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 ¶m) | |
| virtual const char * | type () const |
| Returns the layer type. | |
 Public Member Functions inherited from caffe::BaseConvolutionLayer< Dtype > | |
| 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... | |
| Public Member Functions inherited from caffe::Layer< Dtype > | |
| 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 () |
| 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
◆ ConvolutionLayer()
template<typename Dtype >
|
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
, 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
