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Commit 5e0a71fc by vincent
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check in latest build

parent 96b41df9
Showing with 1177 additions and 1083 deletions
build/Rect.d.ts
...@@ -11,5 +11,6 @@ export declare class Rect implements IRect { ...@@ -11,5 +11,6 @@ export declare class Rect implements IRect {
height: number; height: number;
constructor(x: number, y: number, width: number, height: number); constructor(x: number, y: number, width: number, height: number);
toSquare(): Rect; toSquare(): Rect;
pad(padX: number, padY: number): Rect;
floor(): Rect; floor(): Rect;
} }
build/Rect.js
...@@ -20,6 +20,10 @@ var Rect = /** @class */ (function () { ...@@ -20,6 +20,10 @@ var Rect = /** @class */ (function () {
} }
return new Rect(x, y, width, height); return new Rect(x, y, width, height);
}; };
Rect.prototype.pad = function (padX, padY) {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
};
Rect.prototype.floor = function () { Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height)); return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
}; };
......
build/Rect.js.map
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\ No newline at end of file \ No newline at end of file
build/commons/NeuralNetwork.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from './types'; import { ParamMapping } from './types';
export declare class NeuralNetwork<TNetParams> { export declare class NeuralNetwork<TNetParams> {
private _name;
protected _params: TNetParams | undefined; protected _params: TNetParams | undefined;
protected _paramMappings: ParamMapping[]; protected _paramMappings: ParamMapping[];
constructor(_name: string);
readonly params: TNetParams | undefined; readonly params: TNetParams | undefined;
readonly paramMappings: ParamMapping[]; readonly paramMappings: ParamMapping[];
getParamFromPath(paramPath: string): tf.Tensor; getParamFromPath(paramPath: string): tf.Tensor;
...@@ -21,6 +23,16 @@ export declare class NeuralNetwork<TNetParams> { ...@@ -21,6 +23,16 @@ export declare class NeuralNetwork<TNetParams> {
}[]; }[];
variable(): void; variable(): void;
freeze(): void; freeze(): void;
dispose(): void; dispose(throwOnRedispose?: boolean): void;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void;
private traversePropertyPath(paramPath); private traversePropertyPath(paramPath);
protected loadQuantizedParams(_: any): Promise<{
params: TNetParams;
paramMappings: ParamMapping[];
}>;
protected extractParams(_: any): {
params: TNetParams;
paramMappings: ParamMapping[];
};
} }
build/commons/NeuralNetwork.js
"use strict"; "use strict";
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork = /** @class */ (function () { var NeuralNetwork = /** @class */ (function () {
function NeuralNetwork() { function NeuralNetwork(_name) {
this._name = _name;
this._params = undefined; this._params = undefined;
this._paramMappings = []; this._paramMappings = [];
} }
...@@ -59,10 +61,44 @@ var NeuralNetwork = /** @class */ (function () { ...@@ -59,10 +61,44 @@ var NeuralNetwork = /** @class */ (function () {
_this.reassignParamFromPath(path, tf.tensor(tensor)); _this.reassignParamFromPath(path, tf.tensor(tensor));
}); });
}; };
NeuralNetwork.prototype.dispose = function () { NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
this.getParamList().forEach(function (param) { return param.tensor.dispose(); }); if (throwOnRedispose === void 0) { throwOnRedispose = true; }
this.getParamList().forEach(function (param) {
if (throwOnRedispose && param.tensor.isDisposed) {
throw new Error("param tensor has already been disposed for path " + param.path);
}
param.tensor.dispose();
});
this._params = undefined; this._params = undefined;
}; };
NeuralNetwork.prototype.load = function (weightsOrUrl) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, paramMappings, params;
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(this._name + ".load - expected model uri, or weights as Float32Array");
}
return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
case 1:
_a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
return [2 /*return*/];
}
});
});
};
NeuralNetwork.prototype.extractWeights = function (weights) {
var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
};
NeuralNetwork.prototype.traversePropertyPath = function (paramPath) { NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
if (!this.params) { if (!this.params) {
throw new Error("traversePropertyPath - model has no loaded params"); throw new Error("traversePropertyPath - model has no loaded params");
...@@ -79,6 +115,12 @@ var NeuralNetwork = /** @class */ (function () { ...@@ -79,6 +115,12 @@ var NeuralNetwork = /** @class */ (function () {
} }
return { obj: obj, objProp: objProp }; return { obj: obj, objProp: objProp };
}; };
NeuralNetwork.prototype.loadQuantizedParams = function (_) {
throw new Error(this._name + ".loadQuantizedParams - not implemented");
};
NeuralNetwork.prototype.extractParams = function (_) {
throw new Error(this._name + ".extractParams - not implemented");
};
return NeuralNetwork; return NeuralNetwork;
}()); }());
exports.NeuralNetwork = NeuralNetwork; exports.NeuralNetwork = NeuralNetwork;
......
build/commons/NeuralNetwork.js.map
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\ No newline at end of file \ No newline at end of file
build/commons/disposeUnusedWeightTensors.d.ts 0 → 100644
import { ParamMapping } from './types';
export declare function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]): void;
build/commons/disposeUnusedWeightTensors.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function disposeUnusedWeightTensors(weightMap, paramMappings) {
Object.keys(weightMap).forEach(function (path) {
if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
weightMap[path].dispose();
}
});
}
exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
//# sourceMappingURL=disposeUnusedWeightTensors.js.map
\ No newline at end of file
build/commons/disposeUnusedWeightTensors.js.map 0 → 100644
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\ No newline at end of file
build/commons/extractWeightEntryFactory.d.ts 0 → 100644
import { ParamMapping } from './types';
export declare function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]): <T>(originalPath: string, paramRank: number, mappedPath?: string | undefined) => T;
build/commons/extractWeightEntryFactory.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var isTensor_1 = require("./isTensor");
function extractWeightEntryFactory(weightMap, paramMappings) {
return function (originalPath, paramRank, mappedPath) {
var tensor = weightMap[originalPath];
if (!isTensor_1.isTensor(tensor, paramRank)) {
throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
}
paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
return tensor;
};
}
exports.extractWeightEntryFactory = extractWeightEntryFactory;
//# sourceMappingURL=extractWeightEntryFactory.js.map
\ No newline at end of file
build/commons/extractWeightEntryFactory.js.map 0 → 100644
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\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { NetInput } from '../NetInput'; import { NetInput } from '../NetInput';
import { TNetInput } from '../types'; import { TNetInput } from '../types';
import { FaceDetection } from './FaceDetection'; import { FaceDetection } from './FaceDetection';
export declare class FaceDetectionNet { import { NetParams } from './types';
private _params; export declare class FaceDetectionNet extends NeuralNetwork<NetParams> {
load(weightsOrUrl?: Float32Array | string): Promise<void>; constructor();
extractWeights(weights: Float32Array): void;
forwardInput(input: NetInput): { forwardInput(input: NetInput): {
boxes: tf.Tensor<tf.Rank.R2>[]; boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[]; scores: tf.Tensor<tf.Rank.R1>[];
...@@ -15,4 +15,18 @@ export declare class FaceDetectionNet { ...@@ -15,4 +15,18 @@ export declare class FaceDetectionNet {
scores: tf.Tensor<tf.Rank.R1>[]; scores: tf.Tensor<tf.Rank.R1>[];
}>; }>;
locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>; locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
} }
build/faceDetectionNet/FaceDetectionNet.js
...@@ -2,6 +2,7 @@ ...@@ -2,6 +2,7 @@
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var Rect_1 = require("../Rect"); var Rect_1 = require("../Rect");
var toNetInput_1 = require("../toNetInput"); var toNetInput_1 = require("../toNetInput");
var extractParams_1 = require("./extractParams"); var extractParams_1 = require("./extractParams");
...@@ -11,45 +12,22 @@ var mobileNetV1_1 = require("./mobileNetV1"); ...@@ -11,45 +12,22 @@ var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression"); var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer"); var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer"); var predictionLayer_1 = require("./predictionLayer");
var FaceDetectionNet = /** @class */ (function () { var FaceDetectionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetectionNet, _super);
function FaceDetectionNet() { function FaceDetectionNet() {
return _super.call(this, 'FaceDetectionNet') || this;
} }
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_1.loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
}
});
});
};
FaceDetectionNet.prototype.extractWeights = function (weights) {
this._params = extractParams_1.extractParams(weights);
};
FaceDetectionNet.prototype.forwardInput = function (input) { FaceDetectionNet.prototype.forwardInput = function (input) {
var _this = this; var params = this.params;
if (!this._params) { if (!params) {
throw new Error('FaceDetectionNet - load model before inference'); throw new Error('FaceDetectionNet - load model before inference');
} }
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false); var batchTensor = input.toBatchTensor(512, false);
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)); var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1_1.mobileNetV1(x, _this._params.mobilenetv1_params); var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions; var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer_1.outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params); return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
}); });
}; };
FaceDetectionNet.prototype.forward = function (input) { FaceDetectionNet.prototype.forward = function (input) {
...@@ -112,7 +90,13 @@ var FaceDetectionNet = /** @class */ (function () { ...@@ -112,7 +90,13 @@ var FaceDetectionNet = /** @class */ (function () {
}); });
}); });
}; };
FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceDetectionNet.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceDetectionNet; return FaceDetectionNet;
}()); }(NeuralNetwork_1.NeuralNetwork));
exports.FaceDetectionNet = FaceDetectionNet; exports.FaceDetectionNet = FaceDetectionNet;
//# sourceMappingURL=FaceDetectionNet.js.map //# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/boxPredictionLayer.js
...@@ -5,8 +5,8 @@ var convLayer_1 = require("../commons/convLayer"); ...@@ -5,8 +5,8 @@ var convLayer_1 = require("../commons/convLayer");
function boxPredictionLayer(x, params) { function boxPredictionLayer(x, params) {
return tf.tidy(function () { return tf.tidy(function () {
var batchSize = x.shape[0]; var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convLayer_1.convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]); var boxPredictionEncoding = tf.reshape(convLayer_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer_1.convLayer(x, params.class_predictor_params), [batchSize, -1, 3]); var classPrediction = tf.reshape(convLayer_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return { return {
boxPredictionEncoding: boxPredictionEncoding, boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction classPrediction: classPrediction
......
