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face-api
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fix for wasm compatibility

pull/13/head
Vladimir Mandic 2020-10-25 20:01:36 -04:00
parent 4f17fe476b
commit 7e1de2e57b
29 changed files with 280 additions and 289 deletions
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2
build/package.json
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@ -1,6 +1,6 @@
{ {
"name": "@vladmandic/face-api", "name": "@vladmandic/face-api",
"version": "0.7.4", "version": "0.8.1",
"description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS", "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
"main": "dist/face-api.cjs", "main": "dist/face-api.cjs",
"module": "dist/face-api.esm.js", "module": "dist/face-api.esm.js",

2
build/src/faceFeatureExtractor/FaceFeatureExtractor.js
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@ -15,7 +15,7 @@ export class FaceFeatureExtractor extends NeuralNetwork {
throw new Error('FaceFeatureExtractor - load model before inference'); throw new Error('FaceFeatureExtractor - load model before inference');
} }
return tf.tidy(() => { return tf.tidy(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)); const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
let out = denseBlock4(normalized, params.dense0, true); let out = denseBlock4(normalized, params.dense0, true);

2
build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
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@ -1 +1 @@
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build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
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@ -15,7 +15,7 @@ export class TinyFaceFeatureExtractor extends NeuralNetwork {
throw new Error('TinyFaceFeatureExtractor - load model before inference'); throw new Error('TinyFaceFeatureExtractor - load model before inference');
} }
return tf.tidy(() => { return tf.tidy(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)); const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
let out = denseBlock3(normalized, params.dense0, true); let out = denseBlock3(normalized, params.dense0, true);

2
build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
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@ -1 +1 @@
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build/src/faceLandmarkNet/FaceLandmark68NetBase.js
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@ -15,10 +15,7 @@ export class FaceLandmark68NetBase extends FaceProcessor {
}); });
const batchSize = inputDimensions.length; const batchSize = inputDimensions.length;
return tf.tidy(() => { return tf.tidy(() => {
const createInterleavedTensor = (fillX, fillY) => tf.stack([ const createInterleavedTensor = (fillX, fillY) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();
tf.fill([68], fillX),
tf.fill([68], fillY)
], 1).as2D(1, 136).as1D();
const getPadding = (batchIdx, cond) => { const getPadding = (batchIdx, cond) => {
const { width, height } = inputDimensions[batchIdx]; const { width, height } = inputDimensions[batchIdx];
return cond(width, height) ? Math.abs(width - height) / 2 : 0; return cond(width, height) ? Math.abs(width - height) / 2 : 0;
@ -26,7 +23,7 @@ export class FaceLandmark68NetBase extends FaceProcessor {
const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
const landmarkTensors = output const landmarkTensors = output
.mul(tf.fill([batchSize, 136], inputSize)) .mul(tf.fill([batchSize, 136], inputSize, 'float32'))
.sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))) .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx)))))
.div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height)))); .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
return landmarkTensors; return landmarkTensors;

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build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
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@ -1 +1 @@
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6
build/src/ops/normalize.js
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@ -2,9 +2,9 @@ import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
export function normalize(x, meanRgb) { export function normalize(x, meanRgb) {
return tf.tidy(() => { return tf.tidy(() => {
const [r, g, b] = meanRgb; const [r, g, b] = meanRgb;
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r); const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g); const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b); const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');
const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3); const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
return tf.sub(x, avg_rgb); return tf.sub(x, avg_rgb);
}); });

2
build/src/ops/normalize.js.map
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@ -1 +1 @@
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2
build/src/ops/padToSquare.js
View File

@ -19,7 +19,7 @@ export function padToSquare(imgTensor, isCenterImage = false) {
const createPaddingTensor = (paddingAmount) => { const createPaddingTensor = (paddingAmount) => {
const paddingTensorShape = imgTensor.shape.slice(); const paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount; paddingTensorShape[paddingAxis] = paddingAmount;
return tf.fill(paddingTensorShape, 0); return tf.fill(paddingTensorShape, 0, 'float32');
}; };
const paddingTensorAppend = createPaddingTensor(paddingAmount); const paddingTensorAppend = createPaddingTensor(paddingAmount);
const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];

