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#ifndef CAFFE_CUDNN_CONV_LAYER_HPP_
#define CAFFE_CUDNN_CONV_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/conv_layer.hpp"
namespace caffe {
#ifdef USE_CUDNN
/*
* @brief cuDNN implementation of ConvolutionLayer.
* Fallback to ConvolutionLayer for CPU mode.
*
* cuDNN accelerates convolution through forward kernels for filtering and bias
* plus backward kernels for the gradient w.r.t. the filters, biases, and
* inputs. Caffe + cuDNN further speeds up the computation through forward
* parallelism across groups and backward parallelism across gradients.
*
* The CUDNN engine does not have memory overhead for matrix buffers. For many
* input and filter regimes the CUDNN engine is faster than the CAFFE engine,
* but for fully-convolutional models and large inputs the CAFFE engine can be
* faster as long as it fits in memory.
*/
template <typename Dtype>
class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
public:
explicit CuDNNConvolutionLayer(const LayerParameter& param)
: ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
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 ~CuDNNConvolutionLayer();
protected:
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
bool handles_setup_;
cudnnHandle_t* handle_;
cudaStream_t* stream_;
// algorithms for forward and backwards convolutions
cudnnConvolutionFwdAlgo_t *fwd_algo_;
cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
cudnnTensorDescriptor_t bias_desc_;
cudnnFilterDescriptor_t filter_desc_;
vector<cudnnConvolutionDescriptor_t> conv_descs_;
int bottom_offset_, top_offset_, bias_offset_;
size_t *workspace_fwd_sizes_;
size_t *workspace_bwd_data_sizes_;
size_t *workspace_bwd_filter_sizes_;
size_t workspaceSizeInBytes; // size of underlying storage
void *workspaceData; // underlying storage
void **workspace; // aliases into workspaceData
};
#endif
} // namespace caffe
#endif // CAFFE_CUDNN_CONV_LAYER_HPP_
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