convolution_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_CONVOLUTION_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_CONVOLUTION_IMPL_HPP

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

namespace mlpack {
namespace ann  {

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Convolution()
{
  // Nothing to do here.
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Convolution(
    const size_t inSize,
    const size_t outSize,
    const size_t kernelWidth,
    const size_t kernelHeight,
    const size_t strideWidth,
    const size_t strideHeight,
    const size_t padW,
    const size_t padH,
    const size_t inputWidth,
    const size_t inputHeight,
    const std::string& paddingType) :
    Convolution(
      inSize,
      outSize,
      kernelWidth,
      kernelHeight,
      strideWidth,
      strideHeight,
      std::tuple<size_t, size_t>(padW, padW),
      std::tuple<size_t, size_t>(padH, padH),
      inputWidth,
      inputHeight,
      paddingType)
{
  // Nothing to do here.
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Convolution(
    const size_t inSize,
    const size_t outSize,
    const size_t kernelWidth,
    const size_t kernelHeight,
    const size_t strideWidth,
    const size_t strideHeight,
    const std::tuple<size_t, size_t>& padW,
    const std::tuple<size_t, size_t>& padH,
    const size_t inputWidth,
    const size_t inputHeight,
    const std::string& paddingType) :
    inSize(inSize),
    outSize(outSize),
    kernelWidth(kernelWidth),
    kernelHeight(kernelHeight),
    strideWidth(strideWidth),
    strideHeight(strideHeight),
    padWLeft(std::get<0>(padW)),
    padWRight(std::get<1>(padW)),
    padHBottom(std::get<1>(padH)),
    padHTop(std::get<0>(padH)),
    inputWidth(inputWidth),
    inputHeight(inputHeight),
    outputWidth(0),
    outputHeight(0)
{
  weights.set_size(WeightSize(), 1);

  // Transform paddingType to lowercase.
  const std::string paddingTypeLow = util::ToLower(paddingType);

  if (paddingTypeLow == "valid")
  {
    padWLeft = 0;
    padWRight = 0;
    padHTop = 0;
    padHBottom = 0;
  }
  else if (paddingTypeLow == "same")
  {
    InitializeSamePadding();
  }

  padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom);
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
void Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Reset()
{
    weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight,
        outSize * inSize, false, false);
    bias = arma::mat(weights.memptr() + weight.n_elem,
        outSize, 1, false, false);
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
template<typename eT>
void Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output)
{
  batchSize = input.n_cols;
  arma::cube inputTemp(const_cast<arma::Mat<eT>&>(input).memptr(),
      inputWidth, inputHeight, inSize * batchSize, false, false);

  if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0)
  {
    inputPaddedTemp.set_size(inputTemp.n_rows + padWLeft + padWRight,
        inputTemp.n_cols + padHTop + padHBottom, inputTemp.n_slices);

    for (size_t i = 0; i < inputTemp.n_slices; ++i)
    {
      padding.Forward(inputTemp.slice(i), inputPaddedTemp.slice(i));
    }
  }

  size_t wConv = ConvOutSize(inputWidth, kernelWidth, strideWidth, padWLeft,
      padWRight);
  size_t hConv = ConvOutSize(inputHeight, kernelHeight, strideHeight, padHTop,
      padHBottom);

  output.set_size(wConv * hConv * outSize, batchSize);
  outputTemp = arma::Cube<eT>(output.memptr(), wConv, hConv,
      outSize * batchSize, false, false);
  outputTemp.zeros();

  for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap <
      outSize * batchSize; outMap++)
  {
    if (outMap != 0 && outMap % outSize == 0)
    {
      batchCount++;
      outMapIdx = 0;
    }

    for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++)
    {
      arma::Mat<eT> convOutput;

      if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0)
      {
        ForwardConvolutionRule::Convolution(inputPaddedTemp.slice(inMap +
            batchCount * inSize), weight.slice(outMapIdx), convOutput,
            strideWidth, strideHeight);
      }
      else
      {
        ForwardConvolutionRule::Convolution(inputTemp.slice(inMap +
            batchCount * inSize), weight.slice(outMapIdx), convOutput,
            strideWidth, strideHeight);
      }

      outputTemp.slice(outMap) += convOutput;
    }

    outputTemp.slice(outMap) += bias(outMap % outSize);
  }

  outputWidth = outputTemp.n_rows;
  outputHeight = outputTemp.n_cols;
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
template<typename eT>
void Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Backward(
    const arma::Mat<eT>& /* input */, const arma::Mat<eT>& gy, arma::Mat<eT>& g)
{
  arma::cube mappedError(((arma::Mat<eT>&) gy).memptr(), outputWidth,
      outputHeight, outSize * batchSize, false, false);

  g.set_size(inputWidth * inputHeight * inSize, batchSize);
  gTemp = arma::Cube<eT>(g.memptr(), inputWidth, inputHeight,
      inSize * batchSize, false, false);
  gTemp.zeros();

  for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap <
      outSize * batchSize; outMap++)
  {
    if (outMap != 0 && outMap % outSize == 0)
    {
      batchCount++;
      outMapIdx = 0;
    }

    for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++)
    {
      arma::Mat<eT> output, rotatedFilter;
      Rotate180(weight.slice(outMapIdx), rotatedFilter);

