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Commit 482becb0 by vincent
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load quantized weights for face detection net

parent 91ad7ab0
Showing with 689 additions and 320 deletions
dist/face-api.min.js
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dist/faceDetectionNet/FaceDetectionNet.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
import { FaceDetection } from './FaceDetection';
export declare class FaceDetectionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
private forwardTensor(imgTensor);
forward(input: tf.Tensor | NetInput | TNetInput): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
locateFaces(input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
}
dist/faceDetectionNet/FaceDetectionNet.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 { Rect } from '../Rect';
import { extractParams } from './extractParams';
import { FaceDetection } from './FaceDetection';
import { loadQuantizedParams } from './loadQuantizedParams';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
var FaceDetectionNet = /** @class */ (function () {
function FaceDetectionNet() {
}
FaceDetectionNet.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('FaceDetectionNet.load - expected model uri, or weights as Float32Array');
}
_a = this;
return [4 /*yield*/, loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
}
});
});
};
FaceDetectionNet.prototype.extractWeights = function (weights) {
this._params = extractParams(weights);
};
FaceDetectionNet.prototype.forwardTensor = function (imgTensor) {
var _this = this;
return tf.tidy(function () {
var resized = resizeLayer(imgTensor);
var features = mobileNetV1(resized, _this._params.mobilenetv1_params);
var _a = predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
});
};
FaceDetectionNet.prototype.forward = function (input) {
var _this = this;
return tf.tidy(function () { return _this.forwardTensor(padToSquare(getImageTensor(input))); });
};
FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0:
paddedHeightRelative = 1, paddedWidthRelative = 1;
_a = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
return _this.forwardTensor(imgTensor);
}), _boxes = _a.boxes, _scores = _a.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_c = (_b = Array).from;
return [4 /*yield*/, scores.data()];
case 1:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
}
});
});
};
return FaceDetectionNet;
}());
export { FaceDetectionNet };
//# sourceMappingURL=FaceDetectionNet.js.map
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dist/faceDetectionNet/FaceDetectionNet.js.map 0 → 100644
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dist/faceDetectionNet/boxPredictionLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { BoxPredictionParams } from './types';
export declare function boxPredictionLayer(x: tf.Tensor4D, params: FaceDetectionNet.BoxPredictionParams): { export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
boxPredictionEncoding: tf.Tensor<tf.Rank>; boxPredictionEncoding: tf.Tensor<tf.Rank>;
classPrediction: tf.Tensor<tf.Rank>; classPrediction: tf.Tensor<tf.Rank>;
}; };
dist/faceDetectionNet/boxPredictionLayer.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/extractParams.d.ts
import { FaceDetectionNet } from './types'; import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): FaceDetectionNet.NetParams; export declare function extractParams(weights: Float32Array): NetParams;
dist/faceDetectionNet/extractParams.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core'; import { FaceDetectionNet } from './FaceDetectionNet';
import { NetInput } from '../NetInput'; export * from './FaceDetectionNet';
import { FaceDetection } from './FaceDetection'; export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;
export declare function faceDetectionNet(weights: Float32Array): {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
locateFaces: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
};
dist/faceDetectionNet/index.js
import * as tslib_1 from "tslib"; import { FaceDetectionNet } from './FaceDetectionNet';
import * as tf from '@tensorflow/tfjs-core'; export * from './FaceDetectionNet';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { extractParams } from './extractParams';
import { FaceDetection } from './FaceDetection';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
import { Rect } from '../Rect';
export function faceDetectionNet(weights) { export function faceDetectionNet(weights) {
var params = extractParams(weights); var net = new FaceDetectionNet();
function forwardTensor(imgTensor) { net.extractWeights(weights);
return tf.tidy(function () { return net;
var resized = resizeLayer(imgTensor);
var features = mobileNetV1(resized, params.mobilenetv1_params);
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, params.output_layer_params);
});
}
function forward(input) {
return tf.tidy(function () { return forwardTensor(padToSquare(getImageTensor(input))); });
}
function locateFaces(input, minConfidence, maxResults) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0:
paddedHeightRelative = 1, paddedWidthRelative = 1;
_a = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
return forwardTensor(imgTensor);
}), _boxes = _a.boxes, _scores = _a.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_c = (_b = Array).from;
return [4 /*yield*/, scores.data()];
case 1:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
}
});
});
}
return {
forward: forward,
locateFaces: locateFaces
};
} }
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceDetectionNet/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/loadQuantizedParams.d.ts 0 → 100644
export declare function loadQuantizedParams(uri: string | undefined): Promise<any>;
dist/faceDetectionNet/loadQuantizedParams.js 0 → 100644
import * as tslib_1 from "tslib";
import { isTensor1D, isTensor4D, isTensor3D } from '../commons/isTensor';
import { loadWeightMap } from '../commons/loadWeightMap';
var DEFAULT_MODEL_NAME = 'face_detection_model';
function extractorsFactory(weightMap) {
function extractPointwiseConvParams(prefix, idx) {
var pointwise_conv_params = {
filters: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/weights"],
batch_norm_offset: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset"]
};
if (!isTensor4D(pointwise_conv_params.filters)) {
throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/weights] to be a Tensor4D, instead have " + pointwise_conv_params.filters);
}
if (!isTensor1D(pointwise_conv_params.batch_norm_offset)) {
throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset] to be a Tensor1D, instead have " + pointwise_conv_params.batch_norm_offset);
