.localStorage;\n\n if (modelPath == null || !modelPath) {\n throw new Error(\n 'For local storage, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n this.keys = getModelKeys(this.modelPath);\n }\n\n /**\n * Save model artifacts to browser local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @param modelArtifacts The model artifacts to be stored.\n * @returns An instance of SaveResult.\n */\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n } else {\n const topology = JSON.stringify(modelArtifacts.modelTopology);\n const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n const modelArtifactsInfo: ModelArtifactsInfo =\n getModelArtifactsInfoForJSON(modelArtifacts);\n\n try {\n this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n this.LS.setItem(this.keys.topology, topology);\n this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n this.LS.setItem(\n this.keys.weightData,\n arrayBufferToBase64String(modelArtifacts.weightData));\n this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n userDefinedMetadata: modelArtifacts.userDefinedMetadata\n }));\n\n return {modelArtifactsInfo};\n } catch (err) {\n // If saving failed, clean up all items saved so far.\n this.LS.removeItem(this.keys.info);\n this.LS.removeItem(this.keys.topology);\n this.LS.removeItem(this.keys.weightSpecs);\n this.LS.removeItem(this.keys.weightData);\n this.LS.removeItem(this.keys.modelMetadata);\n\n throw new Error(\n `Failed to save model '${this.modelPath}' to local storage: ` +\n `size quota being exceeded is a possible cause of this failure: ` +\n `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n }\n }\n }\n\n /**\n * Load a model from local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @returns The loaded model (if loading succeeds).\n */\n async load(): Promise {\n const info =\n JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n if (info == null) {\n throw new Error(\n `In local storage, there is no model with name '${this.modelPath}'`);\n }\n\n if (info.modelTopologyType !== 'JSON') {\n throw new Error(\n 'BrowserLocalStorage does not support loading non-JSON model ' +\n 'topology yet.');\n }\n\n const out: ModelArtifacts = {};\n\n // Load topology.\n const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n if (topology == null) {\n throw new Error(\n `In local storage, the topology of model '${this.modelPath}' ` +\n `is missing.`);\n }\n out.modelTopology = topology;\n\n // Load weight specs.\n const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n if (weightSpecs == null) {\n throw new Error(\n `In local storage, the weight specs of model '${this.modelPath}' ` +\n `are missing.`);\n }\n out.weightSpecs = weightSpecs;\n\n // Load meta-data fields.\n const metadataString = this.LS.getItem(this.keys.modelMetadata);\n if (metadataString != null) {\n const metadata = JSON.parse(metadataString) as ModelArtifacts;\n out.format = metadata['format'];\n out.generatedBy = metadata['generatedBy'];\n out.convertedBy = metadata['convertedBy'];\n out.userDefinedMetadata = metadata['userDefinedMetadata'];\n }\n\n // Load weight data.\n const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n if (weightDataBase64 == null) {\n throw new Error(\n `In local storage, the binary weight values of model ` +\n `'${this.modelPath}' are missing.`);\n }\n out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n return out;\n }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n return browserLocalStorage(\n url.slice(BrowserLocalStorage.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n * - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n * model, such as date saved, type of the topology, size in bytes, etc.\n * - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n * style models, this is a stringized JSON.\n * - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n * model, can be used to decode the saved binary weight values (see\n * item below).\n * - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n * weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n * `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n private readonly LS: Storage;\n\n constructor() {\n assert(\n env().getBool('IS_BROWSER'),\n () => 'Current environment is not a web browser');\n assert(\n typeof window === 'undefined' ||\n typeof window.localStorage !== 'undefined',\n () => 'Current browser does not appear to support localStorage');\n this.LS = window.localStorage;\n }\n\n async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n const out: {[path: string]: ModelArtifactsInfo} = {};\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n for (let i = 0; i < this.LS.length; ++i) {\n const key = this.LS.key(i);\n if (key.startsWith(prefix) && key.endsWith(suffix)) {\n const modelPath = getModelPathFromKey(key);\n out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n }\n }\n return out;\n }\n\n async removeModel(path: string): Promise {\n path = maybeStripScheme(path);\n const keys = getModelKeys(path);\n if (this.LS.getItem(keys.info) == null) {\n throw new Error(`Cannot find model at path '${path}'`);\n }\n const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n this.LS.removeItem(keys.info);\n this.LS.removeItem(keys.topology);\n this.LS.removeItem(keys.weightSpecs);\n this.LS.removeItem(keys.weightData);\n return info;\n }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n * string.\n * - Moving or copying model from one path to another in the same medium or from\n * one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n // Singleton instance.\n private static instance: ModelStoreManagerRegistry;\n\n private managers: {[scheme: string]: ModelStoreManager};\n\n private constructor() {\n this.managers = {};\n }\n\n private static getInstance(): ModelStoreManagerRegistry {\n if (ModelStoreManagerRegistry.instance == null) {\n ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n }\n return ModelStoreManagerRegistry.instance;\n }\n\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerManager(scheme: string, manager: ModelStoreManager) {\n assert(scheme != null, () => 'scheme must not be undefined or null.');\n if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n }\n assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n const registry = ModelStoreManagerRegistry.getInstance();\n assert(\n registry.managers[scheme] == null,\n () => `A model store manager is already registered for scheme '${\n scheme}'.