1/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16// Helper functions to run 3d pooling on GPU using CuDNN.
17
18#ifndef TENSORFLOW_CORE_KERNELS_CUDNN_POOLING_GPU_H_
19#define TENSORFLOW_CORE_KERNELS_CUDNN_POOLING_GPU_H_
20
21#include <array>
22
23#include "tensorflow/core/framework/op_kernel.h"
24
25#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
26#include "tensorflow/core/platform/stream_executor.h"
27#endif
28
29#include "tensorflow/core/util/padding.h"
30
31namespace tensorflow {
32
33#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
34
35// Runs (avg/max)pooling on GPU.
36// Dimension order for all array arguments is: x, y, z.
37template <typename T>
38class DnnPooling3dOp {
39 public:
40 static void Compute(OpKernelContext* context,
41 se::dnn::PoolingMode pooling_mode,
42 const std::array<int64, 3>& size,
43 const std::array<int64, 3>& stride,
44 const std::array<int64, 3>& padding,
45 TensorFormat data_format, const Tensor& tensor_in,
46 Tensor* output);
47};
48
49// Computes the gradient of (avg/max)pooling on GPU.
50// Dimension order for all array arguments is: x, y, z.
51template <typename T>
52class DnnPooling3dGradOp {
53 public:
54 static void Compute(OpKernelContext* context,
55 se::dnn::PoolingMode pooling_mode,
56 const std::array<int64, 3>& window,
57 const std::array<int64, 3>& stride,
58 const std::array<int64, 3>& padding,
59 const std::array<int64, 3>& output_size,
60 TensorFormat data_format, const Tensor& out_backprop,
61 const TensorShape& tensor_in_shape,
62 const Tensor* tensor_in, const Tensor* tensor_out,
63 Tensor* input_backprop);
64};
65
66#endif
67
68} // namespace tensorflow
69
70#endif // TENSORFLOW_CORE_KERNELS_CUDNN_POOLING_GPU_H_
71