}
return pointwise_conv_params;
}
function extractConvPairParams(idx) {
var depthwise_conv_params = {
filters: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights"],
batch_norm_scale: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma"],
batch_norm_offset: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta"],
batch_norm_mean: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean"],
batch_norm_variance: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance"],
};
if (!isTensor4D(depthwise_conv_params.filters)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights] to be a Tensor4D, instead have " + depthwise_conv_params.filters);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_scale)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_scale);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_offset)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_offset);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_mean)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_mean);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_variance)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_variance);
}
return {
depthwise_conv_params: depthwise_conv_params,
pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
};
}
function extractMobilenetV1Params() {
return {
conv_0_params: extractPointwiseConvParams('MobilenetV1', 0),
conv_pair_params: Array(13).fill(0).map(function (_, i) { return extractConvPairParams(i + 1); })
};
}
function extractBoxPredictorParams(idx) {
var params = {
box_encoding_predictor_params: {
filters: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights"],
bias: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases"]
},
class_predictor_params: {
filters: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights"],
bias: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases"]
}
};
if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights] to be a Tensor4D, instead have " + params.box_encoding_predictor_params.filters);
}
if (!isTensor1D(params.box_encoding_predictor_params.bias)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases] to be a Tensor1D, instead have " + params.box_encoding_predictor_params.bias);
}
if (!isTensor4D(params.class_predictor_params.filters)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights] to be a Tensor4D, instead have " + params.class_predictor_params.filters);
}
if (!isTensor1D(params.class_predictor_params.bias)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases] to be a Tensor1D, instead have " + params.class_predictor_params.bias);
}
return params;
}
function extractPredictionLayerParams() {
return {
conv_0_params: extractPointwiseConvParams('Prediction', 0),
conv_1_params: extractPointwiseConvParams('Prediction', 1),
conv_2_params: extractPointwiseConvParams('Prediction', 2),
conv_3_params: extractPointwiseConvParams('Prediction', 3),
conv_4_params: extractPointwiseConvParams('Prediction', 4),
conv_5_params: extractPointwiseConvParams('Prediction', 5),
conv_6_params: extractPointwiseConvParams('Prediction', 6),
conv_7_params: extractPointwiseConvParams('Prediction', 7),
box_predictor_0_params: extractBoxPredictorParams(0),
box_predictor_1_params: extractBoxPredictorParams(1),
box_predictor_2_params: extractBoxPredictorParams(2),
box_predictor_3_params: extractBoxPredictorParams(3),
box_predictor_4_params: extractBoxPredictorParams(4),
box_predictor_5_params: extractBoxPredictorParams(5)
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim;
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), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim'];
if (!isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
}
return [2 /*return*/, {
mobilenetv1_params: extractMobilenetV1Params(),
prediction_layer_params: extractPredictionLayerParams(),
output_layer_params: {
extra_dim: extra_dim
}
}];
}
});
});
}
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
dist/faceDetectionNet/loadQuantizedParams.js.map 0 → 100644
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\ No newline at end of file
dist/faceDetectionNet/mobileNetV1.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { MobileNetV1 } from './types';
export declare function mobileNetV1(x: tf.Tensor4D, params: FaceDetectionNet.MobileNetV1.Params): { export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
out: tf.Tensor<tf.Rank.R4>; out: tf.Tensor<tf.Rank.R4>;
conv11: any; conv11: any;
}; };
dist/faceDetectionNet/mobileNetV1.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/outputLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { OutputLayerParams } from './types';
export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: FaceDetectionNet.OutputLayerParams): { export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
boxes: tf.Tensor<tf.Rank.R2>[]; boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[]; scores: tf.Tensor<tf.Rank.R1>[];
}; };
dist/faceDetectionNet/outputLayer.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/pointwiseConvLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { PointwiseConvParams } from './types';
export declare function pointwiseConvLayer(x: tf.Tensor4D, params: FaceDetectionNet.PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>; export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;
dist/faceDetectionNet/pointwiseConvLayer.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/predictionLayer.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { PredictionLayerParams } from './types';
export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: FaceDetectionNet.PredictionLayerParams): { export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
boxPredictions: tf.Tensor<tf.Rank.R4>; boxPredictions: tf.Tensor<tf.Rank.R4>;
classPredictions: tf.Tensor<tf.Rank.R4>; classPredictions: tf.Tensor<tf.Rank.R4>;
}; };
dist/faceDetectionNet/predictionLayer.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceDetectionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types'; import { ConvParams } from '../commons/types';
export declare namespace FaceDetectionNet { export declare type PointwiseConvParams = {
type PointwiseConvParams = {
filters: tf.Tensor4D; filters: tf.Tensor4D;
batch_norm_offset: tf.Tensor1D; batch_norm_offset: tf.Tensor1D;
}; };
namespace MobileNetV1 { export declare namespace MobileNetV1 {
type DepthwiseConvParams = { type DepthwiseConvParams = {
filters: tf.Tensor4D; filters: tf.Tensor4D;
batch_norm_scale: tf.Tensor1D; batch_norm_scale: tf.Tensor1D;
...@@ -21,12 +20,12 @@ export declare namespace FaceDetectionNet { ...@@ -21,12 +20,12 @@ export declare namespace FaceDetectionNet {
