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Commit 91898cde by vincent
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quantization of face recognition model

parent 9cd6d13f
Showing with 555 additions and 259 deletions
dist/face-api.js
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dist/face-api.min.js
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dist/faceRecognitionNet/FaceRecognitionNet.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
export declare class FaceRecognitionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
forward(input: tf.Tensor | NetInput | TNetInput): tf.Tensor<tf.Rank.R2>;
computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput): Promise<Int32Array | Uint8Array | Float32Array>;
}
dist/faceRecognitionNet/FaceRecognitionNet.js 0 → 100644
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { normalize } from './normalize';
import { residual, residualDown } from './residualLayer';
var FaceRecognitionNet = /** @class */ (function () {
function FaceRecognitionNet() {
}
FaceRecognitionNet.prototype.load = function (weightsOrUrl) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
if (weightsOrUrl instanceof Float32Array) {
this.extractWeights(weightsOrUrl);
return [2 /*return*/];
}
if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
}
_a = this;
return [4 /*yield*/, loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
}
});
});
};
FaceRecognitionNet.prototype.extractWeights = function (weights) {
this._params = extractParams(weights);
};
FaceRecognitionNet.prototype.forward = function (input) {
var _this = this;
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return tf.tidy(function () {
var x = padToSquare(getImageTensor(input), true);
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]);
}
x = normalize(x);
var out = convDown(x, _this._params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, _this._params.conv32_1);
out = residual(out, _this._params.conv32_2);
out = residual(out, _this._params.conv32_3);
out = residualDown(out, _this._params.conv64_down);
out = residual(out, _this._params.conv64_1);
out = residual(out, _this._params.conv64_2);
out = residual(out, _this._params.conv64_3);
out = residualDown(out, _this._params.conv128_down);
out = residual(out, _this._params.conv128_1);
out = residual(out, _this._params.conv128_2);
out = residualDown(out, _this._params.conv256_down);
out = residual(out, _this._params.conv256_1);
out = residual(out, _this._params.conv256_2);
out = residualDown(out, _this._params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
});
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var result, data;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0:
result = this.forward(input);
return [4 /*yield*/, result.data()];
case 1:
data = _a.sent();
result.dispose();
return [2 /*return*/, data];
}
});
});
};
FaceRecognitionNet.prototype.computeFaceDescriptorSync = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var result, data;
return tslib_1.__generator(this, function (_a) {
result = this.forward(input);
data = result.dataSync();
result.dispose();
return [2 /*return*/, data];
});
});
};
return FaceRecognitionNet;
}());
export { FaceRecognitionNet };
//# sourceMappingURL=FaceRecognitionNet.js.map
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dist/faceRecognitionNet/FaceRecognitionNet.js.map 0 → 100644
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dist/faceRecognitionNet/convLayer.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceRecognitionNet } from './types';
export declare function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
export declare function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
export declare function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
import { ConvLayerParams } from './types';
export declare function conv(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
export declare function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
export declare function convDown(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
dist/faceRecognitionNet/convLayer.js.map
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dist/faceRecognitionNet/extractParams.d.ts
import { FaceRecognitionNet } from './types';
export declare function extractParams(weights: Float32Array): FaceRecognitionNet.NetParams;
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): NetParams;
dist/faceRecognitionNet/extractParams.js.map
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\ No newline at end of file
dist/faceRecognitionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
export declare function faceRecognitionNet(weights: Float32Array): {
computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Int32Array | Uint8Array | Float32Array;
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => tf.Tensor<tf.Rank.R2>;
};
import { FaceRecognitionNet } from './FaceRecognitionNet';
export * from './FaceRecognitionNet';
export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
dist/faceRecognitionNet/index.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { normalize } from './normalize';
import { residual, residualDown } from './residualLayer';
import { FaceRecognitionNet } from './FaceRecognitionNet';
export * from './FaceRecognitionNet';
export function faceRecognitionNet(weights) {
var _this = this;
var params = extractParams(weights);
function forward(input) {
return tf.tidy(function () {
var x = padToSquare(getImageTensor(input), true);
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]);
}
x = normalize(x);
var out = convDown(x, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1);
out = residual(out, params.conv32_2);
out = residual(out, params.conv32_3);
out = residualDown(out, params.conv64_down);
out = residual(out, params.conv64_1);
out = residual(out, params.conv64_2);
out = residual(out, params.conv64_3);
out = residualDown(out, params.conv128_down);
out = residual(out, params.conv128_1);
out = residual(out, params.conv128_2);
out = residualDown(out, params.conv256_down);
out = residual(out, params.conv256_1);
out = residual(out, params.conv256_2);
out = residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, params.fc);
return fullyConnected;
});
}
var computeFaceDescriptor = function (input) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var result, data;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0:
result = forward(input);
return [4 /*yield*/, result.data()];
case 1:
data = _a.sent();
result.dispose();
return [2 /*return*/, data];
}
});
}); };
var computeFaceDescriptorSync = function (input) {
var result = forward(input);
var data = result.dataSync();
result.dispose();
return data;
};
return {
computeFaceDescriptor: computeFaceDescriptor,
computeFaceDescriptorSync: computeFaceDescriptorSync,
forward: forward
};
var net = new FaceRecognitionNet();
net.extractWeights(weights);
return net;
}
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceRecognitionNet/index.js.map
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\ No newline at end of file
dist/faceRecognitionNet/loadQuantizedParams.d.ts 0 → 100644
export declare function loadQuantizedParams(uri: string | undefined): Promise<any>;
dist/faceRecognitionNet/loadQuantizedParams.js 0 → 100644
import * as tslib_1 from "tslib";
import { isTensor1D, isTensor2D, isTensor4D } from '../commons/isTensor';
import { loadWeightMap } from '../commons/loadWeightMap';
var DEFAULT_MODEL_NAME = 'face_recognition_model';
function extractorsFactory(weightMap) {
function extractScaleLayerParams(prefix) {
var params = {
weights: weightMap[prefix + "/scale/weights"],
biases: weightMap[prefix + "/scale/biases"]
};
if (!isTensor1D(params.weights)) {
throw new Error("expected weightMap[" + prefix + "/scale/weights] to be a Tensor1D, instead have " + params.weights);
}
if (!isTensor1D(params.biases)) {
throw new Error("expected weightMap[" + prefix + "/scale/biases] to be a Tensor1D, instead have " + params.biases);
}
return params;
}
function extractConvLayerParams(prefix) {
var params = {
filters: weightMap[prefix + "/conv/filters"],
bias: weightMap[prefix + "/conv/bias"]
};
if (!isTensor4D(params.filters)) {
throw new Error("expected weightMap[" + prefix + "/conv/filters] to be a Tensor1D, instead have " + params.filters);
}
if (!isTensor1D(params.bias)) {
throw new Error("expected weightMap[" + prefix + "/conv/bias] to be a Tensor1D, instead have " + params.bias);
}
return {
conv: params,
scale: extractScaleLayerParams(prefix)
};
}
function extractResidualLayerParams(prefix) {
return {
conv1: extractConvLayerParams(prefix + "/conv1"),
conv2: extractConvLayerParams(prefix + "/conv2")
};
}
return {
extractConvLayerParams: extractConvLayerParams,
extractResidualLayerParams: extractResidualLayerParams
};
}
export function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
conv32_down = extractConvLayerParams('conv32_down');
conv32_1 = extractResidualLayerParams('conv32_1');
conv32_2 = extractResidualLayerParams('conv32_2');
conv32_3 = extractResidualLayerParams('conv32_3');
conv64_down = extractResidualLayerParams('conv64_down');
conv64_1 = extractResidualLayerParams('conv64_1');
conv64_2 = extractResidualLayerParams('conv64_2');
conv64_3 = extractResidualLayerParams('conv64_3');
conv128_down = extractResidualLayerParams('conv128_down');
