glimpse.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_GLIMPSE_HPP
#define MLPACK_METHODS_ANN_LAYER_GLIMPSE_HPP

#include <mlpack/prereqs.hpp>

#include "layer_types.hpp"
#include <algorithm>

namespace mlpack {
namespace ann  {


/*
 * The mean pooling rule for convolution neural networks. Average all values
 * within the receptive block.
 */
class MeanPoolingRule
{
 public:
  /*
   * Return the average value within the receptive block.
   *
   * @param input Input used to perform the pooling operation.
   */
  template<typename MatType>
  double Pooling(const MatType& input)
  {
    return arma::mean(arma::mean(input));
  }

  /*
   * Set the average value within the receptive block.
   *
   * @param input Input used to perform the pooling operation.
   * @param value The unpooled value.
   * @param output The unpooled output data.
   */
  template<typename MatType>
  void Unpooling(const MatType& input, const double value, MatType& output)
  {
    output = arma::zeros<MatType>(input.n_rows, input.n_cols);
    const double mean = arma::mean(arma::mean(input));

    output.elem(arma::find(mean == input, 1)).fill(value);
  }
};

template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class Glimpse
{
 public:
  Glimpse(const size_t inSize = 0,
          const size_t size = 0,
          const size_t depth = 3,
          const size_t scale = 2,
          const size_t inputWidth = 0,
          const size_t inputHeight = 0);

  template<typename eT>
  void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);

  template<typename eT>
  void Backward(const arma::Mat<eT>& /* input */,
                const arma::Mat<eT>& gy,
                arma::Mat<eT>& g);

  OutputDataType& OutputParameter() const {return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }

  OutputDataType& Delta() const { return delta; }
  OutputDataType& Delta() { return delta; }

  void Location(const arma::mat& location)
  {
    this->location = location;
  }

  size_t const& InputWidth() const { return inputWidth; }
  size_t& InputWidth() { return inputWidth; }

  size_t const& InputHeight() const { return inputHeight; }
  size_t& InputHeight() { return inputHeight; }

  size_t const& OutputWidth() const { return outputWidth; }
  size_t& OutputWidth() { return outputWidth; }

  size_t const& OutputHeight() const { return outputHeight; }
  size_t& OutputHeight() { return outputHeight; }

  bool Deterministic() const { return deterministic; }
  bool& Deterministic() { return deterministic; }

  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);

 private:
  /*
   * Transform the given input by changing rows to columns.
   *
   * @param w The input matrix used to perform the transformation.
   */
  void Transform(arma::mat& w)
  {
    arma::mat t = w;

    for (size_t i = 0, k = 0; i < w.n_elem; k++)
    {
      for (size_t j = 0; j < w.n_cols; j++, i++)
      {
        w(k, j) = t(i);
      }
    }
  }

  /*
   * Transform the given input by changing rows to columns.
   *
   * @param w The input matrix used to perform the transformation.
   */
  void Transform(arma::cube& w)
  {
    for (size_t i = 0; i < w.n_slices; i++)
    {
      arma::mat t = w.slice(i);
      Transform(t);
      w.slice(i) = t;
    }
  }

  template<typename eT>
  void Pooling(const size_t kSize,
               const arma::Mat<eT>& input,
               arma::Mat<eT>& output)
  {
    const size_t rStep = kSize;
    const size_t cStep = kSize;

    for (size_t j = 0; j < input.n_cols; j += cStep)
    {
      for (size_t i = 0; i < input.n_rows; i += rStep)
      {
        output(i / rStep, j / cStep) += pooling.Pooling(
            input(arma::span(i, i + rStep - 1), arma::span(j, j + cStep - 1)));
      }
    }
  }

  template<typename eT>
  void Unpooling(const arma::Mat<eT>& input,
                 const arma::Mat<eT>& error,
                 arma::Mat<eT>& output)
  {
    const size_t rStep = input.n_rows / error.n_rows;
    const size_t cStep = input.n_cols / error.n_cols;

