EVOLUTION-MANAGER

Edit File: avgpooling_op.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_AVGPOOLING_OP_H_
#define TENSORFLOW_CORE_KERNELS_AVGPOOLING_OP_H_
// Functor definition for AvgPoolingOp, must be compilable by nvcc.

#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/kernels/eigen_pooling.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace functor {

template <typename Device, typename T>
struct SpatialAvgPooling {
  void operator()(const Device& d, typename TTypes<T, 4>::Tensor output,
                  typename TTypes<T, 4>::ConstTensor input, int window_rows,
                  int window_cols, int row_stride, int col_stride,
                  const Eigen::PaddingType& padding) {
    if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
      // Use 32bit indexing to speed up the computations
      To32Bit(output).swap_layout().device(d) = Eigen::SpatialAvgPooling(
          To32Bit(input).swap_layout(), window_cols, window_rows, col_stride,
          row_stride, padding);
    } else {
      // Because we swap the layout, we swap the row/cols as well
      output.swap_layout().device(d) = Eigen::SpatialAvgPooling(
          input.swap_layout(), window_cols, window_rows, col_stride, row_stride,
          padding);
    }
  }
};

}  // namespace functor

typedef Eigen::GpuDevice GPUDevice;

// Launch a custom GPU kernels from Yanqing for the avgpooling backward
// operation that works NHWC data formats. Arguments:
//   top_diff: backprop to the output of the pooling layer
//   num: number of input batches
//   height: input height
//   width: input width
//   channels: number of input channels
//   pooled_height: the height of the output to the pooling layer
//   pooled_width: the width of the output to the pooling layer
//   kernel_h: the height of the pooling kernel
//   kernel_w: the width of the pooling kernel
//   stride_h: the height of the vertical stride
//   stride_w: the width of the horizontal stride
//   pad_t: padding size to the top side
//   pad_l: padding size to the left side
//   bottom_diff: backprop to the input of the pooling layer.
template <typename T>
bool RunAvePoolBackwardNHWC(const T* const top_diff, const int num,
                            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, T* const bottom_diff,
                            const GPUDevice& d);

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_AVGPOOLING_OP_H_