conv_0_params: PointwiseConvParams; conv_0_params: PointwiseConvParams;
conv_pair_params: ConvPairParams[]; conv_pair_params: ConvPairParams[];
}; };
} }
type BoxPredictionParams = { export declare type BoxPredictionParams = {
box_encoding_predictor_params: ConvParams; box_encoding_predictor_params: ConvParams;
class_predictor_params: ConvParams; class_predictor_params: ConvParams;
}; };
type PredictionLayerParams = { export declare type PredictionLayerParams = {
conv_0_params: PointwiseConvParams; conv_0_params: PointwiseConvParams;
conv_1_params: PointwiseConvParams; conv_1_params: PointwiseConvParams;
conv_2_params: PointwiseConvParams; conv_2_params: PointwiseConvParams;
...@@ -41,13 +40,12 @@ export declare namespace FaceDetectionNet { ...@@ -41,13 +40,12 @@ export declare namespace FaceDetectionNet {
box_predictor_3_params: BoxPredictionParams; box_predictor_3_params: BoxPredictionParams;
box_predictor_4_params: BoxPredictionParams; box_predictor_4_params: BoxPredictionParams;
box_predictor_5_params: BoxPredictionParams; box_predictor_5_params: BoxPredictionParams;
}; };
type OutputLayerParams = { export declare type OutputLayerParams = {
extra_dim: tf.Tensor3D; extra_dim: tf.Tensor3D;
}; };
type NetParams = { export declare type NetParams = {
mobilenetv1_params: MobileNetV1.Params; mobilenetv1_params: MobileNetV1.Params;
prediction_layer_params: PredictionLayerParams; prediction_layer_params: PredictionLayerParams;
output_layer_params: OutputLayerParams; output_layer_params: OutputLayerParams;
}; };
}
dist/faceLandmarkNet/index.js
import { FaceLandmarkNet } from './FaceLandmarkNet'; import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet'; export * from './FaceLandmarkNet';
export function faceLandmarkNet(weights) { export function faceLandmarkNet(weights) {
var faceLandmarkNet = new FaceLandmarkNet(); var net = new FaceLandmarkNet();
faceLandmarkNet.extractWeights(weights); net.extractWeights(weights);
return faceLandmarkNet; return net;
} }
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
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dist/faceLandmarkNet/index.js.map
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\ No newline at end of file \ No newline at end of file
dist/faceLandmarkNet/loadQuantizedParams.js
...@@ -2,8 +2,7 @@ import * as tslib_1 from "tslib"; ...@@ -2,8 +2,7 @@ import * as tslib_1 from "tslib";
import { loadWeightMap } from '../commons/loadWeightMap'; import { loadWeightMap } from '../commons/loadWeightMap';
import { isTensor4D, isTensor1D, isTensor2D } from '../commons/isTensor'; import { isTensor4D, isTensor1D, isTensor2D } from '../commons/isTensor';
var DEFAULT_MODEL_NAME = 'face_landmark_68_model'; var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
export function loadQuantizedParams(uri) { function extractorsFactory(weightMap) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
function extractConvParams(prefix) { function extractConvParams(prefix) {
var params = { var params = {
filters: weightMap[prefix + "/kernel"], filters: weightMap[prefix + "/kernel"],
...@@ -30,12 +29,20 @@ export function loadQuantizedParams(uri) { ...@@ -30,12 +29,20 @@ export function loadQuantizedParams(uri) {
} }
return params; return params;
} }
var weightMap; return {
return tslib_1.__generator(this, function (_a) { extractConvParams: extractConvParams,
switch (_a.label) { extractFcParams: extractFcParams
};
}
export function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractConvParams, extractFcParams;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)]; case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1: case 1:
weightMap = _a.sent(); weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
return [2 /*return*/, { return [2 /*return*/, {
conv0_params: extractConvParams('conv2d_0'), conv0_params: extractConvParams('conv2d_0'),
conv1_params: extractConvParams('conv2d_1'), conv1_params: extractConvParams('conv2d_1'),
......
dist/faceLandmarkNet/loadQuantizedParams.js.map
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\ No newline at end of file \ No newline at end of file
dist/index.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare'; import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare }; export { euclideanDistance, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces'; export * from './extractFaces';
export * from './extractFaceTensors'; export * from './extractFaceTensors';
export * from './faceDetectionNet';
export * from './faceLandmarkNet'; export * from './faceLandmarkNet';
export * from './utils'; export * from './utils';
dist/index.js
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare'; import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare }; export { euclideanDistance, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces'; export * from './extractFaces';
export * from './extractFaceTensors'; export * from './extractFaceTensors';
export * from './faceDetectionNet';
export * from './faceLandmarkNet'; export * from './faceLandmarkNet';
export * from './utils'; export * from './utils';
//# sourceMappingURL=index.js.map //# sourceMappingURL=index.js.map
\ No newline at end of file
dist/index.js.map
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\ No newline at end of file \ No newline at end of file
examples/public/commons.js
...@@ -12,24 +12,12 @@ async function fetchImage(uri) { ...@@ -12,24 +12,12 @@ async function fetchImage(uri) {
return (await axios.get(uri, { responseType: 'blob' })).data return (await axios.get(uri, { responseType: 'blob' })).data
} }
async function initFaceDetectionNet() {
const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
return faceapi.faceDetectionNet(weights)
}
async function initFaceRecognitionNet() { async function initFaceRecognitionNet() {
const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' }) const res = await axios.get('uncompressed/face_recognition_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data) const weights = new Float32Array(res.data)
return faceapi.faceRecognitionNet(weights) return faceapi.faceRecognitionNet(weights)
} }
async function initFaceLandmarkNet() {
const res = await axios.get('face_landmark_68_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
return faceapi.faceLandmarkNet(weights)
}
// fetch first image of each class and compute their descriptors // fetch first image of each class and compute their descriptors
async function initTrainDescriptorsByClass(net, numImagesForTraining = 1) { async function initTrainDescriptorsByClass(net, numImagesForTraining = 1) {
const maxAvailableImagesPerClass = 5 const maxAvailableImagesPerClass = 5
......
