squeezenet_nchw.js

'use strict';

import {buildConstantByNpy} from '../common/utils.js';

// SqueezeNet 1.1 model with 'nchw' input layout
export class SqueezeNetNchw {
  constructor() {
    this.context_ = null;
    this.builder_ = null;
    this.graph_ = null;
    this.weightsUrl_ = '../test-data/models/squeezenet1.1_nchw/weights/';
    if (location.hostname.toLowerCase().indexOf('github.io') > -1) {
      this.weightsUrl_ = 'https://d3i5xkfad89fac.cloudfront.net/test-data/models/squeezenet1.1_nchw/weights/';
    }
    this.inputOptions = {
      mean: [0.485, 0.456, 0.406],
      std: [0.229, 0.224, 0.225],
      norm: true,
      inputLayout: 'nchw',
      labelUrl: './labels/labels1000.txt',
      inputDimensions: [1, 3, 224, 224],
    };
    this.outputDimensions = [1, 1000];
  }

  async buildConv_(input, name, options = {}) {
    const prefix = this.weightsUrl_ + 'squeezenet0_' + name;
    const weightsName = prefix + '_weight.npy';
    const weights = await buildConstantByNpy(this.builder_, weightsName);
    const biasName = prefix + '_bias.npy';
    const bias = await buildConstantByNpy(this.builder_, biasName);
    options.bias = bias;
    options.activation = this.builder_.relu();
    return this.builder_.conv2d(input, weights, options);
  }

  async buildFire_(input, convName, conv1x1Name, conv3x3Name) {
    const conv = await this.buildConv_(input, convName);
    const conv1x1 = await this.buildConv_(conv, conv1x1Name);
    const conv3x3 = await this.buildConv_(
        conv, conv3x3Name, {padding: [1, 1, 1, 1]});
    return this.builder_.concat([conv1x1, conv3x3], 1);
  }

  async load(contextOptions) {
    this.context_ = await navigator.ml.createContext(contextOptions);
    this.builder_ = new MLGraphBuilder(this.context_);
    const data = this.builder_.input('input', {
      type: 'float32',
      dataType: 'float32',
      dimensions: this.inputOptions.inputDimensions,
    });
    const conv0 = await this.buildConv_(data, 'conv0', {strides: [2, 2]});
    const pool0 = this.builder_.maxPool2d(
        conv0, {windowDimensions: [3, 3], strides: [2, 2]});
    const fire0 = await this.buildFire_(pool0, 'conv1', 'conv2', 'conv3');
    const fire1 = await this.buildFire_(fire0, 'conv4', 'conv5', 'conv6');
    const pool1 = this.builder_.maxPool2d(
        fire1, {windowDimensions: [3, 3], strides: [2, 2]});
    const fire2 = await this.buildFire_(pool1, 'conv7', 'conv8', 'conv9');
    const fire3 = await this.buildFire_(fire2, 'conv10', 'conv11', 'conv12');
    const pool2 = this.builder_.maxPool2d(
        fire3, {windowDimensions: [3, 3], strides: [2, 2]});
    const fire4 = await this.buildFire_(pool2, 'conv13', 'conv14', 'conv15');
    const fire5 = await this.buildFire_(fire4, 'conv16', 'conv17', 'conv18');
    const fire6 = await this.buildFire_(fire5, 'conv19', 'conv20', 'conv21');
    const fire7 = await this.buildFire_(fire6, 'conv22', 'conv23', 'conv24');
    const conv25 = await this.buildConv_(fire7, 'conv25');
    const pool3 = this.builder_.averagePool2d(
        conv25, {windowDimensions: [13, 13], strides: [13, 13]});
    const reshape0 = this.builder_.reshape(pool3, [1, 1000]);
    return this.builder_.softmax(reshape0);
  }

  async build(outputOperand) {
    this.graph_ = await this.builder_.build({'output': outputOperand});
  }

  // Release the constant tensors of a model
  dispose() {
    // dispose() is only available in webnn-polyfill
    if (this.graph_ !== null && 'dispose' in this.graph_) {
      this.graph_.dispose();
    }
  }

  async compute(inputBuffer, outputBuffer) {
    const inputs = {'input': inputBuffer};
    const outputs = {'output': outputBuffer};
    const results = await this.context_.compute(this.graph_, inputs, outputs);
    return results;
  }
}