conv128_1 = extractResidualLayerParams('conv128_1');
conv128_2 = extractResidualLayerParams('conv128_2');
conv256_down = extractResidualLayerParams('conv256_down');
conv256_1 = extractResidualLayerParams('conv256_1');
conv256_2 = extractResidualLayerParams('conv256_2');
conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc'];
if (!isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
return [2 /*return*/, {
conv32_down: conv32_down,
conv32_1: conv32_1,
conv32_2: conv32_2,
conv32_3: conv32_3,
conv64_down: conv64_down,
conv64_1: conv64_1,
conv64_2: conv64_2,
conv64_3: conv64_3,
conv128_down: conv128_down,
conv128_1: conv128_1,
conv128_2: conv128_2,
conv256_down: conv256_down,
conv256_1: conv256_1,
conv256_2: conv256_2,
conv256_down_out: conv256_down_out,
fc: fc
}];
}
});
});
}
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
dist/faceRecognitionNet/loadQuantizedParams.js.map 0 → 100644
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\ No newline at end of file
dist/faceRecognitionNet/residualLayer.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceRecognitionNet } from './types';
export declare function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
export declare function residualDown(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
import { ResidualLayerParams } from './types';
export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
dist/faceRecognitionNet/residualLayer.js.map
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\ No newline at end of file
dist/faceRecognitionNet/scaleLayer.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceRecognitionNet } from './types';
export declare function scale(x: tf.Tensor4D, params: FaceRecognitionNet.ScaleLayerParams): tf.Tensor4D;
import { ScaleLayerParams } from './types';
export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;
dist/faceRecognitionNet/scaleLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceRecognitionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceRecognitionNet {
type ScaleLayerParams = {
weights: tf.Tensor1D;
biases: tf.Tensor1D;
};
type ResidualLayerParams = {
conv1: ConvLayerParams;
conv2: ConvLayerParams;
};
type ConvLayerParams = {
conv: ConvParams;
scale: ScaleLayerParams;
};
type NetParams = {
conv32_down: ConvLayerParams;
conv32_1: ResidualLayerParams;
conv32_2: ResidualLayerParams;
conv32_3: ResidualLayerParams;
conv64_down: ResidualLayerParams;
conv64_1: ResidualLayerParams;
conv64_2: ResidualLayerParams;
conv64_3: ResidualLayerParams;
conv128_down: ResidualLayerParams;
conv128_1: ResidualLayerParams;
conv128_2: ResidualLayerParams;
conv256_down: ResidualLayerParams;
conv256_1: ResidualLayerParams;
conv256_2: ResidualLayerParams;
conv256_down_out: ResidualLayerParams;
fc: tf.Tensor2D;
};
}
export declare type ScaleLayerParams = {
weights: tf.Tensor1D;
biases: tf.Tensor1D;
};
export declare type ResidualLayerParams = {
conv1: ConvLayerParams;
conv2: ConvLayerParams;
};
export declare type ConvLayerParams = {
conv: ConvParams;
scale: ScaleLayerParams;
};
export declare type NetParams = {
conv32_down: ConvLayerParams;
conv32_1: ResidualLayerParams;
conv32_2: ResidualLayerParams;
conv32_3: ResidualLayerParams;
conv64_down: ResidualLayerParams;
conv64_1: ResidualLayerParams;
conv64_2: ResidualLayerParams;
conv64_3: ResidualLayerParams;
conv128_down: ResidualLayerParams;
conv128_1: ResidualLayerParams;
conv128_2: ResidualLayerParams;
conv256_down: ResidualLayerParams;
conv256_1: ResidualLayerParams;
conv256_2: ResidualLayerParams;
conv256_down_out: ResidualLayerParams;
fc: tf.Tensor2D;
};
dist/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, faceRecognitionNet, NetInput, tf, padToSquare };
export { euclideanDistance, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './utils';
dist/index.js
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, faceRecognitionNet, NetInput, tf, padToSquare };
export { euclideanDistance, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './utils';
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/index.js.map
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\ No newline at end of file
examples/public/commons.js
......@@ -12,12 +12,6 @@ async function fetchImage(uri) {
return (await fetch(uri)).blob()
}
async function initFaceRecognitionNet() {
const res = await fetch('uncompressed/face_recognition_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
return faceapi.faceRecognitionNet(weights)
}
// fetch first image of each class and compute their descriptors
async function initTrainDescriptorsByClass(net, numImagesForTraining = 1) {
const maxAvailableImagesPerClass = 5
......
