@tensorflow/tfjs-backend-wasm
Version:
This package adds a WebAssembly backend to TensorFlow.js. It currently supports the following models from our [models](https://github.com/tensorflow/tfjs-models) repo: - BlazeFace - BodyPix - CocoSSD - Face landmarks detection - HandPose - KNN classifier
79 lines • 12.6 kB
JavaScript
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import { backend_util, DepthwiseConv2dNative } from '@tensorflow/tfjs-core';
let wasmDepthwiseConv2d;
function setup(backend) {
wasmDepthwiseConv2d =
backend.wasm.cwrap(DepthwiseConv2dNative, null /* void */, [
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number', // outId
]);
}
function depthwiseConv2d(args) {
const { inputs, attrs, backend } = args;
const { x, filter } = inputs;
const xId = backend.dataIdMap.get(x.dataId).id;
const filterId = backend.dataIdMap.get(filter.dataId).id;
const { strides, dilations, pad, dimRoundingMode } = attrs;
const $dilations = dilations == null ? [1, 1] : dilations;
const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const outputChannels = convInfo.outChannels;
const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
if (convInfo.dataFormat !== 'channelsLast') {
throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'` +
`${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
const out = backend.makeOutput(convInfo.outShape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
return out;
}
export const depthwiseConv2dNativeConfig = {
kernelName: DepthwiseConv2dNative,
backendName: 'wasm',
setupFunc: setup,
kernelFunc: depthwiseConv2d
};
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, KernelFunc, Tensor4D} from '@tensorflow/tfjs-core';\n\nimport {BackendWasm} from '../backend_wasm';\n\nlet wasmDepthwiseConv2d: (\n    xId: number, batchSize: number, inputHeight: number, inputWidth: number,\n    filterId: number, filterHeight: number, filterWidth: number, padTop: number,\n    padRight: number, padBottom: number, padLeft: number, isSamePad: number,\n    dilationHeight: number, dilationWidth: number, strideHeight: number,\n    strideWidth: number, inputChannels: number, outputChannels: number,\n    outId: number) => void;\n\nfunction setup(backend: BackendWasm) {\n  wasmDepthwiseConv2d =\n      backend.wasm.cwrap(DepthwiseConv2dNative, null /* void */, [\n        'number',  // xId\n        'number',  // batchSize\n        'number',  // inputHeight\n        'number',  // inputWidth\n        'number',  // filterId\n        'number',  // filterHeight\n        'number',  // filterWidth\n        'number',  // padTop\n        'number',  // padRight\n        'number',  // padBottom\n        'number',  // padLeft\n        'number',  // isSamePad\n        'number',  // dilationHeight\n        'number',  // dilationWidth\n        'number',  // strideHeight\n        'number',  // strideWidth\n        'number',  // inputChannels\n        'number',  // outputChannels\n        'number',  // outId\n      ]);\n}\n\nfunction depthwiseConv2d(args: {\n  inputs: DepthwiseConv2dNativeInputs,\n  backend: BackendWasm,\n  attrs: DepthwiseConv2dNativeAttrs\n}) {\n  const {inputs, attrs, backend} = args;\n\n  const {x, filter} = inputs;\n  const xId = backend.dataIdMap.get(x.dataId).id;\n  const filterId = backend.dataIdMap.get(filter.dataId).id;\n\n  const {strides, dilations, pad, dimRoundingMode} = attrs;\n\n  const $dilations = dilations == null ? [1, 1] : dilations;\n\n  const convInfo = backend_util.computeConv2DInfo(\n      (x as Tensor4D).shape, (filter as Tensor4D).shape, strides,\n      ($dilations as number | [number, number]), pad, dimRoundingMode,\n      true /* depthwise */);\n\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;\n\n  if (convInfo.dataFormat !== 'channelsLast') {\n    throw new Error(\n        `wasm backend DepthwiseConv2dNative does not support dataFormat:'` +\n        `${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n\n  const out = backend.makeOutput(convInfo.outShape, 'float32');\n  const outId = backend.dataIdMap.get(out.dataId).id;\n  wasmDepthwiseConv2d(\n      xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight,\n      filterWidth, padTop, padRight, padBottom, padLeft, isSamePad,\n      dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels,\n      outputChannels, outId);\n  return out;\n}\n\nexport const depthwiseConv2dNativeConfig: KernelConfig = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: 'wasm',\n  setupFunc: setup,\n  kernelFunc: depthwiseConv2d as unknown as KernelFunc\n};\n"]}