13 #ifndef MLPACK_METHODS_ANN_LAYER_ATROUS_CONVOLUTION_HPP 14 #define MLPACK_METHODS_ANN_LAYER_ATROUS_CONVOLUTION_HPP 44 typename ForwardConvolutionRule = NaiveConvolution<ValidConvolution>,
45 typename BackwardConvolutionRule = NaiveConvolution<FullConvolution>,
46 typename GradientConvolutionRule = NaiveConvolution<ValidConvolution>,
47 typename InputDataType = arma::mat,
48 typename OutputDataType = arma::mat
80 const size_t padW = 0,
81 const size_t padH = 0,
82 const size_t inputWidth = 0,
83 const size_t inputHeight = 0,
84 const size_t dilationW = 1,
85 const size_t dilationH = 1);
100 void Forward(
const arma::Mat<eT>&& input, arma::Mat<eT>&& output);
111 template<
typename eT>
112 void Backward(
const arma::Mat<eT>&& ,
123 template<
typename eT>
124 void Gradient(
const arma::Mat<eT>&& ,
125 arma::Mat<eT>&& error,
126 arma::Mat<eT>&& gradient);
139 OutputDataType
const&
Delta()
const {
return delta; }
141 OutputDataType&
Delta() {
return delta; }
144 OutputDataType
const&
Gradient()
const {
return gradient; }
171 template<
typename Archive>
172 void serialize(Archive& ar,
const unsigned int );
185 size_t ConvOutSize(
const size_t size,
191 return std::floor(size + p * 2 - d * (k - 1) - 1) / s + 1;
200 template<
typename eT>
201 void Rotate180(
const arma::Cube<eT>& input, arma::Cube<eT>& output)
203 output = arma::Cube<eT>(input.n_rows, input.n_cols, input.n_slices);
206 for (
size_t s = 0; s < output.n_slices; s++)
207 output.slice(s) = arma::fliplr(arma::flipud(input.slice(s)));
216 template<
typename eT>
217 void Rotate180(
const arma::Mat<eT>& input, arma::Mat<eT>& output)
220 output = arma::fliplr(arma::flipud(input));
231 template<
typename eT>
232 void Pad(
const arma::Mat<eT>& input,
235 arma::Mat<eT>& output)
237 if (output.n_rows != input.n_rows + wPad * 2 ||
238 output.n_cols != input.n_cols + hPad * 2)
240 output = arma::zeros(input.n_rows + wPad * 2, input.n_cols + hPad * 2);
243 output.submat(wPad, hPad, wPad + input.n_rows - 1,
244 hPad + input.n_cols - 1) = input;
255 template<
typename eT>
256 void Pad(
const arma::Cube<eT>& input,
259 arma::Cube<eT>& output)
261 output = arma::zeros(input.n_rows + wPad * 2,
262 input.n_cols + hPad * 2, input.n_slices);
264 for (
size_t i = 0; i < input.n_slices; ++i)
266 Pad<double>(input.slice(i), wPad, hPad, output.slice(i));
298 OutputDataType weights;
325 arma::cube outputTemp;
328 arma::cube inputTemp;
331 arma::cube inputPaddedTemp;
337 arma::cube gradientTemp;
340 OutputDataType delta;
343 OutputDataType gradient;
346 OutputDataType outputParameter;
353 #include "atrous_convolution_impl.hpp" OutputDataType const & Parameters() const
Get the parameters.
OutputDataType const & OutputParameter() const
Get the output parameter.
void Backward(const arma::Mat< eT > &&, arma::Mat< eT > &&gy, arma::Mat< eT > &&g)
Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backw...
size_t const & OutputHeight() const
Get the output height.
void serialize(Archive &ar, const unsigned int)
Serialize the layer.
OutputDataType & Delta()
Modify the delta.
size_t & InputHeight()
Modify the input height.
The core includes that mlpack expects; standard C++ includes and Armadillo.
size_t const & InputHeight() const
Get the input height.
OutputDataType & OutputParameter()
Modify the output parameter.
OutputDataType & Parameters()
Modify the parameters.
AtrousConvolution()
Create the AtrousConvolution object.
size_t const & OutputWidth() const
Get the output width.
OutputDataType const & Gradient() const
Get the gradient.
void Forward(const arma::Mat< eT > &&input, arma::Mat< eT > &&output)
Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activ...
size_t & InputWidth()
Modify input the width.
size_t const & InputWidth() const
Get the input width.
OutputDataType & Gradient()
Modify the gradient.
size_t & OutputWidth()
Modify the output width.
size_t & OutputHeight()
Modify the output height.
OutputDataType const & Delta() const
Get the delta.
Implementation of the Atrous Convolution class.