2
build/src/ops/padToSquare.js.map
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@ -1 +1 @@
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2
build/src/xception/TinyXception.js
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@ -35,7 +35,7 @@ export class TinyXception extends NeuralNetwork {
throw new Error('TinyXception - load model before inference'); throw new Error('TinyXception - load model before inference');
} }
return tf.tidy(() => { return tf.tidy(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)); const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));

2
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@ -1 +1 @@
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23
dist/face-api.nobundle.cjs vendored
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@ -440,9 +440,9 @@ const tf2 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
function normalize(x, meanRgb) { function normalize(x, meanRgb) {
return tf2.tidy(() => { return tf2.tidy(() => {
const [r, g, b] = meanRgb; const [r, g, b] = meanRgb;
const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r); const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32");
const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g); const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32");
const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b); const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32");
const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3); const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3);
return tf2.sub(x, avg_rgb); return tf2.sub(x, avg_rgb);
}); });
@ -462,7 +462,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
const createPaddingTensor = (paddingAmount2) => { const createPaddingTensor = (paddingAmount2) => {
const paddingTensorShape = imgTensor.shape.slice(); const paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount2; paddingTensorShape[paddingAxis] = paddingAmount2;
return tf3.fill(paddingTensorShape, 0); return tf3.fill(paddingTensorShape, 0, "float32");
}; };
const paddingTensorAppend = createPaddingTensor(paddingAmount); const paddingTensorAppend = createPaddingTensor(paddingAmount);
const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@ -1716,7 +1716,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
throw new Error("FaceFeatureExtractor - load model before inference"); throw new Error("FaceFeatureExtractor - load model before inference");
} }
return tf15.tidy(() => { return tf15.tidy(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = tf15.cast(input.toBatchTensor(112, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(tf15.scalar(255)); const normalized = normalize(batchTensor, meanRgb).div(tf15.scalar(255));
let out = denseBlock4(normalized, params.dense0, true); let out = denseBlock4(normalized, params.dense0, true);
@ -2153,7 +2153,7 @@ class TinyXception extends NeuralNetwork {
throw new Error("TinyXception - load model before inference"); throw new Error("TinyXception - load model before inference");
} }
return tf19.tidy(() => { return tf19.tidy(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = tf19.cast(input.toBatchTensor(112, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(tf19.scalar(256)); const normalized = normalize(batchTensor, meanRgb).div(tf19.scalar(256));
let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
@ -2325,17 +2325,14 @@ class FaceLandmark68NetBase extends FaceProcessor {
}); });
const batchSize = inputDimensions.length; const batchSize = inputDimensions.length;
return tf21.tidy(() => { return tf21.tidy(() => {
const createInterleavedTensor = (fillX, fillY) => tf21.stack([ const createInterleavedTensor = (fillX, fillY) => tf21.stack([tf21.fill([68], fillX, "float32"), tf21.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D();
tf21.fill([68], fillX),
tf21.fill([68], fillY)
], 1).as2D(1, 136).as1D();
const getPadding = (batchIdx, cond) => { const getPadding = (batchIdx, cond) => {
const {width, height} = inputDimensions[batchIdx]; const {width, height} = inputDimensions[batchIdx];
return cond(width, height) ? Math.abs(width - height) / 2 : 0; return cond(width, height) ? Math.abs(width - height) / 2 : 0;
}; };
const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize)).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height)))); const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize, "float32")).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
return landmarkTensors; return landmarkTensors;
}); });
} }
@ -2430,7 +2427,7 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
throw new Error("TinyFaceFeatureExtractor - load model before inference"); throw new Error("TinyFaceFeatureExtractor - load model before inference");
} }
return tf22.tidy(() => { return tf22.tidy(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = tf22.cast(input.toBatchTensor(112, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(tf22.scalar(255)); const normalized = normalize(batchTensor, meanRgb).div(tf22.scalar(255));
let out = denseBlock3(normalized, params.dense0, true); let out = denseBlock3(normalized, params.dense0, true);
@ -4292,7 +4289,7 @@ function resizeResults(results, dimensions) {
} }
// package.json // package.json
var version = "0.7.4"; var version = "0.8.1";
// src/index.ts // src/index.ts
__export(exports, { __export(exports, {

