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#ifndef CAFFE_BASE_CONVOLUTION_LAYER_HPP_
#define CAFFE_BASE_CONVOLUTION_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/im2col.hpp"
namespace caffe {
/**
* @brief Abstract base class that factors out the BLAS code common to
* ConvolutionLayer and DeconvolutionLayer.
*/
template <typename Dtype>
class BaseConvolutionLayer : public Layer<Dtype> {
public:
explicit BaseConvolutionLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline int MinBottomBlobs() const { return 1; }
virtual inline int MinTopBlobs() const { return 1; }
virtual inline bool EqualNumBottomTopBlobs() const { return true; }
protected:
// Helper functions that abstract away the column buffer and gemm arguments.
// The last argument in forward_cpu_gemm is so that we can skip the im2col if
// we just called weight_cpu_gemm with the same input.
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);
#ifndef CPU_ONLY
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);
#endif
/// @brief The spatial dimensions of the input.
inline int input_shape(int i) {
return (*bottom_shape_)[channel_axis_ + i];
}
// reverse_dimensions should return true iff we are implementing deconv, so
// that conv helpers know which dimensions are which.
virtual bool reverse_dimensions() = 0;
// Compute height_out_ and width_out_ from other parameters.
virtual void compute_output_shape() = 0;
/// @brief The spatial dimensions of a filter kernel.
Blob<int> kernel_shape_;
/// @brief The spatial dimensions of the stride.
Blob<int> stride_;
/// @brief The spatial dimensions of the padding.
Blob<int> pad_;
/// @brief The spatial dimensions of the dilation.
Blob<int> dilation_;
/// @brief The spatial dimensions of the convolution input.
Blob<int> conv_input_shape_;
/// @brief The spatial dimensions of the col_buffer.
vector<int> col_buffer_shape_;
/// @brief The spatial dimensions of the output.
vector<int> output_shape_;
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_;
private:
// wrap im2col/col2im so we don't have to remember the (long) argument lists
inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
im2col_cpu(data, conv_in_channels_,
conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
pad_.cpu_data()[0], pad_.cpu_data()[1],
stride_.cpu_data()[0], stride_.cpu_data()[1],
dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
} else {
im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
col_buffer_shape_.data(), kernel_shape_.cpu_data(),
pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), col_buff);
}
}
inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
col2im_cpu(col_buff, conv_in_channels_,
conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
pad_.cpu_data()[0], pad_.cpu_data()[1],
stride_.cpu_data()[0], stride_.cpu_data()[1],
dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
} else {
col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
col_buffer_shape_.data(), kernel_shape_.cpu_data(),
pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), data);
}
}
#ifndef CPU_ONLY
inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
im2col_gpu(data, conv_in_channels_,
conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
pad_.cpu_data()[0], pad_.cpu_data()[1],
stride_.cpu_data()[0], stride_.cpu_data()[1],
dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
} else {
im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
kernel_shape_.gpu_data(), pad_.gpu_data(),
stride_.gpu_data(), dilation_.gpu_data(), col_buff);
}
}
inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
col2im_gpu(col_buff, conv_in_channels_,
conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
pad_.cpu_data()[0], pad_.cpu_data()[1],
stride_.cpu_data()[0], stride_.cpu_data()[1],
dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
} else {
col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
dilation_.gpu_data(), data);
}
}
#endif
int num_kernels_im2col_;
int num_kernels_col2im_;
int conv_out_channels_;
int conv_in_channels_;
int conv_out_spatial_dim_;
int kernel_dim_;
int col_offset_;
int output_offset_;
Blob<Dtype> col_buffer_;
Blob<Dtype> bias_multiplier_;
};
} // namespace caffe
#endif // CAFFE_BASE_CONVOLUTION_LAYER_HPP_
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