`);\n registry.managers[scheme] = manager;\n }\n\n static getManager(scheme: string): ModelStoreManager {\n const manager = this.getInstance().managers[scheme];\n if (manager == null) {\n throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n }\n return manager;\n }\n\n static getSchemes(): string[] {\n return Object.keys(this.getInstance().managers);\n }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n * Scheme: e.g., 'localstorage' in the example above.\n * Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n throw new Error(\n `The url string provided does not contain a scheme. ` +\n `Supported schemes are: ` +\n `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n }\n return {\n scheme: url.split(URL_SCHEME_SUFFIX)[0],\n path: url.split(URL_SCHEME_SUFFIX)[1],\n };\n}\n\nasync function cloneModelInternal(\n sourceURL: string, destURL: string,\n deleteSource = false): Promise {\n assert(\n sourceURL !== destURL,\n () => `Old path and new path are the same: '${sourceURL}'`);\n\n const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n assert(\n loadHandlers.length > 0,\n () => `Copying failed because no load handler is found for source URL ${\n sourceURL}.`);\n assert(\n loadHandlers.length < 2,\n () => `Copying failed because more than one (${loadHandlers.length}) ` +\n `load handlers for source URL ${sourceURL}.`);\n const loadHandler = loadHandlers[0];\n\n const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n assert(\n saveHandlers.length > 0,\n () => `Copying failed because no save handler is found for destination ` +\n `URL ${destURL}.`);\n assert(\n saveHandlers.length < 2,\n () => `Copying failed because more than one (${loadHandlers.length}) ` +\n `save handlers for destination URL ${destURL}.`);\n const saveHandler = saveHandlers[0];\n\n const sourceScheme = parseURL(sourceURL).scheme;\n const sourcePath = parseURL(sourceURL).path;\n const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n const modelArtifacts = await loadHandler.load();\n\n // If moving within the same storage medium, remove the old model as soon as\n // the loading is done. Without doing this, it is possible that the combined\n // size of the two models will cause the cloning to fail.\n if (deleteSource && sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n\n const saveResult = await saveHandler.save(modelArtifacts);\n\n // If moving between mediums, the deletion is done after the save succeeds.\n // This guards against the case in which saving to the destination medium\n // fails.\n if (deleteSource && !sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n\n return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n * 'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n const schemes = ModelStoreManagerRegistry.getSchemes();\n const out: {[url: string]: ModelArtifactsInfo} = {};\n for (const scheme of schemes) {\n const schemeOut =\n await ModelStoreManagerRegistry.getManager(scheme).listModels();\n for (const path in schemeOut) {\n const url = scheme + URL_SCHEME_SUFFIX + path;\n out[url] = schemeOut[path];\n }\n }\n return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n * 'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function removeModel(url: string): Promise {\n const schemeAndPath = parseURL(url);\n const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n return manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function copyModel(\n sourceURL: string, destURL: string): Promise {\n const deleteSource = false;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function moveModel(\n sourceURL: string, destURL: string): Promise {\n const deleteSource = true;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\nimport {BrowserIndexedDB, BrowserIndexedDBManager} from '../io/indexed_db';\nimport {BrowserLocalStorage, BrowserLocalStorageManager} from '../io/local_storage';\nimport {ModelStoreManagerRegistry} from '../io/model_management';\n\nimport {Platform} from './platform';\n\nexport class PlatformBrowser implements Platform {\n // According to the spec, the built-in encoder can do only UTF-8 encoding.\n // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n private textEncoder: TextEncoder;\n\n fetch(path: string, init?: RequestInit): Promise {\n return fetch(path, init);\n }\n\n now(): number {\n return performance.now();\n }\n\n encode(text: string, encoding: string): Uint8Array {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(\n `Browser's encoder only supports utf-8, but got ${encoding}`);\n }\n if (this.textEncoder == null) {\n this.textEncoder = new TextEncoder();\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes: Uint8Array, encoding: string): string {\n return new TextDecoder(encoding).decode(bytes);\n }\n}\n\nif (env().get('IS_BROWSER')) {\n env().setPlatform('browser', new PlatformBrowser());\n\n // Register LocalStorage IOHandler\n try {\n ModelStoreManagerRegistry.registerManager(\n BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n } catch (err) {\n }\n\n // Register IndexedDB IOHandler\n try {\n ModelStoreManagerRegistry.registerManager(\n BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n } catch (err) {\n }\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from '../environment';\n\nimport {Platform} from './platform';\n\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n // tslint:disable-next-line:no-require-imports\n importFetch: () => require('node-fetch')\n};\n\ntype FetchFn = (url: string, init?: RequestInit) => Promise;\nlet systemFetch: FetchFn;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n systemFetch = null;\n}\nexport function setSystemFetch(fetchFn: FetchFn) {\n systemFetch = fetchFn;\n}\nexport function getSystemFetch(): FetchFn {\n return systemFetch;\n}\n\nexport class PlatformNode implements Platform {\n private textEncoder: TextEncoder;\n // tslint:disable-next-line:no-any\n util: any;\n\n constructor() {\n // tslint:disable-next-line:no-require-imports\n this.util = require('util');\n // According to the spec, the built-in encoder can do only UTF-8 encoding.