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@ -447,9 +447,9 @@ import {
function normalize(x, meanRgb) { function normalize(x, meanRgb) {
return tidy(() => { return tidy(() => {
const [r, g, b] = meanRgb; const [r, g, b] = meanRgb;
const avg_r = fill([...x.shape.slice(0, 3), 1], r); const avg_r = fill([...x.shape.slice(0, 3), 1], r, "float32");
const avg_g = fill([...x.shape.slice(0, 3), 1], g); const avg_g = fill([...x.shape.slice(0, 3), 1], g, "float32");
const avg_b = fill([...x.shape.slice(0, 3), 1], b); const avg_b = fill([...x.shape.slice(0, 3), 1], b, "float32");
const avg_rgb = concat([avg_r, avg_g, avg_b], 3); const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
return sub(x, avg_rgb); return sub(x, avg_rgb);
}); });
@ -474,7 +474,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
const createPaddingTensor = (paddingAmount2) => { const createPaddingTensor = (paddingAmount2) => {
const paddingTensorShape = imgTensor.shape.slice(); const paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount2; paddingTensorShape[paddingAxis] = paddingAmount2;
return fill2(paddingTensorShape, 0); return fill2(paddingTensorShape, 0, "float32");
}; };
const paddingTensorAppend = createPaddingTensor(paddingAmount); const paddingTensorAppend = createPaddingTensor(paddingAmount);
const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@ -1762,6 +1762,7 @@ function extractParamsFromWeigthMap(weightMap) {
// src/faceFeatureExtractor/FaceFeatureExtractor.ts // src/faceFeatureExtractor/FaceFeatureExtractor.ts
import { import {
avgPool, avgPool,
cast as cast3,
scalar, scalar,
tidy as tidy9 tidy as tidy9
} from "@tensorflow/tfjs/dist/tf.es2017.js"; } from "@tensorflow/tfjs/dist/tf.es2017.js";
@ -1775,7 +1776,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
throw new Error("FaceFeatureExtractor - load model before inference"); throw new Error("FaceFeatureExtractor - load model before inference");
} }
return tidy9(() => { return tidy9(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = cast3(input.toBatchTensor(112, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(scalar(255)); const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
let out = denseBlock4(normalized, params.dense0, true); let out = denseBlock4(normalized, params.dense0, true);
@ -2194,6 +2195,7 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
// src/xception/TinyXception.ts // src/xception/TinyXception.ts
import { import {
add as add5, add as add5,
cast as cast4,
conv2d as conv2d3, conv2d as conv2d3,
maxPool, maxPool,
relu as relu3, relu as relu3,
@ -2229,7 +2231,7 @@ class TinyXception extends NeuralNetwork {
throw new Error("TinyXception - load model before inference"); throw new Error("TinyXception - load model before inference");
} }
return tidy13(() => { return tidy13(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = cast4(input.toBatchTensor(112, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(scalar2(256)); const normalized = normalize(batchTensor, meanRgb).div(scalar2(256));
let out = relu3(conv(normalized, params.entry_flow.conv_in, [2, 2])); let out = relu3(conv(normalized, params.entry_flow.conv_in, [2, 2]));
@ -2411,17 +2413,14 @@ class FaceLandmark68NetBase extends FaceProcessor {
}); });
const batchSize = inputDimensions.length; const batchSize = inputDimensions.length;
return tidy15(() => { return tidy15(() => {
const createInterleavedTensor = (fillX, fillY) => stack2([ const createInterleavedTensor = (fillX, fillY) => stack2([fill3([68], fillX, "float32"), fill3([68], fillY, "float32")], 1).as2D(1, 136).as1D();
fill3([68], fillX),
fill3([68], fillY)
], 1).as2D(1, 136).as1D();
const getPadding = (batchIdx, cond) => { const getPadding = (batchIdx, cond) => {
const {width, height} = inputDimensions[batchIdx]; const {width, height} = inputDimensions[batchIdx];
return cond(width, height) ? Math.abs(width - height) / 2 : 0; return cond(width, height) ? Math.abs(width - height) / 2 : 0;
}; };
const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize)).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height)))); const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize, "float32")).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