      BackwardConvolutionRule::Convolution(mappedError.slice(outMap),
          rotatedFilter, output, strideWidth, strideHeight);

      if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0)
      {
        gTemp.slice(inMap + batchCount * inSize) += output.submat(padWLeft,
            padHTop, padWLeft + gTemp.n_rows - 1, padHTop + gTemp.n_cols - 1);
      }
      else
      {
        gTemp.slice(inMap + batchCount * inSize) += output;
      }
    }
  }
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
template<typename eT>
void Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::Gradient(
    const arma::Mat<eT>& input,
    const arma::Mat<eT>& error,
    arma::Mat<eT>& gradient)
{
  arma::cube mappedError(((arma::Mat<eT>&) error).memptr(), outputWidth,
      outputHeight, outSize * batchSize, false, false);
  arma::cube inputTemp(((arma::Mat<eT>&) input).memptr(), inputWidth,
      inputHeight, inSize * batchSize, false, false);

  gradient.set_size(weights.n_elem, 1);
  gradientTemp = arma::Cube<eT>(gradient.memptr(), weight.n_rows,
      weight.n_cols, weight.n_slices, false, false);
  gradientTemp.zeros();

  for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap <
      outSize * batchSize; outMap++)
  {
    if (outMap != 0 && outMap % outSize == 0)
    {
      batchCount++;
      outMapIdx = 0;
    }

    for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++)
    {
      arma::Mat<eT> inputSlice;
      if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0)
      {
        inputSlice = inputPaddedTemp.slice(inMap + batchCount * inSize);
      }
      else
      {
        inputSlice = inputTemp.slice(inMap + batchCount * inSize);
      }

      arma::Mat<eT> deltaSlice = mappedError.slice(outMap);

      arma::Mat<eT> output;
      GradientConvolutionRule::Convolution(inputSlice, deltaSlice,
          output, strideWidth, strideHeight);

      if (gradientTemp.n_rows < output.n_rows ||
          gradientTemp.n_cols < output.n_cols)
      {
        gradientTemp.slice(outMapIdx) += output.submat(0, 0,
            gradientTemp.n_rows - 1, gradientTemp.n_cols - 1);
      }
      else if (gradientTemp.n_rows > output.n_rows ||
          gradientTemp.n_cols > output.n_cols)
      {
        gradientTemp.slice(outMapIdx).submat(0, 0, output.n_rows - 1,
            output.n_cols - 1) += output;
      }
      else
      {
        gradientTemp.slice(outMapIdx) += output;
      }
    }

    gradient.submat(weight.n_elem + (outMap % outSize), 0, weight.n_elem +
        (outMap % outSize), 0) = arma::accu(mappedError.slice(outMap));
  }
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
template<typename Archive>
void Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::serialize(Archive& ar, const uint32_t /* version*/)
{
  ar(CEREAL_NVP(inSize));
  ar(CEREAL_NVP(outSize));
  ar(CEREAL_NVP(batchSize));
  ar(CEREAL_NVP(kernelWidth));
  ar(CEREAL_NVP(kernelHeight));
  ar(CEREAL_NVP(strideWidth));
  ar(CEREAL_NVP(strideHeight));
  ar(CEREAL_NVP(padWLeft));
  ar(CEREAL_NVP(padWRight));
  ar(CEREAL_NVP(padHBottom));
  ar(CEREAL_NVP(padHTop));
  ar(CEREAL_NVP(inputWidth));
  ar(CEREAL_NVP(inputHeight));
  ar(CEREAL_NVP(outputWidth));
  ar(CEREAL_NVP(outputHeight));
  ar(CEREAL_NVP(padding));

  if (cereal::is_loading<Archive>())
  {
    weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
        1);
  }
}

template<
    typename ForwardConvolutionRule,
    typename BackwardConvolutionRule,
    typename GradientConvolutionRule,
    typename InputDataType,
    typename OutputDataType
>
void Convolution<
    ForwardConvolutionRule,
    BackwardConvolutionRule,
    GradientConvolutionRule,
    InputDataType,
    OutputDataType
>::InitializeSamePadding()
{
  /*
   * Using O = (W - F + 2P) / s + 1;
   */
  size_t totalVerticalPadding = (strideWidth - 1) * inputWidth + kernelWidth -
      strideWidth;
  size_t totalHorizontalPadding = (strideHeight - 1) * inputHeight +
      kernelHeight - strideHeight;

  padWLeft = totalVerticalPadding / 2;
  padWRight = totalVerticalPadding - totalVerticalPadding / 2;
  padHTop = totalHorizontalPadding / 2;
  padHBottom = totalHorizontalPadding - totalHorizontalPadding / 2;
}

} // namespace ann
} // namespace mlpack

#endif