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Commit 4b49c5a0 by vincent
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check in latest build

parent ef3ecfba
Showing with 617 additions and 178 deletions
build/faceLandmarkNet/FaceLandmark68Net.js
...@@ -2,44 +2,46 @@ ...@@ -2,44 +2,46 @@
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 tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var depthwiseSeparableConv_1 = require("./depthwiseSeparableConv");
var extractParams_1 = require("./extractParams"); var extractParams_1 = require("./extractParams");
var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase"); var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer"); var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams"); var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) { function denseBlock(x, denseBlockParams, isFirstLayer) {
return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true); if (isFirstLayer === void 0) { isFirstLayer = false; }
} return tf.tidy(function () {
function maxPool(x, strides) { var out1 = tf.relu(isFirstLayer
if (strides === void 0) { strides = [2, 2]; } ? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
return tf.maxPool(x, [2, 2], strides, 'valid'); : depthwiseSeparableConv_1.depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
var out2 = depthwiseSeparableConv_1.depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
var in3 = tf.relu(tf.add(out1, out2));
var out3 = depthwiseSeparableConv_1.depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
var in4 = tf.relu(tf.add(out1, tf.add(out2, out3)));
var out4 = depthwiseSeparableConv_1.depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4))));
});
} }
var FaceLandmark68Net = /** @class */ (function (_super) { var FaceLandmark68Net = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68Net, _super); tslib_1.__extends(FaceLandmark68Net, _super);
function FaceLandmark68Net() { function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68Net') || this; return _super.call(this, 'FaceLandmark68LargeNet') || this;
} }
FaceLandmark68Net.prototype.runNet = function (input) { FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params; var params = this.params;
if (!params) { if (!params) {
throw new Error('FaceLandmark68Net - load model before inference'); throw new Error('FaceLandmark68LargeNet - load model before inference');
} }
return tf.tidy(function () { return tf.tidy(function () {
var batchTensor = input.toBatchTensor(128, true).toFloat(); var batchTensor = input.toBatchTensor(112, true);
var out = conv(batchTensor, params.conv0); var meanRgb = [122.782, 117.001, 104.298];
out = maxPool(out); var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
out = conv(out, params.conv1); var out = denseBlock(normalized, params.dense0, true);
out = conv(out, params.conv2); out = denseBlock(out, params.dense1);
out = maxPool(out); out = denseBlock(out, params.dense2);
out = conv(out, params.conv3); out = denseBlock(out, params.dense3);
out = conv(out, params.conv4); out = tf.avgPool(out, [7, 7], [2, 2], 'valid');
out = maxPool(out); return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
out = conv(out, params.conv5);
out = conv(out, params.conv6);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
return fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
}); });
}; };
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) { FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
......
build/faceLandmarkNet/FaceLandmark68Net.js.map
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build/faceLandmarkNet/FaceLandmark68TinyNet.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from 'tfjs-image-recognition-base';
import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
import { TinyNetParams } from './types';
export declare class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyNetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: TinyNetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: TinyNetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/faceLandmarkNet/FaceLandmark68TinyNet.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var depthwiseSeparableConv_1 = require("./depthwiseSeparableConv");
var extractParamsTiny_1 = require("./extractParamsTiny");
var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParamsTiny_1 = require("./loadQuantizedParamsTiny");
function denseBlock(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tf.tidy(function () {
var out1 = tf.relu(isFirstLayer
? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
: depthwiseSeparableConv_1.depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
var out2 = depthwiseSeparableConv_1.depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
var in3 = tf.relu(tf.add(out1, out2));
var out3 = depthwiseSeparableConv_1.depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
return tf.relu(tf.add(out1, tf.add(out2, out3)));
});
}
var FaceLandmark68TinyNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68TinyNet, _super);
function FaceLandmark68TinyNet() {
return _super.call(this, 'FaceLandmark68TinyNet') || this;
}
FaceLandmark68TinyNet.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmark68TinyNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(112, true);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
var out = denseBlock(normalized, params.dense0, true);
out = denseBlock(out, params.dense1);
out = denseBlock(out, params.dense2);
out = tf.avgPool(out, [14, 14], [2, 2], 'valid');
return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68TinyNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParamsTiny_1.loadQuantizedParamsTiny(uri);
};
FaceLandmark68TinyNet.prototype.extractParams = function (weights) {
return extractParamsTiny_1.extractParamsTiny(weights);
};
return FaceLandmark68TinyNet;
}(FaceLandmark68NetBase_1.FaceLandmark68NetBase));
exports.FaceLandmark68TinyNet = FaceLandmark68TinyNet;
//# sourceMappingURL=FaceLandmark68TinyNet.js.map
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build/faceLandmarkNet/FaceLandmark68TinyNet.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/depthwiseSeparableConv.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams, stride: [number, number]): tf.Tensor4D;
