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Merge pull request #226 from mingmingtasd/npu_fp16
Add NPU device type and three fp16 models for image classification
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,6 @@ | ||
| module.exports = { | ||
| globals: { | ||
| 'MLGraphBuilder': 'readonly', | ||
| 'tf': 'readonly', | ||
| }, | ||
| }; |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,176 @@ | ||
| 'use strict'; | ||
|
|
||
| import {buildConstantByNpy, weightsOrigin} from '../common/utils.js'; | ||
|
|
||
| // EfficientNet fp16 model with 'nchw' input layout | ||
| export class EfficientNetFP16Nchw { | ||
| constructor() { | ||
| this.context_ = null; | ||
| this.builder_ = null; | ||
| this.graph_ = null; | ||
| this.weightsUrl_ = weightsOrigin() + | ||
| '/test-data/models/efficientnet_fp16_nchw_optimized/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]; | ||
| } | ||
|
|
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| async buildConv_(input, name, blockName, clip = false, options = {}) { | ||
| let prefix = ''; | ||
| if (blockName !== '') { | ||
| prefix = this.weightsUrl_ + 'block' + blockName + '_conv' + | ||
| name; | ||
| } else { | ||
| prefix = this.weightsUrl_ + 'conv' + name; | ||
| } | ||
| const weight = buildConstantByNpy(this.builder_, prefix + '_w.npy', | ||
| 'float16'); | ||
| options.bias = await buildConstantByNpy(this.builder_, prefix + '_b.npy', | ||
| 'float16'); | ||
| if (clip) { | ||
| return this.builder_.clamp( | ||
| this.builder_.conv2d(await input, await weight, options), | ||
| {minValue: 0, maxValue: 6}); | ||
| } | ||
| return this.builder_.conv2d(await input, await weight, options); | ||
| } | ||
|
|
||
| async buildGemm_(input, name) { | ||
| const prefix = this.weightsUrl_ + 'dense' + name; | ||
| const weightName = prefix + '_w.npy'; | ||
| const weight = buildConstantByNpy(this.builder_, weightName, | ||
| 'float16'); | ||
| const biasName = prefix + '_b.npy'; | ||
| const bias = buildConstantByNpy(this.builder_, biasName, | ||
| 'float16'); | ||
| const options = | ||
| {c: this.builder_.reshape(await bias, [1, 1000])}; | ||
| return await this.builder_.gemm(await input, await weight, options); | ||
| } | ||
|
|
||
| async buildBottleneck_(input, blockName, group, pad = 1) { | ||
| const conv1 = this.buildConv_(input, '0', blockName, true); | ||
| const conv2 = this.buildConv_(conv1, '1', blockName, true, | ||
| {groups: group, padding: [pad, pad, pad, pad]}); | ||
| const conv3 = this.buildConv_(conv2, '2', blockName); | ||
| return this.builder_.add(await conv3, await input); | ||
| } | ||
|
|
||
| async buildBottlenecks_(input, blockNames, group, pad = 1) { | ||
| let result = input; | ||
| for (let i = 0; i < blockNames.length; i++) { | ||
| const bottleneck = await this.buildBottleneck_(result, blockNames[i], | ||
| group, pad); | ||
| result = bottleneck; | ||
| } | ||
| return result; | ||
| } | ||
|
|
||
| async load(contextOptions) { | ||
| this.context_ = await navigator.ml.createContext(contextOptions); | ||
| this.builder_ = new MLGraphBuilder(this.context_); | ||
| let data = this.builder_.input('input', { | ||
| dataType: 'float32', | ||
| dimensions: this.inputOptions.inputDimensions, | ||
| }); | ||