build/faceDetectionNet/boxPredictionLayer.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/extractParams.d.ts
import { ParamMapping } from '../commons/types';
import { NetParams } from './types'; import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): NetParams; export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/faceDetectionNet/extractParams.js
...@@ -2,13 +2,14 @@ ...@@ -2,13 +2,14 @@
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory"); var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractorsFactory(extractWeights) { function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels) { function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
var batch_norm_scale = tf.tensor1d(extractWeights(numChannels)); var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
var batch_norm_offset = tf.tensor1d(extractWeights(numChannels)); var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
var batch_norm_mean = tf.tensor1d(extractWeights(numChannels)); var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
var batch_norm_variance = tf.tensor1d(extractWeights(numChannels)); var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
return { return {
filters: filters, filters: filters,
batch_norm_scale: batch_norm_scale, batch_norm_scale: batch_norm_scale,
...@@ -17,115 +18,116 @@ function extractorsFactory(extractWeights) { ...@@ -17,115 +18,116 @@ function extractorsFactory(extractWeights) {
batch_norm_variance: batch_norm_variance batch_norm_variance: batch_norm_variance
}; };
} }
function extractConvParams(channelsIn, channelsOut, filterSize) { function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]); var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut)); var bias = tf.tensor1d(extractWeights(channelsOut));
return { paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
filters: filters, return { filters: filters, bias: bias };
bias: bias
};
} }
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) { function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias; var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
return { return {
filters: filters, filters: filters,
batch_norm_offset: bias batch_norm_offset: bias
}; };
} }
function extractConvPairParams(channelsIn, channelsOut) { function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_conv_params = extractDepthwiseConvParams(channelsIn); var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
var pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1); var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
return { return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
depthwise_conv_params: depthwise_conv_params,
pointwise_conv_params: pointwise_conv_params
};
} }
function extractMobilenetV1Params() { function extractMobilenetV1Params() {
var conv_0_params = extractPointwiseConvParams(3, 32, 3); var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
var channelNumPairs = [ var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
[32, 64], var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
[64, 128], var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
[128, 128], var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
[128, 256], var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
[256, 256], var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
[256, 512], var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
[512, 512], var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
[512, 512], var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
[512, 512], var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
[512, 512], var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
[512, 512], var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
[512, 1024], var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
[1024, 1024]
];
var conv_pair_params = channelNumPairs.map(function (_a) {
var channelsIn = _a[0], channelsOut = _a[1];
return extractConvPairParams(channelsIn, channelsOut);
});
return { return {
conv_0_params: conv_0_params, conv_0: conv_0,
conv_pair_params: conv_pair_params conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
conv_8: conv_8,
conv_9: conv_9,
conv_10: conv_10,
conv_11: conv_11,
conv_12: conv_12,
conv_13: conv_13
}; };
} }
function extractPredictionLayerParams() { function extractPredictionLayerParams() {
var conv_0_params = extractPointwiseConvParams(1024, 256, 1); var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
var conv_1_params = extractPointwiseConvParams(256, 512, 3); var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
var conv_2_params = extractPointwiseConvParams(512, 128, 1); var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
var conv_3_params = extractPointwiseConvParams(128, 256, 3); var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
var conv_4_params = extractPointwiseConvParams(256, 128, 1); var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
var conv_5_params = extractPointwiseConvParams(128, 256, 3); var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
var conv_6_params = extractPointwiseConvParams(256, 64, 1); var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
var conv_7_params = extractPointwiseConvParams(64, 128, 3); var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
var box_encoding_0_predictor_params = extractConvParams(512, 12, 1); var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
var class_predictor_0_params = extractConvParams(512, 9, 1); var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1); var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
var class_predictor_1_params = extractConvParams(1024, 18, 1); var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
var box_encoding_2_predictor_params = extractConvParams(512, 24, 1); var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
var class_predictor_2_params = extractConvParams(512, 18, 1); var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
var box_encoding_3_predictor_params = extractConvParams(256, 24, 1); var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
var class_predictor_3_params = extractConvParams(256, 18, 1); var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
var box_encoding_4_predictor_params = extractConvParams(256, 24, 1); var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
var class_predictor_4_params = extractConvParams(256, 18, 1); var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
var box_encoding_5_predictor_params = extractConvParams(128, 24, 1); var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
var class_predictor_5_params = extractConvParams(128, 18, 1); var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
var box_predictor_0_params = { var box_predictor_0 = {
box_encoding_predictor_params: box_encoding_0_predictor_params, box_encoding_predictor: box_encoding_0_predictor,
class_predictor_params: class_predictor_0_params class_predictor: class_predictor_0
}; };
var box_predictor_1_params = { var box_predictor_1 = {
box_encoding_predictor_params: box_encoding_1_predictor_params, box_encoding_predictor: box_encoding_1_predictor,
class_predictor_params: class_predictor_1_params class_predictor: class_predictor_1
}; };
var box_predictor_2_params = { var box_predictor_2 = {
box_encoding_predictor_params: box_encoding_2_predictor_params, box_encoding_predictor: box_encoding_2_predictor,
class_predictor_params: class_predictor_2_params class_predictor: class_predictor_2
}; };
var box_predictor_3_params = { var box_predictor_3 = {
box_encoding_predictor_params: box_encoding_3_predictor_params, box_encoding_predictor: box_encoding_3_predictor,
class_predictor_params: class_predictor_3_params class_predictor: class_predictor_3
}; };
var box_predictor_4_params = { var box_predictor_4 = {
box_encoding_predictor_params: box_encoding_4_predictor_params, box_encoding_predictor: box_encoding_4_predictor,
class_predictor_params: class_predictor_4_params class_predictor: class_predictor_4
}; };
var box_predictor_5_params = { var box_predictor_5 = {
box_encoding_predictor_params: box_encoding_5_predictor_params, box_encoding_predictor: box_encoding_5_predictor,
class_predictor_params: class_predictor_5_params class_predictor: class_predictor_5
}; };
return { return {
conv_0_params: conv_0_params, conv_0: conv_0,
conv_1_params: conv_1_params, conv_1: conv_1,
conv_2_params: conv_2_params, conv_2: conv_2,
conv_3_params: conv_3_params, conv_3: conv_3,
conv_4_params: conv_4_params, conv_4: conv_4,
conv_5_params: conv_5_params, conv_5: conv_5,
conv_6_params: conv_6_params, conv_6: conv_6,
conv_7_params: conv_7_params, conv_7: conv_7,
box_predictor_0_params: box_predictor_0_params, box_predictor_0: box_predictor_0,
box_predictor_1_params: box_predictor_1_params, box_predictor_1: box_predictor_1,
box_predictor_2_params: box_predictor_2_params, box_predictor_2: box_predictor_2,
box_predictor_3_params: box_predictor_3_params, box_predictor_3: box_predictor_3,
box_predictor_4_params: box_predictor_4_params, box_predictor_4: box_predictor_4,
box_predictor_5_params: box_predictor_5_params box_predictor_5: box_predictor_5
}; };
} }
return { return {
...@@ -134,21 +136,26 @@ function extractorsFactory(extractWeights) { ...@@ -134,21 +136,26 @@ function extractorsFactory(extractWeights) {
}; };
} }
function extractParams(weights) { function extractParams(weights) {
var paramMappings = [];
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams; var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1_params = extractMobilenetV1Params(); var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer_params = extractPredictionLayerParams(); var prediction_layer = extractPredictionLayerParams();
var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]); var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer_params = { var output_layer = {
extra_dim: extra_dim extra_dim: extra_dim
}; };
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
return { return {
mobilenetv1_params: mobilenetv1_params, params: {
prediction_layer_params: prediction_layer_params, mobilenetv1: mobilenetv1,
output_layer_params: output_layer_params prediction_layer: prediction_layer,
output_layer: output_layer
},
paramMappings: paramMappings
}; };
} }
exports.extractParams = extractParams; exports.extractParams = extractParams;
......
build/faceDetectionNet/extractParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/loadQuantizedParams.d.ts
export declare function loadQuantizedParams(uri: string | undefined): Promise<any>; import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/faceDetectionNet/loadQuantizedParams.js
"use strict"; "use strict";
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var isTensor_1 = require("../commons/isTensor"); var isTensor_1 = require("../commons/isTensor");
var loadWeightMap_1 = require("../commons/loadWeightMap"); var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_detection_model'; var DEFAULT_MODEL_NAME = 'face_detection_model';
function extractorsFactory(weightMap) { function extractorsFactory(weightMap, paramMappings) {
function extractPointwiseConvParams(prefix, idx) { var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
var pointwise_conv_params = { function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
filters: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/weights"], var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
batch_norm_offset: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset"] var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
}; return { filters: filters, batch_norm_offset: batch_norm_offset };
if (!isTensor_1.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 (!isTensor_1.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) { function extractConvPairParams(idx) {
var depthwise_conv_params = { var mappedPrefix = "mobilenetv1/conv_" + idx;
filters: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights"], var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
batch_norm_scale: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma"], var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
batch_norm_offset: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta"], var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
batch_norm_mean: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean"], var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
batch_norm_variance: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance"], var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
}; var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
if (!isTensor_1.isTensor4D(depthwise_conv_params.filters)) { var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights] to be a Tensor4D, instead have " + depthwise_conv_params.filters); var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
}
if (!isTensor_1.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 (!isTensor_1.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 (!isTensor_1.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 (!isTensor_1.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 { return {
depthwise_conv_params: depthwise_conv_params, depthwise_conv: {
pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx) filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
},
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
}; };
} }
function extractMobilenetV1Params() { function extractMobilenetV1Params() {
return { return {
conv_0_params: extractPointwiseConvParams('MobilenetV1', 0), conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
conv_pair_params: Array(13).fill(0).map(function (_, i) { return extractConvPairParams(i + 1); }) conv_1: extractConvPairParams(1),
conv_2: extractConvPairParams(2),
conv_3: extractConvPairParams(3),
conv_4: extractConvPairParams(4),
conv_5: extractConvPairParams(5),
conv_6: extractConvPairParams(6),
conv_7: extractConvPairParams(7),
conv_8: extractConvPairParams(8),
conv_9: extractConvPairParams(9),
conv_10: extractConvPairParams(10),
conv_11: extractConvPairParams(11),
conv_12: extractConvPairParams(12),
conv_13: extractConvPairParams(13)
}; };
} }
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
return { filters: filters, bias: bias };
}
function extractBoxPredictorParams(idx) { function extractBoxPredictorParams(idx) {
var params = { var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
box_encoding_predictor_params: { var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
filters: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights"], return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
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 (!isTensor_1.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 (!isTensor_1.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 (!isTensor_1.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 (!isTensor_1.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() { function extractPredictionLayerParams() {
return { return {
conv_0_params: extractPointwiseConvParams('Prediction', 0), conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
conv_1_params: extractPointwiseConvParams('Prediction', 1), conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
conv_2_params: extractPointwiseConvParams('Prediction', 2), conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
conv_3_params: extractPointwiseConvParams('Prediction', 3), conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
conv_4_params: extractPointwiseConvParams('Prediction', 4), conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
conv_5_params: extractPointwiseConvParams('Prediction', 5), conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
conv_6_params: extractPointwiseConvParams('Prediction', 6), conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
conv_7_params: extractPointwiseConvParams('Prediction', 7), conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
box_predictor_0_params: extractBoxPredictorParams(0), box_predictor_0: extractBoxPredictorParams(0),
box_predictor_1_params: extractBoxPredictorParams(1), box_predictor_1: extractBoxPredictorParams(1),
box_predictor_2_params: extractBoxPredictorParams(2), box_predictor_2: extractBoxPredictorParams(2),
box_predictor_3_params: extractBoxPredictorParams(3), box_predictor_3: extractBoxPredictorParams(3),
box_predictor_4_params: extractBoxPredictorParams(4), box_predictor_4: extractBoxPredictorParams(4),
box_predictor_5_params: extractBoxPredictorParams(5) box_predictor_5: extractBoxPredictorParams(5)
}; };
} }
return { return {
...@@ -102,24 +87,28 @@ function extractorsFactory(weightMap) { ...@@ -102,24 +87,28 @@ function extractorsFactory(weightMap) {
} }
function loadQuantizedParams(uri) { function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim; var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)]; case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams; paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim']; extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!isTensor_1.isTensor3D(extra_dim)) { if (!isTensor_1.isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim); throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
} }
return [2 /*return*/, { params = {
mobilenetv1_params: extractMobilenetV1Params(), mobilenetv1: extractMobilenetV1Params(),
prediction_layer_params: extractPredictionLayerParams(), prediction_layer: extractPredictionLayerParams(),
output_layer_params: { output_layer: {
extra_dim: extra_dim extra_dim: extra_dim
} }
}]; };
disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
......
