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提交 a52af84d 编写于 作者: V Vladislav Vinogradov
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refactored CudaMem (now alloc type assign only in constructor)

上级 cc34a8ac
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Showing with 272 addition and 230 deletion
modules/core/include/opencv2/core/gpu.hpp
浏览文件 @ a52af84d
......@@ -252,66 +252,59 @@ public:
uchar* dataend;
};
//! Creates continuous GPU matrix
//! creates continuous GPU matrix
CV_EXPORTS void createContinuous(int rows, int cols, int type, GpuMat& m);
//! Ensures that size of the given matrix is not less than (rows, cols) size
//! ensures that size of the given matrix is not less than (rows, cols) size
//! and matrix type is match specified one too
CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m);
CV_EXPORTS GpuMat allocMatFromBuf(int rows, int cols, int type, GpuMat& mat);
//////////////////////////////// CudaMem ////////////////////////////////
// CudaMem is limited cv::Mat with page locked memory allocation.
// Page locked memory is only needed for async and faster coping to GPU.
// It is convertable to cv::Mat header without reference counting
// so you can use it with other opencv functions.
// Page-locks the matrix m memory and maps it for the device(s)
CV_EXPORTS void registerPageLocked(Mat& m);
// Unmaps the memory of matrix m, and makes it pageable again.
CV_EXPORTS void unregisterPageLocked(Mat& m);
class CV_EXPORTS CudaMem
{
public:
enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 };
CudaMem();
CudaMem(const CudaMem& m);
explicit CudaMem(AllocType alloc_type = PAGE_LOCKED);
CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
CudaMem(const CudaMem& m);
CudaMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED);
CudaMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED);
//! creates from cv::Mat with coping data
explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);
//! creates from host memory with coping data
explicit CudaMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED);
~CudaMem();
CudaMem& operator = (const CudaMem& m);
CudaMem& operator =(const CudaMem& m);
//! swaps with other smart pointer
void swap(CudaMem& b);
//! returns deep copy of the matrix, i.e. the data is copied
CudaMem clone() const;
//! allocates new matrix data unless the matrix already has specified size and type.
void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
void create(int rows, int cols, int type);
void create(Size size, int type);
//! decrements reference counter and released memory if needed.
void release();
//! returns matrix header with disabled reference counting for CudaMem data.
Mat createMatHeader() const;
operator Mat() const;
//! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware.
GpuMat createGpuMatHeader() const;
operator GpuMat() const;
//returns if host memory can be mapperd to gpu address space;
static bool canMapHostMemory();
// Please see cv::Mat for descriptions
bool isContinuous() const;
......@@ -324,7 +317,6 @@ public:
Size size() const;
bool empty() const;
// Please see cv::Mat for descriptions
int flags;
int rows, cols;
......@@ -336,9 +328,14 @@ public:
uchar* datastart;
uchar* dataend;
int alloc_type;
AllocType alloc_type;
};
//! page-locks the matrix m memory and maps it for the device(s)
CV_EXPORTS void registerPageLocked(Mat& m);
//! unmaps the memory of matrix m, and makes it pageable again
CV_EXPORTS void unregisterPageLocked(Mat& m);
//////////////////////////////// CudaStream ////////////////////////////////
// Encapculates Cuda Stream. Provides interface for async coping.
......@@ -480,6 +477,10 @@ public:
// Checks whether the GPU module can be run on the given device
bool isCompatible() const;
bool canMapHostMemory() const;
size_t textureAlignment() const;
int deviceID() const { return device_id_; }
private:
......
