42 lines
1.6 KiB
JavaScript
42 lines
1.6 KiB
JavaScript
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import * as tf from '@tensorflow/tfjs-core';
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import { toNetInput } from '../dom';
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import { NeuralNetwork } from '../NeuralNetwork';
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import { normalize } from '../ops';
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import { denseBlock4 } from './denseBlock';
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import { extractParams } from './extractParams';
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import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
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export class FaceFeatureExtractor extends NeuralNetwork {
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constructor() {
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super('FaceFeatureExtractor');
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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('FaceFeatureExtractor - 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 = normalize(batchTensor, meanRgb).div(tf.scalar(255));
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let out = denseBlock4(normalized, params.dense0, true);
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out = denseBlock4(out, params.dense1);
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out = denseBlock4(out, params.dense2);
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out = denseBlock4(out, params.dense3);
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out = tf.avgPool(out, [7, 7], [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 toNetInput(input));
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}
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getDefaultModelName() {
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return 'face_feature_extractor_model';
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}
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extractParamsFromWeigthMap(weightMap) {
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return extractParamsFromWeigthMap(weightMap);
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}
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extractParams(weights) {
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return extractParams(weights);
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}
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}
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//# sourceMappingURL=FaceFeatureExtractor.js.map
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