/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
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import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
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export const depthwiseConv2dNativeConfig: KernelConfig = {
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kernelName: DepthwiseConv2dNative,
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backendName: 'tensorflow',
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kernelFunc: (args) => {
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const {x, filter} = args.inputs as DepthwiseConv2dNativeInputs;
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const backend = args.backend as NodeJSKernelBackend;
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const {strides, pad, dilations, dimRoundingMode} =
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args.attrs as unknown as DepthwiseConv2dNativeAttrs;
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let $dilations = dilations;
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if ($dilations == null) {
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$dilations = [1, 1];
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}
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const convInfo = backend_util.computeConv2DInfo(
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x.shape as [number, number, number, number],
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filter.shape as [number, number, number, number], strides, $dilations,
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pad, dimRoundingMode, true /* depthwise */);
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return depthwiseConv2dNativeImpl(x, filter, convInfo, backend);
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}
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};
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export function depthwiseConv2dNativeImpl(
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input: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
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backend: NodeJSKernelBackend): Tensor4D {
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if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME' &&
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convInfo.padInfo.type !== 'EXPLICIT') {
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throw new Error(
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`TF Backend supports only 'valid' and 'same' padding ` +
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`while padding was ${convInfo.padInfo.type}`);
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}
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const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
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const padding = convInfo.padInfo.type;
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const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
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const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
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const opAttrs = [
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createTensorsTypeOpAttr('T', input.dtype),
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{name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
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{name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
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name: 'data_format',
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type: backend.binding.TF_ATTR_STRING,
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value: dataFormat
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},
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{name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
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];
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if (padding === 'EXPLICIT') {
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const padValue = [
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convInfo.padInfo.top, convInfo.padInfo.bottom, convInfo.padInfo.left,
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convInfo.padInfo.right
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];
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opAttrs.push({
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name: 'explicit_paddings',
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type: backend.binding.TF_ATTR_INT,
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value: dataFormat === 'NHWC' ? [0, 0, ...padValue, 0, 0] :
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[0, 0, 0, 0, ...padValue]
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});
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}
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return backend.executeSingleOutput(
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DepthwiseConv2dNative, opAttrs, [input, filter]) as Tensor4D;
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}
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