Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer. More...

#include <base_conv_layer.hpp>

Inheritance diagram for caffe::BaseConvolutionLayer< Dtype >:

Public Member Functions
	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 const char *	type () const
	Returns the layer type.

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
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.

virtual bool	reverse_dimensions ()=0

virtual void	compute_output_shape ()=0

Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void	Forward_cpu (const vector< Blob< Dtype > > &bottom, const vector< Blob< Dtype > > &top)=0
	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)=0
	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 void	CheckBlobCounts (const vector< Blob< Dtype > > &bottom, const vector< Blob< Dtype > > &top)

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

Protected Attributes
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::BaseConvolutionLayer< Dtype >

Abstract base class that factors out the BLAS code common to ConvolutionLayer and DeconvolutionLayer.

Member Function Documentation

◆ EqualNumBottomTopBlobs()

template<typename Dtype >

virtual bool caffe::BaseConvolutionLayer< Dtype >::EqualNumBottomTopBlobs ( ) const

inlinevirtual

Returns true if the layer requires an equal number of bottom and top blobs.

This method should be overridden to return true if your layer expects an equal number of bottom and top blobs.

Reimplemented from caffe::Layer< Dtype >.

◆ LayerSetUp()

template<typename Dtype >

void caffe::BaseConvolutionLayer< Dtype >::LayerSetUp	(	const vector< Blob< Dtype > *> &	bottom,
		const vector< Blob< Dtype > *> &	top
	)

virtual

Does layer-specific setup: your layer should implement this function as well as Reshape.

Parameters

bottom	the preshaped input blobs, whose data fields store the input data for this layer
top	the allocated but unshaped output blobs

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_. Setting up the shapes of top blobs and internal buffers should be done in Reshape, which will be called before the forward pass to adjust the top blob sizes.

Reimplemented from caffe::Layer< Dtype >.

◆ MinBottomBlobs()

template<typename Dtype >

virtual int caffe::BaseConvolutionLayer< Dtype >::MinBottomBlobs ( ) const

inlinevirtual

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

Reimplemented from caffe::Layer< Dtype >.

◆ MinTopBlobs()

template<typename Dtype >

virtual int caffe::BaseConvolutionLayer< Dtype >::MinTopBlobs ( ) const

inlinevirtual

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

Reimplemented from caffe::Layer< Dtype >.

◆ Reshape()

template<typename Dtype >

void caffe::BaseConvolutionLayer< Dtype >::Reshape	(	const vector< Blob< Dtype > *> &	bottom,
		const vector< Blob< Dtype > *> &	top
	)

virtual

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

Parameters

bottom	the input blobs, with the requested input shapes
top	the top blobs, which should be reshaped as needed

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

Implements caffe::Layer< Dtype >.

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

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

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Member Function Documentation

◆ EqualNumBottomTopBlobs()

◆ LayerSetUp()

◆ MinBottomBlobs()

◆ MinTopBlobs()

◆ Reshape()

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