@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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text/typescript
/**
* @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;
}
};