examples/views/detectAndRecognizeFaces.html
......@@ -146,7 +146,8 @@
await detectionNet.load('/')
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
recognitionNet = await initFaceRecognitionNet()
recognitionNet = new faceapi.FaceRecognitionNet()
await recognitionNet.load('/')
trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet, 1)
$('#loader').hide()
onSelectionChanged($('#selectList select').val())
......
examples/views/faceRecognition.html
......@@ -136,7 +136,9 @@
try {
setStatusText('loading model file...')
net = await initFaceRecognitionNet()
net = new faceapi.FaceRecognitionNet()
await net.load('/')
setStatusText('computing initial descriptors...')
trainDescriptorsByClass = await initTrainDescriptorsByClass(net)
......
examples/views/faceSimilarity.html
......@@ -61,7 +61,8 @@
}
async function run() {
net = await initFaceRecognitionNet()
net = new faceapi.FaceRecognitionNet()
await net.load('/')
$('#loader').hide()
await onSelectionChanged(1, $('#selectList1 select').val())
await onSelectionChanged(2, $('#selectList2 select').val())
......
src/faceRecognitionNet/FaceRecognitionNet.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { TNetInput } from '../types';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { normalize } from './normalize';
import { residual, residualDown } from './residualLayer';
import { NetParams } from './types';
export class FaceRecognitionNet {
private _params: NetParams
public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
if (weightsOrUrl instanceof Float32Array) {
this.extractWeights(weightsOrUrl)
return
}
if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array')
}
this._params = await loadQuantizedParams(weightsOrUrl)
}
public extractWeights(weights: Float32Array) {
this._params = extractParams(weights)
}
public forward(input: tf.Tensor | NetInput | TNetInput) {
if (!this._params) {
throw new Error('FaceRecognitionNet - load model before inference')
}
return tf.tidy(() => {
let x = padToSquare(getImageTensor(input), true)
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150])
}
x = normalize(x)
let out = convDown(x, this._params.conv32_down)
out = tf.maxPool(out, 3, 2, 'valid')
out = residual(out, this._params.conv32_1)
out = residual(out, this._params.conv32_2)
out = residual(out, this._params.conv32_3)
out = residualDown(out, this._params.conv64_down)
out = residual(out, this._params.conv64_1)
out = residual(out, this._params.conv64_2)
out = residual(out, this._params.conv64_3)
out = residualDown(out, this._params.conv128_down)
out = residual(out, this._params.conv128_1)
out = residual(out, this._params.conv128_2)
out = residualDown(out, this._params.conv256_down)
out = residual(out, this._params.conv256_1)
out = residual(out, this._params.conv256_2)
out = residualDown(out, this._params.conv256_down_out)
const globalAvg = out.mean([1, 2]) as tf.Tensor2D
const fullyConnected = tf.matMul(globalAvg, this._params.fc)
return fullyConnected
})
}
public async computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput) {
const result = this.forward(input)
const data = await result.data()
result.dispose()
return data
}
public async computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput) {
const result = this.forward(input)
const data = result.dataSync()
result.dispose()
return data
}