build/faceLandmarkNet/depthwiseSeparableConv.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function depthwiseSeparableConv(x, params, stride) {
return tf.tidy(function () {
var out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
out = tf.add(out, params.bias);
return out;
});
}
exports.depthwiseSeparableConv = depthwiseSeparableConv;
//# sourceMappingURL=depthwiseSeparableConv.js.map
\ No newline at end of file
build/faceLandmarkNet/depthwiseSeparableConv.js.map 0 → 100644
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\ 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 tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2"); var extractorsFactory_1 = require("./extractorsFactory");
function extractParams(weights) { function extractParams(weights) {
var paramMappings = []; var paramMappings = [];
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights; var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings); var _b = extractorsFactory_1.extractorsFactory(extractWeights, paramMappings), extractDenseBlock4Params = _b.extractDenseBlock4Params, extractFCParams = _b.extractFCParams;
var extractFCParams = tfjs_tiny_yolov2_1.extractFCParamsFactory(extractWeights, paramMappings); var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
var conv0 = extractConvParams(3, 32, 3, 'conv0'); var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
var conv1 = extractConvParams(32, 64, 3, 'conv1'); var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
var conv2 = extractConvParams(64, 64, 3, 'conv2'); var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
var conv3 = extractConvParams(64, 64, 3, 'conv3'); var fc = extractFCParams(256, 136, 'fc');
var conv4 = extractConvParams(64, 64, 3, 'conv4');
var conv5 = extractConvParams(64, 128, 3, 'conv5');
var conv6 = extractConvParams(128, 128, 3, 'conv6');
var conv7 = extractConvParams(128, 256, 3, 'conv7');
var fc0 = extractFCParams(6400, 1024, 'fc0');
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: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3, fc: fc }
conv0: conv0,
conv1: conv1,
conv2: conv2,
conv3: conv3,
conv4: conv4,
conv5: conv5,
conv6: conv6,
conv7: conv7,
fc0: fc0,
fc1: fc1
}
}; };
} }
exports.extractParams = extractParams; exports.extractParams = extractParams;
......
build/faceLandmarkNet/extractParams.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/extractParamsTiny.d.ts 0 → 100644
import { ParamMapping } from 'tfjs-image-recognition-base';
import { TinyNetParams } from './types';
export declare function extractParamsTiny(weights: Float32Array): {
params: TinyNetParams;
paramMappings: ParamMapping[];
};
build/faceLandmarkNet/extractParamsTiny.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractorsFactory_1 = require("./extractorsFactory");
function extractParamsTiny(weights) {
var paramMappings = [];
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory_1.extractorsFactory(extractWeights, paramMappings), extractDenseBlock3Params = _b.extractDenseBlock3Params, extractFCParams = _b.extractFCParams;
var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);
var dense1 = extractDenseBlock3Params(32, 64, 'dense1');
var dense2 = extractDenseBlock3Params(64, 128, 'dense2');
var fc = extractFCParams(128, 136, 'fc');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
paramMappings: paramMappings,
params: { dense0: dense0, dense1: dense1, dense2: dense2, fc: fc }
};
}
exports.extractParamsTiny = extractParamsTiny;
//# sourceMappingURL=extractParamsTiny.js.map
\ No newline at end of file
build/faceLandmarkNet/extractParamsTiny.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/extractorsFactory.d.ts 0 → 100644
import { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
import { FCParams } from 'tfjs-tiny-yolov2';
import { DenseBlock3Params, DenseBlock4Params } from './types';
export declare function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): {
extractDenseBlock3Params: (channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer?: boolean) => DenseBlock3Params;
extractDenseBlock4Params: (channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer?: boolean) => DenseBlock4Params;
extractFCParams: (channelsIn: number, channelsOut: number, mappedPrefix: string) => FCParams;
};
build/faceLandmarkNet/extractorsFactory.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var types_1 = require("tfjs-tiny-yolov2/build/tinyYolov2/types");
function extractorsFactory(extractWeights, paramMappings) {
function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
function extractFCParams(channelsIn, channelsOut, mappedPrefix) {
var weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
return {
weights: weights,
bias: bias
};
}
var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var conv0 = isFirstLayer
? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv0")
: extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/conv0");
var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv1");
var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv2");
return { conv0: conv0, conv1: conv1, conv2: conv2 };
}
function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;
var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv3");
return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
}
return {
extractDenseBlock3Params: extractDenseBlock3Params,
extractDenseBlock4Params: extractDenseBlock4Params,
extractFCParams: extractFCParams
};
}
exports.extractorsFactory = extractorsFactory;
//# sourceMappingURL=extractorsFactory.js.map
\ No newline at end of file
build/faceLandmarkNet/extractorsFactory.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/index.d.ts
import { FaceLandmark68Net } from './FaceLandmark68Net'; import { FaceLandmark68Net } from './FaceLandmark68Net';
export * from './FaceLandmark68Net'; export * from './FaceLandmark68Net';
export * from './FaceLandmark68TinyNet';
export declare class FaceLandmarkNet extends FaceLandmark68Net { export declare class FaceLandmarkNet extends FaceLandmark68Net {
} }
export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet; export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
......
