65 lines
2.7 KiB
JavaScript
65 lines
2.7 KiB
JavaScript
import * as tf from '@tensorflow/tfjs-core';
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import { depthwiseSeparableConv } from '../common';
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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 { range } from '../utils';
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import { extractParams } from './extractParams';
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import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
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function conv(x, params, stride) {
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return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);
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}
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function reductionBlock(x, params, isActivateInput = true) {
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let out = isActivateInput ? tf.relu(x) : x;
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out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
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out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
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out = tf.maxPool(out, [3, 3], [2, 2], 'same');
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out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));
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return out;
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}
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function mainBlock(x, params) {
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let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);
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out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
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out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);
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out = tf.add(out, x);
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return out;
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}
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export class TinyXception extends NeuralNetwork {
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constructor(numMainBlocks) {
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super('TinyXception');
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this._numMainBlocks = numMainBlocks;
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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('TinyXception - 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(256));
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let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
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out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
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out = reductionBlock(out, params.entry_flow.reduction_block_1);
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range(this._numMainBlocks, 0, 1).forEach((idx) => {
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out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);
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});
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out = reductionBlock(out, params.exit_flow.reduction_block);
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out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
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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 'tiny_xception_model';
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}
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extractParamsFromWeigthMap(weightMap) {
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return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);
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}
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extractParams(weights) {
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return extractParams(weights, this._numMainBlocks);
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}
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}
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//# sourceMappingURL=TinyXception.js.map
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