54 lines
1.8 KiB
TypeScript
54 lines
1.8 KiB
TypeScript
import * as tf from '../../dist/tfjs.esm';
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import { NetInput, TNetInput, toNetInput } from '../dom/index';
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import { NeuralNetwork } from '../NeuralNetwork';
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import { normalize } from '../ops/index';
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import { denseBlock4 } from './denseBlock';
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import { extractParams } from './extractParams';
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import { extractParamsFromWeightMap } from './extractParamsFromWeightMap';
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import { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';
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export class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {
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constructor() {
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super('FaceFeatureExtractor');
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}
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public forwardInput(input: NetInput): tf.Tensor4D {
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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 = tf.cast(input.toBatchTensor(112, true), 'float32');
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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)) as tf.Tensor4D;
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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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public async forward(input: TNetInput): Promise<tf.Tensor4D> {
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return this.forwardInput(await toNetInput(input));
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}
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protected getDefaultModelName(): string {
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return 'face_feature_extractor_model';
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}
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protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {
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return extractParamsFromWeightMap(weightMap);
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}
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protected extractParams(weights: Float32Array) {
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return extractParams(weights);
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}
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}
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