build/faceLandmarkNet/index.js
...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true }); ...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var FaceLandmark68Net_1 = require("./FaceLandmark68Net"); var FaceLandmark68Net_1 = require("./FaceLandmark68Net");
tslib_1.__exportStar(require("./FaceLandmark68Net"), exports); tslib_1.__exportStar(require("./FaceLandmark68Net"), exports);
tslib_1.__exportStar(require("./FaceLandmark68TinyNet"), exports);
var FaceLandmarkNet = /** @class */ (function (_super) { var FaceLandmarkNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarkNet, _super); tslib_1.__extends(FaceLandmarkNet, _super);
function FaceLandmarkNet() { function FaceLandmarkNet() {
......
build/faceLandmarkNet/index.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/loadParamsFactory.d.ts 0 → 100644
import { ParamMapping } from 'tfjs-image-recognition-base';
import { FCParams } from 'tfjs-tiny-yolov2';
import { DenseBlock3Params, DenseBlock4Params } from './types';
export declare function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]): {
extractDenseBlock3Params: (prefix: string, isFirstLayer?: boolean) => DenseBlock3Params;
extractDenseBlock4Params: (prefix: string, isFirstLayer?: boolean) => DenseBlock4Params;
extractFcParams: (prefix: string) => FCParams;
};
build/faceLandmarkNet/loadParamsFactory.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var types_1 = require("tfjs-tiny-yolov2/build/tinyYolov2/types");
function loadParamsFactory(weightMap, paramMappings) {
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix) {
var filters = extractWeightEntry(prefix + "/filters", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { filters: filters, bias: bias };
}
function extractSeparableConvParams(prefix) {
var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
function extractDenseBlock3Params(prefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var conv0 = isFirstLayer
? extractConvParams(prefix + "/conv0")
: extractSeparableConvParams(prefix + "/conv0");
var conv1 = extractSeparableConvParams(prefix + "/conv1");
var conv2 = extractSeparableConvParams(prefix + "/conv2");
return { conv0: conv0, conv1: conv1, conv2: conv2 };
}
function extractDenseBlock4Params(prefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var conv0 = isFirstLayer
? extractConvParams(prefix + "/conv0")
: extractSeparableConvParams(prefix + "/conv0");
var conv1 = extractSeparableConvParams(prefix + "/conv1");
var conv2 = extractSeparableConvParams(prefix + "/conv2");
var conv3 = extractSeparableConvParams(prefix + "/conv3");
return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
}
function extractFcParams(prefix) {
var weights = extractWeightEntry(prefix + "/weights", 2);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { weights: weights, bias: bias };
}
return {
extractDenseBlock3Params: extractDenseBlock3Params,
extractDenseBlock4Params: extractDenseBlock4Params,
extractFcParams: extractFcParams
};
}
exports.loadParamsFactory = loadParamsFactory;
//# sourceMappingURL=loadParamsFactory.js.map
\ No newline at end of file
build/faceLandmarkNet/loadParamsFactory.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/loadQuantizedParams.js
...@@ -2,45 +2,24 @@ ...@@ -2,45 +2,24 @@
Object.defineProperty(exports, "__esModule", { value: true }); Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib"); var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base"); var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
var DEFAULT_MODEL_NAME = 'face_landmark_68_model'; var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
return { filters: filters, bias: bias };
}
function extractFcParams(prefix, mappedPrefix) {
var weights = extractWeightEntry(prefix + "/kernel", 2, mappedPrefix + "/weights");
var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
return { weights: weights, bias: bias };
}
return {
extractConvParams: extractConvParams,
extractFcParams: extractFcParams
};
}
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, paramMappings, _a, extractConvParams, extractFcParams, params; var weightMap, paramMappings, _a, extractDenseBlock4Params, extractFcParams, 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*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)]; case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams; _a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
params = { params = {
conv0: extractConvParams('conv2d_0', 'conv0'), dense0: extractDenseBlock4Params('dense0', true),
conv1: extractConvParams('conv2d_1', 'conv1'), dense1: extractDenseBlock4Params('dense1'),
conv2: extractConvParams('conv2d_2', 'conv2'), dense2: extractDenseBlock4Params('dense2'),
conv3: extractConvParams('conv2d_3', 'conv3'), dense3: extractDenseBlock4Params('dense3'),
conv4: extractConvParams('conv2d_4', 'conv4'), fc: extractFcParams('fc')
conv5: extractConvParams('conv2d_5', 'conv5'),
conv6: extractConvParams('conv2d_6', 'conv6'),
conv7: extractConvParams('conv2d_7', 'conv7'),
fc0: extractFcParams('dense', 'fc0'),
fc1: extractFcParams('logits', 'fc1')
}; };
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings); tfjs_image_recognition_base_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/loadQuantizedParamsTiny.d.ts 0 → 100644
import { ParamMapping } from 'tfjs-image-recognition-base';
import { TinyNetParams } from './types';
export declare function loadQuantizedParamsTiny(uri: string | undefined): Promise<{
params: TinyNetParams;
paramMappings: ParamMapping[];
}>;
build/faceLandmarkNet/loadQuantizedParamsTiny.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
var DEFAULT_MODEL_NAME = 'face_landmark_68_tiny_model';
function loadQuantizedParamsTiny(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractDenseBlock3Params, extractFcParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2'),
fc: extractFcParams('fc')
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParamsTiny = loadQuantizedParamsTiny;
//# sourceMappingURL=loadQuantizedParamsTiny.js.map
\ No newline at end of file
build/faceLandmarkNet/loadQuantizedParamsTiny.js.map 0 → 100644
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\ No newline at end of file
build/faceLandmarkNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams, FCParams } from 'tfjs-tiny-yolov2'; import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
export declare type ConvWithBatchNormParams = BatchNormParams & {
filter: tf.Tensor4D;
};
export declare type BatchNormParams = {
mean: tf.Tensor1D;
variance: tf.Tensor1D;
scale: tf.Tensor1D;
offset: tf.Tensor1D;
};
export declare type SeparableConvWithBatchNormParams = {
depthwise: ConvWithBatchNormParams;