return landmarkTensors; return landmarkTensors;
}); });
} }
@ -2507,6 +2506,7 @@ function extractParamsTiny(weights) {
// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts // src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
import { import {
avgPool as avgPool3, avgPool as avgPool3,
cast as cast5,
scalar as scalar3, scalar as scalar3,
tidy as tidy16 tidy as tidy16
} from "@tensorflow/tfjs/dist/tf.es2017.js"; } from "@tensorflow/tfjs/dist/tf.es2017.js";
@ -2520,7 +2520,7 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
throw new Error("TinyFaceFeatureExtractor - load model before inference"); throw new Error("TinyFaceFeatureExtractor - load model before inference");
} }
return tidy16(() => { return tidy16(() => {
const batchTensor = input.toBatchTensor(112, true); const batchTensor = cast5(input.toBatchTensor(112, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(scalar3(255)); const normalized = normalize(batchTensor, meanRgb).div(scalar3(255));
let out = denseBlock3(normalized, params.dense0, true); let out = denseBlock3(normalized, params.dense0, true);
@ -2804,7 +2804,7 @@ function residualDown(x, params) {
// src/faceRecognitionNet/FaceRecognitionNet.ts // src/faceRecognitionNet/FaceRecognitionNet.ts
import { import {
cast as cast3, cast as cast6,
matMul as matMul2, matMul as matMul2,
maxPool as maxPool2, maxPool as maxPool2,
scalar as scalar4, scalar as scalar4,
@ -2821,7 +2821,7 @@ class FaceRecognitionNet extends NeuralNetwork {
throw new Error("FaceRecognitionNet - load model before inference"); throw new Error("FaceRecognitionNet - load model before inference");
} }
return tidy18(() => { return tidy18(() => {
const batchTensor = cast3(input.toBatchTensor(150, true), "float32"); const batchTensor = cast6(input.toBatchTensor(150, true), "float32");
const meanRgb = [122.782, 117.001, 104.298]; const meanRgb = [122.782, 117.001, 104.298];
const normalized = normalize(batchTensor, meanRgb).div(scalar4(256)); const normalized = normalize(batchTensor, meanRgb).div(scalar4(256));
let out = convDown(normalized, params.conv32_down); let out = convDown(normalized, params.conv32_down);
@ -3445,7 +3445,7 @@ class SsdMobilenetv1Options {
// src/ssdMobilenetv1/SsdMobilenetv1.ts // src/ssdMobilenetv1/SsdMobilenetv1.ts
import { import {
cast as cast4, cast as cast7,
mul as mul3, mul as mul3,
scalar as scalar6, scalar as scalar6,
sub as sub3, sub as sub3,
@ -3461,7 +3461,7 @@ class SsdMobilenetv1 extends NeuralNetwork {
throw new Error("SsdMobilenetv1 - load model before inference"); throw new Error("SsdMobilenetv1 - load model before inference");
} }
return tidy24(() => { return tidy24(() => {
const batchTensor = cast4(input.toBatchTensor(512, false), "float32"); const batchTensor = cast7(input.toBatchTensor(512, false), "float32");
const x = sub3(mul3(batchTensor, scalar6(0.007843137718737125)), scalar6(1)); const x = sub3(mul3(batchTensor, scalar6(0.007843137718737125)), scalar6(1));
const features = mobileNetV1(x, params.mobilenetv1); const features = mobileNetV1(x, params.mobilenetv1);
const { const {
@ -3791,7 +3791,7 @@ class TinyYolov2Options {
// src/tinyYolov2/TinyYolov2Base.ts // src/tinyYolov2/TinyYolov2Base.ts
import { import {
cast as cast5, cast as cast8,
maxPool as maxPool3, maxPool as maxPool3,
scalar as scalar8, scalar as scalar8,
softmax as softmax3, softmax as softmax3,
@ -3853,7 +3853,7 @@ class TinyYolov2Base extends NeuralNetwork {
throw new Error("TinyYolov2 - load model before inference"); throw new Error("TinyYolov2 - load model before inference");
} }
return tidy28(() => { return tidy28(() => {
let batchTensor = cast5(input.toBatchTensor(inputSize, false), "float32"); let batchTensor = cast8(input.toBatchTensor(inputSize, false), "float32");
batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
batchTensor = batchTensor.div(scalar8(256)); batchTensor = batchTensor.div(scalar8(256));
return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
@ -4481,7 +4481,7 @@ function resizeResults(results, dimensions) {
} }
// package.json // package.json
var version = "0.7.4"; var version = "0.8.1";
// src/index.ts // src/index.ts
import * as tf42 from "@tensorflow/tfjs/dist/tf.es2017.js"; import * as tf42 from "@tensorflow/tfjs/dist/tf.es2017.js";

