EVOLUTION-MANAGER

Edit File: winograd_transform.h

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

#ifndef TENSORFLOW_CORE_KERNELS_WINOGRAD_TRANSFORM_H_
#define TENSORFLOW_CORE_KERNELS_WINOGRAD_TRANSFORM_H_

#include "tensorflow/core/kernels/deep_conv2d.h"

namespace tensorflow {

// Winograd DeepConv2DTransform implementation for 3x3 filters.
// Details:
// *) Arithmetic complexity of computations: Shmuel Winograd
// *) Fast Algorithms for Convolutional Neural Networks: Lavin, Gray

template <typename T>
class WinogradTransform : public DeepConv2DTransform<T> {
 public:
  typedef typename DeepConv2DTransform<T>::Shape Shape;

WinogradTransform()
      : filter_shape_(3, 3), input_shape_(4, 4), output_shape_(2, 2) {}

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

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

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

virtual const Shape& filter_shape() const { return filter_shape_; }
  virtual const Shape& input_shape() const { return input_shape_; }
  virtual const Shape& output_shape() const { return output_shape_; }

private:
  const Shape filter_shape_;
  const Shape input_shape_;
  const Shape output_shape_;
};

// The filter transform matrix is the kronecker product 'M * M' of the
// following matrix 'M':
//
//   [ 1    0   0   ]
//   [ 1/2  1/2 1/2 ]
//   [ 1/2 -1/2 1/2 ]
//   [ 0    0   1   ]
//
// The data layout of 'transform_matrix':
//   [input_tile_spatial_size, filter_spatial_size]
//
template <typename T>
void WinogradTransform<T>::GetFilterTransformMatrix(const int64 rows,
                                                    const int64 cols,
                                                    T* transform_matrix) const {
  CHECK_GT(rows, 0);
  CHECK_GT(cols, 0);
  memset(transform_matrix, 0, sizeof(T) * rows * cols);

// Sub matrix [0,0]
  transform_matrix[0 * cols + 0] = T(1.0);

transform_matrix[1 * cols + 0] = T(0.5);
  transform_matrix[1 * cols + 1] = T(0.5);
  transform_matrix[1 * cols + 2] = T(0.5);

transform_matrix[2 * cols + 0] = T(0.5);
  transform_matrix[2 * cols + 1] = T(-0.5);
  transform_matrix[2 * cols + 2] = T(0.5);

transform_matrix[3 * cols + 2] = T(1.0);

// Sub matrix [1,0]
  transform_matrix[4 * cols + 0] = T(0.5);

transform_matrix[5 * cols + 0] = T(0.25);
  transform_matrix[5 * cols + 1] = T(0.25);
  transform_matrix[5 * cols + 2] = T(0.25);

transform_matrix[6 * cols + 0] = T(0.25);
  transform_matrix[6 * cols + 1] = T(-0.25);
  transform_matrix[6 * cols + 2] = T(0.25);

transform_matrix[7 * cols + 2] = T(0.5);

// Sub matrix [1,1]
  transform_matrix[4 * cols + 3] = T(0.5);

transform_matrix[5 * cols + 3] = T(0.25);
  transform_matrix[5 * cols + 4] = T(0.25);
  transform_matrix[5 * cols + 5] = T(0.25);

transform_matrix[6 * cols + 3] = T(0.25);
  transform_matrix[6 * cols + 4] = T(-0.25);
  transform_matrix[6 * cols + 5] = T(0.25);

transform_matrix[7 * cols + 5] = T(0.5);

// Sub matrix [1,2]
  transform_matrix[4 * cols + 6] = T(0.5);

transform_matrix[5 * cols + 6] = T(0.25);
  transform_matrix[5 * cols + 7] = T(0.25);
  transform_matrix[5 * cols + 8] = T(0.25);

transform_matrix[6 * cols + 6] = T(0.25);
  transform_matrix[6 * cols + 7] = T(-0.25);
  transform_matrix[6 * cols + 8] = T(0.25);

transform_matrix[7 * cols + 8] = T(0.5);

// Sub matrix [2,0]
  transform_matrix[8 * cols + 0] = T(0.5);

transform_matrix[9 * cols + 0] = T(0.25);
  transform_matrix[9 * cols + 1] = T(0.25);
  transform_matrix[9 * cols + 2] = T(0.25);

transform_matrix[10 * cols + 0] = T(0.25);
  transform_matrix[10 * cols + 1] = T(-0.25);
  transform_matrix[10 * cols + 2] = T(0.25);

transform_matrix[11 * cols + 2] = T(0.5);