examples/views/detectAndDrawFaces.html
...@@ -80,7 +80,8 @@ ...@@ -80,7 +80,8 @@
} }
async function run() { async function run() {
net = await initFaceDetectionNet() net = new faceapi.FaceDetectionNet()
await net.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/detectAndDrawLandmarks.html
...@@ -89,8 +89,10 @@ ...@@ -89,8 +89,10 @@
} }
async function run() { async function run() {
detectionNet = await initFaceDetectionNet() detectionNet = new faceapi.FaceDetectionNet()
landmarkNet = await initFaceLandmarkNet() await detectionNet.load('/')
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/detectAndRecognizeFaces.html
...@@ -143,9 +143,11 @@ ...@@ -143,9 +143,11 @@
} }
async function run() { async function run() {
detectionNet = await initFaceDetectionNet() detectionNet = new faceapi.FaceDetectionNet()
await detectionNet.load('/')
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
recognitionNet = await initFaceRecognitionNet() recognitionNet = await initFaceRecognitionNet()
landmarkNet = await initFaceLandmarkNet()
trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet, 1) trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet, 1)
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
......
examples/views/faceAlignment.html
...@@ -93,8 +93,10 @@ ...@@ -93,8 +93,10 @@
} }
async function run() { async function run() {
detectionNet = await initFaceDetectionNet() detectionNet = new faceapi.FaceDetectionNet()
landmarkNet = await initFaceLandmarkNet() await detectionNet.load('/')
landmarkNet = new faceapi.FaceLandmarkNet()
await landmarkNet.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/faceDetection.html
...@@ -75,7 +75,8 @@ ...@@ -75,7 +75,8 @@
} }
async function run() { async function run() {
net = await initFaceDetectionNet() net = new faceapi.FaceDetectionNet()
await net.load('/')
$('#loader').hide() $('#loader').hide()
onSelectionChanged($('#selectList select').val()) onSelectionChanged($('#selectList select').val())
} }
......
examples/views/faceDetectionVideo.html
...@@ -87,7 +87,8 @@ ...@@ -87,7 +87,8 @@
} }
async function run() { async function run() {
net = await initFaceDetectionNet() net = new faceapi.FaceDetectionNet()
await net.load('/')
$('#loader').hide() $('#loader').hide()
} }
......
src/faceDetectionNet/FaceDetectionNet.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { Rect } from '../Rect';
import { Dimensions, TNetInput } from '../types';
import { extractParams } from './extractParams';
import { FaceDetection } from './FaceDetection';
import { loadQuantizedParams } from './loadQuantizedParams';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
import { NetParams } from './types';
export class FaceDetectionNet {
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('FaceDetectionNet.load - expected model uri, or weights as Float32Array')
}
this._params = await loadQuantizedParams(weightsOrUrl)
}
public extractWeights(weights: Float32Array) {
this._params = extractParams(weights)
}
private forwardTensor(imgTensor: tf.Tensor4D) {
return tf.tidy(() => {
const resized = resizeLayer(imgTensor) as tf.Tensor4D
const features = mobileNetV1(resized, this._params.mobilenetv1_params)
const {
boxPredictions,
classPredictions
} = predictionLayer(features.out, features.conv11, this._params.prediction_layer_params)
return outputLayer(boxPredictions, classPredictions, this._params.output_layer_params)
})
}
public forward(input: tf.Tensor | NetInput | TNetInput) {
return tf.tidy(
() => this.forwardTensor(padToSquare(getImageTensor(input)))
)
}
public async locateFaces(
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number = 0.8,
maxResults: number = 100,
): Promise<FaceDetection[]> {
let paddedHeightRelative = 1, paddedWidthRelative = 1
let imageDimensions: Dimensions | undefined
const {
boxes: _boxes,
scores: _scores
} = tf.tidy(() => {
let imgTensor = getImageTensor(input)
const [height, width] = imgTensor.shape.slice(1)
imageDimensions = { width, height }
imgTensor = padToSquare(imgTensor)
paddedHeightRelative = imgTensor.shape[1] / height
paddedWidthRelative = imgTensor.shape[2] / width
return this.forwardTensor(imgTensor)
})
// TODO batches
const boxes = _boxes[0]
const scores = _scores[0]
for (let i = 1; i < _boxes.length; i++) {
_boxes[i].dispose()
_scores[i].dispose()
}
// TODO find a better way to filter by minConfidence
const scoresData = Array.from(await scores.data())
const iouThreshold = 0.5
const indices = nonMaxSuppression(
boxes,
scoresData,
maxResults,
iouThreshold,
minConfidence
)
const results = indices
.map(idx => {
const [top, bottom] = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(val => val * paddedHeightRelative)
const [left, right] = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(val => val * paddedWidthRelative)
return new FaceDetection(
scoresData[idx],
new Rect(
left,
top,
right - left,
bottom - top
),
imageDimensions as Dimensions
)
})
boxes.dispose()
scores.dispose()
return results
}
}
\ No newline at end of file
src/faceDetectionNet/boxPredictionLayer.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer'; import { convLayer } from '../commons/convLayer';
import { FaceDetectionNet } from './types'; import { BoxPredictionParams } from './types';
export function boxPredictionLayer( export function boxPredictionLayer(
x: tf.Tensor4D, x: tf.Tensor4D,
params: FaceDetectionNet.BoxPredictionParams params: BoxPredictionParams
) { ) {
return tf.tidy(() => { return tf.tidy(() => {
......
