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deeplabv3_mnv2_nchw.js
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'use strict';
import {buildConstantByNpy} from '../common/utils.js';
/* eslint max-len: ["error", {"code": 120}] */
// DeepLab V3 MobileNet V2 model with 'nchw' input layout
export class DeepLabV3MNV2Nchw {
constructor() {
this.context_ = null;
this.deviceType_ = null;
this.builder_ = null;
this.graph_ = null;
this.weightsUrl_ = '../test-data/models/deeplabv3_mnv2_nchw/weights/';
if (location.hostname.toLowerCase().indexOf('github.io') > -1) {
this.weightsUrl_ = 'https://d3i5xkfad89fac.cloudfront.net/test-data/models/deeplabv3_mnv2_nchw/weights/';
}
// Shares the same bias files with 'nhwc' layout
this.biasUrl_ = '../test-data/models/deeplabv3_mnv2_nhwc/weights/';
if (location.hostname.toLowerCase().indexOf('github.io') > -1) {
this.biasUrl_ = 'https://d3i5xkfad89fac.cloudfront.net/test-data/models/deeplabv3_mnv2_nhwc/weights/';
}
this.inputOptions = {
mean: [127.5, 127.5, 127.5],
std: [127.5, 127.5, 127.5],
scaledFlag: true,
inputLayout: 'nchw',
labelUrl: './labels/labels.txt',
inputDimensions: [1, 3, 513, 513],
};
this.outputDimensions = [1, 21, 513, 513];
}
async buildConv_(input, nameArray, activation = 'relu6', options = {}) {
// nameArray: 0: bias name prefix, 1: depthWise Conv2D's bias name suffix, 2: indice of weight name
const biasPrefix = this.biasUrl_ + nameArray[0];
const weightsName = `${this.weightsUrl_}const_fold_opt__${nameArray[2]}.npy`;
let biasName = biasPrefix + '_bn_offset.npy';
if (nameArray[0].includes('depthwise')) {
biasName = `${biasPrefix}_bn_offset.npy`;
if (nameArray[1] !== '') {
biasName = `${biasPrefix}_${nameArray[1]}.npy`;
}
} else if (nameArray[0] === 'logits_semantic') {
biasName = biasPrefix + '_biases.npy';
}
const weights = await buildConstantByNpy(this.builder_, weightsName);
const bias = await buildConstantByNpy(this.builder_, biasName);
options.bias = bias;
if (activation === 'relu6') {
// TODO: Set clamp activation to options once it's supported in
// WebNN DML backend.
// Implement `clip` by `clamp` of WebNN API
if (this.deviceType_ == 'gpu') {
return this.builder_.clamp(
this.builder_.conv2d(input, weights, options),
{minValue: 0, maxValue: 6});
} else {
options.activation = this.builder_.clamp({minValue: 0, maxValue: 6});
}
} else if (activation === 'relu') {
options.activation = this.builder_.relu();
} else {
options.activation = undefined;
}
return this.builder_.conv2d(input, weights, options);
}
async buildLinearBottleneck_(input, nameArray, dwiseOptions, shortcut = true) {
// nameArray: 0: indice of bias name, 1: indice of conv1x1Relu6's weight name,
// 2: indice of dwise3x3Relu6's weight name, 3: indice of conv1x1Linear's weight name
const biasPrefix = 'MobilenetV2_expanded_conv_' + nameArray[0];
let dwBiasSuffix = 'depthwise_bn_offset';
if (Number.parseInt(nameArray[0]) > 6) {
dwBiasSuffix = 'BatchNorm_FusedBatchNorm';
}
const conv1x1Relu6 = await this.buildConv_(
input,
[`${biasPrefix}_expand_Conv2D`, dwBiasSuffix, nameArray[1]]);
const dwise3x3Relu6 = await this.buildConv_(
conv1x1Relu6,
[`${biasPrefix}_depthwise`, dwBiasSuffix, nameArray[2]],
'relu6',
dwiseOptions);
const conv1x1Linear = await this.buildConv_(
dwise3x3Relu6,
[`${biasPrefix}_project_Conv2D`, dwBiasSuffix, nameArray[3]],
'none');
if (shortcut) {
return this.builder_.add(input, conv1x1Linear);
}
return conv1x1Linear;
}
async load(contextOptions) {
this.context_ = await navigator.ml.createContext(contextOptions);
this.deviceType_ = contextOptions.deviceType;
this.builder_ = new MLGraphBuilder(this.context_);
const strides = [2, 2];
const input = this.builder_.input('input', {
type: 'float32',
dataType: 'float32',