// Sub matrix [2,1]
  transform_matrix[8 * cols + 3] = T(-0.5);

transform_matrix[9 * cols + 3] = T(-0.25);
  transform_matrix[9 * cols + 4] = T(-0.25);
  transform_matrix[9 * cols + 5] = T(-0.25);

transform_matrix[10 * cols + 3] = T(-0.25);
  transform_matrix[10 * cols + 4] = T(0.25);
  transform_matrix[10 * cols + 5] = T(-0.25);

transform_matrix[11 * cols + 5] = T(-0.5);

// Sub matrix [2,2]
  transform_matrix[8 * cols + 6] = T(0.5);

transform_matrix[9 * cols + 6] = T(0.25);
  transform_matrix[9 * cols + 7] = T(0.25);
  transform_matrix[9 * cols + 8] = T(0.25);

transform_matrix[10 * cols + 6] = T(0.25);
  transform_matrix[10 * cols + 7] = T(-0.25);
  transform_matrix[10 * cols + 8] = T(0.25);

transform_matrix[11 * cols + 8] = T(0.5);

// Sub matrix [3,2]
  transform_matrix[12 * cols + 6] = T(1.0);

transform_matrix[13 * cols + 6] = T(0.5);
  transform_matrix[13 * cols + 7] = T(0.5);
  transform_matrix[13 * cols + 8] = T(0.5);

transform_matrix[14 * cols + 6] = T(0.5);
  transform_matrix[14 * cols + 7] = T(-0.5);
  transform_matrix[14 * cols + 8] = T(0.5);

transform_matrix[15 * cols + 8] = T(1.0);
}

// The input transform matrix is the kronecker product 'M * M' of the
// following matrix 'M':
//
//   [1   0  -1   0]
//   [0   1   1   0]
//   [0  -1   1   0]
//   [0   1   0  -1]
//
// Data layout of 'transform_matrix':
//   [tile_spatial_size, tile_spatial_size]
//
template <typename T>
void WinogradTransform<T>::GetInputTransformMatrix(const int64 rows,
                                                   const int64 cols,
                                                   T* transform_matrix) const {
  CHECK_GT(rows, 0);
  CHECK_GT(cols, 0);
  memset(transform_matrix, 0, sizeof(T) * rows * cols);

// Sub matrix [0,0]
  transform_matrix[0 * cols + 0] = T(1.0);
  transform_matrix[0 * cols + 2] = T(-1.0);

transform_matrix[1 * cols + 1] = T(1.0);
  transform_matrix[1 * cols + 2] = T(1.0);

transform_matrix[2 * cols + 1] = T(-1.0);
  transform_matrix[2 * cols + 2] = T(1.0);

transform_matrix[3 * cols + 1] = T(1.0);
  transform_matrix[3 * cols + 3] = T(-1.0);

// Sub matrix [0,2]
  transform_matrix[0 * cols + 8] = T(-1.0);
  transform_matrix[0 * cols + 10] = T(1.0);

transform_matrix[1 * cols + 9] = T(-1.0);
  transform_matrix[1 * cols + 10] = T(-1.0);

transform_matrix[2 * cols + 9] = T(1.0);
  transform_matrix[2 * cols + 10] = T(-1.0);

transform_matrix[3 * cols + 9] = T(-1.0);
  transform_matrix[3 * cols + 11] = T(1.0);

// Sub matrix [1,1]
  transform_matrix[4 * cols + 4] = T(1.0);
  transform_matrix[4 * cols + 6] = T(-1.0);

transform_matrix[5 * cols + 5] = T(1.0);
  transform_matrix[5 * cols + 6] = T(1.0);

transform_matrix[6 * cols + 5] = T(-1.0);
  transform_matrix[6 * cols + 6] = T(1.0);

transform_matrix[7 * cols + 5] = T(1.0);
  transform_matrix[7 * cols + 7] = T(-1.0);

// Sub matrix [1,2]
  transform_matrix[4 * cols + 8] = T(1.0);
  transform_matrix[4 * cols + 10] = T(-1.0);

transform_matrix[5 * cols + 9] = T(1.0);
  transform_matrix[5 * cols + 10] = T(1.0);

transform_matrix[6 * cols + 9] = T(-1.0);
  transform_matrix[6 * cols + 10] = T(1.0);

transform_matrix[7 * cols + 9] = T(1.0);
  transform_matrix[7 * cols + 11] = T(-1.0);

// Sub matrix [2,1]
  transform_matrix[8 * cols + 4] = T(-1.0);
  transform_matrix[8 * cols + 6] = T(1.0);

transform_matrix[9 * cols + 5] = T(-1.0);
  transform_matrix[9 * cols + 6] = T(-1.0);

transform_matrix[10 * cols + 5] = T(1.0);
  transform_matrix[10 * cols + 6] = T(-1.0);

transform_matrix[11 * cols + 5] = T(-1.0);
  transform_matrix[11 * cols + 7] = T(1.0);

