Convolves the input image with a bank of learned filters, and (optionally) adds biases. More...

#include <conv_layer.hpp>

Inheritance diagram for caffe::ConvolutionLayer< Dtype >:

Public Member Functions
	ConvolutionLayer (const LayerParameter &param)

virtual const char *	type () const
	Returns the layer type.

Public Member Functions inherited from caffe::BaseConvolutionLayer< Dtype >
	BaseConvolutionLayer (const LayerParameter &param)

virtual void	LayerSetUp (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
	Does layer-specific setup: your layer should implement this function as well as Reshape. More...

virtual void	Reshape (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
	Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...

virtual int	MinBottomBlobs () const
	Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...

virtual int	MinTopBlobs () const
	Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...

virtual bool	EqualNumBottomTopBlobs () const
	Returns true if the layer requires an equal number of bottom and top blobs. More...

Public Member Functions inherited from caffe::Layer< Dtype >
	Layer (const LayerParameter &param)

void	SetUp (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
	Implements common layer setup functionality. More...

Dtype	Forward (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
	Given the bottom blobs, compute the top blobs and the loss. More...

void	Backward (const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
	Given the top blob error gradients, compute the bottom blob error gradients. More...

vector< shared_ptr< Blob < Dtype > > > &	blobs ()
	Returns the vector of learnable parameter blobs.

const LayerParameter &	layer_param () const
	Returns the layer parameter.

virtual void	ToProto (LayerParameter *param, bool write_diff=false)
	Writes the layer parameter to a protocol buffer.

Dtype	loss (const int top_index) const
	Returns the scalar loss associated with a top blob at a given index.

void	set_loss (const int top_index, const Dtype value)
	Sets the loss associated with a top blob at a given index.

virtual int	ExactNumBottomBlobs () const
	Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...

virtual int	MaxBottomBlobs () const
	Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...

virtual int	ExactNumTopBlobs () const
	Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...

virtual int	MaxTopBlobs () const
	Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...

virtual bool	AutoTopBlobs () const
	Return whether "anonymous" top blobs are created automatically by the layer. More...

virtual bool	AllowForceBackward (const int bottom_index) const
	Return whether to allow force_backward for a given bottom blob index. More...

bool	param_propagate_down (const int param_id)
	Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...

void	set_param_propagate_down (const int param_id, const bool value)
	Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.

Protected Member Functions
virtual void	Forward_cpu (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
	Using the CPU device, compute the layer output.

virtual void	Forward_gpu (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
	Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.

virtual void	Backward_cpu (const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
	Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.

virtual void	Backward_gpu (const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
	Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.

virtual bool	reverse_dimensions ()

virtual void	compute_output_shape ()

Protected Member Functions inherited from caffe::BaseConvolutionLayer< Dtype >
void	forward_cpu_gemm (const Dtype input, const Dtype weights, Dtype *output, bool skip_im2col=false)

void	forward_cpu_bias (Dtype output, const Dtype bias)

void	backward_cpu_gemm (const Dtype input, const Dtype weights, Dtype *output)

void	weight_cpu_gemm (const Dtype input, const Dtype output, Dtype *weights)

void	backward_cpu_bias (Dtype bias, const Dtype input)

void	forward_gpu_gemm (const Dtype col_input, const Dtype weights, Dtype *output, bool skip_im2col=false)

void	forward_gpu_bias (Dtype output, const Dtype bias)

void	backward_gpu_gemm (const Dtype input, const Dtype weights, Dtype *col_output)

void	weight_gpu_gemm (const Dtype col_input, const Dtype output, Dtype *weights)

void	backward_gpu_bias (Dtype bias, const Dtype input)

int	input_shape (int i)
	The spatial dimensions of the input.

Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void	CheckBlobCounts (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)

void	SetLossWeights (const vector< Blob< Dtype > * > &top)

Additional Inherited Members
Protected Attributes inherited from caffe::BaseConvolutionLayer< Dtype >
Blob< int >	kernel_shape_
	The spatial dimensions of a filter kernel.

Blob< int >	stride_
	The spatial dimensions of the stride.

Blob< int >	pad_
	The spatial dimensions of the padding.

Blob< int >	dilation_
	The spatial dimensions of the dilation.

Blob< int >	conv_input_shape_
	The spatial dimensions of the convolution input.

vector< int >	col_buffer_shape_
	The spatial dimensions of the col_buffer.

vector< int >	output_shape_
	The spatial dimensions of the output.

const vector< int > *	bottom_shape_

int	num_spatial_axes_

int	bottom_dim_

int	top_dim_

int	channel_axis_

int	num_

int	channels_

int	group_

int	out_spatial_dim_

int	weight_offset_

int	num_output_

bool	bias_term_

bool	is_1x1_

bool	force_nd_im2col_

Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter	layer_param_

Phase	phase_

vector< shared_ptr< Blob < Dtype > > >	blobs_

vector< bool >	param_propagate_down_

vector< Dtype >	loss_

Detailed Description

template<typename Dtype>
class caffe::ConvolutionLayer< Dtype >

Convolves the input image with a bank of learned filters, and (optionally) adds biases.

Caffe convolves by reduction to matrix multiplication. This achieves high-throughput and generality of input and filter dimensions but comes at the cost of memory for matrices. This makes use of efficiency in BLAS.

The input is "im2col" transformed to a channel K' x H x W data matrix for multiplication with the N x K' x H x W filter matrix to yield a N' x H x W output matrix that is then "col2im" restored. K' is the input channel * kernel height * kernel width dimension of the unrolled inputs so that the im2col matrix has a column for each input region to be filtered. col2im restores the output spatial structure by rolling up the output channel N' columns of the output matrix.

Constructor & Destructor Documentation

template<typename Dtype >

caffe::ConvolutionLayer< Dtype >::ConvolutionLayer ( const LayerParameter & param )

inlineexplicit

Parameters

param

provides ConvolutionParameter convolution_param, with ConvolutionLayer options:

num_output. The number of filters.
kernel_size / kernel_h / kernel_w. The filter dimensions, given by kernel_size for square filters or kernel_h and kernel_w for rectangular filters.
stride / stride_h / stride_w (optional, default 1). The filter stride, given by stride_size for equal dimensions or stride_h and stride_w for different strides. By default the convolution is dense with stride 1.
pad / pad_h / pad_w (optional, default 0). The zero-padding for convolution, given by pad for equal dimensions or pad_h and pad_w for different padding. Input padding is computed implicitly instead of actually padding.
dilation (optional, default 1). The filter dilation, given by dilation_size for equal dimensions for different dilation. By default the convolution has dilation 1.
group (optional, default 1). The number of filter groups. Group convolution is a method for reducing parameterization by selectively connecting input and output channels. The input and output channel dimensions must be divisible by the number of groups. For group $\geq 1$ , the convolutional filters' input and output channels are separated s.t. each group takes 1 / group of the input channels and makes 1 / group of the output channels. Concretely 4 input channels, 8 output channels, and 2 groups separate input channels 1-2 and output channels 1-4 into the first group and input channels 3-4 and output channels 5-8 into the second group.
bias_term (optional, default true). Whether to have a bias.
engine: convolution has CAFFE (matrix multiplication) and CUDNN (library kernels + stream parallelism) engines.

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

include/caffe/layers/conv_layer.hpp
src/caffe/layers/conv_layer.cpp

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

template<typename Dtype> class caffe::ConvolutionLayer< Dtype >

Constructor & Destructor Documentation

template<typename Dtype>
class caffe::ConvolutionLayer< Dtype >