modules/core/include/opencv2/core/gpu.inl.hpp
浏览文件 @ a52af84d
......@@ -373,8 +373,161 @@ void swap(GpuMat& a, GpuMat& b)
a.swap(b);
}
//////////////////////////////// CudaMem ////////////////////////////////
inline
CudaMem::CudaMem(AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
}
inline
CudaMem::CudaMem(const CudaMem& m)
: flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type)
{
if( refcount )
CV_XADD(refcount, 1);
}
inline
CudaMem::CudaMem(int rows_, int cols_, int type_, AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
if (rows_ > 0 && cols_ > 0)
create(rows_, cols_, type_);
}
inline
CudaMem::CudaMem(Size size_, int type_, AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
if (size_.height > 0 && size_.width > 0)
create(size_.height, size_.width, type_);
}
inline
CudaMem::CudaMem(InputArray arr, AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
arr.getMat().copyTo(*this);
}
inline
CudaMem::~CudaMem()
{
release();
}
inline
CudaMem& CudaMem::operator =(const CudaMem& m)
{
if (this != &m)
{
CudaMem temp(m);
swap(temp);
}
return *this;
}
inline
void CudaMem::swap(CudaMem& b)
{
std::swap(flags, b.flags);
std::swap(rows, b.rows);
std::swap(cols, b.cols);
std::swap(step, b.step);
std::swap(data, b.data);
std::swap(datastart, b.datastart);
std::swap(dataend, b.dataend);
std::swap(refcount, b.refcount);
std::swap(alloc_type, b.alloc_type);
}
inline
CudaMem CudaMem::clone() const
{
CudaMem m(size(), type(), alloc_type);
createMatHeader().copyTo(m);
return m;
}
inline
void CudaMem::create(Size size_, int type_)
{
create(size_.height, size_.width, type_);
}
inline
Mat CudaMem::createMatHeader() const
{
return Mat(size(), type(), data, step);
}
inline
bool CudaMem::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
inline
size_t CudaMem::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
inline
size_t CudaMem::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
inline
int CudaMem::type() const
{
return CV_MAT_TYPE(flags);
}
inline
int CudaMem::depth() const
{
return CV_MAT_DEPTH(flags);
}
inline
int CudaMem::channels() const
{
return CV_MAT_CN(flags);
}
inline
size_t CudaMem::step1() const
{
return step / elemSize1();
}
inline
Size CudaMem::size() const
{
return Size(cols, rows);
}
inline
bool CudaMem::empty() const
{
return data == 0;
}
static inline
void swap(CudaMem& a, CudaMem& b)
{
a.swap(b);
}
}} // namespace cv { namespace gpu
//////////////////////////////// Mat ////////////////////////////////
namespace cv {
inline
......
modules/core/src/gpu.cpp
浏览文件 @ a52af84d
......@@ -317,6 +317,16 @@ size_t cv::gpu::DeviceInfo::sharedMemPerBlock() const
return deviceProps.get(device_id_)->sharedMemPerBlock;
}
bool cv::gpu::DeviceInfo::canMapHostMemory() const
{
return deviceProps.get(device_id_)->canMapHostMemory != 0;
}
size_t cv::gpu::DeviceInfo::textureAlignment() const
{
return deviceProps.get(device_id_)->textureAlignment;
}
void cv::gpu::DeviceInfo::queryMemory(size_t& _totalMemory, size_t& _freeMemory) const
{
int prevDeviceID = getDevice();
......
modules/core/src/gpu_cuda_mem.cpp
浏览文件 @ a52af84d
......@@ -7,11 +7,12 @@
// copy or use the software.
//
//
// License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
......@@ -45,217 +46,70 @@
using namespace cv;
using namespace cv::gpu;
cv::gpu::CudaMem::CudaMem()
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(0)
{
}
cv::gpu::CudaMem::CudaMem(int _rows, int _cols, int _type, int _alloc_type)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(0)
{
if( _rows > 0 && _cols > 0 )
create( _rows, _cols, _type, _alloc_type);
}
cv::gpu::CudaMem::CudaMem(Size _size, int _type, int _alloc_type)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(0)
{
if( _size.height > 0 && _size.width > 0 )
