EVOLUTION-MANAGER

Edit File: eigen_attention.h

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

#ifndef TENSORFLOW_CORE_KERNELS_EIGEN_ATTENTION_H_
#define TENSORFLOW_CORE_KERNELS_EIGEN_ATTENTION_H_

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"

namespace Eigen {

// Noise mode used when padding.
enum ExtractGlimpsesNoiseMode {
  UNIFORM = 0,
  GAUSSIAN = 1,
  ZERO = 2,
};

/** ExtractGlimpses
 * \ingroup CXX11_NeuralNetworks_Module
 *
 * \brief Extract glimpses from an input tensor.
 *
 * The input parameter is expected to be a col-major tensor with a rank of 4
 * (depth, x, y, and batch). The width and height parameters specify the
 * extension of the returned glimpses. The offsets parameter specifies the x, y
 * locations of the center of the glimpses relative to the center of the input
 * image. The vector is expected to contain one IndexPair for each image in the
 * batch dimension. The normalized boolean indicates if incoming coordinates are
 * normalized so that 0.0 and 1.0 correspond to the minimum and maximum of each
 * height and width dimension. The centered boolean indicates if incoming
 * coordinates are centered relative to the image, in which case -1.0 and 1.0
 * correspond to minimum and maximum of each dimension while 0.0 corresponds to
 * the center.
 *
 * The result can be assigned to a tensor of rank equal to that of the input.
 * The result will be laid out in col-major order (depth, x, y, batch). The
 * dimensions of the result will be equal to the dimensions of the input except
 * for width and height which will be equal to the requested glimpse size.
 */
namespace {

template <typename Index>
struct GlimpseExtractionOp {
  GlimpseExtractionOp(const Index width, const Index height,
                      const std::vector<IndexPair<float> >& offsets,
                      const bool normalized, const bool centered,
                      const ExtractGlimpsesNoiseMode noise, const int version)
      : width_(width),
        height_(height),
        offsets_(offsets),
        normalized_(normalized),
        centered_(centered),
        noise_(noise),
        version_(version) {}

template <typename Input>
  DSizes<Index, 4> dimensions(const Input& input) const {
    typedef typename internal::traits<Input>::Index IndexType;
    typedef TensorRef<Tensor<typename internal::traits<Input>::Scalar, 4,
                             internal::traits<Input>::Layout, IndexType> >
        Ref;
    Ref in(input);

DSizes<Index, 4> dims = in.dimensions();

dims[0] = in.dimension(0);
    dims[1] = width_;
    dims[2] = height_;
    dims[3] = in.dimension(3);
    return dims;
  }

template <typename Input, typename Output, typename Device>
  EIGEN_DEVICE_FUNC void eval(const Input& input, Output& output,
                              const Device& device) const {
    typedef typename internal::traits<Input>::Index IndexType;
    typedef TensorRef<Tensor<typename internal::traits<Input>::Scalar, 4,
                             internal::traits<Input>::Layout, IndexType> >
        Ref;
    Ref in(input);
    const Index num_channels = in.dimension(0);
    const Index input_width = in.dimension(1);
    const Index input_height = in.dimension(2);
    const Index batch_size = in.dimension(3);
    eigen_assert(input_width > 0);
    eigen_assert(input_height > 0);
    internal::NormalRandomGenerator<float> gen;
    internal::UniformRandomGenerator<float> unigen;

for (Index i = 0; i < batch_size; ++i) {
      float x = offsets_[i].first, y = offsets_[i].second;

if (version_ == 1) {
        // Un-normalize coordinates back to pixel space if normalized.
        if (normalized_) {
          x *= input_width;
          y *= input_height;
        }
        // Un-center if coordinates are centered on the image center.
        if (centered_) {
          x /= 2.0f;
          y /= 2.0f;
          x += input_width / 2.0f;
          y += input_height / 2.0f;
        }
        // Remove half of the glimpse window.
        x -= width_ / 2.0f;
        y -= height_ / 2.0f;
      } else {
        if (normalized_) {
          // Un-normalize coordinates back to pixel space if normalized.
          x *= input_width;
          y *= input_height;
          if (centered_) {
            // Un-center if coordinates are centered on the image center.
            x /= 2.0f;
            y /= 2.0f;
            x += input_width / 2.0f;
            y += input_height / 2.0f;
            // Remove half of the glimpse window.
            x -= width_ / 2.0f;
            y -= height_ / 2.0f;
          }
        } else {
          if (centered_) {
            x += input_width / 2.0f;
            y += input_height / 2.0f;
          }
        }
      }

const Index offset_x = (Index)x;
      const Index offset_y = (Index)y;
      Index glimpse_width = width_;
      Index glimpse_height = height_;
      bool partial_overlap = false;
      DSizes<Index, 3> slice_offset(0, offset_x, offset_y);
      DSizes<Index, 3> slice_extent(num_channels, width_, height_);
      DSizes<Index, 3> base_offset(0, 0, 0);

if (offset_x < 0) {
        slice_offset[1] = 0;
        glimpse_width = (std::max<Index>)(0, width_ + offset_x);
        slice_extent[1] = glimpse_width;
        base_offset[1] = width_ - glimpse_width;
        partial_overlap = true;
      } else if (offset_x + width_ >= input_width) {
        glimpse_width = (std::max<Index>)(0, input_width - offset_x);
        slice_extent[1] = glimpse_width;
        partial_overlap = true;
      }
      if (offset_y < 0) {
        slice_offset[2] = 0;
        glimpse_height = (std::max<Index>)(0, height_ + offset_y);
        slice_extent[2] = glimpse_height;
        base_offset[2] = height_ - glimpse_height;
        partial_overlap = true;
      } else if (offset_y + height_ >= input_height) {
        glimpse_height = (std::max<Index>)(0, input_height - offset_y);
        slice_extent[2] = glimpse_height;
        partial_overlap = true;
      }
      slice_extent[1] = std::min<Index>(input_width, slice_extent[1]);
      slice_extent[2] = std::min<Index>(input_height, slice_extent[2]);