// Sub matrix [2,2]
  transform_matrix[8 * cols + 8] = T(1.0);
  transform_matrix[8 * cols + 10] = T(-1.0);

transform_matrix[9 * cols + 9] = T(1.0);
  transform_matrix[9 * cols + 10] = T(1.0);

transform_matrix[10 * cols + 9] = T(-1.0);
  transform_matrix[10 * cols + 10] = T(1.0);

transform_matrix[11 * cols + 9] = T(1.0);
  transform_matrix[11 * cols + 11] = T(-1.0);

// Sub matrix [3,1]
  transform_matrix[12 * cols + 4] = T(1.0);
  transform_matrix[12 * cols + 6] = T(-1.0);

transform_matrix[13 * cols + 5] = T(1.0);
  transform_matrix[13 * cols + 6] = T(1.0);

transform_matrix[14 * cols + 5] = T(-1.0);
  transform_matrix[14 * cols + 6] = T(1.0);

transform_matrix[15 * cols + 5] = T(1.0);
  transform_matrix[15 * cols + 7] = T(-1.0);

// Sub matrix [3,3]
  transform_matrix[12 * cols + 12] = T(-1.0);
  transform_matrix[12 * cols + 14] = T(1.0);

transform_matrix[13 * cols + 13] = T(-1.0);
  transform_matrix[13 * cols + 14] = T(-1.0);

transform_matrix[14 * cols + 13] = T(1.0);
  transform_matrix[14 * cols + 14] = T(-1.0);

transform_matrix[15 * cols + 13] = T(-1.0);
  transform_matrix[15 * cols + 15] = T(1.0);
};

// The output transform matrix is the kronecker product 'M * M' of the
// following matrix 'M':
//
//   [1  1  1  0]
//   [0  1 -1 -1]
//
// Data layout of 'transform_matrix':
//   [out_tile_spatial_size, tile_spatial_size]
//
template <typename T>
void WinogradTransform<T>::GetOutputTransformMatrix(const int64 rows,
                                                    const int64 cols,
                                                    T* transform_matrix) const {
  CHECK_GT(rows, 0);
  CHECK_GT(cols, 0);
  memset(transform_matrix, 0, sizeof(T) * rows * cols);

// Sub matrix [0,0]
  transform_matrix[0 * cols + 0] = T(1.0);
  transform_matrix[0 * cols + 1] = T(1.0);
  transform_matrix[0 * cols + 2] = T(1.0);

transform_matrix[1 * cols + 1] = T(1.0);
  transform_matrix[1 * cols + 2] = T(-1.0);
  transform_matrix[1 * cols + 3] = T(-1.0);

// Sub matrix [0,1]
  transform_matrix[0 * cols + 4] = T(1.0);
  transform_matrix[0 * cols + 5] = T(1.0);
  transform_matrix[0 * cols + 6] = T(1.0);

transform_matrix[1 * cols + 5] = T(1.0);
  transform_matrix[1 * cols + 6] = T(-1.0);
  transform_matrix[1 * cols + 7] = T(-1.0);

// Sub matrix [0,2]
  transform_matrix[0 * cols + 8] = T(1.0);
  transform_matrix[0 * cols + 9] = T(1.0);
  transform_matrix[0 * cols + 10] = T(1.0);

transform_matrix[1 * cols + 9] = T(1.0);
  transform_matrix[1 * cols + 10] = T(-1.0);
  transform_matrix[1 * cols + 11] = T(-1.0);

// Sub matrix [1,1]
  transform_matrix[2 * cols + 4] = T(1.0);
  transform_matrix[2 * cols + 5] = T(1.0);
  transform_matrix[2 * cols + 6] = T(1.0);

transform_matrix[3 * cols + 5] = T(1.0);
  transform_matrix[3 * cols + 6] = T(-1.0);
  transform_matrix[3 * cols + 7] = T(-1.0);

// Sub matrix [1,2]
  transform_matrix[2 * cols + 8] = T(-1.0);
  transform_matrix[2 * cols + 9] = T(-1.0);
  transform_matrix[2 * cols + 10] = T(-1.0);

transform_matrix[3 * cols + 9] = T(-1.0);
  transform_matrix[3 * cols + 10] = T(1.0);
  transform_matrix[3 * cols + 11] = T(1.0);

// Sub matrix [1,3]
  transform_matrix[2 * cols + 12] = T(-1.0);
  transform_matrix[2 * cols + 13] = T(-1.0);
  transform_matrix[2 * cols + 14] = T(-1.0);

transform_matrix[3 * cols + 13] = T(-1.0);
  transform_matrix[3 * cols + 14] = T(1.0);
  transform_matrix[3 * cols + 15] = T(1.0);
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_WINOGRAD_TRANSFORM_H_