src/faceDetectionNet/extractParams.ts
...@@ -2,11 +2,11 @@ import * as tf from '@tensorflow/tfjs-core'; ...@@ -2,11 +2,11 @@ import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory'; import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { ConvParams } from '../commons/types'; import { ConvParams } from '../commons/types';
import { FaceDetectionNet } from './types'; import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) { function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
function extractDepthwiseConvParams(numChannels: number): FaceDetectionNet.MobileNetV1.DepthwiseConvParams { function extractDepthwiseConvParams(numChannels: number): MobileNetV1.DepthwiseConvParams {
const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]) const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])
const batch_norm_scale = tf.tensor1d(extractWeights(numChannels)) const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))
const batch_norm_offset = tf.tensor1d(extractWeights(numChannels)) const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))
...@@ -43,7 +43,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) ...@@ -43,7 +43,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
channelsIn: number, channelsIn: number,
channelsOut: number, channelsOut: number,
filterSize: number filterSize: number
): FaceDetectionNet.PointwiseConvParams { ): PointwiseConvParams {
const { const {
filters, filters,
bias bias
...@@ -55,7 +55,10 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) ...@@ -55,7 +55,10 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
} }
} }
function extractConvPairParams(channelsIn: number, channelsOut: number): FaceDetectionNet.MobileNetV1.ConvPairParams { function extractConvPairParams(
channelsIn: number,
channelsOut: number
): MobileNetV1.ConvPairParams {
const depthwise_conv_params = extractDepthwiseConvParams(channelsIn) const depthwise_conv_params = extractDepthwiseConvParams(channelsIn)
const pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1) const pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1)
...@@ -65,7 +68,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) ...@@ -65,7 +68,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
} }
} }
function extractMobilenetV1Params(): FaceDetectionNet.MobileNetV1.Params { function extractMobilenetV1Params(): MobileNetV1.Params {
const conv_0_params = extractPointwiseConvParams(3, 32, 3) const conv_0_params = extractPointwiseConvParams(3, 32, 3)
...@@ -96,7 +99,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) ...@@ -96,7 +99,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
} }
function extractPredictionLayerParams(): FaceDetectionNet.PredictionLayerParams { function extractPredictionLayerParams(): PredictionLayerParams {
const conv_0_params = extractPointwiseConvParams(1024, 256, 1) const conv_0_params = extractPointwiseConvParams(1024, 256, 1)
const conv_1_params = extractPointwiseConvParams(256, 512, 3) const conv_1_params = extractPointwiseConvParams(256, 512, 3)
const conv_2_params = extractPointwiseConvParams(512, 128, 1) const conv_2_params = extractPointwiseConvParams(512, 128, 1)
...@@ -170,7 +173,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) ...@@ -170,7 +173,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
} }
export function extractParams(weights: Float32Array): FaceDetectionNet.NetParams { export function extractParams(weights: Float32Array): NetParams {
const { const {
extractWeights, extractWeights,
getRemainingWeights getRemainingWeights
......
