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Commit 4b49c5a0 by vincent
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check in latest build

parent ef3ecfba
Showing with 393 additions and 102 deletions
build/faceLandmarkNet/FaceLandmark68Net.js
......@@ -2,44 +2,46 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
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 FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) {
return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
return tf.maxPool(x, [2, 2], strides, 'valid');
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]);
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) {
tslib_1.__extends(FaceLandmark68Net, _super);
function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68Net') || this;
return _super.call(this, 'FaceLandmark68LargeNet') || this;
}
FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmark68Net - load model before inference');
throw new Error('FaceLandmark68LargeNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(128, true).toFloat();
var out = conv(batchTensor, params.conv0);
out = maxPool(out);
out = conv(out, params.conv1);
out = conv(out, params.conv2);
out = maxPool(out);
out = conv(out, params.conv3);
out = conv(out, params.conv4);
out = maxPool(out);
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);
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 = denseBlock(out, params.dense3);
out = tf.avgPool(out, [7, 7], [2, 2], 'valid');
return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
......
build/faceLandmarkNet/FaceLandmark68Net.js.map
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\ No newline at end of file
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\ No newline at end of file
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
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68TinyNet.js.map 0 → 100644
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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";
Object.defineProperty(exports, "__esModule", { value: true });
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) {
var paramMappings = [];
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 extractFCParams = tfjs_tiny_yolov2_1.extractFCParamsFactory(extractWeights, paramMappings);
var conv0 = extractConvParams(3, 32, 3, 'conv0');
var conv1 = extractConvParams(32, 64, 3, 'conv1');
var conv2 = extractConvParams(64, 64, 3, 'conv2');
var conv3 = extractConvParams(64, 64, 3, 'conv3');
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');
var _b = extractorsFactory_1.extractorsFactory(extractWeights, paramMappings), extractDenseBlock4Params = _b.extractDenseBlock4Params, extractFCParams = _b.extractFCParams;
var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
var fc = extractFCParams(256, 136, 'fc');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
paramMappings: paramMappings,
params: {
conv0: conv0,
conv1: conv1,
conv2: conv2,
conv3: conv3,
conv4: conv4,
conv5: conv5,
conv6: conv6,
conv7: conv7,
fc0: fc0,
fc1: fc1
}
params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3, fc: fc }
};
}
exports.extractParams = extractParams;
......
build/faceLandmarkNet/extractParams.js.map
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\ No newline at end of file
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\ 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';
export * from './FaceLandmark68Net';
export * from './FaceLandmark68TinyNet';
export declare class FaceLandmarkNet extends FaceLandmark68Net {
}
export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
......
build/faceLandmarkNet/index.js
......@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceLandmark68Net_1 = require("./FaceLandmark68Net");
tslib_1.__exportStar(require("./FaceLandmark68Net"), exports);
tslib_1.__exportStar(require("./FaceLandmark68TinyNet"), exports);
var FaceLandmarkNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarkNet, _super);
function FaceLandmarkNet() {
......
build/faceLandmarkNet/index.js.map
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\ No newline at end of file
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\ 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 @@
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_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) {
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) {
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 = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
_a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
params = {
conv0: extractConvParams('conv2d_0', 'conv0'),
conv1: extractConvParams('conv2d_1', 'conv1'),
conv2: extractConvParams('conv2d_2', 'conv2'),
conv3: extractConvParams('conv2d_3', 'conv3'),
conv4: extractConvParams('conv2d_4', 'conv4'),
conv5: extractConvParams('conv2d_5', 'conv5'),
conv6: extractConvParams('conv2d_6', 'conv6'),
conv7: extractConvParams('conv2d_7', 'conv7'),
fc0: extractFcParams('dense', 'fc0'),
fc1: extractFcParams('logits', 'fc1')
dense0: extractDenseBlock4Params('dense0', true),
dense1: extractDenseBlock4Params('dense1'),
dense2: extractDenseBlock4Params('dense2'),
dense3: extractDenseBlock4Params('dense3'),
fc: extractFcParams('fc')
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
......
build/faceLandmarkNet/loadQuantizedParams.js.map
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\ No newline at end of file
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\ 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 { 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 = {
conv0: ConvParams;
conv1: ConvParams;
conv2: ConvParams;
conv3: ConvParams;
conv4: ConvParams;
conv5: ConvParams;
conv6: ConvParams;
conv7: ConvParams;
fc0: FCParams;
fc1: FCParams;
dense0: DenseBlock4Params;
dense1: DenseBlock4Params;
dense2: DenseBlock4Params;
dense3: DenseBlock4Params;
fc: FCParams;
};
build/globalApi.d.ts
......@@ -6,6 +6,7 @@ import { FaceLandmarks68 } from './classes/FaceLandmarks68';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from './faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
......@@ -16,12 +17,14 @@ export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmark68Net;
faceLandmark68TinyNet: FaceLandmark68TinyNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
};
export declare function loadSsdMobilenetv1Model(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 loadMtcnnModel(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
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare const ssdMobilenetv1: typeof locateFaces;
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 mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
......
build/globalApi.js
......@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net");
var FaceLandmark68TinyNet_1 = require("./faceLandmarkNet/FaceLandmark68TinyNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
......@@ -14,6 +15,7 @@ exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
exports.nets = {
ssdMobilenetv1: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2()
......@@ -26,6 +28,10 @@ function loadFaceLandmarkModel(url) {
return exports.nets.faceLandmark68Net.load(url);
}
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceLandmarkTinyModel(url) {
return exports.nets.faceLandmark68TinyNet.load(url);
}
exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
function loadFaceRecognitionModel(url) {
return exports.nets.faceRecognitionNet.load(url);
}
......@@ -43,6 +49,7 @@ function loadFaceDetectionModel(url) {
}
exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadModels(url) {
console.warn('loadModels will be deprecated in future');
return Promise.all([
loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url),
......@@ -61,6 +68,10 @@ function detectLandmarks(input) {
return exports.nets.faceLandmark68Net.detectLandmarks(input);
}
exports.detectLandmarks = detectLandmarks;
function detectLandmarksTiny(input) {
return exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
}
exports.detectLandmarksTiny = detectLandmarksTiny;
function computeFaceDescriptor(input) {
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
}
......
build/globalApi.js.map
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\ No newline at end of file
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 @@
</div>
<script>
const net = new faceapi.FaceLandmark68LargeNet()
const net = new faceapi.FaceLandmark68Net()
const modelCheckpoint = 'tmp/densenet4/checkpoints/landmarks_epoch46_lr00001_12_lr000001_18.weights'
const crops = 4
......@@ -27,9 +27,9 @@
}
async function load() {
//await net.load('./')
const weights = await loadNetWeights(modelCheckpoint)
await net.load(weights)
await net.load('./')
//const weights = await loadNetWeights(modelCheckpoint)
//await net.load(weights)
console.log('model loaded')
}
......
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