99 lines
4.2 KiB
JavaScript
99 lines
4.2 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.AgeGenderNet = void 0;
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const tf = require("@tensorflow/tfjs-core");
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const fullyConnectedLayer_1 = require("../common/fullyConnectedLayer");
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const util_1 = require("../faceProcessor/util");
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const TinyXception_1 = require("../xception/TinyXception");
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const extractParams_1 = require("./extractParams");
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const extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
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const types_1 = require("./types");
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const NeuralNetwork_1 = require("../NeuralNetwork");
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const dom_1 = require("../dom");
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class AgeGenderNet extends NeuralNetwork_1.NeuralNetwork {
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constructor(faceFeatureExtractor = new TinyXception_1.TinyXception(2)) {
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super('AgeGenderNet');
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this._faceFeatureExtractor = faceFeatureExtractor;
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}
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get faceFeatureExtractor() {
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return this._faceFeatureExtractor;
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}
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runNet(input) {
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const { params } = this;
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if (!params) {
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throw new Error(`${this._name} - load model before inference`);
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}
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return tf.tidy(() => {
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const bottleneckFeatures = input instanceof dom_1.NetInput
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? this.faceFeatureExtractor.forwardInput(input)
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: input;
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const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);
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const age = fullyConnectedLayer_1.fullyConnectedLayer(pooled, params.fc.age).as1D();
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const gender = fullyConnectedLayer_1.fullyConnectedLayer(pooled, params.fc.gender);
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return { age, gender };
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});
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}
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forwardInput(input) {
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return tf.tidy(() => {
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const { age, gender } = this.runNet(input);
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return { age, gender: tf.softmax(gender) };
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});
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}
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async forward(input) {
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return this.forwardInput(await dom_1.toNetInput(input));
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}
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async predictAgeAndGender(input) {
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const netInput = await dom_1.toNetInput(input);
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const out = await this.forwardInput(netInput);
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const ages = tf.unstack(out.age);
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const genders = tf.unstack(out.gender);
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const ageAndGenderTensors = ages.map((ageTensor, i) => ({
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ageTensor,
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genderTensor: genders[i]
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}));
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const predictionsByBatch = await Promise.all(ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {
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const age = (await ageTensor.data())[0];
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const probMale = (await genderTensor.data())[0];
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const isMale = probMale > 0.5;
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const gender = isMale ? types_1.Gender.MALE : types_1.Gender.FEMALE;
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const genderProbability = isMale ? probMale : (1 - probMale);
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ageTensor.dispose();
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genderTensor.dispose();
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return { age, gender, genderProbability };
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}));
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out.age.dispose();
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out.gender.dispose();
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return netInput.isBatchInput
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? predictionsByBatch
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: predictionsByBatch[0];
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}
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getDefaultModelName() {
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return 'age_gender_model';
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}
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dispose(throwOnRedispose = true) {
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this.faceFeatureExtractor.dispose(throwOnRedispose);
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super.dispose(throwOnRedispose);
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}
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loadClassifierParams(weights) {
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const { params, paramMappings } = this.extractClassifierParams(weights);
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this._params = params;
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this._paramMappings = paramMappings;
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}
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extractClassifierParams(weights) {
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return extractParams_1.extractParams(weights);
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}
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extractParamsFromWeigthMap(weightMap) {
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const { featureExtractorMap, classifierMap } = util_1.seperateWeightMaps(weightMap);
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this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
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return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(classifierMap);
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}
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extractParams(weights) {
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const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);
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const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
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const classifierWeights = weights.slice(weights.length - classifierWeightSize);
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this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
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return this.extractClassifierParams(classifierWeights);
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}
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}
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exports.AgeGenderNet = AgeGenderNet;
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//# sourceMappingURL=AgeGenderNet.js.map
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