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230 lines
8.7 KiB
230 lines
8.7 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. 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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/platform/device_context.h"
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#include "paddle/fluid/platform/hostdevice.h"
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#include "paddle/fluid/platform/macros.h"
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namespace paddle {
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namespace operators {
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namespace math {
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/*
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* \brief Extracting simple operations from pooling.
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* Both MaxPool and AvgPool need "initial", "compute" and "finalize"
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* operation.
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* MaxPool initializes temp variable to the negative maximum to find the
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* maximum value in the pooling field.
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* AvgPool initializes temp variable to the zero to accumulate all values
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* in pool pooling, and finally takes the average.
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* MaxPoolGrad and AvgPoolGrad are gradient operations respectively.
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*/
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template <class T>
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class MaxPool {
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public:
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DEVICE inline T initial() { return static_cast<T>(-FLT_MAX); }
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DEVICE inline void compute(const T& x, T* y) { *y = *y > x ? *y : x; }
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DEVICE inline void finalize(const T& pool_field, T* y) {}
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};
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template <class T>
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class AvgPool {
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public:
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DEVICE inline T initial() { return static_cast<T>(0); }
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DEVICE inline void compute(const T& x, T* y) { *y += x; }
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DEVICE inline void finalize(const T& pool_field, T* y) { *y /= pool_field; }
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};
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template <class T>
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class MaxPoolGrad {
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public:
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DEVICE inline void compute(const T& x, const T& y, const T& dy, T scale,
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T* dx) {
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*dx += dy * (x == y);
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}
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};
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template <class T>
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class AvgPoolGrad {
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public:
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DEVICE inline void compute(const T& x, const T& y, const T& dy, T scale,
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T* dx) {
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*dx += (scale * dy);
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}
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};
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/* used for adaptive pool to calculate start and end index of each divided grid
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*/
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HOSTDEVICE inline int AdaptStartIndex(int ph, int input_size, int output_size) {
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return static_cast<int>(
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floor(static_cast<double>(ph * input_size) / output_size));
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}
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HOSTDEVICE inline int AdaptEndIndex(int ph, int input_size, int output_size) {
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return static_cast<int>(
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ceil(static_cast<double>((ph + 1) * input_size) / output_size));
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}
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/*
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* \brief Getting pooling results, and calculating gradient.
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*
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* In pool2d, all tensors are in NCHW format. Where N is batch size, C is the
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* number of channels, H and W is the height and width of feature.
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* In pool3d, all tensors are in NCDHW format. Where N is batch size, C is the
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* number of channels, D, H and W is the depth, height and width of feature.
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*
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* In max pooling, it is possible that the pooling region has multiple maximum
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* elements. In this case, we should compute the gradient of the first maximum
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* element.
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* This is different from average pooling. So we rewrite the max_pool_grad:
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* MaxPool2dGradFunctor, MaxPool3dGradFunctor.
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*/
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#ifdef PADDLE_WITH_CUDA
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template <typename PoolProcess, typename T>
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class Pool2dDirectCUDAFunctor {
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public:
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void operator()(const T* input, const std::vector<int>& input_shape,
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const std::vector<int>& output_shape,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, PoolProcess pool_compute,
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bool exclusive, T* output, cudaStream_t stream);
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};
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#endif
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template <typename DeviceContext, typename PoolProcess, typename T>
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class Pool2dFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, PoolProcess pool_compute,
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bool exclusive, bool adaptive, framework::Tensor* output);
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};
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template <typename DeviceContext, typename PoolProcess, typename T>
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class Pool2dGradFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const framework::Tensor& output,
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const framework::Tensor& output_grad,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, PoolProcess pool_compute,
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bool exclusive, bool adaptive, framework::Tensor* input_grad);
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};
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template <typename DeviceContext, class T>
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class MaxPool2dGradFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const framework::Tensor& output,
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const framework::Tensor& output_grad,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings,
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framework::Tensor* input_grad);
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};
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template <typename DeviceContext, typename PoolProcess, typename T>
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class Pool3dFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, PoolProcess pool_compute,
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bool exclusive, bool adaptive, framework::Tensor* output);
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};
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template <typename DeviceContext, typename PoolProcess, typename T>
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class Pool3dGradFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const framework::Tensor& output,
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const framework::Tensor& output_grad,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, PoolProcess pool_compute,
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bool exclusive, bool adaptive, framework::Tensor* input_grad);
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};
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template <typename DeviceContext, class T>
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class MaxPool3dGradFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const framework::Tensor& output,
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const framework::Tensor& output_grad,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings,
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framework::Tensor* input_grad);
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};
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/*
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* \brief Getting max pooling results and corresponding max index, and
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* calculating gradient.
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* In up-sampling-pooling, it is necessary to know max element index.
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* In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in
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* NCDHW format.
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*/
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template <typename DeviceContext, typename T1, typename T2>
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class MaxPool2dWithIndexFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, bool adaptive,
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framework::Tensor* output, framework::Tensor* mask);
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};
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template <typename DeviceContext, typename T1, typename T2>
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class MaxPool2dWithIndexGradFunctor {
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public:
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void operator()(const DeviceContext& context,
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const framework::Tensor& output_grad,
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const framework::Tensor& mask, const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, bool adaptive,
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framework::Tensor* input_grad);
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};
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template <typename DeviceContext, typename T1, typename T2>
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class MaxPool3dWithIndexFunctor {
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public:
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void operator()(const DeviceContext& context, const framework::Tensor& input,
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const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, bool adaptive,
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framework::Tensor* output, framework::Tensor* mask);
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};
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template <typename DeviceContext, typename T1, typename T2>
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class MaxPool3dWithIndexGradFunctor {
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public:
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void operator()(const DeviceContext& context,
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const framework::Tensor& output_grad,
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const framework::Tensor& mask, const std::vector<int>& ksize,
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const std::vector<int>& strides,
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const std::vector<int>& paddings, bool adaptive,
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framework::Tensor* input_grad);
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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