Convolves the input image with a bank of learned filters, and (optionally) adds biases. More...
#include <conv_layer.hpp>
Public Member Functions | |
ConvolutionLayer (const LayerParameter ¶m) | |
virtual const char * | type () const |
Returns the layer type. | |
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BaseConvolutionLayer (const LayerParameter ¶m) | |
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... | |
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Layer (const LayerParameter ¶m) | |
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 () |
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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. | |
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virtual void | CheckBlobCounts (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top) |
void | SetLossWeights (const vector< Blob< Dtype > * > &top) |
Additional Inherited Members | |
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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_ |
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LayerParameter | layer_param_ |
Phase | phase_ |
vector< shared_ptr< Blob< Dtype > > > | blobs_ |
vector< bool > | param_propagate_down_ |
vector< Dtype > | loss_ |
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.
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inlineexplicit |
param | provides ConvolutionParameter convolution_param, with ConvolutionLayer options:
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