pointwise: ConvWithBatchNormParams;
};
export declare type FCWithBatchNormParams = BatchNormParams & {
weights: tf.Tensor2D;
};
export declare type DenseBlock3Params = {
conv0: SeparableConvParams | ConvParams;
conv1: SeparableConvParams;
conv2: SeparableConvParams;
};
export declare type DenseBlock4Params = DenseBlock3Params & {
conv3: SeparableConvParams;
};
export declare type TinyNetParams = {
dense0: DenseBlock3Params;
dense1: DenseBlock3Params;
dense2: DenseBlock3Params;
fc: FCParams;
};
export declare type NetParams = { export declare type NetParams = {
conv0: ConvParams; dense0: DenseBlock4Params;
conv1: ConvParams; dense1: DenseBlock4Params;
conv2: ConvParams; dense2: DenseBlock4Params;
conv3: ConvParams; dense3: DenseBlock4Params;
conv4: ConvParams; fc: FCParams;
conv5: ConvParams;
conv6: ConvParams;
conv7: ConvParams;
fc0: FCParams;
fc1: FCParams;
}; };
build/globalApi.d.ts
...@@ -6,6 +6,7 @@ import { FaceLandmarks68 } from './classes/FaceLandmarks68'; ...@@ -6,6 +6,7 @@ import { FaceLandmarks68 } from './classes/FaceLandmarks68';
import { FullFaceDescription } from './classes/FullFaceDescription'; import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet'; import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net'; import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from './faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet'; import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn'; import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types'; import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
...@@ -16,12 +17,14 @@ export declare const recognitionNet: FaceRecognitionNet; ...@@ -16,12 +17,14 @@ export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: { export declare const nets: {
ssdMobilenetv1: FaceDetectionNet; ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmark68Net; faceLandmark68Net: FaceLandmark68Net;
faceLandmark68TinyNet: FaceLandmark68TinyNet;
faceRecognitionNet: FaceRecognitionNet; faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn; mtcnn: Mtcnn;
tinyYolov2: TinyYolov2; tinyYolov2: TinyYolov2;
}; };
export declare function loadSsdMobilenetv1Model(url: string): Promise<void>; export declare function loadSsdMobilenetv1Model(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>; export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceLandmarkTinyModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>; export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadMtcnnModel(url: string): Promise<void>; export declare function loadMtcnnModel(url: string): Promise<void>;
export declare function loadTinyYolov2Model(url: string): Promise<void>; export declare function loadTinyYolov2Model(url: string): Promise<void>;
...@@ -30,6 +33,7 @@ export declare function loadModels(url: string): Promise<[void, void, void, void ...@@ -30,6 +33,7 @@ export declare function loadModels(url: string): Promise<[void, void, void, void
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>; export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare const ssdMobilenetv1: typeof locateFaces; export declare const ssdMobilenetv1: typeof locateFaces;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>; export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function detectLandmarksTiny(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>; export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>; export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>; export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
......
build/globalApi.js
...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true }); ...@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory"); var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet"); var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net"); var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net");
var FaceLandmark68TinyNet_1 = require("./faceLandmarkNet/FaceLandmark68TinyNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet"); var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn"); var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2"); var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
...@@ -14,6 +15,7 @@ exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet(); ...@@ -14,6 +15,7 @@ exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
exports.nets = { exports.nets = {
ssdMobilenetv1: exports.detectionNet, ssdMobilenetv1: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet, faceLandmark68Net: exports.landmarkNet,
faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
faceRecognitionNet: exports.recognitionNet, faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn(), mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2() tinyYolov2: new TinyYolov2_1.TinyYolov2()
...@@ -26,6 +28,10 @@ function loadFaceLandmarkModel(url) { ...@@ -26,6 +28,10 @@ function loadFaceLandmarkModel(url) {
return exports.nets.faceLandmark68Net.load(url); return exports.nets.faceLandmark68Net.load(url);
} }
exports.loadFaceLandmarkModel = loadFaceLandmarkModel; exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceLandmarkTinyModel(url) {
return exports.nets.faceLandmark68TinyNet.load(url);
}
exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
function loadFaceRecognitionModel(url) { function loadFaceRecognitionModel(url) {
return exports.nets.faceRecognitionNet.load(url); return exports.nets.faceRecognitionNet.load(url);
} }
...@@ -43,6 +49,7 @@ function loadFaceDetectionModel(url) { ...@@ -43,6 +49,7 @@ function loadFaceDetectionModel(url) {
} }
exports.loadFaceDetectionModel = loadFaceDetectionModel; exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadModels(url) { function loadModels(url) {
console.warn('loadModels will be deprecated in future');
return Promise.all([ return Promise.all([
loadSsdMobilenetv1Model(url), loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url), loadFaceLandmarkModel(url),
...@@ -61,6 +68,10 @@ function detectLandmarks(input) { ...@@ -61,6 +68,10 @@ function detectLandmarks(input) {
return exports.nets.faceLandmark68Net.detectLandmarks(input); return exports.nets.faceLandmark68Net.detectLandmarks(input);
} }
exports.detectLandmarks = detectLandmarks; exports.detectLandmarks = detectLandmarks;
function detectLandmarksTiny(input) {
return exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
}
exports.detectLandmarksTiny = detectLandmarksTiny;
function computeFaceDescriptor(input) { function computeFaceDescriptor(input) {
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input); return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
} }
......
