max_pooling.hpp

Go to the documentation of this file.

 
#ifndef MLPACK_METHODS_ANN_LAYER_MAX_POOLING_HPP
#define MLPACK_METHODS_ANN_LAYER_MAX_POOLING_HPP
 
#include <mlpack/prereqs.hpp>
 
namespace mlpack {
namespace ann  {
 
/*
 * The max pooling rule for convolution neural networks. Take the maximum value
 * within the receptive block.
 */
class MaxPoolingRule
{
 public:
  /*
   * Return the maximum value within the receptive block.
   *
   * @param input Input used to perform the pooling operation.
   */
  template<typename MatType>
  size_t Pooling(const MatType& input)
  {
    return arma::as_scalar(arma::find(input.max() == input, 1));
  }
};
 
template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class MaxPooling
{
 public:
  MaxPooling();
 
  MaxPooling(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);
 
  const OutputDataType& OutputParameter() const { return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }
 
  const OutputDataType& Delta() const { return delta; }
  OutputDataType& Delta() { return delta; }
 
  size_t InputWidth() const { return inputWidth; }
  size_t& InputWidth() { return inputWidth; }
 
  size_t InputHeight() const { return inputHeight; }
  size_t& InputHeight() { return inputHeight; }
 
  size_t OutputWidth() const { return outputWidth; }
  size_t& OutputWidth() { return outputWidth; }
 
  size_t 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 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 PoolingOperation(const arma::Mat<eT>& input,
                        arma::Mat<eT>& output,
                        arma::Mat<eT>& poolingIndices)
  {
    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));
 
        const size_t idx = pooling.Pooling(subInput);
        output(i, j) = subInput(idx);
 
        if (!deterministic)
        {
          arma::Mat<size_t> subIndices = indices(arma::span(rowidx,
              rowidx + kernelWidth - 1 - offset),
              arma::span(colidx, colidx + kernelHeight - 1 - offset));
 
          poolingIndices(i, j) = subIndices(idx);
        }
      }
    }
  }
 
  template<typename eT>
  void Unpooling(const arma::Mat<eT>& error,
                 arma::Mat<eT>& output,
                 arma::Mat<eT>& poolingIndices)
  {
    for (size_t i = 0; i < poolingIndices.n_elem; ++i)
    {
      output(poolingIndices(i)) += error(i);
    }
  }
 
  size_t kernelWidth;
 
  size_t kernelHeight;
 
  size_t strideWidth;
 
  size_t strideHeight;
 
  bool floor;
 
  size_t inSize;
 
  size_t outSize;
 
  bool reset;
 
  size_t inputWidth;
 
  size_t inputHeight;
 
  size_t outputWidth;
 
  size_t outputHeight;
 
  bool deterministic;
 
  size_t offset;
 
  size_t batchSize;
 
  arma::cube outputTemp;
 
  arma::cube inputTemp;
 
  arma::cube gTemp;
 
  MaxPoolingRule pooling;
 
  OutputDataType delta;
 
  OutputDataType gradient;
 
  OutputDataType outputParameter;
 
  arma::Mat<size_t> indices;
 
  arma::Col<size_t> indicesCol;
 
  std::vector<arma::cube> poolingIndices;
}; // class MaxPooling
 
} // namespace ann
} // namespace mlpack
 
// Include implementation.
#include "max_pooling_impl.hpp"
 
#endif