build/faceDetectionNet/loadQuantizedParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/mobileNetV1.js
...@@ -16,12 +16,27 @@ function getStridesForLayerIdx(layerIdx) { ...@@ -16,12 +16,27 @@ function getStridesForLayerIdx(layerIdx) {
function mobileNetV1(x, params) { function mobileNetV1(x, params) {
return tf.tidy(function () { return tf.tidy(function () {
var conv11 = null; var conv11 = null;
var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0_params, [2, 2]); var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
params.conv_pair_params.forEach(function (param, i) { var convPairParams = [
params.conv_1,
params.conv_2,
params.conv_3,
params.conv_4,
params.conv_5,
params.conv_6,
params.conv_7,
params.conv_8,
params.conv_9,
params.conv_10,
params.conv_11,
params.conv_12,
params.conv_13
];
convPairParams.forEach(function (param, i) {
var layerIdx = i + 1; var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx); var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides); out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]); out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
if (layerIdx === 11) { if (layerIdx === 11) {
conv11 = out; conv11 = out;
} }
......
build/faceDetectionNet/mobileNetV1.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/predictionLayer.js
...@@ -5,20 +5,20 @@ var boxPredictionLayer_1 = require("./boxPredictionLayer"); ...@@ -5,20 +5,20 @@ var boxPredictionLayer_1 = require("./boxPredictionLayer");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer"); var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
function predictionLayer(x, conv11, params) { function predictionLayer(x, conv11, params) {
return tf.tidy(function () { return tf.tidy(function () {
var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0_params, [1, 1]); var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]); var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]); var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]); var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]); var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]); var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]); var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]); var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0_params); var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1_params); var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2_params); var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3_params); var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4_params); var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5_params); var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
var boxPredictions = tf.concat([ var boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding, boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding, boxPrediction1.boxPredictionEncoding,
......
build/faceDetectionNet/predictionLayer.js.map
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\ No newline at end of file \ No newline at end of file
build/faceDetectionNet/types.d.ts
...@@ -13,39 +13,51 @@ export declare namespace MobileNetV1 { ...@@ -13,39 +13,51 @@ export declare namespace MobileNetV1 {
batch_norm_variance: tf.Tensor1D; batch_norm_variance: tf.Tensor1D;
}; };
type ConvPairParams = { type ConvPairParams = {
depthwise_conv_params: DepthwiseConvParams; depthwise_conv: DepthwiseConvParams;
pointwise_conv_params: PointwiseConvParams; pointwise_conv: PointwiseConvParams;
}; };
type Params = { type Params = {
conv_0_params: PointwiseConvParams; conv_0: PointwiseConvParams;
conv_pair_params: ConvPairParams[]; conv_1: ConvPairParams;
conv_2: ConvPairParams;
conv_3: ConvPairParams;
conv_4: ConvPairParams;
conv_5: ConvPairParams;
conv_6: ConvPairParams;
conv_7: ConvPairParams;
conv_8: ConvPairParams;
conv_9: ConvPairParams;
conv_10: ConvPairParams;
conv_11: ConvPairParams;
conv_12: ConvPairParams;
conv_13: ConvPairParams;
}; };
} }
export declare type BoxPredictionParams = { export declare type BoxPredictionParams = {
box_encoding_predictor_params: ConvParams; box_encoding_predictor: ConvParams;
class_predictor_params: ConvParams; class_predictor: ConvParams;
}; };
export declare type PredictionLayerParams = { export declare type PredictionLayerParams = {
conv_0_params: PointwiseConvParams; conv_0: PointwiseConvParams;
conv_1_params: PointwiseConvParams; conv_1: PointwiseConvParams;
conv_2_params: PointwiseConvParams; conv_2: PointwiseConvParams;
conv_3_params: PointwiseConvParams; conv_3: PointwiseConvParams;
conv_4_params: PointwiseConvParams; conv_4: PointwiseConvParams;
conv_5_params: PointwiseConvParams; conv_5: PointwiseConvParams;
conv_6_params: PointwiseConvParams; conv_6: PointwiseConvParams;
conv_7_params: PointwiseConvParams; conv_7: PointwiseConvParams;
box_predictor_0_params: BoxPredictionParams; box_predictor_0: BoxPredictionParams;
box_predictor_1_params: BoxPredictionParams; box_predictor_1: BoxPredictionParams;
box_predictor_2_params: BoxPredictionParams; box_predictor_2: BoxPredictionParams;
box_predictor_3_params: BoxPredictionParams; box_predictor_3: BoxPredictionParams;
box_predictor_4_params: BoxPredictionParams; box_predictor_4: BoxPredictionParams;
box_predictor_5_params: BoxPredictionParams; box_predictor_5: BoxPredictionParams;
}; };
export declare type OutputLayerParams = { export declare type OutputLayerParams = {
extra_dim: tf.Tensor3D; extra_dim: tf.Tensor3D;
}; };
export declare type NetParams = { export declare type NetParams = {
mobilenetv1_params: MobileNetV1.Params; mobilenetv1: MobileNetV1.Params;
prediction_layer_params: PredictionLayerParams; prediction_layer: PredictionLayerParams;
output_layer_params: OutputLayerParams; output_layer: OutputLayerParams;
}; };
build/faceLandmarkNet/FaceLandmarkNet.d.ts
...@@ -5,9 +5,22 @@ import { TNetInput } from '../types'; ...@@ -5,9 +5,22 @@ import { TNetInput } from '../types';
import { FaceLandmarks } from './FaceLandmarks'; import { FaceLandmarks } from './FaceLandmarks';
import { NetParams } from './types'; import { NetParams } from './types';
export declare class FaceLandmarkNet extends NeuralNetwork<NetParams> { export declare class FaceLandmarkNet extends NeuralNetwork<NetParams> {
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>; constructor();
extractWeights(weights: Float32Array): void;
forwardInput(input: NetInput): tf.Tensor2D; forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>; forward(input: TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>; detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
} }
build/faceLandmarkNet/FaceLandmarkNet.js
...@@ -21,57 +21,29 @@ function maxPool(x, strides) { ...@@ -21,57 +21,29 @@ function maxPool(x, strides) {
var FaceLandmarkNet = /** @class */ (function (_super) { var FaceLandmarkNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarkNet, _super); tslib_1.__extends(FaceLandmarkNet, _super);
function FaceLandmarkNet() { function FaceLandmarkNet() {
return _super !== null && _super.apply(this, arguments) || this; return _super.call(this, 'FaceLandmarkNet') || this;
} }
FaceLandmarkNet.prototype.load = function (weightsOrUrl) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, paramMappings, params;
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');
}
return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
case 1:
_a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
return [2 /*return*/];
}
});
});
};
FaceLandmarkNet.prototype.extractWeights = function (weights) {
var _a = extractParams_1.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
};
FaceLandmarkNet.prototype.forwardInput = function (input) { FaceLandmarkNet.prototype.forwardInput = function (input) {
var params = this._params; var params = this.params;
if (!params) { if (!params) {
throw new Error('FaceLandmarkNet - load model before inference'); throw new Error('FaceLandmarkNet - load model before inference');
} }
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(128, true); var batchTensor = input.toBatchTensor(128, true);
var out = conv(batchTensor, params.conv0_params); var out = conv(batchTensor, params.conv0);
out = maxPool(out); out = maxPool(out);
out = conv(out, params.conv1_params); out = conv(out, params.conv1);
out = conv(out, params.conv2_params); out = conv(out, params.conv2);
out = maxPool(out); out = maxPool(out);
out = conv(out, params.conv3_params); out = conv(out, params.conv3);
out = conv(out, params.conv4_params); out = conv(out, params.conv4);
out = maxPool(out); out = maxPool(out);
out = conv(out, params.conv5_params); out = conv(out, params.conv5);
out = conv(out, params.conv6_params); out = conv(out, params.conv6);
out = maxPool(out, [1, 1]); out = maxPool(out, [1, 1]);
out = conv(out, params.conv7_params); out = conv(out, params.conv7);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params)); var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1_params); var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
var createInterleavedTensor = function (fillX, fillY) { var createInterleavedTensor = function (fillX, fillY) {
return tf.stack([ return tf.stack([
tf.fill([68], fillX), tf.fill([68], fillX),
...@@ -146,6 +118,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) { ...@@ -146,6 +118,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
}); });
}); });
}; };
FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceLandmarkNet.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceLandmarkNet; return FaceLandmarkNet;
}(NeuralNetwork_1.NeuralNetwork)); }(NeuralNetwork_1.NeuralNetwork));
exports.FaceLandmarkNet = FaceLandmarkNet; exports.FaceLandmarkNet = FaceLandmarkNet;
......
build/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/extractParams.js
"use strict"; "use strict";
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory"); var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractParams(weights) { function extractParams(weights) {
var paramMappings = []; var paramMappings = [];
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings); function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return { filters: filters, bias: bias };
}
function extractFcParams(channelsIn, channelsOut, mappedPrefix) { function extractFcParams(channelsIn, channelsOut, mappedPrefix) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut)); var fc_bias = tf.tensor1d(extractWeights(channelsOut));
...@@ -16,32 +20,32 @@ function extractParams(weights) { ...@@ -16,32 +20,32 @@ function extractParams(weights) {
bias: fc_bias bias: fc_bias
}; };
} }
var conv0_params = extractConvParams(3, 32, 3, 'conv0_params'); var conv0 = extractConvParams(3, 32, 3, 'conv0');
var conv1_params = extractConvParams(32, 64, 3, 'conv1_params'); var conv1 = extractConvParams(32, 64, 3, 'conv1');
var conv2_params = extractConvParams(64, 64, 3, 'conv2_params'); var conv2 = extractConvParams(64, 64, 3, 'conv2');
var conv3_params = extractConvParams(64, 64, 3, 'conv3_params'); var conv3 = extractConvParams(64, 64, 3, 'conv3');
var conv4_params = extractConvParams(64, 64, 3, 'conv4_params'); var conv4 = extractConvParams(64, 64, 3, 'conv4');
var conv5_params = extractConvParams(64, 128, 3, 'conv5_params'); var conv5 = extractConvParams(64, 128, 3, 'conv5');
var conv6_params = extractConvParams(128, 128, 3, 'conv6_params'); var conv6 = extractConvParams(128, 128, 3, 'conv6');
var conv7_params = extractConvParams(128, 256, 3, 'conv7_params'); var conv7 = extractConvParams(128, 256, 3, 'conv7');
var fc0_params = extractFcParams(6400, 1024, 'fc0_params'); var fc0 = extractFcParams(6400, 1024, 'fc0');
var fc1_params = extractFcParams(1024, 136, 'fc1_params'); var fc1 = extractFcParams(1024, 136, 'fc1');
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
return { return {
paramMappings: paramMappings, paramMappings: paramMappings,
params: { params: {
conv0_params: conv0_params, conv0: conv0,
conv1_params: conv1_params, conv1: conv1,
conv2_params: conv2_params, conv2: conv2,
conv3_params: conv3_params, conv3: conv3,
conv4_params: conv4_params, conv4: conv4,
conv5_params: conv5_params, conv5: conv5,
conv6_params: conv6_params, conv6: conv6,
conv7_params: conv7_params, conv7: conv7,
fc0_params: fc0_params, fc0: fc0,
fc1_params: fc1_params fc1: fc1
} }
}; };
} }
......