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@ -23,7 +23,7 @@ export class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorPara
} }
return tf.tidy(() => { return tf.tidy(() => {
const batchTensor = input.toBatchTensor(112, true) const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
const meanRgb = [122.782, 117.001, 104.298] const meanRgb = [122.782, 117.001, 104.298]
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D

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@ -23,7 +23,7 @@ export class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtra
} }
return tf.tidy(() => { return tf.tidy(() => {
const batchTensor = input.toBatchTensor(112, true) const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
const meanRgb = [122.782, 117.001, 104.298] const meanRgb = [122.782, 117.001, 104.298]
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D

7
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@ -26,10 +26,7 @@ export abstract class FaceLandmark68NetBase<
return tf.tidy(() => { return tf.tidy(() => {
const createInterleavedTensor = (fillX: number, fillY: number) => const createInterleavedTensor = (fillX: number, fillY: number) =>
tf.stack([ tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()
tf.fill([68], fillX),
tf.fill([68], fillY)
], 1).as2D(1, 136).as1D()
const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => { const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {
const { width, height } = inputDimensions[batchIdx] const { width, height } = inputDimensions[batchIdx]
@ -39,7 +36,7 @@ export abstract class FaceLandmark68NetBase<
const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w) const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)
const landmarkTensors = output const landmarkTensors = output
.mul(tf.fill([batchSize, 136], inputSize)) .mul(tf.fill([batchSize, 136], inputSize, 'float32'))
.sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
createInterleavedTensor( createInterleavedTensor(
getPaddingX(batchIdx), getPaddingX(batchIdx),

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@ -3,9 +3,9 @@ import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D { export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {
return tf.tidy(() => { return tf.tidy(() => {
const [r, g, b] = meanRgb const [r, g, b] = meanRgb
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r) const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g) const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b) const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')
const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3) const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
return tf.sub(x, avg_rgb) return tf.sub(x, avg_rgb)

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@ -26,7 +26,7 @@ export function padToSquare(
const createPaddingTensor = (paddingAmount: number): tf.Tensor => { const createPaddingTensor = (paddingAmount: number): tf.Tensor => {
const paddingTensorShape = imgTensor.shape.slice() const paddingTensorShape = imgTensor.shape.slice()
paddingTensorShape[paddingAxis] = paddingAmount paddingTensorShape[paddingAxis] = paddingAmount
return tf.fill(paddingTensorShape, 0) return tf.fill(paddingTensorShape, 0, 'float32')
} }
const paddingTensorAppend = createPaddingTensor(paddingAmount) const paddingTensorAppend = createPaddingTensor(paddingAmount)

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@ -48,7 +48,7 @@ export class TinyXception extends NeuralNetwork<TinyXceptionParams> {
} }
return tf.tidy(() => { return tf.tidy(() => {
const batchTensor = input.toBatchTensor(112, true) const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
const meanRgb = [122.782, 117.001, 104.298] const meanRgb = [122.782, 117.001, 104.298]
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D