    arma::Mat<eT> unpooledError;
    for (size_t j = 0; j < input.n_cols; j += cStep)
    {
      for (size_t i = 0; i < input.n_rows; i += rStep)
      {
        const arma::Mat<eT>& inputArea = input(arma::span(i, i + rStep - 1),
                                               arma::span(j, j + cStep - 1));

        pooling.Unpooling(inputArea, error(i / rStep, j / cStep),
            unpooledError);

        output(arma::span(i, i + rStep - 1),
            arma::span(j, j + cStep - 1)) += unpooledError;
      }
    }
  }

  template<typename eT>
  void ReSampling(const arma::Mat<eT>& input, arma::Mat<eT>& output)
  {
    double wRatio = (double) (input.n_rows - 1) / (size - 1);
    double hRatio = (double) (input.n_cols - 1) / (size - 1);

    double iWidth = input.n_rows - 1;
    double iHeight = input.n_cols - 1;

    for (size_t y = 0; y < size; y++)
    {
      for (size_t x = 0; x < size; x++)
      {
        double ix = wRatio * x;
        double iy = hRatio * y;

        // Get the 4 nearest neighbors.
        double ixNw = std::floor(ix);
        double iyNw = std::floor(iy);
        double ixNe = ixNw + 1;
        double iySw = iyNw + 1;

        // Get surfaces to each neighbor.
        double se = (ix - ixNw) * (iy - iyNw);
        double sw = (ixNe - ix) * (iy - iyNw);
        double ne = (ix - ixNw) * (iySw - iy);
        double nw = (ixNe - ix) * (iySw - iy);

        // Calculate the weighted sum.
        output(y, x) = input(iyNw, ixNw) * nw +
            input(iyNw, std::min(ixNe,  iWidth)) * ne +
            input(std::min(iySw, iHeight), ixNw) * sw +
            input(std::min(iySw, iHeight), std::min(ixNe, iWidth)) * se;
      }
    }
  }

  template<typename eT>
  void DownwardReSampling(const arma::Mat<eT>& input,
                          const arma::Mat<eT>& error,
                          arma::Mat<eT>& output)
  {
    double iWidth = input.n_rows - 1;
    double iHeight = input.n_cols - 1;

    double wRatio = iWidth / (size - 1);
    double hRatio = iHeight / (size - 1);

    for (size_t y = 0; y < size; y++)
    {
      for (size_t x = 0; x < size; x++)
      {
        double ix = wRatio * x;
        double iy = hRatio * y;

        // Get the 4 nearest neighbors.
        double ixNw = std::floor(ix);
        double iyNw = std::floor(iy);
        double ixNe = ixNw + 1;
        double iySw = iyNw + 1;

        // Get surfaces to each neighbor.
        double se = (ix - ixNw) * (iy - iyNw);
        double sw = (ixNe - ix) * (iy - iyNw);
        double ne = (ix - ixNw) * (iySw - iy);
        double nw = (ixNe - ix) * (iySw - iy);

        double ograd = error(y, x);

        output(iyNw, ixNw) = output(iyNw, ixNw) + nw * ograd;
        output(iyNw, std::min(ixNe, iWidth)) = output(iyNw,
            std::min(ixNe, iWidth)) + ne * ograd;
        output(std::min(iySw, iHeight), ixNw) = output(std::min(iySw, iHeight),
            ixNw) + sw * ograd;
        output(std::min(iySw, iHeight), std::min(ixNe, iWidth)) = output(
            std::min(iySw, iHeight), std::min(ixNe, iWidth)) + se * ograd;
      }
    }
  }

  size_t inSize;

  size_t size;

  size_t depth;

  size_t scale;

  size_t inputWidth;

  size_t inputHeight;

  size_t outputWidth;

  size_t outputHeight;

  OutputDataType delta;

  OutputDataType outputParameter;

  size_t inputDepth;

  arma::cube inputTemp;

  arma::cube outputTemp;

  arma::mat location;

  MeanPoolingRule pooling;

  std::vector<arma::mat> locationParameter;

  arma::cube gTemp;

  bool deterministic;
}; // class GlimpseLayer

} // namespace ann
} // namespace mlpack

// Include implementation.
#include "glimpse_impl.hpp"

#endif