build/faceLandmarkNet/extractParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/loadQuantizedParams.js
"use strict"; "use strict";
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var extractWeightEntry_1 = require("../commons/extractWeightEntry"); var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var loadWeightMap_1 = require("../commons/loadWeightMap"); var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_landmark_68_model'; var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
function extractorsFactory(weightMap, paramMappings) { function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix, mappedPrefix) { function extractConvParams(prefix, mappedPrefix) {
var filtersEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/kernel", 4); var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
var biasEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/bias", 1); var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
paramMappings.push({ originalPath: filtersEntry.path, paramPath: mappedPrefix + "/filters" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" }); return { filters: filters, bias: bias };
return {
filters: filtersEntry.tensor,
bias: biasEntry.tensor
};
} }
function extractFcParams(prefix, mappedPrefix) { function extractFcParams(prefix, mappedPrefix) {
var weightsEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/kernel", 2); var weights = extractWeightEntry(prefix + "/kernel", 2, mappedPrefix + "/weights");
var biasEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/bias", 1); var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
paramMappings.push({ originalPath: weightsEntry.path, paramPath: mappedPrefix + "/weights" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" }); return { weights: weights, bias: bias };
return {
weights: weightsEntry.tensor,
bias: biasEntry.tensor
};
} }
return { return {
extractConvParams: extractConvParams, extractConvParams: extractConvParams,
...@@ -39,17 +33,18 @@ function loadQuantizedParams(uri) { ...@@ -39,17 +33,18 @@ function loadQuantizedParams(uri) {
paramMappings = []; paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams; _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
params = { params = {
conv0_params: extractConvParams('conv2d_0', 'conv0_params'), conv0: extractConvParams('conv2d_0', 'conv0'),
conv1_params: extractConvParams('conv2d_1', 'conv1_params'), conv1: extractConvParams('conv2d_1', 'conv1'),
conv2_params: extractConvParams('conv2d_2', 'conv2_params'), conv2: extractConvParams('conv2d_2', 'conv2'),
conv3_params: extractConvParams('conv2d_3', 'conv3_params'), conv3: extractConvParams('conv2d_3', 'conv3'),
conv4_params: extractConvParams('conv2d_4', 'conv4_params'), conv4: extractConvParams('conv2d_4', 'conv4'),
conv5_params: extractConvParams('conv2d_5', 'conv5_params'), conv5: extractConvParams('conv2d_5', 'conv5'),
conv6_params: extractConvParams('conv2d_6', 'conv6_params'), conv6: extractConvParams('conv2d_6', 'conv6'),
conv7_params: extractConvParams('conv2d_7', 'conv7_params'), conv7: extractConvParams('conv2d_7', 'conv7'),
fc0_params: extractFcParams('dense', 'fc0_params'), fc0: extractFcParams('dense', 'fc0'),
fc1_params: extractFcParams('logits', 'fc1_params') fc1: extractFcParams('logits', 'fc1')
}; };
disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }]; return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
......
build/faceLandmarkNet/loadQuantizedParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/types.d.ts
...@@ -5,14 +5,14 @@ export declare type FCParams = { ...@@ -5,14 +5,14 @@ export declare type FCParams = {
bias: tf.Tensor1D; bias: tf.Tensor1D;
}; };
export declare type NetParams = { export declare type NetParams = {
conv0_params: ConvParams; conv0: ConvParams;
conv1_params: ConvParams; conv1: ConvParams;
conv2_params: ConvParams; conv2: ConvParams;
conv3_params: ConvParams; conv3: ConvParams;
conv4_params: ConvParams; conv4: ConvParams;
conv5_params: ConvParams; conv5: ConvParams;
conv6_params: ConvParams; conv6: ConvParams;
conv7_params: ConvParams; conv7: ConvParams;
fc0_params: FCParams; fc0: FCParams;
fc1_params: FCParams; fc1: FCParams;
}; };
build/faceRecognitionNet/FaceRecognitionNet.d.ts
import * as tf from '@tensorflow/tfjs-core'; import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { NetInput } from '../NetInput'; import { NetInput } from '../NetInput';
import { TNetInput } from '../types'; import { TNetInput } from '../types';
export declare class FaceRecognitionNet { import { NetParams } from './types';
private _params; export declare class FaceRecognitionNet extends NeuralNetwork<NetParams> {
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>; constructor();
extractWeights(weights: Float32Array): void;
forwardInput(input: NetInput): tf.Tensor2D; forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>; forward(input: TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>; computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
} }
build/faceRecognitionNet/FaceRecognitionNet.js
...@@ -2,66 +2,44 @@ ...@@ -2,66 +2,44 @@
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var toNetInput_1 = require("../toNetInput"); var toNetInput_1 = require("../toNetInput");
var convLayer_1 = require("./convLayer"); var convLayer_1 = require("./convLayer");
var extractParams_1 = require("./extractParams"); var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams"); var loadQuantizedParams_1 = require("./loadQuantizedParams");
var normalize_1 = require("./normalize"); var normalize_1 = require("./normalize");
var residualLayer_1 = require("./residualLayer"); var residualLayer_1 = require("./residualLayer");
var FaceRecognitionNet = /** @class */ (function () { var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super);
function FaceRecognitionNet() { function FaceRecognitionNet() {
return _super.call(this, 'FaceRecognitionNet') || this;
} }
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_1.loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
}
});
});
};
FaceRecognitionNet.prototype.extractWeights = function (weights) {
this._params = extractParams_1.extractParams(weights);
};
FaceRecognitionNet.prototype.forwardInput = function (input) { FaceRecognitionNet.prototype.forwardInput = function (input) {
var _this = this; var params = this.params;
if (!this._params) { if (!params) {
throw new Error('FaceRecognitionNet - load model before inference'); throw new Error('FaceRecognitionNet - load model before inference');
} }
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true); var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize_1.normalize(batchTensor); var normalized = normalize_1.normalize(batchTensor);
var out = convLayer_1.convDown(normalized, _this._params.conv32_down); var out = convLayer_1.convDown(normalized, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid'); out = tf.maxPool(out, 3, 2, 'valid');
out = residualLayer_1.residual(out, _this._params.conv32_1); out = residualLayer_1.residual(out, params.conv32_1);
out = residualLayer_1.residual(out, _this._params.conv32_2); out = residualLayer_1.residual(out, params.conv32_2);
out = residualLayer_1.residual(out, _this._params.conv32_3); out = residualLayer_1.residual(out, params.conv32_3);
out = residualLayer_1.residualDown(out, _this._params.conv64_down); out = residualLayer_1.residualDown(out, params.conv64_down);
out = residualLayer_1.residual(out, _this._params.conv64_1); out = residualLayer_1.residual(out, params.conv64_1);
out = residualLayer_1.residual(out, _this._params.conv64_2); out = residualLayer_1.residual(out, params.conv64_2);
out = residualLayer_1.residual(out, _this._params.conv64_3); out = residualLayer_1.residual(out, params.conv64_3);
out = residualLayer_1.residualDown(out, _this._params.conv128_down); out = residualLayer_1.residualDown(out, params.conv128_down);
out = residualLayer_1.residual(out, _this._params.conv128_1); out = residualLayer_1.residual(out, params.conv128_1);
out = residualLayer_1.residual(out, _this._params.conv128_2); out = residualLayer_1.residual(out, params.conv128_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down); out = residualLayer_1.residualDown(out, params.conv256_down);
out = residualLayer_1.residual(out, _this._params.conv256_1); out = residualLayer_1.residual(out, params.conv256_1);
out = residualLayer_1.residual(out, _this._params.conv256_2); out = residualLayer_1.residual(out, params.conv256_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down_out); out = residualLayer_1.residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]); var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc); var fullyConnected = tf.matMul(globalAvg, params.fc);
return fullyConnected; return fullyConnected;
}); });
}; };
...@@ -99,7 +77,13 @@ var FaceRecognitionNet = /** @class */ (function () { ...@@ -99,7 +77,13 @@ var FaceRecognitionNet = /** @class */ (function () {
}); });
}); });
}; };
FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceRecognitionNet; return FaceRecognitionNet;
}()); }(NeuralNetwork_1.NeuralNetwork));
exports.FaceRecognitionNet = FaceRecognitionNet; exports.FaceRecognitionNet = FaceRecognitionNet;
//# sourceMappingURL=FaceRecognitionNet.js.map //# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file \ No newline at end of file
build/faceRecognitionNet/extractParams.d.ts
import { ParamMapping } from '../commons/types';
import { NetParams } from './types'; import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): NetParams; export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/faceRecognitionNet/extractParams.js
...@@ -3,43 +3,40 @@ Object.defineProperty(exports, "__esModule", { value: true }); ...@@ -3,43 +3,40 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory"); var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
var utils_1 = require("../utils"); var utils_1 = require("../utils");
function extractorsFactory(extractWeights) { function extractorsFactory(extractWeights, paramMappings) {
function extractFilterValues(numFilterValues, numFilters, filterSize) { function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues); var weights = extractWeights(numFilterValues);
var depth = weights.length / (numFilters * filterSize * filterSize); var depth = weights.length / (numFilters * filterSize * filterSize);
if (utils_1.isFloat(depth)) { if (utils_1.isFloat(depth)) {
throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize); throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
} }
return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); return tf.tidy(function () { return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
} }
function extractScaleLayerParams(numWeights) { function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
var bias = tf.tensor1d(extractWeights(numFilters));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return { filters: filters, bias: bias };
}
function extractScaleLayerParams(numWeights, mappedPrefix) {
var weights = tf.tensor1d(extractWeights(numWeights)); var weights = tf.tensor1d(extractWeights(numWeights));
var biases = tf.tensor1d(extractWeights(numWeights)); var biases = tf.tensor1d(extractWeights(numWeights));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
return { return {
weights: weights, weights: weights,
biases: biases biases: biases
}; };
} }
function extractConvLayerParams(numFilterValues, numFilters, filterSize) { function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize); var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
var conv_bias = tf.tensor1d(extractWeights(numFilters)); var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
var scale = extractScaleLayerParams(numFilters); return { conv: conv, scale: scale };
return {
conv: {
filters: conv_filters,
bias: conv_bias
},
scale: scale
};
} }
function extractResidualLayerParams(numFilterValues, numFilters, filterSize, isDown) { function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
if (isDown === void 0) { isDown = false; } if (isDown === void 0) { isDown = false; }
var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize); var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize); var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
return { return { conv1: conv1, conv2: conv2 };
conv1: conv1,
conv2: conv2
};
} }
return { return {
extractConvLayerParams: extractConvLayerParams, extractConvLayerParams: extractConvLayerParams,
...@@ -48,27 +45,29 @@ function extractorsFactory(extractWeights) { ...@@ -48,27 +45,29 @@ function extractorsFactory(extractWeights) {
} }
function extractParams(weights) { function extractParams(weights) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams; var paramMappings = [];
var conv32_down = extractConvLayerParams(4704, 32, 7); var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_1 = extractResidualLayerParams(9216, 32, 3); var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
var conv32_2 = extractResidualLayerParams(9216, 32, 3); var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
var conv32_3 = extractResidualLayerParams(9216, 32, 3); var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
var conv64_down = extractResidualLayerParams(36864, 64, 3, true); var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
var conv64_1 = extractResidualLayerParams(36864, 64, 3); var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
var conv64_2 = extractResidualLayerParams(36864, 64, 3); var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
var conv64_3 = extractResidualLayerParams(36864, 64, 3); var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
var conv128_down = extractResidualLayerParams(147456, 128, 3, true); var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
var conv128_1 = extractResidualLayerParams(147456, 128, 3); var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
