45 lines
1.8 KiB
JavaScript
45 lines
1.8 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.TinyFaceFeatureExtractor = void 0;
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const tf = require("@tensorflow/tfjs-core");
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const dom_1 = require("../dom");
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const NeuralNetwork_1 = require("../NeuralNetwork");
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const ops_1 = require("../ops");
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const denseBlock_1 = require("./denseBlock");
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const extractParamsFromWeigthMapTiny_1 = require("./extractParamsFromWeigthMapTiny");
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const extractParamsTiny_1 = require("./extractParamsTiny");
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class TinyFaceFeatureExtractor extends NeuralNetwork_1.NeuralNetwork {
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constructor() {
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super('TinyFaceFeatureExtractor');
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}
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forwardInput(input) {
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const { params } = this;
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if (!params) {
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throw new Error('TinyFaceFeatureExtractor - load model before inference');
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}
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return tf.tidy(() => {
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const batchTensor = input.toBatchTensor(112, true);
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const meanRgb = [122.782, 117.001, 104.298];
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const normalized = ops_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
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let out = denseBlock_1.denseBlock3(normalized, params.dense0, true);
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out = denseBlock_1.denseBlock3(out, params.dense1);
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out = denseBlock_1.denseBlock3(out, params.dense2);
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out = tf.avgPool(out, [14, 14], [2, 2], 'valid');
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return out;
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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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getDefaultModelName() {
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return 'face_feature_extractor_tiny_model';
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}
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extractParamsFromWeigthMap(weightMap) {
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return extractParamsFromWeigthMapTiny_1.extractParamsFromWeigthMapTiny(weightMap);
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}
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extractParams(weights) {
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return extractParamsTiny_1.extractParamsTiny(weights);
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}
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}
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exports.TinyFaceFeatureExtractor = TinyFaceFeatureExtractor;
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//# sourceMappingURL=TinyFaceFeatureExtractor.js.map
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