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#ifndef CAFFE_CONV_LAYER_HPP_

#define CAFFE_CONV_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"

#include "caffe/layer.hpp"

#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/base_conv_layer.hpp"

namespace caffe {

/**

* @brief 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.

template <typename Dtype>

class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {

public:

/**

* @param 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 (\b 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 (\b 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 (\b optional, default 1). The filter

* dilation, given by dilation_size for equal dimensions for different

* dilation. By default the convolution has dilation 1.

* - group (\b 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 @f$ \geq 1 @f$, 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 (\b optional, default true). Whether to have a bias.

* - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library

* kernels + stream parallelism) engines.

explicit ConvolutionLayer(const LayerParameter& param)

: BaseConvolutionLayer<Dtype>(param) {}

virtual inline const char* type() const { return "Convolution"; }

protected:

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);

virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,

const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,

const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

virtual inline bool reverse_dimensions() { return false; }

virtual void compute_output_shape();

};

} // namespace caffe

#endif // CAFFE_CONV_LAYER_HPP_

caffe/include/caffe/layers/conv_layer.hpp at master · BVLC/caffe · GitHub

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