if (partial_overlap) {
        switch (noise_) {
          case ZERO: {
            // Initialize the glimpse with zero noise.
            output.template chip<3>(i).device(device) =
                output.template chip<3>(i).constant(0);
          } break;
          case UNIFORM: {
            // Initialize the glimpse with uniform noise.
            typedef typename internal::remove_const<
                typename internal::traits<Input>::Scalar>::type Scalar;
            TensorFixedSize<Scalar, Sizes<> > mini;
            mini.device(device) = input.template chip<3>(i).minimum();
            TensorFixedSize<float, Sizes<> > range;
            range.device(device) = (input.template chip<3>(i).maximum() - mini)
                                       .template cast<float>();

DSizes<Index, 3> glimpse_size(num_channels, width_, height_);
            TensorMap<Tensor<float, 3> > tmp(NULL, glimpse_size);
            output.template chip<3>(i).device(device) =
                mini.reshape(Sizes<1, 1, 1>()).broadcast(glimpse_size) +
                (tmp.random(unigen) *
                 range.reshape(Sizes<1, 1, 1>()).broadcast(glimpse_size))
                    .template cast<Scalar>();
          } break;
          case GAUSSIAN: {
            // Initialize the glimpse with white noise: compute the mean and
            // sigma
            // of each channel, and use them to shape the gaussian.
            DSizes<Index, 2> glimpse_size(width_, height_);
            DSizes<Index, 2> input_size(input_width, input_height);
            typedef typename internal::remove_const<
                typename internal::traits<Input>::Scalar>::type Scalar;

for (int j = 0; j < num_channels; ++j) {
              TensorFixedSize<Scalar, Sizes<> > mean;
              mean.device(device) = input.template chip<3>(i)
                                        .template chip<0>(j)
                                        .template cast<float>()
                                        .mean();
              TensorFixedSize<float, Sizes<> > sigma;
              sigma.device(device) =
                  (input.template chip<3>(i)
                       .template chip<0>(j)
                       .template cast<float>() -
                   mean.reshape(Sizes<1, 1>()).broadcast(input_size))
                      .square()
                      .mean()
                      .sqrt();
              TensorFixedSize<Scalar, Sizes<> > mini;
              mini.device(device) =
                  input.template chip<3>(i).template chip<0>(j).minimum();
              TensorFixedSize<float, Sizes<> > maxi;
              maxi.device(device) =
                  input.template chip<3>(i).template chip<0>(j).maximum();

TensorMap<Tensor<float, 2> > tmp(NULL, glimpse_size);
              output.template chip<3>(i).template chip<0>(j).device(device) =
                  (mean.reshape(Sizes<1, 1>()).broadcast(glimpse_size) +
                   (tmp.random(gen) *
                    sigma.reshape(Sizes<1, 1>()).broadcast(glimpse_size))
                       .template cast<Scalar>())
                      .cwiseMin(
                          maxi.reshape(Sizes<1, 1>()).broadcast(glimpse_size))
                      .cwiseMax(
                          mini.reshape(Sizes<1, 1>()).broadcast(glimpse_size));
            }
          } break;
        }

// Copy the part of the glimpse that cover the input image if any.
        if (glimpse_width == 0 || glimpse_height == 0) {
          continue;
        }
        output.template chip<3>(i)
            .slice(base_offset, slice_extent)
            .device(device) =
            input.template chip<3>(i).slice(slice_offset, slice_extent);
      } else {
        output.template chip<3>(i).device(device) =
            input.template chip<3>(i).slice(slice_offset, slice_extent);
      }
    }
  }

private:
  const Index width_;
  const Index height_;
  const std::vector<IndexPair<float> > offsets_;
  const bool normalized_;
  const bool centered_;
  const ExtractGlimpsesNoiseMode noise_;
  const int version_;
};
}  // namespace

template <typename Input>
EIGEN_ALWAYS_INLINE static const TensorCustomUnaryOp<
    const GlimpseExtractionOp<typename internal::traits<Input>::Index>,
    const Input>
ExtractGlimpses(
    const Input& input, const typename internal::traits<Input>::Index width,
    const typename internal::traits<Input>::Index height,
    const std::vector<IndexPair<float> >& offsets, const bool normalized = true,
    const bool centered = true,
    const ExtractGlimpsesNoiseMode noise = ExtractGlimpsesNoiseMode::UNIFORM,
    const int version = 2) {
  EIGEN_STATIC_ASSERT(internal::traits<Input>::Layout == ColMajor,
                      YOU_MADE_A_PROGRAMMING_MISTAKE);
  EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 4,
                      YOU_MADE_A_PROGRAMMING_MISTAKE);

typedef typename internal::traits<Input>::Index Index;
  const GlimpseExtractionOp<Index> op(width, height, offsets, normalized,
                                      centered, noise, version);
  return input.customOp(op);
}

}  // end namespace Eigen

#endif  // TENSORFLOW_CORE_KERNELS_EIGEN_ATTENTION_H_