EVOLUTION-MANAGER

Edit File: deep_conv2d.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_DEEP_CONV2D_H_
#define TENSORFLOW_CORE_KERNELS_DEEP_CONV2D_H_

#include "tensorflow/core/framework/types.h"

namespace tensorflow {

class OpKernelContext;

// DeepConv2D is a Conv2D implementation specialized for deep (i.e. large
// in_depth * out_depth product) convolutions (see deep_conv2d.cc for details).

// DeepConv2DTransform is an interface for implementing transforms for
// DeepConv2D. Implementations must specify transform matrices and
// input/output/filter shapes. DeepConv2d computes:
//
//   y = C[Ad * Bg]
//
//   C: output transform matrix
//   A: input data transform matrix
//   B: filter transform matrix
//   d: vectorized 2D data tile
//   g: vectorized 2D filter tile
//   y: vectorized 2D output tile

template <typename T>
class DeepConv2DTransform {
 public:
  virtual ~DeepConv2DTransform() {}

virtual void GetFilterTransformMatrix(const int64 rows, const int64 cols,
                                        T* transform_matrix) const = 0;

virtual void GetInputTransformMatrix(const int64 rows, const int64 cols,
                                       T* transform_matrix) const = 0;

virtual void GetOutputTransformMatrix(const int64 rows, const int64 cols,
                                        T* transform_matrix) const = 0;

struct Shape {
    Shape(int64 r, int64 c) : rows(r), cols(c) {}
    int64 rows;
    int64 cols;
  };

virtual const Shape& filter_shape() const = 0;
  virtual const Shape& input_shape() const = 0;
  virtual const Shape& output_shape() const = 0;
};

// Conv2D arguments used by DeepConv2D implementation.
struct Conv2DArgs {
  // Input layer dimensions
  int batch;
  int in_rows;
  int in_cols;
  int in_depth;
  int filter_rows;
  int filter_cols;
  int pad_rows;
  int pad_cols;

// Output layer dimensions
  int out_rows;
  int out_cols;
  int out_depth;

Conv2DArgs()
      : batch(0),
        in_rows(0),
        in_cols(0),
        in_depth(0),
        filter_rows(0),
        filter_cols(0),
        pad_rows(0),
        pad_cols(0),
        out_rows(0),
        out_cols(0),
        out_depth(0) {}
};

// Returns true if convolution operation specified by function arguments
// can use DeepConv2D implementation, and false otherwise.
// May return false based on parameters, cost, or whether feature is disabled.
bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows,
                      int filter_cols, int in_depth, int out_depth,
                      int out_rows, int out_cols);

namespace functor {

// Calls DeepConv2D implementation (see deep_conv2d.cc for details).
template <typename Device, typename T>
struct DeepConv2D {
  void operator()(OpKernelContext* ctx, const Conv2DArgs& args, const T* input,
                  const T* filter, T* output);
};

}  // namespace functor

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_DEEP_CONV2D_H_