src/faceDetectionNet/index.ts
import * as tf from '@tensorflow/tfjs-core'; import { FaceDetectionNet } from './FaceDetectionNet';
import { getImageTensor } from '../getImageTensor'; export * from './FaceDetectionNet';
import { NetInput } from '../NetInput';
import { padToSquare } from '../padToSquare';
import { TNetInput, Dimensions } from '../types';
import { extractParams } from './extractParams';
import { FaceDetection } from './FaceDetection';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
import { Rect } from '../Rect';
export function faceDetectionNet(weights: Float32Array) { export function faceDetectionNet(weights: Float32Array) {
const params = extractParams(weights) const net = new FaceDetectionNet()
net.extractWeights(weights)
function forwardTensor(imgTensor: tf.Tensor4D) { return net
return tf.tidy(() => {
const resized = resizeLayer(imgTensor) as tf.Tensor4D
const features = mobileNetV1(resized, params.mobilenetv1_params)
const {
boxPredictions,
classPredictions
} = predictionLayer(features.out, features.conv11, params.prediction_layer_params)
return outputLayer(boxPredictions, classPredictions, params.output_layer_params)
})
}
function forward(input: tf.Tensor | NetInput | TNetInput) {
return tf.tidy(
() => forwardTensor(padToSquare(getImageTensor(input)))
)
}
async function locateFaces(
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number = 0.8,
maxResults: number = 100,
): Promise<FaceDetection[]> {
let paddedHeightRelative = 1, paddedWidthRelative = 1
let imageDimensions: Dimensions | undefined
const {
boxes: _boxes,
scores: _scores
} = tf.tidy(() => {
let imgTensor = getImageTensor(input)
const [height, width] = imgTensor.shape.slice(1)
imageDimensions = { width, height }
imgTensor = padToSquare(imgTensor)
paddedHeightRelative = imgTensor.shape[1] / height
paddedWidthRelative = imgTensor.shape[2] / width
return forwardTensor(imgTensor)
})
// TODO batches
const boxes = _boxes[0]
const scores = _scores[0]
for (let i = 1; i < _boxes.length; i++) {
_boxes[i].dispose()
_scores[i].dispose()
}
// TODO find a better way to filter by minConfidence
const scoresData = Array.from(await scores.data())
const iouThreshold = 0.5
const indices = nonMaxSuppression(
boxes,
scoresData,
maxResults,
iouThreshold,
minConfidence
)
const results = indices
.map(idx => {
const [top, bottom] = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(val => val * paddedHeightRelative)
const [left, right] = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(val => val * paddedWidthRelative)
return new FaceDetection(
scoresData[idx],
new Rect(
left,
top,
right - left,
bottom - top
),
imageDimensions as Dimensions
)
})
boxes.dispose()
scores.dispose()
return results
}
return {
forward,
locateFaces
}
} }
\ No newline at end of file
src/faceDetectionNet/loadQuantizedParams.ts 0 → 100644
import { isTensor1D, isTensor4D, isTensor3D } from '../commons/isTensor';
import { loadWeightMap } from '../commons/loadWeightMap';
import { BoxPredictionParams, MobileNetV1, PointwiseConvParams, PredictionLayerParams } from './types';
const DEFAULT_MODEL_NAME = 'face_detection_model'
function extractorsFactory(weightMap: any) {
function extractPointwiseConvParams(prefix: string, idx: number): PointwiseConvParams {
const pointwise_conv_params = {
filters: weightMap[`${prefix}/Conv2d_${idx}_pointwise/weights`],
batch_norm_offset: weightMap[`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`]
}
if (!isTensor4D(pointwise_conv_params.filters)) {
throw new Error(`expected weightMap[${prefix}/Conv2d_${idx}_pointwise/weights] to be a Tensor4D, instead have ${pointwise_conv_params.filters}`)
}
if (!isTensor1D(pointwise_conv_params.batch_norm_offset)) {
throw new Error(`expected weightMap[${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset] to be a Tensor1D, instead have ${pointwise_conv_params.batch_norm_offset}`)
}
return pointwise_conv_params
}
function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {
const depthwise_conv_params = {
filters: weightMap[`MobilenetV1/Conv2d_${idx}_depthwise/depthwise_weights`],
batch_norm_scale: weightMap[`MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/gamma`],
batch_norm_offset: weightMap[`MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/beta`],
batch_norm_mean: weightMap[`MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/moving_mean`],
batch_norm_variance: weightMap[`MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/moving_variance`],
}
if (!isTensor4D(depthwise_conv_params.filters)) {
throw new Error(`expected weightMap[MobilenetV1/Conv2d_${idx}_depthwise/depthwise_weights] to be a Tensor4D, instead have ${depthwise_conv_params.filters}`)
}
if (!isTensor1D(depthwise_conv_params.batch_norm_scale)) {
throw new Error(`expected weightMap[MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/gamma] to be a Tensor1D, instead have ${depthwise_conv_params.batch_norm_scale}`)
}
if (!isTensor1D(depthwise_conv_params.batch_norm_offset)) {
throw new Error(`expected weightMap[MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/beta] to be a Tensor1D, instead have ${depthwise_conv_params.batch_norm_offset}`)
}
if (!isTensor1D(depthwise_conv_params.batch_norm_mean)) {
throw new Error(`expected weightMap[MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/moving_mean] to be a Tensor1D, instead have ${depthwise_conv_params.batch_norm_mean}`)
}
if (!isTensor1D(depthwise_conv_params.batch_norm_variance)) {
throw new Error(`expected weightMap[MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/moving_variance] to be a Tensor1D, instead have ${depthwise_conv_params.batch_norm_variance}`)
}
return {
depthwise_conv_params,
pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
}
}
function extractMobilenetV1Params(): MobileNetV1.Params {
return {
conv_0_params: extractPointwiseConvParams('MobilenetV1', 0),
conv_pair_params: Array(13).fill(0).map((_, i) => extractConvPairParams(i + 1))
}
}
function extractBoxPredictorParams(idx: number): BoxPredictionParams {