\n // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n this.textEncoder = new this.util.TextEncoder();\n }\n\n fetch(path: string, requestInits?: RequestInit): Promise {\n if (env().global.fetch != null) {\n return env().global.fetch(path, requestInits);\n }\n\n if (systemFetch == null) {\n systemFetch = getNodeFetch.importFetch();\n }\n return systemFetch(path, requestInits);\n }\n\n now(): number {\n const time = process.hrtime();\n return time[0] * 1000 + time[1] / 1000000;\n }\n\n encode(text: string, encoding: string): Uint8Array {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(\n `Node built-in encoder only supports utf-8, but got ${encoding}`);\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes: Uint8Array, encoding: string): string {\n if (bytes.length === 0) {\n return '';\n }\n return new this.util.TextDecoder(encoding).decode(bytes);\n }\n}\n\nif (env().get('IS_NODE')) {\n env().setPlatform('node', new PlatformNode());\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, Rank, ShapeMap} from '../types';\nimport * as util from '../util';\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(\n shape: ShapeMap[R], dtype: D = 'float32' as D,\n values?: DataTypeMap[D]): TensorBuffer {\n dtype = dtype || 'float32' as D;\n util.assertNonNegativeIntegerDimensions(shape);\n return new TensorBuffer(shape, dtype, values);\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Cast, CastAttrs, CastInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {DataType, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x: T|TensorLike, dtype: DataType): T {\n const $x = convertToTensor(x, 'x', 'cast');\n\n // Sanity checks.\n if (!util.isValidDtype(dtype)) {\n throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n }\n if (dtype === 'string' && $x.dtype !== 'string' ||\n dtype !== 'string' && $x.dtype === 'string') {\n throw new Error('Only strings can be casted to strings');\n }\n\n const inputs: CastInputs = {x: $x};\n const attrs: CastAttrs = {dtype};\n\n return ENGINE.runKernelFunc(\n backend => backend.cast($x, dtype), inputs as {} as NamedTensorMap,\n null /* grad */, Cast, attrs as {} as NamedAttrMap);\n}\n\nexport const cast = op({cast_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Identity, IdentityInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'clone', null);\n const forward = () =>\n ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype) as T;\n\n const inputs: IdentityInputs = {x: $x};\n\n // Note this op is called tf.identity in python. Hence the kernel name used\n // here.\n return ENGINE.runKernelFunc(\n forward, inputs as {} as NamedTensorMap, null /* grad */, Identity);\n}\n\nexport const clone = op({clone_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x: T, verbose = false): void {\n console.log(x.toString(verbose));\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Required side effectful code for tfjs-core\n\n// Set up Engine and ENV\nimport {getOrMakeEngine} from './engine';\ngetOrMakeEngine();\n\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n\n// Set up OpHandler\nimport {buffer} from './ops/buffer';\nimport {cast} from './ops/cast';\nimport {clone} from './ops/clone';\nimport {print} from './ops/print';\nimport {OpHandler, setOpHandler} from './tensor';\nconst opHandler: OpHandler = {\n buffer,\n cast,\n clone,\n print\n};\nsetOpHandler(opHandler);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelJSON, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nfunction defer(f: () => T): Promise {\n return new Promise(resolve => setTimeout(resolve)).then(f);\n}\n\nexport class BrowserDownloads implements IOHandler {\n private readonly modelTopologyFileName: string;\n private readonly weightDataFileName: string;\n private readonly jsonAnchor: HTMLAnchorElement;\n private readonly weightDataAnchor: HTMLAnchorElement;\n\n static readonly URL_SCHEME = 'downloads://';\n\n constructor(fileNamePrefix?: string) {\n if (!env().getBool('IS_BROWSER')) {\n // TODO(cais): Provide info on what IOHandlers are available under the\n // current environment.\n throw new Error(\n 'browserDownloads() cannot proceed because the current environment ' +\n 'is not a browser.');\n }\n\n if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n }\n if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n }\n\n this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n this.weightDataFileName =\n fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (typeof (document) === 'undefined') {\n throw new Error(\n 'Browser downloads are not supported in ' +\n 'this environment since `document` is not present');\n }\n const weightsURL = window.URL.createObjectURL(new Blob(\n [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserDownloads.save() does not support saving model topology ' +\n 'in binary formats yet.');\n } else {\n const weightsManifest: WeightsManifestConfig = [{\n paths: ['./' + this.weightDataFileName],\n weights: modelArtifacts.weightSpecs\n }];\n const modelTopologyAndWeightManifest: ModelJSON = {\n modelTopology: modelArtifacts.modelTopology,\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n weightsManifest\n };\n const modelTopologyAndWeightManifestURL =\n window.URL.createObjectURL(new Blob(\n [JSON.stringify(modelTopologyAndWeightManifest)],\n {type: 'application/json'}));\n\n // If anchor elements are not provided, create them without attaching them\n // to parents, so that the downloaded file names can be controlled.\n const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n this.jsonAnchor;\n jsonAnchor.download = this.modelTopologyFileName;\n jsonAnchor.href = modelTopologyAndWeightManifestURL;\n // Trigger downloads by evoking a click event on the download anchors.\n // When multiple downloads are started synchronously, Firefox will only\n // save the last one.\n await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n\n if (modelArtifacts.weightData != null) {\n const weightDataAnchor = this.weightDataAnchor == null ?\n document.createElement('a') :\n this.weightDataAnchor;\n weightDataAnchor.download = this.weightDataFileName;\n weightDataAnchor.href = weightsURL;\n await defer(\n () => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n }\n\n return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n }\n }\n}\n\nclass BrowserFiles implements IOHandler {\n private readonly files: File[];\n\n constructor(files: File[]) {\n if (files == null || files.length < 1) {\n throw new Error(\n `When calling browserFiles, at least 1 file is required, ` +\n `but received ${files}`);\n }\n this.files = files;\n }\n\n async load(): Promise {\n const jsonFile = this.files[0];\n const weightFiles = this.files.slice(1);\n\n return new Promise((resolve, reject) => {\n const jsonReader = new FileReader();\n jsonReader.onload = (event: Event) => {\n // tslint:disable-next-line:no-any\n const modelJSON = JSON.parse((event.target as any).result) as ModelJSON;\n const modelTopology = modelJSON.modelTopology;\n if (modelTopology == null) {\n reject(new Error(\n `modelTopology field is missing from file ${jsonFile.name}`));\n return;\n }\n\n if (weightFiles.length === 0) {\n resolve({modelTopology});\n }\n\n const weightsManifest = modelJSON.weightsManifest;\n if (weightsManifest == null) {\n reject(new Error(\n `weightManifest field is missing from file ${jsonFile.name}`));\n return;\n }\n\n let pathToFile: {[path: string]: File};\n try {\n pathToFile =\n this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n } catch (err) {\n reject(err);\n return;\n }\n\n const weightSpecs: WeightsManifestEntry[] = [];\n const paths: string[] = [];\n const perFileBuffers: ArrayBuffer[] = [];\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n paths.push(path);\n perFileBuffers.push(null);\n });\n weightSpecs.push(...weightsGroup.weights);\n });\n\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n const weightFileReader = new FileReader();\n weightFileReader.onload = (event: Event) => {\n // tslint:disable-next-line:no-any\n const weightData = (event.target as any).result as ArrayBuffer;\n const index = paths.indexOf(path);\n perFileBuffers[index] = weightData;\n if (perFileBuffers.indexOf(null) === -1) {\n resolve({\n modelTopology,\n weightSpecs,\n weightData: concatenateArrayBuffers(perFileBuffers),\n format: modelJSON.format,\n generatedBy: modelJSON.generatedBy,\n convertedBy: modelJSON.convertedBy,\n userDefinedMetadata: modelJSON.userDefinedMetadata\n });\n }\n };\n weightFileReader.onerror = error =>\n reject(`Failed to weights data from file of path '${path}'.`);\n weightFileReader.readAsArrayBuffer(pathToFile[path]);\n });\n });\n };\n jsonReader.onerror = error => reject(\n `Failed to read model topology and weights manifest JSON ` +\n `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n `Keras-style tf.Model artifacts only.`);\n jsonReader.readAsText(jsonFile);\n });\n }\n\n /**\n * Check the compatibility between weights manifest and weight files.\n */\n private checkManifestAndWeightFiles(\n manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n const basenames: string[] = [];\n const fileNames = files.map(file => basename(file.name));\n const pathToFile: {[path: string]: File} = {};\n for (const group of manifest) {\n group.paths.forEach(path => {\n const pathBasename = basename(path);\n if (basenames.indexOf(pathBasename) !== -1) {\n throw new Error(\n `Duplicate file basename found in weights manifest: ` +\n `'${pathBasename}'`);\n }\n basenames.push(pathBasename);\n if (fileNames.indexOf(pathBasename) === -1) {\n throw new Error(\n `Weight file with basename '${pathBasename}' is not provided.`);\n } else {\n pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n }\n });\n }\n\n if (basenames.length !== files.length) {\n throw new Error(\n `Mismatch in the number of files in weights manifest ` +\n `(${basenames.length}) and the number of weight files provided ` +\n `(${files.length}).`);\n }\n return pathToFile;\n }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * // 'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n * `tf.Model`, `fileNamePrefix` should follow either of the following two\n * formats:\n * 1. `null` or `undefined`, in which case the default file\n * names will be used:\n * - 'model.json' for the JSON file containing the model topology and\n * weights manifest.\n * - 'model.weights.bin' for the binary file containing the binary weight\n * values.\n * 2. A single string or an Array of a single string, as the file name prefix.\n * For example, if `'foo'` is provided, the downloaded JSON\n * file and binary weights file will be named 'foo.json' and\n * 'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * // elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (``)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n * [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n * loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n * which an `Array` of `File`s is expected (in that order):\n * - A JSON file containing the model topology and weight manifest.\n * - Optionally, One or more binary files containing the binary weights.\n * These files must have names that match the paths in the `weightsManifest`\n * contained by the aforementioned JSON file, or errors will be thrown\n * during loading. These weights files have the same format as the ones\n * generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n * Python PIP package. If no weights files are provided, only the model\n * topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function browserFiles(files: File[]): IOHandler {\n return new BrowserFiles(files);\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from '../util';\n\nimport {OnProgressCallback} from './types';\n\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(\n promises: Array>, onProgress: OnProgressCallback,\n startFraction?: number, endFraction?: number) {\n checkPromises(promises);\n startFraction = startFraction == null ? 0 : startFraction;\n endFraction = endFraction == null ? 1 : endFraction;\n checkFraction(startFraction, endFraction);\n let resolvedPromise = 0;\n\n const registerMonitor = (promise: Promise<{}>) => {\n promise.then(value => {\n const fraction = startFraction +\n ++resolvedPromise / promises.length * (endFraction - startFraction);\n // pass fraction as parameter to callback function.\n onProgress(fraction);\n return value;\n });\n return promise;\n };\n\n function checkPromises(promises: Array>): void {\n assert(\n promises != null && Array.isArray(promises) && promises.length > 0,\n () => 'promises must be a none empty array');\n }\n\n function checkFraction(startFraction: number, endFraction: number): void {\n assert(\n startFraction >= 0 && startFraction <= 1,\n () => `Progress fraction must be in range [0, 1], but ` +\n `got startFraction ${startFraction}`);\n assert(\n endFraction >= 0 && endFraction <= 1,\n () => `Progress fraction must be in range [0, 1], but ` +\n `got endFraction ${endFraction}`);\n assert(\n endFraction >= startFraction,\n () => `startFraction must be no more than endFraction, but ` +\n `got startFraction ${startFraction} and endFraction ` +\n `${endFraction}`);\n }\n\n return Promise.all(promises.map(registerMonitor));\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from '../environment';\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\nimport {decodeWeights} from './io_utils';\nimport {monitorPromisesProgress} from './progress';\nimport {DTYPE_VALUE_SIZE_MAP, LoadOptions, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n * length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n fetchURLs: string[], loadOptions?: LoadOptions): Promise {\n if (loadOptions == null) {\n loadOptions = {};\n }\n\n const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n loadOptions.fetchFunc;\n\n // Create the requests for all of the weights in parallel.