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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| 'use strict'; | ||
|
|
||
| importScripts('../common/utils_worker.js'); | ||
|
|
||
| // MobileNet V2 model with 'nchw' input layout | ||
| class MobileNetV2NchwSync { | ||
| constructor() { | ||
| this.context_ = null; | ||
| this.builder_ = null; | ||
| this.graph_ = null; | ||
| this.weightsUrl_ = '../test-data/models/mobilenetv2_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, relu6 = true, options = {}) { | ||
| const prefix = this.weightsUrl_ + 'conv_' + 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; | ||
| if (relu6) { | ||
| return this.builder_.clamp( | ||
| this.builder_.conv2d(input, weights, options), | ||
| {minValue: 0, maxValue: 6}); | ||
| } else { | ||
| return this.builder_.conv2d(input, weights, options); | ||
| } | ||
| } | ||
|
|
||
| async buildGemm_(input, name) { | ||
| const prefix = this.weightsUrl_ + 'gemm_' + 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); | ||
| const options = {c: bias, bTranspose: true}; | ||
| return this.builder_.gemm(input, weights, options); | ||
| } | ||
|
|
||
| async buildLinearBottleneck_( | ||
| input, convNameArray, group, stride, shortcut = true) { | ||
| const conv1x1Relu6 = await this.buildConv_(input, convNameArray[0]); | ||
| const options = { | ||
| padding: [1, 1, 1, 1], | ||
| groups: group, | ||
| strides: [stride, stride], | ||
| }; | ||
| const dwise3x3Relu6 = await this.buildConv_( | ||
| conv1x1Relu6, convNameArray[1], true, options); | ||
| const conv1x1Linear = await this.buildConv_( | ||
| dwise3x3Relu6, convNameArray[2], false); | ||
|
|
||
| if (shortcut) { | ||
| return this.builder_.add(input, conv1x1Linear); | ||
| } | ||
| return conv1x1Linear; | ||
| } | ||
|
|
||
| async load(contextOptions) { | ||
| this.context_ = navigator.ml.createContextSync(contextOptions); | ||
| this.builder_ = new MLGraphBuilder(this.context_); | ||
| const data = this.builder_.input('input', | ||
| {type: 'float32', dimensions: this.inputOptions.inputDimensions}); | ||
| const conv0 = await this.buildConv_( | ||
| data, '0', true, {padding: [1, 1, 1, 1], strides: [2, 2]}); | ||
| const conv1 = await this.buildConv_( | ||
| conv0, '2', true, {padding: [1, 1, 1, 1], groups: 32}); | ||
| const conv2 = await this.buildConv_(conv1, '4', false); | ||
| const bottleneck0 = await this.buildLinearBottleneck_( | ||
| conv2, ['5', '7', '9'], 96, 2, false); | ||
| const bottleneck1 = await this.buildLinearBottleneck_( | ||
| bottleneck0, ['10', '12', '14'], 144, 1); | ||
| const bottleneck2 = await this.buildLinearBottleneck_( | ||
| bottleneck1, ['16', '18', '20'], 144, 2, false); | ||
| const bottleneck3 = await this.buildLinearBottleneck_( | ||
| bottleneck2, ['21', '23', '25'], 192, 1); | ||
| const bottleneck4 = await this.buildLinearBottleneck_( | ||
| bottleneck3, ['27', '29', '31'], 192, 1); | ||
| const bottleneck5 = await this.buildLinearBottleneck_( | ||
| bottleneck4, ['33', '35', '37'], 192, 2, false); | ||
| const bottleneck6 = await this.buildLinearBottleneck_( | ||
| bottleneck5, ['38', '40', '42'], 384, 1); | ||
| const bottleneck7 = await this.buildLinearBottleneck_( | ||
| bottleneck6, ['44', '46', '48'], 384, 1); | ||
| const bottleneck8 = await this.buildLinearBottleneck_( | ||
| bottleneck7, ['50', '52', '54'], 384, 1); | ||
| const bottleneck9 = await this.buildLinearBottleneck_( | ||
| bottleneck8, ['56', '58', '60'], 384, 1, false); | ||
| const bottleneck10 = await this.buildLinearBottleneck_( | ||
| bottleneck9, ['61', '63', '65'], 576, 1); | ||
| const bottleneck11 = await this.buildLinearBottleneck_( | ||
| bottleneck10, ['67', '69', '71'], 576, 1); | ||
| const bottleneck12 = await this.buildLinearBottleneck_( | ||
| bottleneck11, ['73', '75', '77'], 576, 2, false); | ||
| const bottleneck13 = await this.buildLinearBottleneck_( | ||
| bottleneck12, ['78', '80', '82'], 960, 1); | ||
| const bottleneck14 = await this.buildLinearBottleneck_( | ||
| bottleneck13, ['84', '86', '88'], 960, 1); | ||
| const bottleneck15 = await this.buildLinearBottleneck_( | ||
| bottleneck14, ['90', '92', '94'], 960, 1, false); | ||
|
|
||
| const conv3 = await this.buildConv_(bottleneck15, '95', true); | ||
| const pool = this.builder_.averagePool2d(conv3); | ||
| const reshape = this.builder_.reshape(pool, [1, null]); | ||
| const gemm = await this.buildGemm_(reshape, '104'); | ||
| return this.builder_.softmax(gemm); | ||
| } | ||
|
|
||
| build(outputOperand) { | ||
| this.graph_ = this.builder_.buildSync({'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(); | ||
| } | ||
| } | ||
|
|
||
| compute(inputBuffer, outputBuffer) { | ||
| const inputs = {'input': inputBuffer}; | ||
| const outputs = {'output': outputBuffer}; | ||
| this.context_.computeSync(this.graph_, inputs, outputs); | ||
| } | ||
| } |
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