| data = this.builder_.cast(data, 'float16'); | ||
| // Block 0 | ||
| const conv1 = this.buildConv_( | ||
| data, '0', '0', true, {padding: [0, 1, 0, 1], strides: [2, 2]}); | ||
| const conv2 = this.buildConv_(conv1, '1', '0', true, | ||
| {groups: 32, padding: [1, 1, 1, 1]}); | ||
| const conv3 = this.buildConv_(conv2, '2', '0'); | ||
|
|
||
| // Block 1 | ||
| const conv4 = this.buildConv_(conv3, '0', '1', true); | ||
| const conv5 = this.buildConv_(conv4, '1', '1', true, | ||
| {groups: 144, padding: [0, 1, 0, 1], strides: [2, 2]}); | ||
| const conv6 = this.buildConv_(conv5, '2', '1'); | ||
|
|
||
| // Block 2~4 | ||
| const bottleneck4 = this.buildBottlenecks_(conv6, | ||
| ['2', '3', '4'], 192); | ||
|
|
||
| // Block 5 | ||
| const conv7 = this.buildConv_(bottleneck4, '0', '5', true); | ||
| const conv8 = this.buildConv_(conv7, '1', '5', true, | ||
| {groups: 192, padding: [1, 2, 1, 2], strides: [2, 2]}); | ||
| const conv9 = this.buildConv_(conv8, '2', '5'); | ||
|
|
||
| // Block 6~8 | ||
| const bottleneck8 = this.buildBottlenecks_(conv9, | ||
| ['6', '7', '8'], 336, 2); | ||
|
|
||
| // Block 9 | ||
| const conv10 = this.buildConv_(bottleneck8, '0', '9', true); | ||
| const conv11 = this.buildConv_(conv10, '1', '9', true, | ||
| {groups: 336, padding: [0, 1, 0, 1], strides: [2, 2]}); | ||
| const conv12 = this.buildConv_(conv11, '2', '9'); | ||
|
|
||
| // Block 10~14 | ||
| const bottleneck14 = this.buildBottlenecks_(conv12, | ||
| ['10', '11', '12', '13', '14'], 672); | ||
|
|
||
| // Block 15 | ||
| const conv13 = this.buildConv_(bottleneck14, '0', '15', true); | ||
| const conv14 = this.buildConv_(conv13, '1', '15', true, | ||
| {groups: 672, padding: [2, 2, 2, 2]}); | ||
| const conv15 = this.buildConv_(conv14, '2', '15'); | ||
|
|
||
| // Block 16~20 | ||
| const bottleneck20 = await this.buildBottlenecks_(conv15, | ||
| ['16', '17', '18', '19', '20'], 960, 2); | ||
|
|
||
| // Block 21 | ||
| const conv16 = this.buildConv_(bottleneck20, '0', '21', true); | ||
| const conv17 = this.buildConv_(conv16, '1', '21', true, | ||
| {groups: 960, padding: [1, 2, 1, 2], strides: [2, 2]}); | ||
| const conv18 = this.buildConv_(conv17, '2', '21'); | ||
|
|
||
| // Block 22~28 | ||
| const bottleneck28 = this.buildBottlenecks_(conv18, | ||
| ['22', '23', '24', '25', '26', '27', '28'], 1632, 2); | ||
|
|
||
| // Block 29 | ||
| const conv19 = this.buildConv_(bottleneck28, '0', '29', true); | ||
| const conv20 = this.buildConv_(conv19, '1', '29', true, | ||
| {groups: 1632, padding: [1, 1, 1, 1]}); | ||
| const conv21 = this.buildConv_(conv20, '2', '29'); | ||
|
|
||
| const conv22 = this.buildConv_(conv21, '0', '', true); | ||
| const pool1 = this.builder_.averagePool2d(await conv22); | ||
| const reshape = this.builder_.reshape(pool1, [1, 1280]); | ||
| const gemm = this.buildGemm_(reshape, '0'); | ||
| if (contextOptions.deviceType === 'npu') { | ||
| return this.builder_.cast(await gemm, 'float32'); | ||
| } else { | ||
| const softmax = this.builder_.softmax(await gemm); | ||
| return this.builder_.cast(softmax, 'float32'); | ||
| } | ||
| } | ||
|
|
||
| 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; | ||
| } | ||
| } |
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