\n const requests = fetchURLs.map(\n fetchURL =>\n fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));\n\n const fetchStartFraction = 0;\n const fetchEndFraction = 0.5;\n\n const responses = loadOptions.onProgress == null ?\n await Promise.all(requests) :\n await monitorPromisesProgress(\n requests, loadOptions.onProgress, fetchStartFraction,\n fetchEndFraction);\n\n const bufferPromises = responses.map(response => response.arrayBuffer());\n\n const bufferStartFraction = 0.5;\n const bufferEndFraction = 1;\n\n const buffers = loadOptions.onProgress == null ?\n await Promise.all(bufferPromises) :\n await monitorPromisesProgress(\n bufferPromises, loadOptions.onProgress, bufferStartFraction,\n bufferEndFraction);\n return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n * Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n manifest: WeightsManifestConfig, filePathPrefix = '',\n weightNames?: string[],\n requestInit?: RequestInit): Promise {\n // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n // single weight from a group, the whole group will be fetched. At a future\n // date, we should support fetching only the individual shards within a\n // group that are needed to reconstruct the requested weight.\n // TODO(cais): Use `decodeWeights` for implementation.\n\n const fetchWeights = (fetchUrls: string[]) =>\n loadWeightsAsArrayBuffer(fetchUrls, {requestInit});\n const loadWeights = weightsLoaderFactory(fetchWeights);\n\n return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n * filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n * fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n fetchWeightsFunction: (fetchUrls: string[]) => Promise):\n (manifest: WeightsManifestConfig, filePathPrefix?: string,\n weightNames?: string[]) => Promise {\n return async(\n manifest: WeightsManifestConfig, filePathPrefix = '',\n weightNames?: string[]): Promise => {\n // Collect all the groups, weights, and their relative offsets to be\n // fetched.\n const groupIndicesToFetchMap = manifest.map(() => false);\n const groupWeightsToFetch: {\n [group: number]: Array<{\n manifestEntry: WeightsManifestEntry; groupOffset: number;\n sizeBytes: number;\n }>\n } = {};\n const weightsFound =\n weightNames != null ? weightNames.map(() => false) : [];\n const allManifestWeightNames: string[] = [];\n manifest.forEach((manifestGroupConfig, groupIndex) => {\n let groupOffset = 0;\n manifestGroupConfig.weights.forEach(weightsEntry => {\n const rawDtype = ('quantization' in weightsEntry) ?\n weightsEntry.quantization.dtype :\n weightsEntry.dtype;\n\n const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n util.sizeFromShape(weightsEntry.shape);\n\n const enqueueWeightsForFetchingFn = () => {\n groupIndicesToFetchMap[groupIndex] = true;\n if (groupWeightsToFetch[groupIndex] == null) {\n groupWeightsToFetch[groupIndex] = [];\n }\n\n groupWeightsToFetch[groupIndex].push({\n manifestEntry: weightsEntry,\n groupOffset,\n sizeBytes: weightsBytes\n });\n };\n\n if (weightNames != null) {\n weightNames.forEach((weightName, weightIndex) => {\n if (weightName === weightsEntry.name) {\n enqueueWeightsForFetchingFn();\n weightsFound[weightIndex] = true;\n }\n });\n } else {\n enqueueWeightsForFetchingFn();\n }\n\n allManifestWeightNames.push(weightsEntry.name);\n groupOffset += weightsBytes;\n });\n });\n\n if (!weightsFound.every(found => found)) {\n const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n throw new Error(\n `Could not find weights in manifest with names: ` +\n `${weightsNotFound.join(', ')}. \\n` +\n `Manifest JSON has weights with names: ` +\n `${allManifestWeightNames.join(', ')}.`);\n }\n\n // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n // IDs.\n const groupIndicesToFetch =\n groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n if (shouldFetch) {\n accumulator.push(i);\n }\n return accumulator;\n }, []);\n\n const fetchUrls: string[] = [];\n groupIndicesToFetch.forEach(i => {\n manifest[i].paths.forEach(filepath => {\n const fetchUrl = filePathPrefix +\n (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n fetchUrls.push(fetchUrl);\n });\n });\n const buffers = await fetchWeightsFunction(fetchUrls);\n\n const weightsTensorMap: NamedTensorMap = {};\n let bufferIndexOffset = 0;\n groupIndicesToFetch.forEach(i => {\n const numBuffers = manifest[i].paths.length;\n\n let groupBytes = 0;\n for (let i = 0; i < numBuffers; i++) {\n groupBytes += buffers[bufferIndexOffset + i].byteLength;\n }\n\n // Create a buffer for the whole group.\n const groupBuffer = new ArrayBuffer(groupBytes);\n const groupByteBuffer = new Uint8Array(groupBuffer);\n let groupBufferOffset = 0;\n for (let i = 0; i < numBuffers; i++) {\n const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n groupByteBuffer.set(buffer, groupBufferOffset);\n groupBufferOffset += buffer.byteLength;\n }\n\n const weightsEntries = groupWeightsToFetch[i];\n weightsEntries.forEach(weightsEntry => {\n const byteBuffer = groupBuffer.slice(\n weightsEntry.groupOffset,\n weightsEntry.groupOffset + weightsEntry.sizeBytes);\n const nameToTensorMap =\n decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n for (const name in nameToTensorMap) {\n weightsTensorMap[name] = nameToTensorMap[name];\n }\n });\n\n bufferIndexOffset += numBuffers;\n });\n\n return weightsTensorMap;\n };\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadOptions, ModelArtifacts, ModelJSON, OnProgressCallback, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest implements IOHandler {\n protected readonly path: string;\n protected readonly requestInit: RequestInit;\n\n private readonly fetch: Function;\n private readonly weightUrlConverter: (weightName: string) => Promise;\n\n readonly DEFAULT_METHOD = 'POST';\n\n static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n private readonly weightPathPrefix: string;\n private readonly onProgress: OnProgressCallback;\n\n constructor(path: string, loadOptions?: LoadOptions) {\n if (loadOptions == null) {\n loadOptions = {};\n }\n this.weightPathPrefix = loadOptions.weightPathPrefix;\n this.onProgress = loadOptions.onProgress;\n this.weightUrlConverter = loadOptions.weightUrlConverter;\n\n if (loadOptions.fetchFunc != null) {\n assert(\n typeof loadOptions.fetchFunc === 'function',\n () => 'Must pass a function that matches the signature of ' +\n '`fetch` (see ' +\n 'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n this.fetch = loadOptions.fetchFunc;\n } else {\n this.fetch = env().platform.fetch;\n }\n\n assert(\n path != null && path.length > 0,\n () => 'URL path for http must not be null, undefined or ' +\n 'empty.');\n\n if (Array.isArray(path)) {\n assert(\n path.length === 2,\n () => 'URL paths for http must have a length of 2, ' +\n `(actual length is ${path.length}).