const params = {
box_encoding_predictor_params: {
filters: weightMap[`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/weights`],
bias: weightMap[`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/biases`]
},
class_predictor_params: {
filters: weightMap[`Prediction/BoxPredictor_${idx}/ClassPredictor/weights`],
bias: weightMap[`Prediction/BoxPredictor_${idx}/ClassPredictor/biases`]
}
}
if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/weights] to be a Tensor4D, instead have ${params.box_encoding_predictor_params.filters}`)
}
if (!isTensor1D(params.box_encoding_predictor_params.bias)) {
throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/biases] to be a Tensor1D, instead have ${params.box_encoding_predictor_params.bias}`)
}
if (!isTensor4D(params.class_predictor_params.filters)) {
throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/ClassPredictor/weights] to be a Tensor4D, instead have ${params.class_predictor_params.filters}`)
}
if (!isTensor1D(params.class_predictor_params.bias)) {
throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/ClassPredictor/biases] to be a Tensor1D, instead have ${params.class_predictor_params.bias}`)
}
return params
}
function extractPredictionLayerParams(): PredictionLayerParams {
return {
conv_0_params: extractPointwiseConvParams('Prediction', 0),
conv_1_params: extractPointwiseConvParams('Prediction', 1),
conv_2_params: extractPointwiseConvParams('Prediction', 2),
conv_3_params: extractPointwiseConvParams('Prediction', 3),
conv_4_params: extractPointwiseConvParams('Prediction', 4),
conv_5_params: extractPointwiseConvParams('Prediction', 5),
conv_6_params: extractPointwiseConvParams('Prediction', 6),
conv_7_params: extractPointwiseConvParams('Prediction', 7),
box_predictor_0_params: extractBoxPredictorParams(0),
box_predictor_1_params: extractBoxPredictorParams(1),
box_predictor_2_params: extractBoxPredictorParams(2),
box_predictor_3_params: extractBoxPredictorParams(3),
box_predictor_4_params: extractBoxPredictorParams(4),
box_predictor_5_params: extractBoxPredictorParams(5)
}
}
return {
extractMobilenetV1Params,
extractPredictionLayerParams
}
}
export async function loadQuantizedParams(uri: string | undefined): Promise<any> {//Promise<NetParams> {
const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
const {
extractMobilenetV1Params,
extractPredictionLayerParams
} = extractorsFactory(weightMap)
const extra_dim = weightMap['Output/extra_dim']
if (!isTensor3D(extra_dim)) {
throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)
}
return {
mobilenetv1_params: extractMobilenetV1Params(),
prediction_layer_params: extractPredictionLayerParams(),
output_layer_params: {
extra_dim
}
}
}
\ No newline at end of file
src/faceDetectionNet/mobileNetV1.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { pointwiseConvLayer } from './pointwiseConvLayer'; import { pointwiseConvLayer } from './pointwiseConvLayer';
import { FaceDetectionNet } from './types'; import { MobileNetV1 } from './types';
const epsilon = 0.0010000000474974513 const epsilon = 0.0010000000474974513
function depthwiseConvLayer( function depthwiseConvLayer(
x: tf.Tensor4D, x: tf.Tensor4D,
params: FaceDetectionNet.MobileNetV1.DepthwiseConvParams, params: MobileNetV1.DepthwiseConvParams,
strides: [number, number] strides: [number, number]
) { ) {
return tf.tidy(() => { return tf.tidy(() => {
...@@ -30,7 +30,7 @@ function getStridesForLayerIdx(layerIdx: number): [number, number] { ...@@ -30,7 +30,7 @@ function getStridesForLayerIdx(layerIdx: number): [number, number] {
return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1] return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]
} }
export function mobileNetV1(x: tf.Tensor4D, params: FaceDetectionNet.MobileNetV1.Params) { export function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {
return tf.tidy(() => { return tf.tidy(() => {
let conv11 = null let conv11 = null
......
src/faceDetectionNet/outputLayer.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { OutputLayerParams } from './types';
function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) { function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
const vec = tf.unstack(tf.transpose(x, [1, 0])) const vec = tf.unstack(tf.transpose(x, [1, 0]))
...@@ -49,7 +50,7 @@ function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) { ...@@ -49,7 +50,7 @@ function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
export function outputLayer( export function outputLayer(
boxPredictions: tf.Tensor4D, boxPredictions: tf.Tensor4D,
classPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D,
params: FaceDetectionNet.OutputLayerParams params: OutputLayerParams
) { ) {
return tf.tidy(() => { return tf.tidy(() => {
......
src/faceDetectionNet/pointwiseConvLayer.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionNet } from './types'; import { PointwiseConvParams } from './types';
export function pointwiseConvLayer( export function pointwiseConvLayer(
x: tf.Tensor4D, x: tf.Tensor4D,
params: FaceDetectionNet.PointwiseConvParams, params: PointwiseConvParams,
strides: [number, number] strides: [number, number]
) { ) {
return tf.tidy(() => { return tf.tidy(() => {
......
src/faceDetectionNet/predictionLayer.ts
...@@ -2,12 +2,12 @@ import * as tf from '@tensorflow/tfjs-core'; ...@@ -2,12 +2,12 @@ import * as tf from '@tensorflow/tfjs-core';
import { boxPredictionLayer } from './boxPredictionLayer'; import { boxPredictionLayer } from './boxPredictionLayer';
import { pointwiseConvLayer } from './pointwiseConvLayer'; import { pointwiseConvLayer } from './pointwiseConvLayer';
import { FaceDetectionNet } from './types'; import { PredictionLayerParams } from './types';
export function predictionLayer( export function predictionLayer(
x: tf.Tensor4D, x: tf.Tensor4D,
conv11: tf.Tensor4D, conv11: tf.Tensor4D,
params: FaceDetectionNet.PredictionLayerParams params: PredictionLayerParams
) { ) {
return tf.tidy(() => { return tf.tidy(() => {
......