var conv128_2 = extractResidualLayerParams(147456, 128, 3); var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
var conv256_down = extractResidualLayerParams(589824, 256, 3, true); var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
var conv256_1 = extractResidualLayerParams(589824, 256, 3); var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
var conv256_2 = extractResidualLayerParams(589824, 256, 3); var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
var conv256_down_out = extractResidualLayerParams(589824, 256, 3); var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
var fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
var fc = tf.tidy(function () { return tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });
paramMappings.push({ paramPath: "fc" });
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
return { var params = {
conv32_down: conv32_down, conv32_down: conv32_down,
conv32_1: conv32_1, conv32_1: conv32_1,
conv32_2: conv32_2, conv32_2: conv32_2,
...@@ -86,6 +85,7 @@ function extractParams(weights) { ...@@ -86,6 +85,7 @@ function extractParams(weights) {
conv256_down_out: conv256_down_out, conv256_down_out: conv256_down_out,
fc: fc fc: fc
}; };
return { params: params, paramMappings: paramMappings };
} }
exports.extractParams = extractParams; exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map //# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/faceRecognitionNet/extractParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceRecognitionNet/loadQuantizedParams.d.ts
export declare function loadQuantizedParams(uri: string | undefined): Promise<any>; import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/faceRecognitionNet/loadQuantizedParams.js
"use strict"; "use strict";
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var isTensor_1 = require("../commons/isTensor"); var isTensor_1 = require("../commons/isTensor");
var loadWeightMap_1 = require("../commons/loadWeightMap"); var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_recognition_model'; var DEFAULT_MODEL_NAME = 'face_recognition_model';
function extractorsFactory(weightMap) { function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) { function extractScaleLayerParams(prefix) {
var params = { var weights = extractWeightEntry(prefix + "/scale/weights", 1);
weights: weightMap[prefix + "/scale/weights"], var biases = extractWeightEntry(prefix + "/scale/biases", 1);
biases: weightMap[prefix + "/scale/biases"] return { weights: weights, biases: biases };
};
if (!isTensor_1.isTensor1D(params.weights)) {
throw new Error("expected weightMap[" + prefix + "/scale/weights] to be a Tensor1D, instead have " + params.weights);
}
if (!isTensor_1.isTensor1D(params.biases)) {
throw new Error("expected weightMap[" + prefix + "/scale/biases] to be a Tensor1D, instead have " + params.biases);
}
return params;
} }
function extractConvLayerParams(prefix) { function extractConvLayerParams(prefix) {
var params = { var filters = extractWeightEntry(prefix + "/conv/filters", 4);
filters: weightMap[prefix + "/conv/filters"], var bias = extractWeightEntry(prefix + "/conv/bias", 1);
bias: weightMap[prefix + "/conv/bias"] var scale = extractScaleLayerParams(prefix);
}; return { conv: { filters: filters, bias: bias }, scale: scale };
if (!isTensor_1.isTensor4D(params.filters)) {
throw new Error("expected weightMap[" + prefix + "/conv/filters] to be a Tensor1D, instead have " + params.filters);
}
if (!isTensor_1.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) { function extractResidualLayerParams(prefix) {
return { return {
...@@ -47,13 +32,14 @@ function extractorsFactory(weightMap) { ...@@ -47,13 +32,14 @@ function extractorsFactory(weightMap) {
} }
function loadQuantizedParams(uri) { function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () { 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; var weightMap, paramMappings, _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, params;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)]; case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams; paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
conv32_down = extractConvLayerParams('conv32_down'); conv32_down = extractConvLayerParams('conv32_down');
conv32_1 = extractResidualLayerParams('conv32_1'); conv32_1 = extractResidualLayerParams('conv32_1');
conv32_2 = extractResidualLayerParams('conv32_2'); conv32_2 = extractResidualLayerParams('conv32_2');
...@@ -70,27 +56,30 @@ function loadQuantizedParams(uri) { ...@@ -70,27 +56,30 @@ function loadQuantizedParams(uri) {
conv256_2 = extractResidualLayerParams('conv256_2'); conv256_2 = extractResidualLayerParams('conv256_2');
conv256_down_out = extractResidualLayerParams('conv256_down_out'); conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc']; fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!isTensor_1.isTensor2D(fc)) { if (!isTensor_1.isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc); throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
} }
return [2 /*return*/, { params = {
conv32_down: conv32_down, conv32_down: conv32_down,
conv32_1: conv32_1, conv32_1: conv32_1,
conv32_2: conv32_2, conv32_2: conv32_2,
conv32_3: conv32_3, conv32_3: conv32_3,
conv64_down: conv64_down, conv64_down: conv64_down,
conv64_1: conv64_1, conv64_1: conv64_1,
conv64_2: conv64_2, conv64_2: conv64_2,
conv64_3: conv64_3, conv64_3: conv64_3,
conv128_down: conv128_down, conv128_down: conv128_down,
conv128_1: conv128_1, conv128_1: conv128_1,
conv128_2: conv128_2, conv128_2: conv128_2,
conv256_down: conv256_down, conv256_down: conv256_down,
conv256_1: conv256_1, conv256_1: conv256_1,
conv256_2: conv256_2, conv256_2: conv256_2,
conv256_down_out: conv256_down_out, conv256_down_out: conv256_down_out,
fc: fc fc: fc
}]; };
disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
......
build/faceRecognitionNet/loadQuantizedParams.js.map
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\ No newline at end of file \ No newline at end of file
dist/face-api.js
...@@ -271,9 +271,6 @@ ...@@ -271,9 +271,6 @@
function isTensor(tensor$$1, dim) { function isTensor(tensor$$1, dim) {
return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim; return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
} }
function isTensor1D(tensor$$1) {
return isTensor(tensor$$1, 1);
}
function isTensor2D(tensor$$1) { function isTensor2D(tensor$$1) {
return isTensor(tensor$$1, 2); return isTensor(tensor$$1, 2);
} }
...@@ -663,6 +660,10 @@ ...@@ -663,6 +660,10 @@
} }
return new Rect(x, y, width, height); return new Rect(x, y, width, height);
}; };
Rect.prototype.pad = function (padX, padY) {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
};
Rect.prototype.floor = function () { Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height)); return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
}; };
...@@ -955,6 +956,128 @@ ...@@ -955,6 +956,128 @@
}); });
} }
var NeuralNetwork = /** @class */ (function () {
function NeuralNetwork(_name) {
this._name = _name;
this._params = undefined;
this._paramMappings = [];
}
Object.defineProperty(NeuralNetwork.prototype, "params", {
get: function () {
return this._params;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
get: function () {
return this._paramMappings;
},
enumerable: true,
configurable: true
});
NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
return obj[objProp];
};
NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
obj[objProp].dispose();
obj[objProp] = tensor$$1;
};
NeuralNetwork.prototype.getParamList = function () {
var _this = this;
return this._paramMappings.map(function (_a) {
var paramPath = _a.paramPath;
return ({
path: paramPath,
tensor: _this.getParamFromPath(paramPath)
});
});
};
NeuralNetwork.prototype.getTrainableParams = function () {
return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
};
NeuralNetwork.prototype.getFrozenParams = function () {
return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
};
NeuralNetwork.prototype.variable = function () {
var _this = this;
this.getFrozenParams().forEach(function (_a) {
var path = _a.path, tensor$$1 = _a.tensor;
_this.reassignParamFromPath(path, variable(tensor$$1));
});
};
NeuralNetwork.prototype.freeze = function () {
var _this = this;
this.getTrainableParams().forEach(function (_a) {
var path = _a.path, tensor$$1 = _a.tensor;
_this.reassignParamFromPath(path, tensor(tensor$$1));
});
};
NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
this.getParamList().forEach(function (param) {
if (throwOnRedispose && param.tensor.isDisposed) {
throw new Error("param tensor has already been disposed for path " + param.path);
}
param.tensor.dispose();
});
this._params = undefined;
};
NeuralNetwork.prototype.load = function (weightsOrUrl) {
return __awaiter$1(this, void 0, void 0, function () {
var _a, paramMappings, params;
return __generator$1(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(this._name + ".load - expected model uri, or weights as Float32Array");
}
return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
case 1:
_a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
return [2 /*return*/];
}
});
});
};
NeuralNetwork.prototype.extractWeights = function (weights) {
var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
};
NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
if (!this.params) {
throw new Error("traversePropertyPath - model has no loaded params");
}
var result = paramPath.split('/').reduce(function (res, objProp) {
if (!res.nextObj.hasOwnProperty(objProp)) {
throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
}
return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
}, { nextObj: this.params });
var obj = result.obj, objProp = result.objProp;
if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
}
return { obj: obj, objProp: objProp };
};
NeuralNetwork.prototype.loadQuantizedParams = function (_) {
throw new Error(this._name + ".loadQuantizedParams - not implemented");
};
NeuralNetwork.prototype.extractParams = function (_) {
throw new Error(this._name + ".extractParams - not implemented");
};
return NeuralNetwork;
}());
function extractWeightsFactory(weights) { function extractWeightsFactory(weights) {
var remainingWeights = weights; var remainingWeights = weights;
function extractWeights(numWeights) { function extractWeights(numWeights) {
...@@ -971,13 +1094,14 @@ ...@@ -971,13 +1094,14 @@
}; };
} }
function extractorsFactory(extractWeights) { function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels) { function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); var filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
var batch_norm_scale = tensor1d(extractWeights(numChannels)); var batch_norm_scale = tensor1d(extractWeights(numChannels));
var batch_norm_offset = tensor1d(extractWeights(numChannels)); var batch_norm_offset = tensor1d(extractWeights(numChannels));
var batch_norm_mean = tensor1d(extractWeights(numChannels)); var batch_norm_mean = tensor1d(extractWeights(numChannels));
var batch_norm_variance = tensor1d(extractWeights(numChannels)); var batch_norm_variance = tensor1d(extractWeights(numChannels));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
return { return {
filters: filters, filters: filters,
batch_norm_scale: batch_norm_scale, batch_norm_scale: batch_norm_scale,
...@@ -986,115 +1110,116 @@ ...@@ -986,115 +1110,116 @@
batch_norm_variance: batch_norm_variance batch_norm_variance: batch_norm_variance
}; };
} }
function extractConvParams(channelsIn, channelsOut, filterSize) { function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]); var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut)); var bias = tensor1d(extractWeights(channelsOut));
return { paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
filters: filters, return { filters: filters, bias: bias };
bias: bias
};
} }
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) { function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias; var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
return { return {
filters: filters, filters: filters,
batch_norm_offset: bias batch_norm_offset: bias
}; };
} }
function extractConvPairParams(channelsIn, channelsOut) { function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_conv_params = extractDepthwiseConvParams(channelsIn); var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
var pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1); var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
return { return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
depthwise_conv_params: depthwise_conv_params,
pointwise_conv_params: pointwise_conv_params
};
} }
function extractMobilenetV1Params() { function extractMobilenetV1Params() {
var conv_0_params = extractPointwiseConvParams(3, 32, 3); var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
var channelNumPairs = [ var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
[32, 64], var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
[64, 128], var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
[128, 128], var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
[128, 256], var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
[256, 256], var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
[256, 512], var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
[512, 512], var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
[512, 512], var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
[512, 512], var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
[512, 512], var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
[512, 512], var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
[512, 1024], var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
[1024, 1024]
];
var conv_pair_params = channelNumPairs.map(function (_a) {
var channelsIn = _a[0], channelsOut = _a[1];
