/**
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* @license
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* Copyright 2019 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 { ENGINE } from '../../engine';
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import { customGrad } from '../../gradients';
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import { FusedDepthwiseConv2D } from '../../kernel_names';
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import { makeTypesMatch } from '../../tensor_util';
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import { convertToTensor } from '../../tensor_util_env';
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import * as util from '../../util';
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import { add } from '../add';
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import * as broadcast_util from '../broadcast_util';
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import * as conv_util from '../conv_util';
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import { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';
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import { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';
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import { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';
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import { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';
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import { op } from '../operation';
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import { reshape } from '../reshape';
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/**
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* Computes depthwise 2D convolution, optionally fused with adding a
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* bias and applying an activation.
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*
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* Given a 4D `input` array and a `filter` array of shape
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* `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
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* `inChannels` convolutional filters of depth 1, this op applies a
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* different filter to each input channel (expanding from 1 channel to
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* `channelMultiplier` channels for each), then concatenates the results
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* together. The output has `inChannels * channelMultiplier` channels.
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*
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* See
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* [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
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* https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
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* for more details.
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*
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* @param obj An object with the following properties:
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* @param x The input tensor, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter tensor, rank 4, of shape
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* `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`. If strides is a single number, then `strideHeight ==
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* strideWidth`.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
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* https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
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* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
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* in which we sample input values across the height and width dimensions
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* in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
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* number, then `dilationHeight == dilationWidth`. If it is greater than
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* 1, then all values of `strides` must be 1.
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* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
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* "NHWC". Specify the data format of the input and output data. With the
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* default format "NHWC", the data is stored in the order of: [batch,
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* height, width, channels]. Only "NHWC" is currently supported.
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* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
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* provided, it will default to truncate.
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* @param bias Tensor to be added to the result.
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* @param activation Name of activation kernel (defaults to `linear`).
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* @param preluActivationWeights Tensor of prelu weights to be applied as part
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* of a `prelu` activation, typically the same shape as `x`.
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* @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
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* activation.
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*/
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function fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {
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if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
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let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
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if (bias != null) {
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result = add(result, bias);
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}
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return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
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}
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const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');
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const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
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let x4D = $x;
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let reshapedTo4D = false;
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if ($x.rank === 3) {
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reshapedTo4D = true;
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x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
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}
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util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +
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`rank ${x4D.rank}.`);
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util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +
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`but got rank ${$filter.rank}.`);
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util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +
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`(${x4D.shape[3]}) must match the inChannels dimension in ` +
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`filter ${$filter.shape[2]}.`);
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if (dilations == null) {
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dilations = [1, 1];
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}
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util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +
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`be 1. Got strides ${strides} and dilations '${dilations}'`);
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conv_util.checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode);
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const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
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let $bias;
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if (bias != null) {
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$bias = convertToTensor(bias, 'bias', 'fused conv2d');
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[$bias] = makeTypesMatch($bias, $x);
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broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
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}
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let $preluActivationWeights;
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if (preluActivationWeights != null) {
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$preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');
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}
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const grad = (dy, saved) => {
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util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +
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`greater than 1 are not yet supported. Got dilations ` +
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`'${dilations}'`);
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const [$filter, x4D, y, bias] = saved;
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const dyActivation = getFusedDyActivation(dy, y, activation);
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const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);
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const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);
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if (bias != null) {
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const biasDer = getFusedBiasGradient($bias, dyActivation);
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return [xDer, filterDer, biasDer];
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}
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return [xDer, filterDer];
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};
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const inputs = {
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x: x4D,
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filter: $filter,
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bias: $bias,
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preluActivationWeights: $preluActivationWeights
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};
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const attrs = {
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strides,
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pad,
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dataFormat,
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dilations,
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dimRoundingMode,
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activation,
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leakyreluAlpha
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};
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// Depending on the the params passed in we will have different number of
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// inputs and thus a a different number of elements in the gradient.
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if (bias == null) {
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const customOp = customGrad((x4D, filter, save) => {
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// tslint:disable-next-line: no-unnecessary-type-assertion
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let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
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save([filter, x4D, res]);
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if (reshapedTo4D) {
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// tslint:disable-next-line: no-unnecessary-type-assertion
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res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
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}
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return { value: res, gradFunc: grad };
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});
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return customOp(x4D, $filter);
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}
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else {
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const customOpWithBias = customGrad((x4D, filter, bias, save) => {
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// tslint:disable-next-line: no-unnecessary-type-assertion
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let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
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save([filter, x4D, res, bias]);
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if (reshapedTo4D) {
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// tslint:disable-next-line: no-unnecessary-type-assertion
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res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
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}
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return { value: res, gradFunc: grad };
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});
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return customOpWithBias(x4D, $filter, $bias);
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}
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}
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export const depthwiseConv2d = /* @__PURE__ */ op({ fusedDepthwiseConv2d_ });
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* @license\n * Copyright 2019 Google LLC. 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 {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport {depthwiseConv2d as unfusedDepthwiseConv2d} from '../depthwise_conv2d';\nimport {depthwiseConv2dNativeBackpropFilter} from '../depthwise_conv2d_native_backprop_filter';\nimport {depthwiseConv2dNativeBackpropInput} from '../depthwise_conv2d_native_backprop_input';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n *     activation.\n */\nfunction fusedDepthwiseConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor,\n  leakyreluAlpha?: number\n}): T {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedDepthwiseConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(\n               result, activation, preluActivationWeights, leakyreluAlpha) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n  const $filter =\n      convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n          `but got rank ${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in fused depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () =>\n          'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n          `be 1. Got strides ${strides} and dilations '${dilations}'`);\n  conv_util.checkPadOnDimRoundingMode(\n      'fused depthwiseConv2d', pad, dimRoundingMode);\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n    const [$filter, x4D, y, bias] = saved;\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    const xDer = depthwiseConv2dNativeBackpropInput(\n        (x4D as Tensor4D).shape, dyActivation, $filter as Tensor4D, strides,\n        pad, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(\n        x4D as Tensor4D, dyActivation, ($filter as Tensor4D).shape, strides,\n        pad, dilations, dimRoundingMode);\n\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n\n  const inputs: FusedDepthwiseConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs: FusedDepthwiseConv2DAttrs = {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  };\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          // tslint:disable-next-line: no-unnecessary-type-assertion\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedDepthwiseConv2D, inputs as unknown as NamedTensorMap,\n              attrs as unknown as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          // tslint:disable-next-line: no-unnecessary-type-assertion\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedDepthwiseConv2D, inputs as unknown as NamedTensorMap,\n              attrs as unknown as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const depthwiseConv2d = /* @__PURE__ */ op({fusedDepthwiseConv2d_});\n"]}
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