build/globalApi.js.map
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\ No newline at end of file \ No newline at end of file
dist/face-api.js
...@@ -3148,38 +3148,69 @@ ...@@ -3148,38 +3148,69 @@
return createFaceDetectionNet(weights); return createFaceDetectionNet(weights);
} }
function depthwiseSeparableConv(x, params, stride) {
return tidy(function () {
var out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
out = add(out, params.bias);
return out;
});
}
function extractorsFactory$4(extractWeights, paramMappings) {
function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
function extractFCParams(channelsIn, channelsOut, mappedPrefix) {
var weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
return {
weights: weights,
bias: bias
};
}
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var conv0 = isFirstLayer
? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv0")
: extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/conv0");
var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv1");
var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv2");
return { conv0: conv0, conv1: conv1, conv2: conv2 };
}
function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;
var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv3");
return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
}
return {
extractDenseBlock3Params: extractDenseBlock3Params,
extractDenseBlock4Params: extractDenseBlock4Params,
extractFCParams: extractFCParams
};
}
function extractParams$2(weights) { function extractParams$2(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); var _b = extractorsFactory$4(extractWeights, paramMappings), extractDenseBlock4Params = _b.extractDenseBlock4Params, extractFCParams = _b.extractFCParams;
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
var conv0 = extractConvParams(3, 32, 3, 'conv0'); var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
var conv1 = extractConvParams(32, 64, 3, 'conv1'); var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
var conv2 = extractConvParams(64, 64, 3, 'conv2'); var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
var conv3 = extractConvParams(64, 64, 3, 'conv3'); var fc = extractFCParams(256, 136, 'fc');
var conv4 = extractConvParams(64, 64, 3, 'conv4');
var conv5 = extractConvParams(64, 128, 3, 'conv5');
var conv6 = extractConvParams(128, 128, 3, 'conv6');
var conv7 = extractConvParams(128, 256, 3, 'conv7');
var fc0 = extractFCParams(6400, 1024, 'fc0');
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: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3, fc: fc }
conv0: conv0,
conv1: conv1,
conv2: conv2,
conv3: conv3,
conv4: conv4,
conv5: conv5,
conv6: conv6,
conv7: conv7,
fc0: fc0,
fc1: fc1
}
}; };
} }
...@@ -3297,45 +3328,67 @@ ...@@ -3297,45 +3328,67 @@
}); });
} }
var DEFAULT_MODEL_NAME$1 = 'face_landmark_68_model'; function loadParamsFactory(weightMap, paramMappings) {
function extractorsFactory$4(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix, mappedPrefix) { function extractConvParams(prefix) {
var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters"); var filters = extractWeightEntry(prefix + "/filters", 4);
var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias"); var bias = extractWeightEntry(prefix + "/bias", 1);
return { filters: filters, bias: bias }; return { filters: filters, bias: bias };
} }
function extractFcParams(prefix, mappedPrefix) { function extractSeparableConvParams(prefix) {
var weights = extractWeightEntry(prefix + "/kernel", 2, mappedPrefix + "/weights"); var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias"); var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
function extractDenseBlock3Params(prefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var conv0 = isFirstLayer
? extractConvParams(prefix + "/conv0")
: extractSeparableConvParams(prefix + "/conv0");
var conv1 = extractSeparableConvParams(prefix + "/conv1");
var conv2 = extractSeparableConvParams(prefix + "/conv2");
return { conv0: conv0, conv1: conv1, conv2: conv2 };
}
function extractDenseBlock4Params(prefix, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
var conv0 = isFirstLayer
? extractConvParams(prefix + "/conv0")
: extractSeparableConvParams(prefix + "/conv0");
var conv1 = extractSeparableConvParams(prefix + "/conv1");
var conv2 = extractSeparableConvParams(prefix + "/conv2");
var conv3 = extractSeparableConvParams(prefix + "/conv3");
return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
}
function extractFcParams(prefix) {
var weights = extractWeightEntry(prefix + "/weights", 2);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { weights: weights, bias: bias }; return { weights: weights, bias: bias };
} }
return { return {
extractConvParams: extractConvParams, extractDenseBlock3Params: extractDenseBlock3Params,
extractDenseBlock4Params: extractDenseBlock4Params,
