convolution_layer.hpp

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#ifndef __OPENCV_DNN_LAYERS_CONVOLUTION_LAYER_HPP__
#define __OPENCV_DNN_LAYERS_CONVOLUTION_LAYER_HPP__
#include "../precomp.hpp"
#include <opencv2/dnn/all_layers.hpp>

namespace cv
{
namespace dnn
{

//TODO: simultaneously convolution and bias addition for cache optimization
class ConvolutionLayerImpl : public ConvolutionLayer
{
public:

    ConvolutionLayerImpl();
    virtual void allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
    virtual void forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
    virtual void init();

protected:
    int numOutput, group;
    int inpH, inpW, inpCn;
    int outH, outW, outCn;
    int topH, topW, topCn; //switched between inp/out on deconv/conv
    int inpGroupCn, outGroupCn;
    int ksize;

    bool bias;
    bool tryUseOpenCL, useOpenCL;

    Blob colBlob, biasOnesBlob;

    bool is1x1() const;
    virtual void computeInpOutShape(const Blob &inpBlob);

    template<typename XMat>
    void forward_(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
    void im2col(const  Mat &srcImg,  Mat &dstCol);
    void im2col(const UMat &srcImg, UMat &dstCol);
};

class DeConvolutionLayerImpl : public ConvolutionLayerImpl
{
public:
    DeConvolutionLayerImpl();
    virtual void forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);

protected:

    virtual void computeInpOutShape(const Blob &inpBlob);

    template<typename XMat>
    void forward_(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
    void col2im(const  Mat &colMat, Mat  &dstImg);
    void col2im(const UMat &colMat, UMat &dstImg);
};

//Importers
Ptr<Layer> createConvolutionLayerFromCaffe(LayerParams &params);
Ptr<Layer> createDeconvolutionLayerFromCaffe(LayerParams &params);

}
}
#endif