mean_pooling.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_HPP
#define MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_HPP
 
#include <mlpack/prereqs.hpp>
 
namespace mlpack {
namespace ann  {
 
template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class MeanPooling
{
 public:
  MeanPooling();
 
  MeanPooling(const size_t kernelWidth,
              const size_t kernelHeight,
              const size_t strideWidth = 1,
              const size_t strideHeight = 1,
              const bool floor = true);
 
  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 const& OutputParameter() const { return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }
 
  OutputDataType const& Delta() const { return delta; }
  OutputDataType& Delta() { return delta; }
 
  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; }
 
  size_t InputSize() const { return inSize; }
 
  size_t OutputSize() const { return outSize; }
 
  size_t KernelWidth() const { return kernelWidth; }
  size_t& KernelWidth() { return kernelWidth; }
 
  size_t KernelHeight() const { return kernelHeight; }
  size_t& KernelHeight() { return kernelHeight; }
 
  size_t StrideWidth() const { return strideWidth; }
  size_t& StrideWidth() { return strideWidth; }
 
  size_t StrideHeight() const { return strideHeight; }
  size_t& StrideHeight() { return strideHeight; }
 
  bool const& Floor() const { return floor; }
  bool& Floor() { return floor; }
 
  bool Deterministic() const { return deterministic; }
  bool& Deterministic() { return deterministic; }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);
 
 private:
  template<typename eT>
  void Pooling(const arma::Mat<eT>& input, arma::Mat<eT>& output)
  {
    for (size_t j = 0, colidx = 0; j < output.n_cols;
         ++j, colidx += strideHeight)
    {
      for (size_t i = 0, rowidx = 0; i < output.n_rows;
           ++i, rowidx += strideWidth)
      {
        arma::mat subInput = input(
            arma::span(rowidx, rowidx + kernelWidth - 1 - offset),
            arma::span(colidx, colidx + kernelHeight - 1 - offset));
 
        output(i, j) = arma::mean(arma::mean(subInput));
      }
    }
  }
 
  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 - offset;
    const size_t cStep = input.n_cols / error.n_cols - offset;
 
    arma::Mat<eT> unpooledError;
    for (size_t j = 0; j < input.n_cols - cStep; j += cStep)
    {
      for (size_t i = 0; i < input.n_rows - rStep; i += rStep)
      {
        const arma::Mat<eT>& inputArea = input(arma::span(i, i + rStep - 1),
            arma::span(j, j + cStep - 1));
 
        unpooledError = arma::Mat<eT>(inputArea.n_rows, inputArea.n_cols);
        unpooledError.fill(error(i / rStep, j / cStep) / inputArea.n_elem);
 
        output(arma::span(i, i + rStep - 1 - offset),
            arma::span(j, j + cStep - 1 - offset)) += unpooledError;
      }
    }
  }
 
  size_t kernelWidth;
 
  size_t kernelHeight;
 
  size_t strideWidth;
 
  size_t strideHeight;
 
  bool floor;
 
  size_t inSize;
 
  size_t outSize;
 
  size_t inputWidth;
 
  size_t inputHeight;
 
  size_t outputWidth;
 
  size_t outputHeight;
 
  bool reset;
 
  bool deterministic;
 
  size_t offset;
 
  size_t batchSize;
 
  arma::cube outputTemp;
 
  arma::cube inputTemp;
 
  arma::cube gTemp;
 
  OutputDataType delta;
 
  OutputDataType gradient;
 
  OutputDataType outputParameter;
}; // class MeanPooling
 
 
} // namespace ann
} // namespace mlpack
 
// Include implementation.
#include "mean_pooling_impl.hpp"
 
#endif