extractFcParams: extractFcParams extractFcParams: extractFcParams
}; };
} }
var DEFAULT_MODEL_NAME$1 = 'face_landmark_68_model';
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, paramMappings, _a, extractConvParams, extractFcParams, params; var weightMap, paramMappings, _a, extractDenseBlock4Params, extractFcParams, 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$1)]; case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$1)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
_a = extractorsFactory$4(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams; _a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
params = { params = {
conv0: extractConvParams('conv2d_0', 'conv0'), dense0: extractDenseBlock4Params('dense0', true),
conv1: extractConvParams('conv2d_1', 'conv1'), dense1: extractDenseBlock4Params('dense1'),
conv2: extractConvParams('conv2d_2', 'conv2'), dense2: extractDenseBlock4Params('dense2'),
conv3: extractConvParams('conv2d_3', 'conv3'), dense3: extractDenseBlock4Params('dense3'),
conv4: extractConvParams('conv2d_4', 'conv4'), fc: extractFcParams('fc')
conv5: extractConvParams('conv2d_5', 'conv5'),
conv6: extractConvParams('conv2d_6', 'conv6'),
conv7: extractConvParams('conv2d_7', 'conv7'),
fc0: extractFcParams('dense', 'fc0'),
fc1: extractFcParams('logits', 'fc1')
}; };
disposeUnusedWeightTensors(weightMap, paramMappings); disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }]; return [2 /*return*/, { params: params, paramMappings: paramMappings }];
...@@ -3344,39 +3397,40 @@ ...@@ -3344,39 +3397,40 @@
}); });
} }
function conv(x, params) { function denseBlock(x, denseBlockParams, isFirstLayer) {
return convLayer(x, params, 'valid', true); if (isFirstLayer === void 0) { isFirstLayer = false; }
} return tidy(function () {
function maxPool$1(x, strides) { var out1 = relu(isFirstLayer
if (strides === void 0) { strides = [2, 2]; } ? add(conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
return maxPool(x, [2, 2], strides, 'valid'); : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
var in3 = relu(add(out1, out2));
var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
var in4 = relu(add(out1, add(out2, out3)));
var out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
return relu(add(out1, add(out2, add(out3, out4))));
});
} }
var FaceLandmark68Net = /** @class */ (function (_super) { var FaceLandmark68Net = /** @class */ (function (_super) {
__extends$1(FaceLandmark68Net, _super); __extends$1(FaceLandmark68Net, _super);
function FaceLandmark68Net() { function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68Net') || this; return _super.call(this, 'FaceLandmark68LargeNet') || this;
} }
FaceLandmark68Net.prototype.runNet = function (input) { FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params; var params = this.params;
if (!params) { if (!params) {
throw new Error('FaceLandmark68Net - load model before inference'); throw new Error('FaceLandmark68LargeNet - load model before inference');
} }
return tidy(function () { return tidy(function () {
var batchTensor = input.toBatchTensor(128, true).toFloat(); var batchTensor = input.toBatchTensor(112, true);
var out = conv(batchTensor, params.conv0); var meanRgb = [122.782, 117.001, 104.298];
out = maxPool$1(out); var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
out = conv(out, params.conv1); var out = denseBlock(normalized, params.dense0, true);
out = conv(out, params.conv2); out = denseBlock(out, params.dense1);
out = maxPool$1(out); out = denseBlock(out, params.dense2);
out = conv(out, params.conv3); out = denseBlock(out, params.dense3);
out = conv(out, params.conv4); out = avgPool(out, [7, 7], [2, 2], 'valid');
out = maxPool$1(out); return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
out = conv(out, params.conv5);
out = conv(out, params.conv6);
out = maxPool$1(out, [1, 1]);
out = conv(out, params.conv7);
var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
return fullyConnectedLayer(fc0, params.fc1);
}); });
}; };
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) { FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
...@@ -3388,6 +3442,89 @@ ...@@ -3388,6 +3442,89 @@
return FaceLandmark68Net; return FaceLandmark68Net;
}(FaceLandmark68NetBase)); }(FaceLandmark68NetBase));
function extractParamsTiny(weights) {
var paramMappings = [];
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory$4(extractWeights, paramMappings), extractDenseBlock3Params = _b.extractDenseBlock3Params, extractFCParams = _b.extractFCParams;
var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);
var dense1 = extractDenseBlock3Params(32, 64, 'dense1');
var dense2 = extractDenseBlock3Params(64, 128, 'dense2');
var fc = extractFCParams(128, 136, 'fc');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
paramMappings: paramMappings,
params: { dense0: dense0, dense1: dense1, dense2: dense2, fc: fc }
};
}
var DEFAULT_MODEL_NAME$2 = 'face_landmark_68_tiny_model';