return extractConvPairParams(channelsIn, channelsOut);
});
return { return {
conv_0_params: conv_0_params, conv_0: conv_0,
conv_pair_params: conv_pair_params conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
conv_8: conv_8,
conv_9: conv_9,
conv_10: conv_10,
conv_11: conv_11,
conv_12: conv_12,
conv_13: conv_13
}; };
} }
function extractPredictionLayerParams() { function extractPredictionLayerParams() {
var conv_0_params = extractPointwiseConvParams(1024, 256, 1); var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
var conv_1_params = extractPointwiseConvParams(256, 512, 3); var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
var conv_2_params = extractPointwiseConvParams(512, 128, 1); var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
var conv_3_params = extractPointwiseConvParams(128, 256, 3); var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
var conv_4_params = extractPointwiseConvParams(256, 128, 1); var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
var conv_5_params = extractPointwiseConvParams(128, 256, 3); var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
var conv_6_params = extractPointwiseConvParams(256, 64, 1); var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
var conv_7_params = extractPointwiseConvParams(64, 128, 3); var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
var box_encoding_0_predictor_params = extractConvParams(512, 12, 1); var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
var class_predictor_0_params = extractConvParams(512, 9, 1); var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1); var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
var class_predictor_1_params = extractConvParams(1024, 18, 1); var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
var box_encoding_2_predictor_params = extractConvParams(512, 24, 1); var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
var class_predictor_2_params = extractConvParams(512, 18, 1); var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
var box_encoding_3_predictor_params = extractConvParams(256, 24, 1); var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
var class_predictor_3_params = extractConvParams(256, 18, 1); var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
var box_encoding_4_predictor_params = extractConvParams(256, 24, 1); var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
var class_predictor_4_params = extractConvParams(256, 18, 1); var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
var box_encoding_5_predictor_params = extractConvParams(128, 24, 1); var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
var class_predictor_5_params = extractConvParams(128, 18, 1); var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
var box_predictor_0_params = { var box_predictor_0 = {
box_encoding_predictor_params: box_encoding_0_predictor_params, box_encoding_predictor: box_encoding_0_predictor,
class_predictor_params: class_predictor_0_params class_predictor: class_predictor_0
}; };
var box_predictor_1_params = { var box_predictor_1 = {
box_encoding_predictor_params: box_encoding_1_predictor_params, box_encoding_predictor: box_encoding_1_predictor,
class_predictor_params: class_predictor_1_params class_predictor: class_predictor_1
}; };
var box_predictor_2_params = { var box_predictor_2 = {
box_encoding_predictor_params: box_encoding_2_predictor_params, box_encoding_predictor: box_encoding_2_predictor,
class_predictor_params: class_predictor_2_params class_predictor: class_predictor_2
}; };
var box_predictor_3_params = { var box_predictor_3 = {
box_encoding_predictor_params: box_encoding_3_predictor_params, box_encoding_predictor: box_encoding_3_predictor,
class_predictor_params: class_predictor_3_params class_predictor: class_predictor_3
}; };
var box_predictor_4_params = { var box_predictor_4 = {
box_encoding_predictor_params: box_encoding_4_predictor_params, box_encoding_predictor: box_encoding_4_predictor,
class_predictor_params: class_predictor_4_params class_predictor: class_predictor_4
}; };
var box_predictor_5_params = { var box_predictor_5 = {
box_encoding_predictor_params: box_encoding_5_predictor_params, box_encoding_predictor: box_encoding_5_predictor,
class_predictor_params: class_predictor_5_params class_predictor: class_predictor_5
}; };
return { return {
conv_0_params: conv_0_params, conv_0: conv_0,
conv_1_params: conv_1_params, conv_1: conv_1,
conv_2_params: conv_2_params, conv_2: conv_2,
conv_3_params: conv_3_params, conv_3: conv_3,
conv_4_params: conv_4_params, conv_4: conv_4,
conv_5_params: conv_5_params, conv_5: conv_5,
conv_6_params: conv_6_params, conv_6: conv_6,
conv_7_params: conv_7_params, conv_7: conv_7,
box_predictor_0_params: box_predictor_0_params, box_predictor_0: box_predictor_0,
box_predictor_1_params: box_predictor_1_params, box_predictor_1: box_predictor_1,
box_predictor_2_params: box_predictor_2_params, box_predictor_2: box_predictor_2,
box_predictor_3_params: box_predictor_3_params, box_predictor_3: box_predictor_3,
box_predictor_4_params: box_predictor_4_params, box_predictor_4: box_predictor_4,
box_predictor_5_params: box_predictor_5_params box_predictor_5: box_predictor_5
}; };
} }
return { return {
...@@ -1103,21 +1228,45 @@ ...@@ -1103,21 +1228,45 @@
}; };
} }
function extractParams(weights) { function extractParams(weights) {
var paramMappings = [];
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams; var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1_params = extractMobilenetV1Params(); var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer_params = extractPredictionLayerParams(); var prediction_layer = extractPredictionLayerParams();
var extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]); var extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer_params = { var output_layer = {
extra_dim: extra_dim extra_dim: extra_dim
}; };
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
return { return {
mobilenetv1_params: mobilenetv1_params, params: {
prediction_layer_params: prediction_layer_params, mobilenetv1: mobilenetv1,
output_layer_params: output_layer_params prediction_layer: prediction_layer,
output_layer: output_layer
},
paramMappings: paramMappings
};
}
function disposeUnusedWeightTensors(weightMap, paramMappings) {
Object.keys(weightMap).forEach(function (path) {
if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
weightMap[path].dispose();
}
});
}
function extractWeightEntryFactory(weightMap, paramMappings) {
return function (originalPath, paramRank, mappedPath) {
var tensor = weightMap[originalPath];
if (!isTensor(tensor, paramRank)) {
throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
}
paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
return tensor;
}; };
} }
...@@ -1152,95 +1301,78 @@ ...@@ -1152,95 +1301,78 @@
} }
var DEFAULT_MODEL_NAME = 'face_detection_model'; var DEFAULT_MODEL_NAME = 'face_detection_model';
function extractorsFactory$1(weightMap) { function extractorsFactory$1(weightMap, paramMappings) {
function extractPointwiseConvParams(prefix, idx) { var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
var pointwise_conv_params = { function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
filters: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/weights"], var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
batch_norm_offset: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset"] var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
}; return { filters: filters, batch_norm_offset: batch_norm_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) { function extractConvPairParams(idx) {
var depthwise_conv_params = { var mappedPrefix = "mobilenetv1/conv_" + idx;
filters: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights"], var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
batch_norm_scale: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma"], var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
batch_norm_offset: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta"], var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
batch_norm_mean: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean"], var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
batch_norm_variance: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance"], var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
}; var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
if (!isTensor4D(depthwise_conv_params.filters)) { var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights] to be a Tensor4D, instead have " + depthwise_conv_params.filters); var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
}
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 { return {
depthwise_conv_params: depthwise_conv_params, depthwise_conv: {
pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx) filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
},
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
}; };
} }
function extractMobilenetV1Params() { function extractMobilenetV1Params() {
return { return {
conv_0_params: extractPointwiseConvParams('MobilenetV1', 0), conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
conv_pair_params: Array(13).fill(0).map(function (_, i) { return extractConvPairParams(i + 1); }) conv_1: extractConvPairParams(1),
conv_2: extractConvPairParams(2),
conv_3: extractConvPairParams(3),
conv_4: extractConvPairParams(4),
conv_5: extractConvPairParams(5),
conv_6: extractConvPairParams(6),
conv_7: extractConvPairParams(7),
conv_8: extractConvPairParams(8),
conv_9: extractConvPairParams(9),
conv_10: extractConvPairParams(10),
conv_11: extractConvPairParams(11),
conv_12: extractConvPairParams(12),
conv_13: extractConvPairParams(13)
}; };
} }
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
return { filters: filters, bias: bias };
}
function extractBoxPredictorParams(idx) { function extractBoxPredictorParams(idx) {
var params = { var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
box_encoding_predictor_params: { var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
filters: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights"], return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
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() { function extractPredictionLayerParams() {
return { return {
conv_0_params: extractPointwiseConvParams('Prediction', 0), conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
conv_1_params: extractPointwiseConvParams('Prediction', 1), conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
conv_2_params: extractPointwiseConvParams('Prediction', 2), conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
conv_3_params: extractPointwiseConvParams('Prediction', 3), conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
conv_4_params: extractPointwiseConvParams('Prediction', 4), conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
conv_5_params: extractPointwiseConvParams('Prediction', 5), conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
conv_6_params: extractPointwiseConvParams('Prediction', 6), conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
conv_7_params: extractPointwiseConvParams('Prediction', 7), conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
box_predictor_0_params: extractBoxPredictorParams(0), box_predictor_0: extractBoxPredictorParams(0),
box_predictor_1_params: extractBoxPredictorParams(1), box_predictor_1: extractBoxPredictorParams(1),
box_predictor_2_params: extractBoxPredictorParams(2), box_predictor_2: extractBoxPredictorParams(2),
box_predictor_3_params: extractBoxPredictorParams(3), box_predictor_3: extractBoxPredictorParams(3),
box_predictor_4_params: extractBoxPredictorParams(4), box_predictor_4: extractBoxPredictorParams(4),
box_predictor_5_params: extractBoxPredictorParams(5) box_predictor_5: extractBoxPredictorParams(5)
}; };
} }
return { return {
...@@ -1250,24 +1382,28 @@ ...@@ -1250,24 +1382,28 @@
} }
function loadQuantizedParams(uri) { function loadQuantizedParams(uri) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim; var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
return __generator$1(this, function (_b) { return __generator$1(this, function (_b) {
switch (_b.label) { 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 = _b.sent(); weightMap = _b.sent();
_a = extractorsFactory$1(weightMap), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams; paramMappings = [];
_a = extractorsFactory$1(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim']; extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!isTensor3D(extra_dim)) { if (!isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim); throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
} }
return [2 /*return*/, { params = {
mobilenetv1_params: extractMobilenetV1Params(), mobilenetv1: extractMobilenetV1Params(),
prediction_layer_params: extractPredictionLayerParams(), prediction_layer: extractPredictionLayerParams(),
output_layer_params: { output_layer: {
extra_dim: extra_dim extra_dim: extra_dim
} }
}]; };
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
...@@ -1295,12 +1431,27 @@ ...@@ -1295,12 +1431,27 @@
function mobileNetV1(x, params) { function mobileNetV1(x, params) {
return tidy(function () { return tidy(function () {
var conv11 = null; var conv11 = null;
var out = pointwiseConvLayer(x, params.conv_0_params, [2, 2]); var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
params.conv_pair_params.forEach(function (param, i) { var convPairParams = [
params.conv_1,
params.conv_2,
params.conv_3,
params.conv_4,
params.conv_5,
params.conv_6,
params.conv_7,
params.conv_8,
params.conv_9,
params.conv_10,
params.conv_11,
params.conv_12,
params.conv_13
];
convPairParams.forEach(function (param, i) {
var layerIdx = i + 1; var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx); var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides); out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
out = pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]); out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
if (layerIdx === 11) { if (layerIdx === 11) {
conv11 = out; conv11 = out;
} }
...@@ -1426,8 +1577,8 @@ ...@@ -1426,8 +1577,8 @@
function boxPredictionLayer(x, params) { function boxPredictionLayer(x, params) {
return tidy(function () { return tidy(function () {
var batchSize = x.shape[0]; var batchSize = x.shape[0];
var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]); var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = reshape(convLayer(x, params.class_predictor_params), [batchSize, -1, 3]); var classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return { return {
boxPredictionEncoding: boxPredictionEncoding, boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction classPrediction: classPrediction
...@@ -1437,20 +1588,20 @@ ...@@ -1437,20 +1588,20 @@
function predictionLayer(x, conv11, params) { function predictionLayer(x, conv11, params) {
return tidy(function () { return tidy(function () {
var conv0 = pointwiseConvLayer(x, params.conv_0_params, [1, 1]); var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]); var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]); var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]); var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]); var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]); var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]); var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]); var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0_params); var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1_params); var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2_params); var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3_params); var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4_params); var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5_params); var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
var boxPredictions = concat([ var boxPredictions = concat([
boxPrediction0.boxPredictionEncoding, boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding, boxPrediction1.boxPredictionEncoding,
...@@ -1474,45 +1625,22 @@ ...@@ -1474,45 +1625,22 @@
}); });
} }
var FaceDetectionNet = /** @class */ (function () { var FaceDetectionNet = /** @class */ (function (_super) {
__extends$1(FaceDetectionNet, _super);
function FaceDetectionNet() { function FaceDetectionNet() {
return _super.call(this, 'FaceDetectionNet') || this;
} }
FaceDetectionNet.prototype.load = function (weightsOrUrl) {
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(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.forwardInput = function (input) { FaceDetectionNet.prototype.forwardInput = function (input) {
var _this = this; var params = this.params;
if (!this._params) { if (!params) {
throw new Error('FaceDetectionNet - load model before inference'); throw new Error('FaceDetectionNet - load model before inference');
} }
return tidy(function () { return tidy(function () {
var batchTensor = input.toBatchTensor(512, false); var batchTensor = input.toBatchTensor(512, false);
var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1)); var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
var features = mobileNetV1(x, _this._params.mobilenetv1_params); var features = mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions; var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params); return outputLayer(boxPredictions, classPredictions, params.output_layer);
}); });
}; };
FaceDetectionNet.prototype.forward = function (input) { FaceDetectionNet.prototype.forward = function (input) {
...@@ -1575,8 +1703,14 @@ ...@@ -1575,8 +1703,14 @@
}); });
}); });
}; };
FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
};
FaceDetectionNet.prototype.extractParams = function (weights) {
return extractParams(weights);
};
return FaceDetectionNet; return FaceDetectionNet;
}()); }(NeuralNetwork));
function faceDetectionNet(weights) { function faceDetectionNet(weights) {
var net = new FaceDetectionNet(); var net = new FaceDetectionNet();
...@@ -1584,103 +1718,15 @@ ...@@ -1584,103 +1718,15 @@
return net; return net;
} }
var NeuralNetwork = /** @class */ (function () {
function NeuralNetwork() {
this._params = undefined;
this._paramMappings = [];
}
Object.defineProperty(NeuralNetwork.prototype, "params", {
get: function () {
return this._params;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
get: function () {
return this._paramMappings;
},
enumerable: true,
configurable: true
});
NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
return obj[objProp];
};
NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
obj[objProp].dispose();
obj[objProp] = tensor$$1;
};
NeuralNetwork.prototype.getParamList = function () {
var _this = this;
return this._paramMappings.map(function (_a) {
var paramPath = _a.paramPath;
return ({
path: paramPath,
tensor: _this.getParamFromPath(paramPath)
});
});
};
NeuralNetwork.prototype.getTrainableParams = function () {
return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
};
NeuralNetwork.prototype.getFrozenParams = function () {
return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
};
NeuralNetwork.prototype.variable = function () {
var _this = this;
this.getFrozenParams().forEach(function (_a) {
var path = _a.path, tensor$$1 = _a.tensor;
_this.reassignParamFromPath(path, variable(tensor$$1));
});
};
NeuralNetwork.prototype.freeze = function () {
var _this = this;
this.getTrainableParams().forEach(function (_a) {
var path = _a.path, tensor$$1 = _a.tensor;
_this.reassignParamFromPath(path, tensor(tensor$$1));
});
};
NeuralNetwork.prototype.dispose = function () {
this.getParamList().forEach(function (param) { return param.tensor.dispose(); });
this._params = undefined;
};
NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
if (!this.params) {
throw new Error("traversePropertyPath - model has no loaded params");
}
var result = paramPath.split('/').reduce(function (res, objProp) {
if (!res.nextObj.hasOwnProperty(objProp)) {
throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
}
return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
}, { nextObj: this.params });
var obj = result.obj, objProp = result.objProp;
if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
}
return { obj: obj, objProp: objProp };
};
return NeuralNetwork;
}());
function extractConvParamsFactory(extractWeights, paramMappings) {
return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return {
filters: filters,
bias: bias
};
};
}
function extractParams$1(weights) { function extractParams$1(weights) {
var paramMappings = []; var paramMappings = [];
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return { filters: filters, bias: bias };
}
function extractFcParams(channelsIn, channelsOut, mappedPrefix) { function extractFcParams(channelsIn, channelsOut, mappedPrefix) {
var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tensor1d(extractWeights(channelsOut)); var fc_bias = tensor1d(extractWeights(channelsOut));
...@@ -1690,32 +1736,32 @@ ...@@ -1690,32 +1736,32 @@
bias: fc_bias bias: fc_bias
}; };
} }
var conv0_params = extractConvParams(3, 32, 3, 'conv0_params'); var conv0 = extractConvParams(3, 32, 3, 'conv0');
var conv1_params = extractConvParams(32, 64, 3, 'conv1_params'); var conv1 = extractConvParams(32, 64, 3, 'conv1');
var conv2_params = extractConvParams(64, 64, 3, 'conv2_params'); var conv2 = extractConvParams(64, 64, 3, 'conv2');
var conv3_params = extractConvParams(64, 64, 3, 'conv3_params'); var conv3 = extractConvParams(64, 64, 3, 'conv3');
var conv4_params = extractConvParams(64, 64, 3, 'conv4_params'); var conv4 = extractConvParams(64, 64, 3, 'conv4');
var conv5_params = extractConvParams(64, 128, 3, 'conv5_params'); var conv5 = extractConvParams(64, 128, 3, 'conv5');
var conv6_params = extractConvParams(128, 128, 3, 'conv6_params'); var conv6 = extractConvParams(128, 128, 3, 'conv6');
var conv7_params = extractConvParams(128, 256, 3, 'conv7_params'); var conv7 = extractConvParams(128, 256, 3, 'conv7');
var fc0_params = extractFcParams(6400, 1024, 'fc0_params'); var fc0 = extractFcParams(6400, 1024, 'fc0');
var fc1_params = extractFcParams(1024, 136, 'fc1_params'); var fc1 = extractFcParams(1024, 136, 'fc1');
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
return { return {
paramMappings: paramMappings, paramMappings: paramMappings,
params: { params: {
conv0_params: conv0_params, conv0: conv0,
conv1_params: conv1_params, conv1: conv1,
conv2_params: conv2_params, conv2: conv2,
conv3_params: conv3_params, conv3: conv3,
conv4_params: conv4_params, conv4: conv4,
conv5_params: conv5_params, conv5: conv5,
conv6_params: conv6_params, conv6: conv6,
conv7_params: conv7_params, conv7: conv7,
fc0_params: fc0_params, fc0: fc0,
fc1_params: fc1_params fc1: fc1
} }
}; };
} }
...@@ -1826,33 +1872,18 @@ ...@@ -1826,33 +1872,18 @@
}); });
} }
function extractWeightEntry(weightMap, path, paramRank) {
var tensor = weightMap[path];
if (!isTensor(tensor, paramRank)) {
throw new Error("expected weightMap[" + path + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
}
return { path: path, tensor: tensor };
}
var DEFAULT_MODEL_NAME$1 = 'face_landmark_68_model'; var DEFAULT_MODEL_NAME$1 = 'face_landmark_68_model';
function extractorsFactory$2(weightMap, paramMappings) { function extractorsFactory$2(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix, mappedPrefix) { function extractConvParams(prefix, mappedPrefix) {
var filtersEntry = extractWeightEntry(weightMap, prefix + "/kernel", 4); var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
var biasEntry = extractWeightEntry(weightMap, prefix + "/bias", 1); var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
paramMappings.push({ originalPath: filtersEntry.path, paramPath: mappedPrefix + "/filters" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" }); return { filters: filters, bias: bias };
return {
filters: filtersEntry.tensor,
bias: biasEntry.tensor
};
} }
function extractFcParams(prefix, mappedPrefix) { function extractFcParams(prefix, mappedPrefix) {
var weightsEntry = extractWeightEntry(weightMap, prefix + "/kernel", 2); var weights = extractWeightEntry(prefix + "/kernel", 2, mappedPrefix + "/weights");
var biasEntry = extractWeightEntry(weightMap, prefix + "/bias", 1); var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
paramMappings.push({ originalPath: weightsEntry.path, paramPath: mappedPrefix + "/weights" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" }); return { weights: weights, bias: bias };
return {
weights: weightsEntry.tensor,
bias: biasEntry.tensor
};
} }
return { return {
extractConvParams: extractConvParams, extractConvParams: extractConvParams,
...@@ -1870,17 +1901,18 @@ ...@@ -1870,17 +1901,18 @@
paramMappings = []; paramMappings = [];
_a = extractorsFactory$2(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams; _a = extractorsFactory$2(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
params = { params = {
conv0_params: extractConvParams('conv2d_0', 'conv0_params'), conv0: extractConvParams('conv2d_0', 'conv0'),
conv1_params: extractConvParams('conv2d_1', 'conv1_params'), conv1: extractConvParams('conv2d_1', 'conv1'),
conv2_params: extractConvParams('conv2d_2', 'conv2_params'), conv2: extractConvParams('conv2d_2', 'conv2'),
conv3_params: extractConvParams('conv2d_3', 'conv3_params'), conv3: extractConvParams('conv2d_3', 'conv3'),
conv4_params: extractConvParams('conv2d_4', 'conv4_params'), conv4: extractConvParams('conv2d_4', 'conv4'),
conv5_params: extractConvParams('conv2d_5', 'conv5_params'), conv5: extractConvParams('conv2d_5', 'conv5'),
conv6_params: extractConvParams('conv2d_6', 'conv6_params'), conv6: extractConvParams('conv2d_6', 'conv6'),
conv7_params: extractConvParams('conv2d_7', 'conv7_params'), conv7: extractConvParams('conv2d_7', 'conv7'),
fc0_params: extractFcParams('dense', 'fc0_params'), fc0: extractFcParams('dense', 'fc0'),
fc1_params: extractFcParams('logits', 'fc1_params') fc1: extractFcParams('logits', 'fc1')
}; };
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }]; return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
...@@ -1897,57 +1929,29 @@ ...@@ -1897,57 +1929,29 @@
var FaceLandmarkNet = /** @class */ (function (_super) { var FaceLandmarkNet = /** @class */ (function (_super) {
__extends$1(FaceLandmarkNet, _super); __extends$1(FaceLandmarkNet, _super);
function FaceLandmarkNet() { function FaceLandmarkNet() {
return _super !== null && _super.apply(this, arguments) || this; return _super.call(this, 'FaceLandmarkNet') || this;
} }
FaceLandmarkNet.prototype.load = function (weightsOrUrl) {
return __awaiter$1(this, void 0, void 0, function () {
var _a, paramMappings, params;
return __generator$1(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');
}
return [4 /*yield*/, loadQuantizedParams$1(weightsOrUrl)];
case 1:
_a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
return [2 /*return*/];
}
});
});
};
FaceLandmarkNet.prototype.extractWeights = function (weights) {
var _a = extractParams$1(weights), paramMappings = _a.paramMappings, params = _a.params;
this._paramMappings = paramMappings;
this._params = params;
};
FaceLandmarkNet.prototype.forwardInput = function (input) { FaceLandmarkNet.prototype.forwardInput = function (input) {
var params = this._params; var params = this.params;
if (!params) { if (!params) {
throw new Error('FaceLandmarkNet - load model before inference'); throw new Error('FaceLandmarkNet - load model before inference');
} }
return tidy(function () { return tidy(function () {
var batchTensor = input.toBatchTensor(128, true); var batchTensor = input.toBatchTensor(128, true);
var out = conv(batchTensor, params.conv0_params); var out = conv(batchTensor, params.conv0);