`);\n }\n this.path = path;\n\n if (loadOptions.requestInit != null &&\n loadOptions.requestInit.body != null) {\n throw new Error(\n 'requestInit is expected to have no pre-existing body, but has one.');\n }\n this.requestInit = loadOptions.requestInit || {};\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserHTTPRequest.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n\n const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n init.body = new FormData();\n\n const weightsManifest: WeightsManifestConfig = [{\n paths: ['./model.weights.bin'],\n weights: modelArtifacts.weightSpecs,\n }];\n const modelTopologyAndWeightManifest: ModelJSON = {\n modelTopology: modelArtifacts.modelTopology,\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n weightsManifest\n };\n\n init.body.append(\n 'model.json',\n new Blob(\n [JSON.stringify(modelTopologyAndWeightManifest)],\n {type: JSON_TYPE}),\n 'model.json');\n\n if (modelArtifacts.weightData != null) {\n init.body.append(\n 'model.weights.bin',\n new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}),\n 'model.weights.bin');\n }\n\n const response = await this.fetch(this.path, init);\n\n if (response.ok) {\n return {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n responses: [response],\n };\n } else {\n throw new Error(\n `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n `${response.status}.`);\n }\n }\n\n /**\n * Load model artifacts via HTTP request(s).\n *\n * See the documentation to `tf.io.http` for details on the saved\n * artifacts.\n *\n * @returns The loaded model artifacts (if loading succeeds).\n */\n async load(): Promise {\n const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n\n if (!modelConfigRequest.ok) {\n throw new Error(\n `Request to ${this.path} failed with status code ` +\n `${modelConfigRequest.status}. Please verify this URL points to ` +\n `the model JSON of the model to load.`);\n }\n let modelConfig: ModelJSON;\n try {\n modelConfig = await modelConfigRequest.json();\n } catch (e) {\n let message = `Failed to parse model JSON of response from ${this.path}.`;\n // TODO(nsthorat): Remove this after some time when we're comfortable that\n // .pb files are mostly gone.\n if (this.path.endsWith('.pb')) {\n message += ' Your path contains a .pb file extension. ' +\n 'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n 'in favor of .json models. You can re-convert your Python ' +\n 'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n 'or you can convert your.pb models with the \\'pb2json\\'' +\n 'NPM script in the tensorflow/tfjs-converter repository.';\n } else {\n message += ' Please make sure the server is serving valid ' +\n 'JSON for this request.';\n }\n throw new Error(message);\n }\n const modelTopology = modelConfig.modelTopology;\n const weightsManifest = modelConfig.weightsManifest;\n const generatedBy = modelConfig.generatedBy;\n const convertedBy = modelConfig.convertedBy;\n const format = modelConfig.format;\n const userDefinedMetadata = modelConfig.userDefinedMetadata;\n\n // We do not allow both modelTopology and weightsManifest to be missing.\n if (modelTopology == null && weightsManifest == null) {\n throw new Error(\n `The JSON from HTTP path ${this.path} contains neither model ` +\n `topology or manifest for weights.`);\n }\n\n let weightSpecs: WeightsManifestEntry[];\n let weightData: ArrayBuffer;\n if (weightsManifest != null) {\n const results = await this.loadWeights(weightsManifest);\n [weightSpecs, weightData] = results;\n }\n\n const artifacts: ModelArtifacts = {\n modelTopology,\n weightSpecs,\n weightData,\n userDefinedMetadata,\n generatedBy,\n convertedBy,\n format\n };\n\n const initializer = modelConfig.modelInitializer;\n if (initializer) {\n artifacts.modelInitializer = initializer;\n }\n\n return artifacts;\n }\n\n private async loadWeights(weightsManifest: WeightsManifestConfig):\n Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n const [prefix, suffix] = parseUrl(weightPath);\n const pathPrefix = this.weightPathPrefix || prefix;\n\n const weightSpecs = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n }\n\n const fetchURLs: string[] = [];\n const urlPromises: Array> = [];\n for (const weightsGroup of weightsManifest) {\n for (const path of weightsGroup.paths) {\n if (this.weightUrlConverter != null) {\n urlPromises.push(this.weightUrlConverter(path));\n } else {\n fetchURLs.push(pathPrefix + path + suffix);\n }\n }\n }\n\n if (this.weightUrlConverter) {\n fetchURLs.push(...await Promise.all(urlPromises));\n }\n\n const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n requestInit: this.requestInit,\n fetchFunc: this.fetch,\n onProgress: this.onProgress\n });\n return [weightSpecs, concatenateArrayBuffers(buffers)];\n }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n const lastSlash = url.lastIndexOf('/');\n const lastSearchParam = url.lastIndexOf('?');\n const prefix = url.substring(0, lastSlash);\n const suffix =\n lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRouter: IORouter =\n (url: string, loadOptions?: LoadOptions) => {\n if (typeof fetch === 'undefined' &&\n (loadOptions == null || loadOptions.fetchFunc == null)) {\n // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n // an environment that is not the browser or node they have to setup a\n // global fetch polyfill.\n return null;\n } else {\n let isHTTP = true;\n if (Array.isArray(url)) {\n isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n } else {\n isHTTP = isHTTPScheme(url);\n }\n if (isHTTP) {\n return http(url, loadOptions);\n }\n }\n return null;\n };\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n * - A JSON file consisting of `modelTopology` and `weightsManifest`.\n * - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n * 'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n * Can be an absolute HTTP path (e.g.,\n * 'http://localhost:8000/model-upload)') or a relative path (e.g.,\n * './model-upload').\n * @param requestInit Request configurations to be used when sending\n * HTTP request to server using `fetch`. It can contain fields such as\n * `method`, `credentials`, `headers`, `mode`, etc. See\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n * for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n * following fields:\n * - weightPathPrefix Optional, this specifies the path prefix for weight\n * files, by default this is calculated from the path param.