function loadQuantizedParamsTiny(uri) {
return __awaiter$1(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractDenseBlock3Params, extractFcParams, params;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$2)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2'),
fc: extractFcParams('fc')
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
function denseBlock$1(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tidy(function () {
var out1 = relu(isFirstLayer
? add(conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
: depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
var in3 = relu(add(out1, out2));
var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
return relu(add(out1, add(out2, out3)));
});
}
var FaceLandmark68TinyNet = /** @class */ (function (_super) {
__extends$1(FaceLandmark68TinyNet, _super);
function FaceLandmark68TinyNet() {
return _super.call(this, 'FaceLandmark68TinyNet') || this;
}
FaceLandmark68TinyNet.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmark68TinyNet - load model before inference');
}
return tidy(function () {
var batchTensor = input.toBatchTensor(112, true);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
var out = denseBlock$1(normalized, params.dense0, true);
out = denseBlock$1(out, params.dense1);
out = denseBlock$1(out, params.dense2);
out = avgPool(out, [14, 14], [2, 2], 'valid');
return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68TinyNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParamsTiny(uri);
};
FaceLandmark68TinyNet.prototype.extractParams = function (weights) {
return extractParamsTiny(weights);
};
return FaceLandmark68TinyNet;
}(FaceLandmark68NetBase));
var FaceLandmarkNet = /** @class */ (function (_super) { var FaceLandmarkNet = /** @class */ (function (_super) {
__extends$1(FaceLandmarkNet, _super); __extends$1(FaceLandmarkNet, _super);
function FaceLandmarkNet() { function FaceLandmarkNet() {
...@@ -3417,7 +3554,7 @@ ...@@ -3417,7 +3554,7 @@
out = scale(out, params.scale); out = scale(out, params.scale);
return withRelu ? relu(out) : out; return withRelu ? relu(out) : out;
} }
function conv$1(x, params) { function conv(x, params) {
return convLayer$1(x, params, [1, 1], true); return convLayer$1(x, params, [1, 1], true);
} }
function convNoRelu(x, params) { function convNoRelu(x, params) {
...@@ -3512,7 +3649,7 @@ ...@@ -3512,7 +3649,7 @@
return { params: params, paramMappings: paramMappings }; return { params: params, paramMappings: paramMappings };
} }
var DEFAULT_MODEL_NAME$2 = 'face_recognition_model'; var DEFAULT_MODEL_NAME$3 = 'face_recognition_model';
function extractorsFactory$6(weightMap, paramMappings) { function extractorsFactory$6(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) { function extractScaleLayerParams(prefix) {
...@@ -3542,7 +3679,7 @@ ...@@ -3542,7 +3679,7 @@
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; 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$3)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
...@@ -3593,7 +3730,7 @@ ...@@ -3593,7 +3730,7 @@
} }
function residual(x, params) { function residual(x, params) {
var out = conv$1(x, params.conv1); var out = conv(x, params.conv1);
out = convNoRelu(out, params.conv2); out = convNoRelu(out, params.conv2);
out = add(out, x); out = add(out, x);
out = relu(out); out = relu(out);
...@@ -3916,7 +4053,7 @@ ...@@ -3916,7 +4053,7 @@
}; };
} }
var DEFAULT_MODEL_NAME$3 = 'mtcnn_model'; var DEFAULT_MODEL_NAME$4 = 'mtcnn_model';
function extractorsFactory$8(weightMap, paramMappings) { function extractorsFactory$8(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix) { function extractConvParams(prefix) {
...@@ -3977,7 +4114,7 @@ ...@@ -3977,7 +4114,7 @@
var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet; var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet;
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$3)]; case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$4)];
case 1: case 1:
weightMap = _b.sent(); weightMap = _b.sent();
paramMappings = []; paramMappings = [];
...@@ -4478,7 +4615,7 @@ ...@@ -4478,7 +4615,7 @@
new Point(9.041765, 10.66308) new Point(9.041765, 10.66308)
]; ];
var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
var DEFAULT_MODEL_NAME$4 = 'tiny_yolov2_model'; var DEFAULT_MODEL_NAME$5 = 'tiny_yolov2_model';
var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'; var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
var TinyYolov2$1 = /** @class */ (function (_super) { var TinyYolov2$1 = /** @class */ (function (_super) {
...@@ -4530,7 +4667,7 @@ ...@@ -4530,7 +4667,7 @@
}); });
}; };
TinyYolov2$$1.prototype.loadQuantizedParams = function (modelUri) { TinyYolov2$$1.prototype.loadQuantizedParams = function (modelUri) {
var defaultModelName = this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME$4; var defaultModelName = this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME$5;