}
\ No newline at end of file
src/faceRecognitionNet/convLayer.ts
import * as tf from '@tensorflow/tfjs-core';
import { scale } from './scaleLayer';
import { FaceRecognitionNet } from './types';
import { ConvLayerParams } from './types';
function convLayer(
x: tf.Tensor4D,
params: FaceRecognitionNet.ConvLayerParams,
params: ConvLayerParams,
strides: [number, number],
withRelu: boolean,
padding: 'valid' | 'same' = 'same'
......@@ -19,14 +19,14 @@ function convLayer(
return withRelu ? tf.relu(out) : out
}
export function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
export function conv(x: tf.Tensor4D, params: ConvLayerParams) {
return convLayer(x, params, [1, 1], true)
}
export function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
export function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {
return convLayer(x, params, [1, 1], false)
}
export function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams) {
export function convDown(x: tf.Tensor4D, params: ConvLayerParams) {
return convLayer(x, params, [2, 2], true, 'valid')
}
\ No newline at end of file
src/faceRecognitionNet/extractParams.ts
......@@ -3,7 +3,7 @@ import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { ExtractWeightsFunction } from '../commons/types';
import { isFloat } from '../utils';
import { FaceRecognitionNet } from './types';
import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
function extractorsFactory(extractWeights: ExtractWeightsFunction) {
......@@ -21,7 +21,7 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
)
}
function extractScaleLayerParams(numWeights: number): FaceRecognitionNet.ScaleLayerParams {
function extractScaleLayerParams(numWeights: number): ScaleLayerParams {
const weights = tf.tensor1d(extractWeights(numWeights))
const biases = tf.tensor1d(extractWeights(numWeights))
return {
......@@ -34,7 +34,7 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
numFilterValues: number,
numFilters: number,
filterSize: number
): FaceRecognitionNet.ConvLayerParams {
): ConvLayerParams {
const conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize)
const conv_bias = tf.tensor1d(extractWeights(numFilters))
const scale = extractScaleLayerParams(numFilters)
......@@ -48,9 +48,14 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
}
}
function extractResidualLayerParams(numFilterValues: number, numFilters: number, filterSize: number, isDown: boolean = false): FaceRecognitionNet.ResidualLayerParams {
const conv1: FaceRecognitionNet.ConvLayerParams = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize)
const conv2: FaceRecognitionNet.ConvLayerParams = extractConvLayerParams(numFilterValues, numFilters, filterSize)
function extractResidualLayerParams(
numFilterValues: number,
numFilters: number,
filterSize: number,
isDown: boolean = false
): ResidualLayerParams {
const conv1: ConvLayerParams = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize)
const conv2: ConvLayerParams = extractConvLayerParams(numFilterValues, numFilters, filterSize)
return {
conv1,
......@@ -65,7 +70,7 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
}
export function extractParams(weights: Float32Array): FaceRecognitionNet.NetParams {
export function extractParams(weights: Float32Array): NetParams {
const {
extractWeights,
getRemainingWeights
......