dimensions: this.inputOptions.inputDimensions,
});
const conv0 = await this.buildConv_(
input, ['MobilenetV2_Conv_Conv2D', '', '551'], 'relu6', {strides, padding: [1, 1, 1, 1]});
const conv1 = await this.buildConv_(
conv0, ['MobilenetV2_expanded_conv_depthwise_depthwise', '', '543'], 'relu6',
{padding: [1, 1, 1, 1], groups: 32});
const conv2 = await this.buildConv_(
conv1, ['MobilenetV2_expanded_conv_project_Conv2D', '', '511'], 'none');
const bottleneck0 = await this.buildLinearBottleneck_(
conv2, ['1', '537', '494', '534'], {strides, padding: [1, 1, 1, 1], groups: 96}, false);
const bottleneck1 = await this.buildLinearBottleneck_(
bottleneck0, ['2', '447', '555', '523'], {padding: [1, 1, 1, 1], groups: 144});
const bottleneck2 = await this.buildLinearBottleneck_(
bottleneck1, ['3', '520', '562', '542'], {strides, padding: [1, 1, 1, 1], groups: 144}, false);
const bottleneck3 = await this.buildLinearBottleneck_(
bottleneck2, ['4', '503', '505', '489'], {padding: [1, 1, 1, 1], groups: 192});
const bottleneck4 = await this.buildLinearBottleneck_(
bottleneck3, ['5', '446', '530', '522'], {padding: [1, 1, 1, 1], groups: 192});
const bottleneck5 = await this.buildLinearBottleneck_(
bottleneck4, ['6', '491', '561', '538'], {padding: [1, 1, 1, 1], groups: 192}, false);
const bottleneck6 = await this.buildLinearBottleneck_(
bottleneck5, ['7', '487', '560', '478'], {padding: [2, 2, 2, 2], groups: 384, dilations: [2, 2]});
const bottleneck7 = await this.buildLinearBottleneck_(
bottleneck6, ['8', '467', '536', '455'], {padding: [2, 2, 2, 2], groups: 384, dilations: [2, 2]});
const bottleneck8 = await this.buildLinearBottleneck_(
bottleneck7, ['9', '474', '524', '558'], {padding: [2, 2, 2, 2], groups: 384, dilations: [2, 2]});
const bottleneck9 = await this.buildLinearBottleneck_(
bottleneck8, ['10', '465', '556', '462'], {padding: [2, 2, 2, 2], groups: 384, dilations: [2, 2]}, false);
const bottleneck10 = await this.buildLinearBottleneck_(
bottleneck9, ['11', '453', '532', '450'], {padding: [2, 2, 2, 2], groups: 576, dilations: [2, 2]});
const bottleneck11 = await this.buildLinearBottleneck_(
bottleneck10, ['12', '441', '554', '517'], {padding: [2, 2, 2, 2], groups: 576, dilations: [2, 2]});
const bottleneck12 = await this.buildLinearBottleneck_(
bottleneck11, ['13', '544', '509', '479'], {padding: [2, 2, 2, 2], groups: 576, dilations: [2, 2]}, false);
const bottleneck13 = await this.buildLinearBottleneck_(
bottleneck12, ['14', '482', '552', '512'], {padding: [4, 4, 4, 4], groups: 960, dilations: [4, 4]});
const bottleneck14 = await this.buildLinearBottleneck_(
bottleneck13, ['15', '475', '495', '563'], {padding: [4, 4, 4, 4], groups: 960, dilations: [4, 4]});
const bottleneck15 = await this.buildLinearBottleneck_(
bottleneck14, ['16', '500', '459', '539'], {padding: [4, 4, 4, 4], groups: 960, dilations: [4, 4]}, false);
const conv3 = await this.buildConv_(bottleneck15, ['aspp0_Conv2D', '', '553'], 'relu');
const averagePool2d = this.builder_.averagePool2d(
bottleneck15, {windowDimensions: [65, 65], layout: 'nchw'});
const conv4 = await this.buildConv_(averagePool2d, ['image_pooling_Conv2D', '', '546'], 'relu');
const resample0 = this.builder_.resample2d(
conv4, {sizes: [65, 65], mode: 'linear'});
const concat = this.builder_.concat([resample0, conv3], 1);
const conv5 = await this.buildConv_(concat, ['concat_projection_Conv2D', '', '502'], 'relu');
const conv6 = await this.buildConv_(conv5, ['logits_semantic', '', '541'], 'none');
const resample1 = this.builder_.resample2d(
conv6, {sizes: [65, 65], mode: 'linear'});
return this.builder_.resample2d(
resample1, {sizes: [513, 513], mode: 'linear'});
}
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;
}
}