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#ifndef CAFFE_DECONV_LAYER_HPP_
#define CAFFE_DECONV_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 Convolve the input with a bank of learned filters, and (optionally)
* add biases, treating filters and convolution parameters in the
* opposite sense as ConvolutionLayer.
*
* ConvolutionLayer computes each output value by dotting an input window with
* a filter; DeconvolutionLayer multiplies each input value by a filter
* elementwise, and sums over the resulting output windows. In other words,
* DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
* reversed. DeconvolutionLayer reuses ConvolutionParameter for its
* parameters, but they take the opposite sense as in ConvolutionLayer (so
* padding is removed from the output rather than added to the input, and
* stride results in upsampling rather than downsampling).
*/
template <typename Dtype>
class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
public:
explicit DeconvolutionLayer(const LayerParameter& param)
: BaseConvolutionLayer<Dtype>(param) {}
virtual inline const char* type() const { return "Deconvolution"; }
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 true; }
virtual void compute_output_shape();
};
} // namespace caffe
#endif // CAFFE_DECONV_LAYER_HPP_
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