\n * - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n * the `fetch` from node-fetch can be used here.\n * - onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function http(path: string, loadOptions?: LoadOptions): IOHandler {\n return new HTTPRequest(path, loadOptions);\n}\n\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(\n path: string, loadOptions?: LoadOptions): IOHandler {\n return http(path, loadOptions);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, TrainingConfig, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n constructor(private readonly modelArtifacts?: ModelArtifacts) {}\n\n async load(): Promise {\n return this.modelArtifacts;\n }\n}\n\nclass PassthroughSaver implements IOHandler {\n constructor(\n private readonly saveHandler:\n (artifacts: ModelArtifacts) => Promise) {}\n\n async save(modelArtifacts: ModelArtifacts) {\n return this.saveHandler(modelArtifacts);\n }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n * modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n * the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n * names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n * concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],\n weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler {\n if (arguments.length === 1) {\n const isModelArtifacts =\n (modelArtifacts as ModelArtifacts).modelTopology != null ||\n (modelArtifacts as ModelArtifacts).weightSpecs != null;\n if (isModelArtifacts) {\n return new PassthroughLoader(modelArtifacts as ModelArtifacts);\n } else {\n // Legacy support: with only modelTopology.\n // TODO(cais): Remove this deprecated API.\n console.warn(\n 'Please call tf.io.fromMemory() with only one argument. ' +\n 'The argument should be of type ModelArtifacts. ' +\n 'The multi-argument signature of tf.io.fromMemory() has been ' +\n 'deprecated and will be removed in a future release.');\n return new PassthroughLoader({modelTopology: modelArtifacts as {}});\n }\n } else {\n // Legacy support.\n // TODO(cais): Remove this deprecated API.\n console.warn(\n 'Please call tf.io.fromMemory() with only one argument. ' +\n 'The argument should be of type ModelArtifacts. ' +\n 'The multi-argument signature of tf.io.fromMemory() has been ' +\n 'deprecated and will be removed in a future release.');\n return new PassthroughLoader({\n modelTopology: modelArtifacts as {},\n weightSpecs,\n weightData,\n trainingConfig\n });\n }\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n * // ... do something with the artifacts ...\n * return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n * `SaveResult`.\n */\nexport function withSaveHandler(\n saveHandler: (artifacts: ModelArtifacts) =>\n Promise): IOHandler {\n return new PassthroughSaver(saveHandler);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, http, isHTTPScheme} from './http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter} from './router_registry';\nimport {IOHandler, LoadHandler, LoadOptions, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, OnProgressCallback, RequestDetails, SaveConfig, SaveHandler, SaveResult, WeightGroup, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\nexport {\n browserFiles,\n browserHTTPRequest,\n concatenateArrayBuffers,\n decodeWeights,\n encodeWeights,\n fromMemory,\n getLoadHandlers,\n getModelArtifactsInfoForJSON,\n getSaveHandlers,\n http,\n IOHandler,\n isHTTPScheme,\n LoadHandler,\n LoadOptions,\n loadWeights,\n ModelArtifacts,\n ModelArtifactsInfo,\n ModelJSON,\n ModelStoreManager,\n OnProgressCallback,\n registerLoadRouter,\n registerSaveRouter,\n RequestDetails,\n SaveConfig,\n SaveHandler,\n SaveResult,\n WeightGroup,\n weightsLoaderFactory,\n WeightsManifestConfig,\n WeightsManifestEntry,\n withSaveHandler\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Reshape, ReshapeAttrs, ReshapeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(\n x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor {\n const $x = convertToTensor(x, 'x', 'reshape', null);\n\n const inputs: ReshapeInputs = {x: $x};\n const attrs: ReshapeAttrs = {shape};\n const forward: ForwardFunc<\n Tensor> = (backend: KernelBackend, save: GradSaveFunc) => {\n shape = util.inferFromImplicitShape(shape, $x.size) as ShapeMap[R];\n util.assert(\n $x.size === util.sizeFromShape(shape),\n () => 'new shape and old shape must have the same number of elements.');\n save([$x]);\n return backend.reshape($x, shape);\n };\n return ENGINE.runKernelFunc(\n forward, inputs as {} as NamedTensorMap, null /* grad */, Reshape,\n attrs as {} as NamedAttrMap);\n}\nexport const reshape = op({reshape_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print(); // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(\n a: T|TensorLike, b: T|TensorLike, transposeA = false,\n transposeB = false): T {\n let $a = convertToTensor(a, 'a', 'matMul');\n let $b = convertToTensor(b, 'b', 'matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n\n util.assert(\n $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n `got ranks ${$a.rank} and ${$b.rank}.`);\n\n const innerShapeA =\n transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n const innerShapeB =\n transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n const outerShapeA =\n transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n const outerShapeB =\n transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n const outerDimsA = $a.shape.slice(0, -2);\n const outerDimsB = $b.shape.slice(0, -2);\n const batchDimA = util.sizeFromShape(outerDimsA);\n const batchDimB = util.sizeFromShape(outerDimsB);\n\n util.assert(\n util.arraysEqual(outerDimsA, outerDimsB),\n () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} must match.`);\n\n util.assert(\n innerShapeA === innerShapeB,\n () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} and transposeA=${transposeA}` +\n ` and transposeB=${transposeB} must match.`);\n\n const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n\n const forward: ForwardFunc = (backend, save) => {\n save([a3D, b3D]);\n\n return backend.batchMatMul(\n a3D as Tensor3D, b3D as Tensor3D, transposeA, transposeB);\n };\n\n const inputs: BatchMatMulInputs = {a: a3D, b: b3D};\n\n const attrs: BatchMatMulAttrs = {transposeA, transposeB};\n\n const res = ENGINE.runKernelFunc(\n forward, inputs as {} as NamedTensorMap, null /* grad */, BatchMatMul,\n attrs as {} as NamedAttrMap);\n\n return reshape(res, outShape) as T;\n}\n\nexport const matMul = op({matMul_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {OneHot, OneHotAttrs, OneHotInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n * not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(\n indices: Tensor|TensorLike, depth: number, onValue = 1,\n offValue = 0): Tensor {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n const outShape = [...