54 lines
1.7 KiB
TypeScript
54 lines
1.7 KiB
TypeScript
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import * as tf from '@tensorflow/tfjs-core';
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import { NetInput, TNetInput, toNetInput } from '../dom';
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import { NeuralNetwork } from '../NeuralNetwork';
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import { normalize } from '../ops';
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import { denseBlock3 } from './denseBlock';
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import { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';
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import { extractParamsTiny } from './extractParamsTiny';
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import { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';
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export class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {
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constructor() {
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super('TinyFaceFeatureExtractor')
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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('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 = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
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let out = denseBlock3(normalized, params.dense0, true)
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out = denseBlock3(out, params.dense1)
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out = 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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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_tiny_model'
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}
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protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
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return extractParamsFromWeigthMapTiny(weightMap)
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}
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protected extractParams(weights: Float32Array) {
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return extractParamsTiny(weights)
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}
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}
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