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@tensorflow/tfjs-node

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This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).

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/** * @license * Copyright 2020 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 {add, backend_util, FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, Tensor} from '@tensorflow/tfjs'; import {NodeJSKernelBackend} from '../nodejs_kernel_backend'; import {depthwiseConv2dNativeImpl} from './DepthwiseConv2dNative'; export const fusedDepthwiseConv2DConfig: KernelConfig = { kernelName: FusedDepthwiseConv2D, backendName: 'tensorflow', kernelFunc: (args) => { const {x, filter, bias, preluActivationWeights} = args.inputs as FusedDepthwiseConv2DInputs; const backend = args.backend as NodeJSKernelBackend; const { strides, pad, dilations, dimRoundingMode, activation, leakyreluAlpha } = args.attrs as unknown as FusedDepthwiseConv2DAttrs; let $dilations = dilations; if ($dilations == null) { $dilations = [1, 1]; } const convInfo = backend_util.computeConv2DInfo( x.shape as [number, number, number, number], filter.shape as [number, number, number, number], strides, $dilations, pad, dimRoundingMode, true /* depthwise */); let result = depthwiseConv2dNativeImpl(x, filter, convInfo, backend); const toDispose = []; if (bias != null) { toDispose.push(result); result = add(result, bias as Tensor); } const temp = result; result = backend.applyActivation( result, activation, preluActivationWeights as Tensor, leakyreluAlpha); if (temp !== result) { toDispose.push(temp); } toDispose.forEach(t => t.dispose()); return result; } };