create( _size.height, _size.width, _type, _alloc_type);
}
cv::gpu::CudaMem::CudaMem(const CudaMem& m)
: flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type)
{
if( refcount )
CV_XADD(refcount, 1);
}
cv::gpu::CudaMem::CudaMem(const Mat& m, int _alloc_type)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(0)
{
if( m.rows > 0 && m.cols > 0 )
create( m.size(), m.type(), _alloc_type);
Mat tmp = createMatHeader();
m.copyTo(tmp);
}
cv::gpu::CudaMem::~CudaMem()
{
release();
}
CudaMem& cv::gpu::CudaMem::operator = (const CudaMem& m)
{
if( this != &m )
{
if( m.refcount )
CV_XADD(m.refcount, 1);
release();
flags = m.flags;
rows = m.rows; cols = m.cols;
step = m.step; data = m.data;
datastart = m.datastart;
dataend = m.dataend;
refcount = m.refcount;
alloc_type = m.alloc_type;
}
return *this;
}
CudaMem cv::gpu::CudaMem::clone() const
{
CudaMem m(size(), type(), alloc_type);
Mat to = m;
Mat from = *this;
from.copyTo(to);
return m;
}
void cv::gpu::CudaMem::create(Size _size, int _type, int _alloc_type)
{
create(_size.height, _size.width, _type, _alloc_type);
}
Mat cv::gpu::CudaMem::createMatHeader() const
{
return Mat(size(), type(), data, step);
}
cv::gpu::CudaMem::operator Mat() const
{
return createMatHeader();
}
cv::gpu::CudaMem::operator GpuMat() const
{
return createGpuMatHeader();
}
bool cv::gpu::CudaMem::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
size_t cv::gpu::CudaMem::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
size_t cv::gpu::CudaMem::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
int cv::gpu::CudaMem::type() const
{
return CV_MAT_TYPE(flags);
}
int cv::gpu::CudaMem::depth() const
{
return CV_MAT_DEPTH(flags);
}
int cv::gpu::CudaMem::channels() const
{
return CV_MAT_CN(flags);
}
size_t cv::gpu::CudaMem::step1() const
{
return step/elemSize1();
}
Size cv::gpu::CudaMem::size() const
{
return Size(cols, rows);
}
bool cv::gpu::CudaMem::empty() const
{
return data == 0;
}
#if !defined (HAVE_CUDA)
void cv::gpu::registerPageLocked(Mat&) { throw_no_cuda(); }
void cv::gpu::unregisterPageLocked(Mat&) { throw_no_cuda(); }
void cv::gpu::CudaMem::create(int, int, int, int) { throw_no_cuda(); }
bool cv::gpu::CudaMem::canMapHostMemory() { throw_no_cuda(); return false; }
void cv::gpu::CudaMem::release() { throw_no_cuda(); }
GpuMat cv::gpu::CudaMem::createGpuMatHeader () const { throw_no_cuda(); return GpuMat(); }
#else /* !defined (HAVE_CUDA) */
void cv::gpu::registerPageLocked(Mat& m)
{
cudaSafeCall( cudaHostRegister(m.ptr(), m.step * m.rows, cudaHostRegisterPortable) );
}
void cv::gpu::unregisterPageLocked(Mat& m)
{
cudaSafeCall( cudaHostUnregister(m.ptr()) );
}
bool cv::gpu::CudaMem::canMapHostMemory()
{
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, getDevice()) );
return (prop.canMapHostMemory != 0) ? true : false;
}
namespace
{
size_t alignUpStep(size_t what, size_t alignment)
{
size_t alignMask = alignment-1;
size_t alignMask = alignment - 1;
size_t inverseAlignMask = ~alignMask;
size_t res = (what + alignMask) & inverseAlignMask;
return res;
}
}
void cv::gpu::CudaMem::create(int _rows, int _cols, int _type, int _alloc_type)
void cv::gpu::CudaMem::create(int rows_, int cols_, int type_)
{
if (_alloc_type == ALLOC_ZEROCOPY && !canMapHostMemory())
CV_Error(cv::Error::GpuApiCallError, "ZeroCopy is not supported by current device");
#ifndef HAVE_CUDA
(void) rows_;
(void) cols_;
(void) type_;
throw_no_cuda();
#else
if (alloc_type == SHARED)
{
DeviceInfo devInfo;
CV_Assert( devInfo.canMapHostMemory() );
}
type_ &= Mat::TYPE_MASK;
_type &= Mat::TYPE_MASK;
if( rows == _rows && cols == _cols && type() == _type && data )
if (rows == rows_ && cols == cols_ && type() == type_ && data)
return;
if( data )
if (data)