out = maxPool$1(out); out = maxPool$1(out);
out = conv(out, params.conv1_params); out = conv(out, params.conv1);
out = conv(out, params.conv2_params); out = conv(out, params.conv2);
out = maxPool$1(out); out = maxPool$1(out);
out = conv(out, params.conv3_params); out = conv(out, params.conv3);
out = conv(out, params.conv4_params); out = conv(out, params.conv4);
out = maxPool$1(out); out = maxPool$1(out);
out = conv(out, params.conv5_params); out = conv(out, params.conv5);
out = conv(out, params.conv6_params); out = conv(out, params.conv6);
out = maxPool$1(out, [1, 1]); out = maxPool$1(out, [1, 1]);
out = conv(out, params.conv7_params); out = conv(out, params.conv7);
var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params)); var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer(fc0, params.fc1_params); var fc1 = fullyConnectedLayer(fc0, params.fc1);
var createInterleavedTensor = function (fillX, fillY) { var createInterleavedTensor = function (fillX, fillY) {
return stack([ return stack([
fill([68], fillX), fill([68], fillX),
...@@ -2022,6 +2026,12 @@ ...@@ -2022,6 +2026,12 @@
}); });
}); });
}; };
FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams$1(uri);
};
FaceLandmarkNet.prototype.extractParams = function (weights) {
return extractParams$1(weights);
};
return FaceLandmarkNet; return FaceLandmarkNet;
}(NeuralNetwork)); }(NeuralNetwork));
...@@ -2053,43 +2063,40 @@ ...@@ -2053,43 +2063,40 @@
return convLayer$1(x, params, [2, 2], true, 'valid'); return convLayer$1(x, params, [2, 2], true, 'valid');
} }
function extractorsFactory$3(extractWeights) { function extractorsFactory$3(extractWeights, paramMappings) {
function extractFilterValues(numFilterValues, numFilters, filterSize) { function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues); var weights = extractWeights(numFilterValues);
var depth = weights.length / (numFilters * filterSize * filterSize); var depth = weights.length / (numFilters * filterSize * filterSize);
if (isFloat(depth)) { if (isFloat(depth)) {
throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize); throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
} }
return transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); return tidy(function () { return transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
} }
function extractScaleLayerParams(numWeights) { function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
var bias = tensor1d(extractWeights(numFilters));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
return { filters: filters, bias: bias };
}
function extractScaleLayerParams(numWeights, mappedPrefix) {
var weights = tensor1d(extractWeights(numWeights)); var weights = tensor1d(extractWeights(numWeights));
var biases = tensor1d(extractWeights(numWeights)); var biases = tensor1d(extractWeights(numWeights));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
return { return {
weights: weights, weights: weights,
biases: biases biases: biases
}; };
} }
function extractConvLayerParams(numFilterValues, numFilters, filterSize) { function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize); var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
var conv_bias = tensor1d(extractWeights(numFilters)); var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
var scale = extractScaleLayerParams(numFilters); return { conv: conv, scale: scale };
return {
conv: {
filters: conv_filters,
bias: conv_bias
},
scale: scale
};
} }
function extractResidualLayerParams(numFilterValues, numFilters, filterSize, isDown) { function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
if (isDown === void 0) { isDown = false; } if (isDown === void 0) { isDown = false; }
var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize); var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize); var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
return { return { conv1: conv1, conv2: conv2 };
conv1: conv1,
conv2: conv2
};
} }
return { return {
extractConvLayerParams: extractConvLayerParams, extractConvLayerParams: extractConvLayerParams,
...@@ -2098,27 +2105,29 @@ ...@@ -2098,27 +2105,29 @@
} }
function extractParams$2(weights) { function extractParams$2(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory$3(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams; var paramMappings = [];
var conv32_down = extractConvLayerParams(4704, 32, 7); var _b = extractorsFactory$3(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_1 = extractResidualLayerParams(9216, 32, 3); var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
var conv32_2 = extractResidualLayerParams(9216, 32, 3); var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
var conv32_3 = extractResidualLayerParams(9216, 32, 3); var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
var conv64_down = extractResidualLayerParams(36864, 64, 3, true); var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
var conv64_1 = extractResidualLayerParams(36864, 64, 3); var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
var conv64_2 = extractResidualLayerParams(36864, 64, 3); var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
var conv64_3 = extractResidualLayerParams(36864, 64, 3); var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
var conv128_down = extractResidualLayerParams(147456, 128, 3, true); var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
var conv128_1 = extractResidualLayerParams(147456, 128, 3); var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
var conv128_2 = extractResidualLayerParams(147456, 128, 3); var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
var conv256_down = extractResidualLayerParams(589824, 256, 3, true); var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
var conv256_1 = extractResidualLayerParams(589824, 256, 3); var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
var conv256_2 = extractResidualLayerParams(589824, 256, 3); var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
var conv256_down_out = extractResidualLayerParams(589824, 256, 3); var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
var fc = transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
var fc = tidy(function () { return transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });
paramMappings.push({ paramPath: "fc" });
if (getRemainingWeights().length !== 0) { if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length); throw new Error("weights remaing after extract: " + getRemainingWeights().length);
} }
return { var params = {
conv32_down: conv32_down, conv32_down: conv32_down,
conv32_1: conv32_1, conv32_1: conv32_1,
conv32_2: conv32_2, conv32_2: conv32_2,
...@@ -2136,38 +2145,22 @@ ...@@ -2136,38 +2145,22 @@
conv256_down_out: conv256_down_out, conv256_down_out: conv256_down_out,
fc: fc fc: fc
}; };
return { params: params, paramMappings: paramMappings };
} }
var DEFAULT_MODEL_NAME$2 = 'face_recognition_model'; var DEFAULT_MODEL_NAME$2 = 'face_recognition_model';
function extractorsFactory$4(weightMap) { function extractorsFactory$4(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) { function extractScaleLayerParams(prefix) {
var params = { var weights = extractWeightEntry(prefix + "/scale/weights", 1);
weights: weightMap[prefix + "/scale/weights"], var biases = extractWeightEntry(prefix + "/scale/biases", 1);
biases: weightMap[prefix + "/scale/biases"] return { weights: weights, biases: 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) { function extractConvLayerParams(prefix) {
var params = { var filters = extractWeightEntry(prefix + "/conv/filters", 4);
filters: weightMap[prefix + "/conv/filters"], var bias = extractWeightEntry(prefix + "/conv/bias", 1);
bias: weightMap[prefix + "/conv/bias"] var scale = extractScaleLayerParams(prefix);
}; return { conv: { filters: filters, bias: bias }, scale: scale };
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) { function extractResidualLayerParams(prefix) {
return { return {
...@@ -2182,13 +2175,14 @@ ...@@ -2182,13 +2175,14 @@
} }
function loadQuantizedParams$2(uri) { function loadQuantizedParams$2(uri) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(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; var weightMap, paramMappings, _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, params;
return __generator$1(this, function (_b) { return __generator$1(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$2)]; case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$2)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
_a = extractorsFactory$4(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams; paramMappings = [];
_a = extractorsFactory$4(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
conv32_down = extractConvLayerParams('conv32_down'); conv32_down = extractConvLayerParams('conv32_down');
conv32_1 = extractResidualLayerParams('conv32_1'); conv32_1 = extractResidualLayerParams('conv32_1');
conv32_2 = extractResidualLayerParams('conv32_2'); conv32_2 = extractResidualLayerParams('conv32_2');
...@@ -2205,27 +2199,30 @@ ...@@ -2205,27 +2199,30 @@
conv256_2 = extractResidualLayerParams('conv256_2'); conv256_2 = extractResidualLayerParams('conv256_2');
conv256_down_out = extractResidualLayerParams('conv256_down_out'); conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc']; fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!isTensor2D(fc)) { if (!isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc); throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
} }
return [2 /*return*/, { params = {
conv32_down: conv32_down, conv32_down: conv32_down,
conv32_1: conv32_1, conv32_1: conv32_1,
conv32_2: conv32_2, conv32_2: conv32_2,
conv32_3: conv32_3, conv32_3: conv32_3,
conv64_down: conv64_down, conv64_down: conv64_down,
conv64_1: conv64_1, conv64_1: conv64_1,
conv64_2: conv64_2, conv64_2: conv64_2,
conv64_3: conv64_3, conv64_3: conv64_3,
conv128_down: conv128_down, conv128_down: conv128_down,
conv128_1: conv128_1, conv128_1: conv128_1,
conv128_2: conv128_2, conv128_2: conv128_2,
conv256_down: conv256_down, conv256_down: conv256_down,
conv256_1: conv256_1, conv256_1: conv256_1,
conv256_2: conv256_2, conv256_2: conv256_2,
conv256_down_out: conv256_down_out, conv256_down_out: conv256_down_out,
fc: fc fc: fc
}]; };
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
} }
}); });
}); });
...@@ -2271,60 +2268,37 @@ ...@@ -2271,60 +2268,37 @@
return out; return out;
} }
var FaceRecognitionNet = /** @class */ (function () { var FaceRecognitionNet = /** @class */ (function (_super) {
__extends$1(FaceRecognitionNet, _super);
function FaceRecognitionNet() { function FaceRecognitionNet() {
return _super.call(this, 'FaceRecognitionNet') || this;
} }
FaceRecognitionNet.prototype.load = function (weightsOrUrl) {
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(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$2(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
}
});
});
};
FaceRecognitionNet.prototype.extractWeights = function (weights) {
this._params = extractParams$2(weights);
};
FaceRecognitionNet.prototype.forwardInput = function (input) { FaceRecognitionNet.prototype.forwardInput = function (input) {
var _this = this; var params = this.params;
if (!this._params) { if (!params) {
throw new Error('FaceRecognitionNet - load model before inference'); throw new Error('FaceRecognitionNet - load model before inference');
} }
return tidy(function () { return tidy(function () {
var batchTensor = input.toBatchTensor(150, true); var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize(batchTensor); var normalized = normalize(batchTensor);
var out = convDown(normalized, _this._params.conv32_down); var out = convDown(normalized, params.conv32_down);
out = maxPool(out, 3, 2, 'valid'); out = maxPool(out, 3, 2, 'valid');
out = residual(out, _this._params.conv32_1); out = residual(out, params.conv32_1);
out = residual(out, _this._params.conv32_2); out = residual(out, params.conv32_2);
out = residual(out, _this._params.conv32_3); out = residual(out, params.conv32_3);
out = residualDown(out, _this._params.conv64_down); out = residualDown(out, params.conv64_down);
out = residual(out, _this._params.conv64_1); out = residual(out, params.conv64_1);
out = residual(out, _this._params.conv64_2); out = residual(out, params.conv64_2);
out = residual(out, _this._params.conv64_3); out = residual(out, params.conv64_3);
out = residualDown(out, _this._params.conv128_down); out = residualDown(out, params.conv128_down);
out = residual(out, _this._params.conv128_1); out = residual(out, params.conv128_1);
out = residual(out, _this._params.conv128_2); out = residual(out, params.conv128_2);
out = residualDown(out, _this._params.conv256_down); out = residualDown(out, params.conv256_down);
out = residual(out, _this._params.conv256_1); out = residual(out, params.conv256_1);
out = residual(out, _this._params.conv256_2); out = residual(out, params.conv256_2);
out = residualDown(out, _this._params.conv256_down_out); out = residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]); var globalAvg = out.mean([1, 2]);
var fullyConnected = matMul(globalAvg, _this._params.fc); var fullyConnected = matMul(globalAvg, params.fc);
return fullyConnected; return fullyConnected;
}); });
}; };
...@@ -2362,8 +2336,14 @@ ...@@ -2362,8 +2336,14 @@
}); });
}); });
}; };
FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams$2(uri);
};
FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams$2(weights);
};
return FaceRecognitionNet; return FaceRecognitionNet;
}()); }(NeuralNetwork));
function faceRecognitionNet(weights) { function faceRecognitionNet(weights) {
var net = new FaceRecognitionNet(); var net = new FaceRecognitionNet();
......
dist/face-api.js.map
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dist/face-api.min.js
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