src/faceDetectionNet/types.ts
...@@ -2,14 +2,12 @@ import * as tf from '@tensorflow/tfjs-core'; ...@@ -2,14 +2,12 @@ import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types'; import { ConvParams } from '../commons/types';
export namespace FaceDetectionNet { export type PointwiseConvParams = {
export type PointwiseConvParams = {
filters: tf.Tensor4D filters: tf.Tensor4D
batch_norm_offset: tf.Tensor1D batch_norm_offset: tf.Tensor1D
} }
export namespace MobileNetV1 { export namespace MobileNetV1 {
export type DepthwiseConvParams = { export type DepthwiseConvParams = {
filters: tf.Tensor4D filters: tf.Tensor4D
...@@ -29,14 +27,14 @@ export namespace FaceDetectionNet { ...@@ -29,14 +27,14 @@ export namespace FaceDetectionNet {
conv_pair_params: ConvPairParams[] conv_pair_params: ConvPairParams[]
} }
} }
export type BoxPredictionParams = { export type BoxPredictionParams = {
box_encoding_predictor_params: ConvParams box_encoding_predictor_params: ConvParams
class_predictor_params: ConvParams class_predictor_params: ConvParams
} }
export type PredictionLayerParams = { export type PredictionLayerParams = {
conv_0_params: PointwiseConvParams conv_0_params: PointwiseConvParams
conv_1_params: PointwiseConvParams conv_1_params: PointwiseConvParams
conv_2_params: PointwiseConvParams conv_2_params: PointwiseConvParams
...@@ -51,15 +49,14 @@ export namespace FaceDetectionNet { ...@@ -51,15 +49,14 @@ export namespace FaceDetectionNet {
box_predictor_3_params: BoxPredictionParams box_predictor_3_params: BoxPredictionParams
box_predictor_4_params: BoxPredictionParams box_predictor_4_params: BoxPredictionParams
box_predictor_5_params: BoxPredictionParams box_predictor_5_params: BoxPredictionParams
} }
export type OutputLayerParams = { export type OutputLayerParams = {
extra_dim: tf.Tensor3D extra_dim: tf.Tensor3D
} }
export type NetParams = { export type NetParams = {
mobilenetv1_params: MobileNetV1.Params, mobilenetv1_params: MobileNetV1.Params,
prediction_layer_params: PredictionLayerParams, prediction_layer_params: PredictionLayerParams,
output_layer_params: OutputLayerParams output_layer_params: OutputLayerParams
}
} }
src/faceLandmarkNet/index.ts
...@@ -3,7 +3,7 @@ import { FaceLandmarkNet } from './FaceLandmarkNet'; ...@@ -3,7 +3,7 @@ import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet'; export * from './FaceLandmarkNet';
export function faceLandmarkNet(weights: Float32Array) { export function faceLandmarkNet(weights: Float32Array) {
const faceLandmarkNet = new FaceLandmarkNet() const net = new FaceLandmarkNet()
faceLandmarkNet.extractWeights(weights) net.extractWeights(weights)
return faceLandmarkNet return net
} }
\ No newline at end of file
src/faceLandmarkNet/loadQuantizedParams.ts
...@@ -7,8 +7,7 @@ import { isTensor4D, isTensor1D, isTensor2D } from '../commons/isTensor'; ...@@ -7,8 +7,7 @@ import { isTensor4D, isTensor1D, isTensor2D } from '../commons/isTensor';
const DEFAULT_MODEL_NAME = 'face_landmark_68_model' const DEFAULT_MODEL_NAME = 'face_landmark_68_model'
export async function loadQuantizedParams(uri: string | undefined): Promise<NetParams> { function extractorsFactory(weightMap: any) {
const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
function extractConvParams(prefix: string): ConvParams { function extractConvParams(prefix: string): ConvParams {
const params = { const params = {
...@@ -45,6 +44,20 @@ export async function loadQuantizedParams(uri: string | undefined): Promise<NetP ...@@ -45,6 +44,20 @@ export async function loadQuantizedParams(uri: string | undefined): Promise<NetP
} }
return { return {
extractConvParams,
extractFcParams
}
}
export async function loadQuantizedParams(uri: string | undefined): Promise<NetParams> {
const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
const {
extractConvParams,
extractFcParams
} = extractorsFactory(weightMap)
return {
conv0_params: extractConvParams('conv2d_0'), conv0_params: extractConvParams('conv2d_0'),
conv1_params: extractConvParams('conv2d_1'), conv1_params: extractConvParams('conv2d_1'),
conv2_params: extractConvParams('conv2d_2'), conv2_params: extractConvParams('conv2d_2'),
......
src/index.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance'; import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet'; import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput'; import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare'; import { padToSquare } from './padToSquare';
export { export {
euclideanDistance, euclideanDistance,
faceDetectionNet,
faceRecognitionNet, faceRecognitionNet,
NetInput, NetInput,
tf, tf,
...@@ -17,5 +15,6 @@ export { ...@@ -17,5 +15,6 @@ export {
export * from './extractFaces' export * from './extractFaces'
export * from './extractFaceTensors' export * from './extractFaceTensors'
export * from './faceDetectionNet';
export * from './faceLandmarkNet'; export * from './faceLandmarkNet';
export * from './utils' export * from './utils'
\ No newline at end of file
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