94 lines
2.8 KiB
TypeScript
94 lines
2.8 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 { convDown } from './convLayer';
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import { extractParams } from './extractParams';
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import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
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import { residual, residualDown } from './residualLayer';
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import { NetParams } from './types';
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export class FaceRecognitionNet extends NeuralNetwork<NetParams> {
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constructor() {
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super('FaceRecognitionNet')
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}
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public forwardInput(input: NetInput): tf.Tensor2D {
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const { params } = this
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if (!params) {
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throw new Error('FaceRecognitionNet - load model before inference')
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}
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return tf.tidy(() => {
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const batchTensor = input.toBatchTensor(150, true).toFloat()
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const meanRgb = [122.782, 117.001, 104.298]
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const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D
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let out = convDown(normalized, params.conv32_down)
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out = tf.maxPool(out, 3, 2, 'valid')
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out = residual(out, params.conv32_1)
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out = residual(out, params.conv32_2)
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out = residual(out, params.conv32_3)
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out = residualDown(out, params.conv64_down)
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out = residual(out, params.conv64_1)
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out = residual(out, params.conv64_2)
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out = residual(out, params.conv64_3)
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out = residualDown(out, params.conv128_down)
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out = residual(out, params.conv128_1)
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out = residual(out, params.conv128_2)
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out = residualDown(out, params.conv256_down)
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out = residual(out, params.conv256_1)
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out = residual(out, params.conv256_2)
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out = residualDown(out, params.conv256_down_out)
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const globalAvg = out.mean([1, 2]) as tf.Tensor2D
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const fullyConnected = tf.matMul(globalAvg, params.fc)
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return fullyConnected as tf.Tensor2D
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})
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}
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public async forward(input: TNetInput): Promise<tf.Tensor2D> {
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return this.forwardInput(await toNetInput(input))
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}
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public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {
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const netInput = await toNetInput(input)
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const faceDescriptorTensors = tf.tidy(
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() => tf.unstack(this.forwardInput(netInput))
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)
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const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(
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t => t.data()
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)) as Float32Array[]
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faceDescriptorTensors.forEach(t => t.dispose())
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return netInput.isBatchInput
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? faceDescriptorsForBatch
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: faceDescriptorsForBatch[0]
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}
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protected getDefaultModelName(): string {
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return 'face_recognition_model'
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}
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protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
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return extractParamsFromWeigthMap(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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