cudnn.hpp
#ifndef CAFFE_UTIL_CUDNN_H_
#define CAFFE_UTIL_CUDNN_H_
#ifdef USE_CUDNN
#include <cudnn.h>
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#define CUDNN_VERSION_MIN(major, minor, patch) \
(CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
#define CUDNN_CHECK(condition) \
do { \
cudnnStatus_t status = condition; \
CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " "\
<< cudnnGetErrorString(status); \
} while (0)
inline const char* cudnnGetErrorString(cudnnStatus_t status) {
switch (status) {
case CUDNN_STATUS_SUCCESS:
return "CUDNN_STATUS_SUCCESS";
case CUDNN_STATUS_NOT_INITIALIZED:
return "CUDNN_STATUS_NOT_INITIALIZED";
case CUDNN_STATUS_ALLOC_FAILED:
return "CUDNN_STATUS_ALLOC_FAILED";
case CUDNN_STATUS_BAD_PARAM:
return "CUDNN_STATUS_BAD_PARAM";
case CUDNN_STATUS_INTERNAL_ERROR:
return "CUDNN_STATUS_INTERNAL_ERROR";
case CUDNN_STATUS_INVALID_VALUE:
return "CUDNN_STATUS_INVALID_VALUE";
case CUDNN_STATUS_ARCH_MISMATCH:
return "CUDNN_STATUS_ARCH_MISMATCH";
case CUDNN_STATUS_MAPPING_ERROR:
return "CUDNN_STATUS_MAPPING_ERROR";
case CUDNN_STATUS_EXECUTION_FAILED:
return "CUDNN_STATUS_EXECUTION_FAILED";
case CUDNN_STATUS_NOT_SUPPORTED:
return "CUDNN_STATUS_NOT_SUPPORTED";
case CUDNN_STATUS_LICENSE_ERROR:
return "CUDNN_STATUS_LICENSE_ERROR";
#if CUDNN_VERSION_MIN(6, 0, 0)
case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
#endif
#if CUDNN_VERSION_MIN(7, 0, 0)
case CUDNN_STATUS_RUNTIME_IN_PROGRESS:
return "CUDNN_STATUS_RUNTIME_IN_PROGRESS";
case CUDNN_STATUS_RUNTIME_FP_OVERFLOW:
return "CUDNN_STATUS_RUNTIME_FP_OVERFLOW";
#endif
}
return "Unknown cudnn status";
}
namespace caffe {
namespace cudnn {
template <typename Dtype> class dataType;
template<> class dataType<float> {
public:
static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
static float oneval, zeroval;
static const void *one, *zero;
};
template<> class dataType<double> {
public:
static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
static double oneval, zeroval;
static const void *one, *zero;
};
template <typename Dtype>
inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
}
template <typename Dtype>
inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
int n, int c, int h, int w,
int stride_n, int stride_c, int stride_h, int stride_w) {
CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
n, c, h, w, stride_n, stride_c, stride_h, stride_w));
}
template <typename Dtype>
inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
int n, int c, int h, int w) {
const int stride_w = 1;
const int stride_h = w * stride_w;
const int stride_c = h * stride_h;
const int stride_n = c * stride_c;
setTensor4dDesc<Dtype>(desc, n, c, h, w,
stride_n, stride_c, stride_h, stride_w);
}
template <typename Dtype>
inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
int n, int c, int h, int w) {
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
CUDNN_TENSOR_NCHW, n, c, h, w));
#else
CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
CUDNN_TENSOR_NCHW, n, c, h, w));
#endif
}
template <typename Dtype>
inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
}
template <typename Dtype>
inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
int pad_h, int pad_w, int stride_h, int stride_w) {
#if CUDNN_VERSION_MIN(6, 0, 0)
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION,
dataType<Dtype>::type));
#else
CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
#endif
}
template <typename Dtype>
inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
switch (poolmethod) {
case PoolingParameter_PoolMethod_MAX:
*mode = CUDNN_POOLING_MAX;
break;
case PoolingParameter_PoolMethod_AVE:
*mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
break;
default:
LOG(FATAL) << "Unknown pooling method.";
}
CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
#else
CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
#endif
}
template <typename Dtype>
inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
cudnnActivationMode_t mode) {
CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
CUDNN_PROPAGATE_NAN, Dtype(0)));
}
} // namespace cudnn
} // namespace caffe
#endif // USE_CUDNN
#endif // CAFFE_UTIL_CUDNN_H_
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14