src/faceRecognitionNet/index.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceRecognitionNet } from './FaceRecognitionNet';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { TNetInput } from '../types';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { normalize } from './normalize';
import { residual, residualDown } from './residualLayer';
export * from './FaceRecognitionNet';
export function faceRecognitionNet(weights: Float32Array) {
const params = extractParams(weights)
function forward(input: tf.Tensor | NetInput | TNetInput) {
return tf.tidy(() => {
let x = padToSquare(getImageTensor(input), true)
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150])
}
x = normalize(x)
let out = convDown(x, params.conv32_down)
out = tf.maxPool(out, 3, 2, 'valid')
out = residual(out, params.conv32_1)
out = residual(out, params.conv32_2)
out = residual(out, params.conv32_3)
out = residualDown(out, params.conv64_down)
out = residual(out, params.conv64_1)
out = residual(out, params.conv64_2)
out = residual(out, params.conv64_3)
out = residualDown(out, params.conv128_down)
out = residual(out, params.conv128_1)
out = residual(out, params.conv128_2)
out = residualDown(out, params.conv256_down)
out = residual(out, params.conv256_1)
out = residual(out, params.conv256_2)
out = residualDown(out, params.conv256_down_out)
const globalAvg = out.mean([1, 2]) as tf.Tensor2D
const fullyConnected = tf.matMul(globalAvg, params.fc)
return fullyConnected
})
}
const computeFaceDescriptor = async (input: tf.Tensor | NetInput | TNetInput) => {
const result = forward(input)
const data = await result.data()
result.dispose()
return data
}
const computeFaceDescriptorSync = (input: tf.Tensor | NetInput | TNetInput) => {
const result = forward(input)
const data = result.dataSync()
result.dispose()
return data
}
return {
computeFaceDescriptor,
computeFaceDescriptorSync,
forward
}
const net = new FaceRecognitionNet()
net.extractWeights(weights)
return net
}
\ No newline at end of file
src/faceRecognitionNet/loadQuantizedParams.ts 0 → 100644
import { isTensor1D, isTensor2D, isTensor4D } from '../commons/isTensor';
import { loadWeightMap } from '../commons/loadWeightMap';
import { ConvLayerParams, ResidualLayerParams, ScaleLayerParams } from './types';
const DEFAULT_MODEL_NAME = 'face_recognition_model'
function extractorsFactory(weightMap: any) {
function extractScaleLayerParams(prefix: string): ScaleLayerParams {
const params = {
weights: weightMap[`${prefix}/scale/weights`],
biases: weightMap[`${prefix}/scale/biases`]
}
if (!isTensor1D(params.weights)) {
throw new Error(`expected weightMap[${prefix}/scale/weights] to be a Tensor1D, instead have ${params.weights}`)
}
if (!isTensor1D(params.biases)) {
throw new Error(`expected weightMap[${prefix}/scale/biases] to be a Tensor1D, instead have ${params.biases}`)
}
return params
}
function extractConvLayerParams(prefix: string): ConvLayerParams {
const params = {
filters: weightMap[`${prefix}/conv/filters`],
bias: weightMap[`${prefix}/conv/bias`]
}
if (!isTensor4D(params.filters)) {
throw new Error(`expected weightMap[${prefix}/conv/filters] to be a Tensor1D, instead have ${params.filters}`)
}
if (!isTensor1D(params.bias)) {
throw new Error(`expected weightMap[${prefix}/conv/bias] to be a Tensor1D, instead have ${params.bias}`)
}
return {
conv: params,
scale: extractScaleLayerParams(prefix)
}
}
function extractResidualLayerParams(prefix: string): ResidualLayerParams {
return {
conv1: extractConvLayerParams(`${prefix}/conv1`),
conv2: extractConvLayerParams(`${prefix}/conv2`)
}
}
return {
extractConvLayerParams,
extractResidualLayerParams
}
}
export async function loadQuantizedParams(uri: string | undefined): Promise<any> {
const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
const {
extractConvLayerParams,
extractResidualLayerParams
} = extractorsFactory(weightMap)
const conv32_down = extractConvLayerParams('conv32_down')
const conv32_1 = extractResidualLayerParams('conv32_1')
const conv32_2 = extractResidualLayerParams('conv32_2')
const conv32_3 = extractResidualLayerParams('conv32_3')
const conv64_down = extractResidualLayerParams('conv64_down')
const conv64_1 = extractResidualLayerParams('conv64_1')
const conv64_2 = extractResidualLayerParams('conv64_2')
const conv64_3 = extractResidualLayerParams('conv64_3')
const conv128_down = extractResidualLayerParams('conv128_down')
const conv128_1 = extractResidualLayerParams('conv128_1')
const conv128_2 = extractResidualLayerParams('conv128_2')
const conv256_down = extractResidualLayerParams('conv256_down')
const conv256_1 = extractResidualLayerParams('conv256_1')
const conv256_2 = extractResidualLayerParams('conv256_2')
const conv256_down_out = extractResidualLayerParams('conv256_down_out')
const fc = weightMap['fc']
if (!isTensor2D(fc)) {
throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)
}
return {
conv32_down,
conv32_1,
conv32_2,
conv32_3,
conv64_down,
conv64_1,
conv64_2,
conv64_3,
conv128_down,
conv128_1,
conv128_2,
conv256_down,
conv256_1,
conv256_2,
conv256_down_out,
fc
}
}
\ No newline at end of file
src/faceRecognitionNet/residualLayer.ts
import * as tf from '@tensorflow/tfjs-core';
import { conv, convDown, convNoRelu } from './convLayer';
import { FaceRecognitionNet } from './types';
import { ResidualLayerParams } from './types';
export function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D {
export function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {
let out = conv(x, params.conv1)
out = convNoRelu(out, params.conv2)
out = tf.add(out, x)
......@@ -11,7 +11,7 @@ export function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLaye
return out
}
export function residualDown(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D {
export function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {
let out = convDown(x, params.conv1)
out = convNoRelu(out, params.conv2)
......
