text: /* Copyright 2015 The TensorFlow Authors. 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.
  ==============================================================================*/
  
  #if !GOOGLE_CUDA
  #error This file must only be included when building with Cuda support
  #endif
  
  #ifndef TENSORFLOW_CORE_KERNELS_MAXPOOLING_OP_GPU_H_
  #define TENSORFLOW_CORE_KERNELS_MAXPOOLING_OP_GPU_H_
  
  #define EIGEN_USE_GPU
  
  #include "tensorflow/core/framework/tensor_types.h"
  #include "tensorflow/core/platform/types.h"
  
  namespace tensorflow {
  
  // Run the forward pass of max pooling, optionally writing the argmax indices to
  // the mask array, if it is not nullptr. If mask is passed in as nullptr, the
  // argmax indices are not written.
  bool MaxPoolForwardWithOptionalArgmax(
      const float* bottom_data, const int batch, const int height,
      const int width, const int channels, const int pooled_height,
      const int pooled_width, const int kernel_h, const int kernel_w,
      const int stride_h, const int stride_w, const int pad_t, const int pad_l,
      float* top_data, int64* mask, const Eigen::GpuDevice& d);
  
  bool MaxPoolForwardWithOptionalArgmax(
      const Eigen::half* bottom_data, const int batch, const int height,
      const int width, const int channels, const int pooled_height,
      const int pooled_width, const int kernel_h, const int kernel_w,
      const int stride_h, const int stride_w, const int pad_t, const int pad_l,
      Eigen::half* top_data, int64* mask, const Eigen::GpuDevice& d);
  
  bool MaxPoolBackwardWithArgmax(const int output_size, const int input_size,
                                 const float* top_diff, const int64* mask,
                                 const int top_offset, const int bottom_offset,
                                 float* bottom_diff, const Eigen::GpuDevice& d);
  
  bool MaxPoolBackwardWithArgmax(const int output_size, const int input_size,
                                 const Eigen::half* top_diff, const int64* mask,
                                 const int top_offset, const int bottom_offset,
                                 Eigen::half* bottom_diff,
                                 const Eigen::GpuDevice& d);
  
  bool MaxPoolBackwardNoMask(const float* bottom_data, const int batch,
                             const int height, const int width,
                             const int channels, const int pooled_height,
                             const int pooled_width, const int kernel_h,
                             const int kernel_w, const int stride_h,
                             const int stride_w, const int pad_t, const int pad_l,
                             const float* top_diff, float* bottom_diff,
                             const Eigen::GpuDevice& d);
  
  bool MaxPoolBackwardNoMask(const Eigen::half* bottom_data, const int batch,
                             const int height, const int width,
                             const int channels, const int pooled_height,
                             const int pooled_width, const int kernel_h,
                             const int kernel_w, const int stride_h,
                             const int stride_w, const int pad_t, const int pad_l,
                             const Eigen::half* top_diff, Eigen::half* bottom_diff,
                             const Eigen::GpuDevice& d);
  
  }  // namespace tensorflow
  
  #endif  // TENSORFLOW_CORE_KERNELS_MAXPOOLING_OP_GPU_H_
  