release();
CV_DbgAssert( _rows >= 0 && _cols >= 0 );
if( _rows > 0 && _cols > 0 )
CV_DbgAssert( rows_ >= 0 && cols_ >= 0 );
if (rows_ > 0 && cols_ > 0)
{
flags = Mat::MAGIC_VAL + Mat::CONTINUOUS_FLAG + _type;
rows = _rows;
cols = _cols;
step = elemSize()*cols;
if (_alloc_type == ALLOC_ZEROCOPY)
flags = Mat::MAGIC_VAL + Mat::CONTINUOUS_FLAG + type_;
rows = rows_;
cols = cols_;
step = elemSize() * cols;
if (alloc_type == SHARED)
{
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, getDevice()) );
step = alignUpStep(step, prop.textureAlignment);
DeviceInfo devInfo;
step = alignUpStep(step, devInfo.textureAlignment());
}
int64 _nettosize = (int64)step*rows;
size_t nettosize = (size_t)_nettosize;
if( _nettosize != (int64)nettosize )
CV_Error(CV_StsNoMem, "Too big buffer is allocated");
if (_nettosize != (int64)nettosize)
CV_Error(cv::Error::StsNoMem, "Too big buffer is allocated");
size_t datasize = alignSize(nettosize, (int)sizeof(*refcount));
//datastart = data = (uchar*)fastMalloc(datasize + sizeof(*refcount));
alloc_type = _alloc_type;
void *ptr = 0;
void* ptr = 0;
switch (alloc_type)
{
case ALLOC_PAGE_LOCKED: cudaSafeCall( cudaHostAlloc( &ptr, datasize, cudaHostAllocDefault) ); break;
case ALLOC_ZEROCOPY: cudaSafeCall( cudaHostAlloc( &ptr, datasize, cudaHostAllocMapped) ); break;
case ALLOC_WRITE_COMBINED: cudaSafeCall( cudaHostAlloc( &ptr, datasize, cudaHostAllocWriteCombined) ); break;
default: CV_Error(cv::Error::StsBadFlag, "Invalid alloc type");
case PAGE_LOCKED: cudaSafeCall( cudaHostAlloc(&ptr, datasize, cudaHostAllocDefault) ); break;
case SHARED: cudaSafeCall( cudaHostAlloc(&ptr, datasize, cudaHostAllocMapped) ); break;
case WRITE_COMBINED: cudaSafeCall( cudaHostAlloc(&ptr, datasize, cudaHostAllocWriteCombined) ); break;
default: CV_Error(cv::Error::StsBadFlag, "Invalid alloc type");
}
datastart = data = (uchar*)ptr;
......@@ -264,31 +118,55 @@ void cv::gpu::CudaMem::create(int _rows, int _cols, int _type, int _alloc_type)
refcount = (int*)cv::fastMalloc(sizeof(*refcount));
*refcount = 1;
}
}
GpuMat cv::gpu::CudaMem::createGpuMatHeader () const
{
CV_Assert( alloc_type == ALLOC_ZEROCOPY );
GpuMat res;
void *pdev;
cudaSafeCall( cudaHostGetDevicePointer( &pdev, data, 0 ) );
res = GpuMat(rows, cols, type(), pdev, step);
return res;
#endif
}
void cv::gpu::CudaMem::release()
{
if( refcount && CV_XADD(refcount, -1) == 1 )
#ifdef HAVE_CUDA
if (refcount && CV_XADD(refcount, -1) == 1)
{
cudaSafeCall( cudaFreeHost(datastart ) );
cudaFreeHost(datastart);
fastFree(refcount);
}
data = datastart = dataend = 0;
step = rows = cols = 0;
refcount = 0;
#endif
}
GpuMat cv::gpu::CudaMem::createGpuMatHeader() const
{
#ifndef HAVE_CUDA
throw_no_cuda();
return GpuMat();
#else
CV_Assert( alloc_type == SHARED );
void *pdev;
cudaSafeCall( cudaHostGetDevicePointer(&pdev, data, 0) );
return GpuMat(rows, cols, type(), pdev, step);
#endif
}
#endif /* !defined (HAVE_CUDA) */
void cv::gpu::registerPageLocked(Mat& m)
{
#ifndef HAVE_CUDA
(void) m;
throw_no_cuda();
#else
CV_Assert( m.isContinuous() );
cudaSafeCall( cudaHostRegister(m.data, m.step * m.rows, cudaHostRegisterPortable) );
#endif
}
void cv::gpu::unregisterPageLocked(Mat& m)
{
#ifndef HAVE_CUDA
(void) m;
#else
cudaSafeCall( cudaHostUnregister(m.data) );
#endif
}
modules/core/src/gpu_stream.cpp
浏览文件 @ a52af84d
......@@ -145,7 +145,7 @@ void cv::gpu::Stream::enqueueDownload(const GpuMat& src, Mat& dst)
void cv::gpu::Stream::enqueueDownload(const GpuMat& src, CudaMem& dst)
{
dst.create(src.size(), src.type(), CudaMem::ALLOC_PAGE_LOCKED);
dst.create(src.size(), src.type());
cudaStream_t stream = Impl::getStream(impl);
size_t bwidth = src.cols * src.elemSize();
......
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