adaptive_mean_pooling.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_HPP
#define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_HPP
 
#include <mlpack/prereqs.hpp>
#include "layer_types.hpp"
 
namespace mlpack {
namespace ann  {
 
template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class AdaptiveMeanPooling
{
 public:
  AdaptiveMeanPooling();
 
  AdaptiveMeanPooling(const size_t outputWidth,
                      const size_t outputHeight);
 
  AdaptiveMeanPooling(const std::tuple<size_t, size_t>& outputShape);
 
  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 poolingLayer.OutputParameter(); }
 
  OutputDataType& OutputParameter() { return poolingLayer.OutputParameter(); }
 
  const OutputDataType& Delta() const { return poolingLayer.Delta(); }
  OutputDataType& Delta() { return poolingLayer.Delta(); }
 
  size_t InputWidth() const { return poolingLayer.InputWidth(); }
  size_t& InputWidth() { return poolingLayer.InputWidth(); }
 
  size_t InputHeight() const { return poolingLayer.InputHeight(); }
  size_t& InputHeight() { return poolingLayer.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 poolingLayer.InputSize(); }
 
  size_t OutputSize() const { return poolingLayer.OutputSize(); }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int version);
 
 private:
  void IntializeAdaptivePadding()
  {
    poolingLayer.StrideWidth() = std::floor(poolingLayer.InputWidth() /
        outputWidth);
    poolingLayer.StrideHeight() = std::floor(poolingLayer.InputHeight() /
        outputHeight);
 
    poolingLayer.KernelWidth() = poolingLayer.InputWidth() -
        (outputWidth - 1) * poolingLayer.StrideWidth();
    poolingLayer.KernelHeight() = poolingLayer.InputHeight() -
        (outputHeight - 1) * poolingLayer.StrideHeight();
 
    if (poolingLayer.KernelHeight() <= 0 || poolingLayer.KernelWidth() <= 0 ||
        poolingLayer.StrideWidth() <= 0 || poolingLayer.StrideHeight() <= 0)
    {
      Log::Fatal << "Given output shape (" << outputWidth << ", "
        << outputHeight << ") is not possible for given input shape ("
        << poolingLayer.InputWidth() << ", " << poolingLayer.InputHeight()
        << ")." << std::endl;
    }
  }
 
  MeanPooling<InputDataType, OutputDataType> poolingLayer;
 
  size_t outputWidth;
 
  size_t outputHeight;
 
  bool reset;
}; // class AdaptiveMeanPooling
 
} // namespace ann
} // namespace mlpack
 
// Include implementation.
#include "adaptive_mean_pooling_impl.hpp"
 
#endif