Caffe
Public Member Functions | Protected Member Functions | List of all members
caffe::DeconvolutionLayer< Dtype > Class Template Reference

Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer. More...

#include <deconv_layer.hpp>

Inheritance diagram for caffe::DeconvolutionLayer< Dtype >:
caffe::BaseConvolutionLayer< Dtype > caffe::Layer< Dtype >

Public Member Functions

 DeconvolutionLayer (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::DeconvolutionLayer< Dtype >

Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer.

ConvolutionLayer computes each output value by dotting an input window with a filter; DeconvolutionLayer multiplies each input value by a filter elementwise, and sums over the resulting output windows. In other words, DeconvolutionLayer is ConvolutionLayer with the forward and backward passes reversed. DeconvolutionLayer reuses ConvolutionParameter for its parameters, but they take the opposite sense as in ConvolutionLayer (so padding is removed from the output rather than added to the input, and stride results in upsampling rather than downsampling).


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