src/faceRecognitionNet/scaleLayer.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceRecognitionNet } from './types';
export function scale(x: tf.Tensor4D, params: FaceRecognitionNet.ScaleLayerParams): tf.Tensor4D {
import { ScaleLayerParams } from './types';
export function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {
return tf.add(tf.mul(x, params.weights), params.biases)
}
src/faceRecognitionNet/types.ts
......@@ -2,39 +2,35 @@ import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export namespace FaceRecognitionNet {
export type ScaleLayerParams = {
weights: tf.Tensor1D
biases: tf.Tensor1D
}
export type ResidualLayerParams = {
conv1: ConvLayerParams
conv2: ConvLayerParams
}
export type ScaleLayerParams = {
weights: tf.Tensor1D
biases: tf.Tensor1D
}
export type ResidualLayerParams = {
conv1: ConvLayerParams
conv2: ConvLayerParams
}
export type ConvLayerParams = {
conv: ConvParams
scale: ScaleLayerParams
}
export type NetParams = {
conv32_down: ConvLayerParams
conv32_1: ResidualLayerParams
conv32_2: ResidualLayerParams
conv32_3: ResidualLayerParams
conv64_down: ResidualLayerParams
conv64_1: ResidualLayerParams
conv64_2: ResidualLayerParams
conv64_3: ResidualLayerParams
conv128_down: ResidualLayerParams
conv128_1: ResidualLayerParams
conv128_2: ResidualLayerParams
conv256_down: ResidualLayerParams
conv256_1: ResidualLayerParams
conv256_2: ResidualLayerParams
conv256_down_out: ResidualLayerParams
fc: tf.Tensor2D
}
export type ConvLayerParams = {
conv: ConvParams
scale: ScaleLayerParams
}
export type NetParams = {
conv32_down: ConvLayerParams
conv32_1: ResidualLayerParams
conv32_2: ResidualLayerParams
conv32_3: ResidualLayerParams
conv64_down: ResidualLayerParams
conv64_1: ResidualLayerParams
conv64_2: ResidualLayerParams
conv64_3: ResidualLayerParams
conv128_down: ResidualLayerParams
conv128_1: ResidualLayerParams
conv128_2: ResidualLayerParams
conv256_down: ResidualLayerParams
conv256_1: ResidualLayerParams
conv256_2: ResidualLayerParams
conv256_down_out: ResidualLayerParams
fc: tf.Tensor2D
}
\ No newline at end of file
src/index.ts
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export {
euclideanDistance,
faceRecognitionNet,
NetInput,
tf,
padToSquare
......@@ -17,4 +15,5 @@ export * from './extractFaces'
export * from './extractFaceTensors'
export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './utils'
\ No newline at end of file
weights/face_recognition_model-shard2 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
weights/face_recognition_model-weights_manifest.json 0 → 100644
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