return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName); return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
}; };
return TinyYolov2$$1; return TinyYolov2$$1;
...@@ -4544,6 +4681,7 @@ ...@@ -4544,6 +4681,7 @@
var nets = { var nets = {
ssdMobilenetv1: detectionNet, ssdMobilenetv1: detectionNet,
faceLandmark68Net: landmarkNet, faceLandmark68Net: landmarkNet,
faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
faceRecognitionNet: recognitionNet, faceRecognitionNet: recognitionNet,
mtcnn: new Mtcnn(), mtcnn: new Mtcnn(),
tinyYolov2: new TinyYolov2$1() tinyYolov2: new TinyYolov2$1()
...@@ -4554,6 +4692,9 @@ ...@@ -4554,6 +4692,9 @@
function loadFaceLandmarkModel(url) { function loadFaceLandmarkModel(url) {
return nets.faceLandmark68Net.load(url); return nets.faceLandmark68Net.load(url);
} }
function loadFaceLandmarkTinyModel(url) {
return nets.faceLandmark68TinyNet.load(url);
}
function loadFaceRecognitionModel(url) { function loadFaceRecognitionModel(url) {
return nets.faceRecognitionNet.load(url); return nets.faceRecognitionNet.load(url);
} }
...@@ -4567,6 +4708,7 @@ ...@@ -4567,6 +4708,7 @@
return loadSsdMobilenetv1Model(url); return loadSsdMobilenetv1Model(url);
} }
function loadModels(url) { function loadModels(url) {
console.warn('loadModels will be deprecated in future');
return Promise.all([ return Promise.all([
loadSsdMobilenetv1Model(url), loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url), loadFaceLandmarkModel(url),
...@@ -4582,6 +4724,9 @@ ...@@ -4582,6 +4724,9 @@
function detectLandmarks(input) { function detectLandmarks(input) {
return nets.faceLandmark68Net.detectLandmarks(input); return nets.faceLandmark68Net.detectLandmarks(input);
} }
function detectLandmarksTiny(input) {
return nets.faceLandmark68TinyNet.detectLandmarks(input);
}
function computeFaceDescriptor(input) { function computeFaceDescriptor(input) {
return nets.faceRecognitionNet.computeFaceDescriptor(input); return nets.faceRecognitionNet.computeFaceDescriptor(input);
} }
...@@ -4678,6 +4823,7 @@ ...@@ -4678,6 +4823,7 @@
exports.createFaceLandmarkNet = createFaceLandmarkNet; exports.createFaceLandmarkNet = createFaceLandmarkNet;
exports.faceLandmarkNet = faceLandmarkNet; exports.faceLandmarkNet = faceLandmarkNet;
exports.FaceLandmark68Net = FaceLandmark68Net; exports.FaceLandmark68Net = FaceLandmark68Net;
exports.FaceLandmark68TinyNet = FaceLandmark68TinyNet;
exports.createFaceRecognitionNet = createFaceRecognitionNet; exports.createFaceRecognitionNet = createFaceRecognitionNet;
exports.faceRecognitionNet = faceRecognitionNet; exports.faceRecognitionNet = faceRecognitionNet;
exports.FaceRecognitionNet = FaceRecognitionNet; exports.FaceRecognitionNet = FaceRecognitionNet;
...@@ -4687,6 +4833,7 @@ ...@@ -4687,6 +4833,7 @@
exports.nets = nets; exports.nets = nets;
exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model; exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
exports.loadFaceLandmarkModel = loadFaceLandmarkModel; exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
exports.loadFaceRecognitionModel = loadFaceRecognitionModel; exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
exports.loadMtcnnModel = loadMtcnnModel; exports.loadMtcnnModel = loadMtcnnModel;
exports.loadTinyYolov2Model = loadTinyYolov2Model; exports.loadTinyYolov2Model = loadTinyYolov2Model;
...@@ -4695,6 +4842,7 @@ ...@@ -4695,6 +4842,7 @@
exports.locateFaces = locateFaces; exports.locateFaces = locateFaces;
exports.ssdMobilenetv1 = ssdMobilenetv1; exports.ssdMobilenetv1 = ssdMobilenetv1;
exports.detectLandmarks = detectLandmarks; exports.detectLandmarks = detectLandmarks;
exports.detectLandmarksTiny = detectLandmarksTiny;
exports.computeFaceDescriptor = computeFaceDescriptor; exports.computeFaceDescriptor = computeFaceDescriptor;
exports.mtcnn = mtcnn; exports.mtcnn = mtcnn;
exports.tinyYolov2 = tinyYolov2; exports.tinyYolov2 = tinyYolov2;
......
dist/face-api.js.map
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dist/face-api.min.js
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tools/train/faceLandmarks/test.html
...@@ -18,7 +18,7 @@ ...@@ -18,7 +18,7 @@
</div> </div>
<script> <script>
const net = new faceapi.FaceLandmark68LargeNet() const net = new faceapi.FaceLandmark68Net()
const modelCheckpoint = 'tmp/densenet4/checkpoints/landmarks_epoch46_lr00001_12_lr000001_18.weights' const modelCheckpoint = 'tmp/densenet4/checkpoints/landmarks_epoch46_lr00001_12_lr000001_18.weights'
const crops = 4 const crops = 4
...@@ -27,9 +27,9 @@ ...@@ -27,9 +27,9 @@
} }
async function load() { async function load() {
//await net.load('./') await net.load('./')
const weights = await loadNetWeights(modelCheckpoint) //const weights = await loadNetWeights(modelCheckpoint)
await net.load(weights) //await net.load(weights)
console.log('model loaded') console.log('model loaded')
} }
......
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