adaptive_mean_pooling_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_IMPL_HPP

// In case it hasn't yet been included.
#include "adaptive_mean_pooling.hpp"

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
AdaptiveMeanPooling<InputDataType, OutputDataType>::AdaptiveMeanPooling()
{
  // Nothing to do here.
}

template <typename InputDataType, typename OutputDataType>
AdaptiveMeanPooling<InputDataType, OutputDataType>::AdaptiveMeanPooling(
    const size_t outputWidth,
    const size_t outputHeight) :
    AdaptiveMeanPooling(std::tuple<size_t, size_t>(outputWidth, outputHeight))
{
  // Nothing to do here.
}

template <typename InputDataType, typename OutputDataType>
AdaptiveMeanPooling<InputDataType, OutputDataType>::AdaptiveMeanPooling(
    const std::tuple<size_t, size_t>& outputShape):
    outputWidth(std::get<0>(outputShape)),
    outputHeight(std::get<1>(outputShape)),
    reset(false)
{
  poolingLayer = ann::MeanPooling<>(0, 0);
}

template<typename InputDataType, typename OutputDataType>
template<typename eT>
void AdaptiveMeanPooling<InputDataType, OutputDataType>::Forward(
    const arma::Mat<eT>& input, arma::Mat<eT>& output)
{
  if (!reset)
  {
    IntializeAdaptivePadding();
    reset = true;
  }

  poolingLayer.Forward(input, output);
}

template<typename InputDataType, typename OutputDataType>
template<typename eT>
void AdaptiveMeanPooling<InputDataType, OutputDataType>::Backward(
  const arma::Mat<eT>& input,
  const arma::Mat<eT>& gy,
  arma::Mat<eT>& g)
{
  poolingLayer.Backward(input, gy, g);
}

template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void AdaptiveMeanPooling<InputDataType, OutputDataType>::serialize(
    Archive& ar,
    const uint32_t /* version */)
{
  ar(CEREAL_NVP(outputWidth));
  ar(CEREAL_NVP(outputHeight));
  ar(CEREAL_NVP(reset));
  ar(CEREAL_NVP(poolingLayer));
}

} // namespace ann
} // namespace mlpack

#endif