Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set. More...

#include <scale_layer.hpp>

Inheritance diagram for caffe::ScaleLayer< Dtype >:

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

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	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...

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	MinTopBlobs () const
	Returns the minimum number of top blobs required by the layer, or -1 if no minimum 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	EqualNumBottomTopBlobs () const
	Returns true if the layer requires an equal number of bottom and top blobs. 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)

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.

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)

Protected Attributes
shared_ptr< Layer< Dtype > >	bias_layer_

vector< Blob< Dtype > * >	bias_bottom_vec_

vector< bool >	bias_propagate_down_

int	bias_param_id_

Blob< Dtype >	sum_multiplier_

Blob< Dtype >	sum_result_

Blob< Dtype >	temp_

int	axis_

int	outer_dim_

int	scale_dim_

int	inner_dim_

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::ScaleLayer< Dtype >

Computes the elementwise product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when bias_term: true is set.

The latter, scale input may be omitted, in which case it's learned as parameter of the layer (as is the bias, if it is included).

Member Function Documentation

◆ ExactNumTopBlobs()

template<typename Dtype >

virtual int caffe::ScaleLayer< Dtype >::ExactNumTopBlobs ( ) const

inlinevirtual

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

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

Reimplemented from caffe::Layer< Dtype >.

◆ Forward_cpu()

template<typename Dtype >

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

protectedvirtual

In the below shape specifications, $ i $ denotes the value of the axis field given by this->layer_param_.scale_param().axis(), after canonicalization (i.e., conversion from negative to positive index, if applicable).

Parameters

bottom	input Blob vector (length 2) $(d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n)$ the first factor $(d_i \times ... \times d_j)$ the second factor
top	output Blob vector (length 1) $(d_0 \times ... \times d_i \times ... \times d_j \times ... \times d_n)$ the product computed after "broadcasting" y. Equivalent to tiling to have the same shape as , then computing the elementwise product.

Implements caffe::Layer< Dtype >.

◆ LayerSetUp()

template<typename Dtype >

void caffe::ScaleLayer< 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 >.

◆ MaxBottomBlobs()

template<typename Dtype >

virtual int caffe::ScaleLayer< Dtype >::MaxBottomBlobs ( ) const

inlinevirtual

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

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

Reimplemented from caffe::Layer< Dtype >.

◆ MinBottomBlobs()

template<typename Dtype >

virtual int caffe::ScaleLayer< 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 >.

◆ Reshape()

template<typename Dtype >

void caffe::ScaleLayer< 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/scale_layer.hpp
src/caffe/layers/scale_layer.cpp

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Member Function Documentation

◆ ExactNumTopBlobs()

◆ Forward_cpu()

◆ LayerSetUp()

◆ MaxBottomBlobs()

◆ MinBottomBlobs()

◆ Reshape()

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