$indices.shape, depth];\n\n const forward: ForwardFunc = (backend, save) => {\n save([$indices]);\n return reshape(\n backend.oneHot(\n reshape($indices, [$indices.size]), depth, onValue, offValue),\n outShape);\n };\n\n const inputs: OneHotInputs = {indices: $indices};\n const attrs: OneHotAttrs = {depth, onValue, offValue};\n\n return ENGINE.runKernelFunc(\n forward, inputs as unknown as NamedTensorMap, null /* grad */, OneHot,\n attrs as unknown as NamedAttrMap);\n}\n\nexport const oneHot = op({oneHot_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Transpose, TransposeAttrs, TransposeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print(); // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x: T|TensorLike, perm?: number[]): T {\n const $x = convertToTensor(x, 'x', 'transpose');\n\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n util.assert(\n $x.rank === perm.length,\n () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of perm ${perm}.`);\n perm.forEach(axis => {\n util.assert(\n axis >= 0 && axis < $x.rank,\n () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n ` but got ${perm}`);\n });\n\n if ($x.rank <= 1) {\n return $x.clone();\n }\n\n const inputs: TransposeInputs = {x: $x};\n const attrs: TransposeAttrs = {perm};\n\n return ENGINE.runKernelFunc(\n backend => backend.transpose($x, perm), inputs as {} as NamedTensorMap,\n null /* gradient */, Transpose, attrs as {} as NamedAttrMap);\n}\n\nexport const transpose = op({transpose_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {matMul} from './mat_mul';\nimport {oneHot} from './one_hot';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * // [0, 1, 1],\n * // [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n * for the classes. The shape is `[numExamples]`, where\n * `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n * 0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n * Its value must be larger than the largest element in `labels` and\n * `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n * row `r` and column `c` is the number of times examples of actual class\n * `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(\n labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n numClasses: number): Tensor2D {\n const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n util.assert(\n numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n () => `If provided, numClasses must be a positive integer, ` +\n `but got ${numClasses}`);\n util.assert(\n $labels.rank === 1,\n () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n util.assert(\n $predictions.rank === 1,\n () => `Expected the rank of predictions to be 1, ` +\n `but got ${$predictions.rank}`);\n util.assert(\n $labels.shape[0] === $predictions.shape[0],\n () => `Mismatch in the number of examples: ` +\n `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n `Labels and predictions should have the same number of elements.`);\n util.assert(\n numClasses > 0 && Number.isInteger(numClasses),\n () => `numClasses is required to be a positive integer, but got ` +\n `${numClasses}`);\n // TODO(cais): In the future, if oneHot supports tensors inputs for\n // `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses) as Tensor2D;\n const oneHotPredictions =\n oneHot(cast($predictions, 'int32'), numClasses) as Tensor2D;\n const oneHotLabelsT: Tensor2D = transpose(oneHotLabels);\n return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Exports under the tf.math.* namespace.\n */\n\nimport {confusionMatrix} from './ops/confusion_matrix';\n\nexport {confusionMatrix};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike3D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(\n values: TensorLike3D, shape?: [number, number, number],\n dtype?: DataType): Tensor3D {\n assertNonNull(values);\n if (shape != null && shape.length !== 3) {\n throw new Error('tensor3d() requires shape to have three numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n throw new Error(\n 'tensor3d() requires values to be number[][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\n 'tensor3d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype) as Tensor3D;\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {FromPixels, FromPixelsAttrs, FromPixelsInputs} from '../kernel_names';\nimport {getKernel, NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor2D, Tensor3D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {PixelData, TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {tensor3d} from './tensor3d';\n\nlet fromPixels2DContext: CanvasRenderingContext2D;\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(\n pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n HTMLVideoElement,\n numChannels = 3): Tensor3D {\n // Sanity checks.\n if (numChannels > 4) {\n throw new Error(\n 'Cannot construct Tensor with more than 4 channels from pixels.');\n }\n if (pixels == null) {\n throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n }\n let isPixelData = false;\n let isImageData = false;\n let isVideo = false;\n let isImage = false;\n let isCanvasLike = false;\n if ((pixels as PixelData).data instanceof Uint8Array) {\n isPixelData = true;\n } else if (\n typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n isImageData = true;\n } else if (\n typeof (HTMLVideoElement) !== 'undefined' &&\n pixels instanceof HTMLVideoElement) {\n isVideo = true;\n } else if (\n typeof (HTMLImageElement) !== 'undefined' &&\n pixels instanceof HTMLImageElement) {\n isImage = true;\n // tslint:disable-next-line: no-any\n } else if ((pixels as any).getContext != null) {\n isCanvasLike = true;\n } else {\n throw new Error(\n 'pixels passed to tf.browser.fromPixels() must be either an ' +\n `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n `in browser, or OffscreenCanvas, ImageData in webworker` +\n ` or {data: Uint32Array, width: number, height: number}, ` +\n `but was ${(pixels as {}).constructor.name}`);\n }\n if (isVideo) {\n const HAVE_CURRENT_DATA_READY_STATE = 2;\n if (isVideo &&\n (pixels as HTMLVideoElement).readyState <\n HAVE_CURRENT_DATA_READY_STATE) {\n throw new Error(\n 'The video element has not loaded data yet. Please wait for ' +\n '`loadeddata` event on the