mlpack::ann::ChannelShuffle< InputDataType, OutputDataType > Class Template Reference

Definition and implementation of the Channel Shuffle Layer. More...

#include <channel_shuffle.hpp>

Public Member Functions
	ChannelShuffle ()
	Create the Channel Shuffle object.
	ChannelShuffle (const size_t inRowSize, const size_t inColSize, const size_t depth, const size_t groupCount)
	The constructor for the Channel Shuffle. More...
template<typename eT >
void	Forward (const arma::Mat< eT > &input, arma::Mat< eT > &output)
	Forward pass through the layer. More...
template<typename eT >
void	Backward (const arma::Mat< eT > &, const arma::Mat< eT > &gradient, arma::Mat< eT > &output)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...
OutputDataType const &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
size_t const &	InRowSize () const
	Get the row size of the input.
size_t &	InRowSize ()
	Modify the row size of the input.
size_t const &	InColSize () const
	Get the column size of the input.
size_t &	InColSize ()
	Modify the column size of the input.
size_t const &	InDepth () const
	Get the depth of the input.
size_t &	InDepth ()
	Modify the depth of the input.
size_t const &	InGroupCount () const
	Get the number of groups the channels is divided into.
size_t &	InGroupCount ()
	Modify the number of groups the channels is divided into.
size_t	InputShape () const
	Get the shape of the input.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::ChannelShuffle< InputDataType, OutputDataType >

Definition and implementation of the Channel Shuffle Layer.

Channel Shuffle divides the channels/units in a tensor into groups and rearrange while keeping the original tensor shape.

For more information, refer to the following paper,

@article{zhang2018shufflenet,
  author    = {Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun and
               Megvii Inc},
  title     = {Shufflenet: An extremely efficient convolutional neural
               network for mobile devices},
  year      = {2018},
  url       = {https://arxiv.org/pdf/1707.01083},
}

Template Parameters

InputDataType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataType	Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Constructor & Destructor Documentation

ChannelShuffle()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::ChannelShuffle< InputDataType, OutputDataType >::ChannelShuffle	(	const size_t	inRowSize,
		const size_t	inColSize,
		const size_t	depth,
		const size_t	groupCount
	)

The constructor for the Channel Shuffle.

Parameters

inRowSize	Number of input rows.
inColSize	Number of input columns.
depth	Number of input slices.
group	Number of groups for shuffling channels.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::ChannelShuffle< InputDataType, OutputDataType >::Backward	(	const arma::Mat< eT > &	,
		const arma::Mat< eT > &	gradient,
		arma::Mat< eT > &	output
	)

Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f.

Using the results from the feed forward pass. Since the layer does not have any learn-able parameters, we just have to down-sample the gradient to make its size compatible with the input size.

Parameters

*	(input) The input matrix.
gradient	The computed backward gradient.
output	The resulting down-sampled output.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::ChannelShuffle< InputDataType, OutputDataType >::Forward	(	const arma::Mat< eT > &	input,
		arma::Mat< eT > &	output
	)

Forward pass through the layer.

Parameters

input	The input matrix.
output	The resulting interpolated output matrix.

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The documentation for this class was generated from the following files:

• src/mlpack/methods/ann/layer/channel_shuffle.hpp
• src/mlpack/methods/ann/layer/channel_shuffle_impl.hpp