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fixed bugs in page locked memory allocation

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avoid extra gpu memory allocation in BP and CSBP
pull/13383/head
Anatoly Baksheev 15 years ago
parent ba713f28f9
commit 9a669b1ceb
6 changed files with 97 additions and 102 deletions
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  1. 56
      modules/gpu/include/opencv2/gpu/gpu.hpp
  2. 58
      modules/gpu/include/opencv2/gpu/matrix_operations.hpp
  3. 2
      modules/gpu/src/beliefpropagation_gpu.cpp
  4. 2
      modules/gpu/src/constantspacebp_gpu.cpp
  5. 8
      modules/gpu/src/cudastream.cpp
  6. 73
      modules/gpu/src/matrix_operations.cpp

56
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -68,7 +68,7 @@ namespace cv
//////////////////////////////// GpuMat ////////////////////////////////
class Stream;
class MatPL;
class CudaMem;
//! Smart pointer for GPU memory with reference counting. Its interface is mostly similar with cv::Mat.
class CV_EXPORTS GpuMat
@ -111,12 +111,16 @@ namespace cv
//! pefroms blocking upload data to GpuMat. .
void upload(const cv::Mat& m);
void upload(const MatPL& m, Stream& stream);
//! Downloads data from device to host memory. Blocking calls.
//! upload async
void upload(const CudaMem& m, Stream& stream);
//! downloads data from device to host memory. Blocking calls.
operator Mat() const;
void download(cv::Mat& m) const;
void download(MatPL& m, Stream& stream) const;
//! download async
void download(CudaMem& m, Stream& stream) const;
//! returns a new GpuMatrix header for the specified row
GpuMat row(int y) const;
@ -223,52 +227,50 @@ namespace cv
uchar* dataend;
};
//////////////////////////////// MatPL ////////////////////////////////
// MatPL is limited cv::Mat with page locked memory allocation.
//////////////////////////////// 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.
class CV_EXPORTS MatPL
class CV_EXPORTS CudaMem
{
public:
public:
enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
//Supported. Now behaviour is like ALLOC_DEFAULT.
enum { ALLOC_PAGE_LOCKED = 0, ALLOC_ZEROCOPY = 1, ALLOC_WRITE_COMBINED = 4 };
CudaMem();
CudaMem(const CudaMem& m);
MatPL();
MatPL(const MatPL& m);
MatPL(int _rows, int _cols, int _type, int type_alloc = ALLOC_PAGE_LOCKED);
MatPL(Size _size, int _type, int type_alloc = ALLOC_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);
//! creates from cv::Mat with coping data
explicit MatPL(const Mat& m, int type_alloc = ALLOC_PAGE_LOCKED);
explicit CudaMem(const Mat& m, int _alloc_type = ALLOC_PAGE_LOCKED);
~MatPL();
~CudaMem();
MatPL& operator = (const MatPL& m);
CudaMem& operator = (const CudaMem& m);
//! returns deep copy of the matrix, i.e. the data is copied
MatPL clone() const;
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 type_alloc = ALLOC_PAGE_LOCKED);
void create(Size _size, int _type, int type_alloc = ALLOC_PAGE_LOCKED);
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);
//! decrements reference counter and released memory if needed.
void release();
//! returns matrix header with disabled reference counting for MatPL data.
//! returns matrix header with disabled reference counting for CudaMem data.
Mat createMatHeader() const;
operator Mat() const;
operator GpuMat() const;
//returns if host memory can be mapperd to gpu address space;
static bool can_device_map_to_host();
// Please see cv::Mat for descriptions
bool isContinuous() const;
size_t elemSize() const;
@ -314,13 +316,13 @@ namespace cv
void waitForCompletion();
//! downloads asynchronously.
// Warning! cv::Mat must point to page locked memory (i.e. to MatPL data or to its subMat)
void enqueueDownload(const GpuMat& src, MatPL& dst);
// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
void enqueueDownload(const GpuMat& src, CudaMem& dst);
void enqueueDownload(const GpuMat& src, Mat& dst);
//! uploads asynchronously.
// Warning! cv::Mat must point to page locked memory (i.e. to MatPL data or to its ROI)
void enqueueUpload(const MatPL& src, GpuMat& dst);
// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
void enqueueUpload(const CudaMem& src, GpuMat& dst);
void enqueueUpload(const Mat& src, GpuMat& dst);
void enqueueCopy(const GpuMat& src, GpuMat& dst);

58
modules/gpu/include/opencv2/gpu/matrix_operations.hpp
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@ -339,43 +339,43 @@ static inline void swap( GpuMat& a, GpuMat& b ) { a.swap(b); }
///////////////////////////////////////////////////////////////////////
//////////////////////////////// MatPL ////////////////////////////////
//////////////////////////////// CudaMem ////////////////////////////////
///////////////////////////////////////////////////////////////////////
inline MatPL::MatPL() : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) {}
inline MatPL::MatPL(int _rows, int _cols, int _type, int type_alloc) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
inline CudaMem::CudaMem() : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(0) {}
inline 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 , type_alloc);
create( _rows, _cols, _type, _alloc_type);
}
inline MatPL::MatPL(Size _size, int _type, int type_alloc) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
inline 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, type_alloc );
create( _size.height, _size.width, _type, _alloc_type);
}
inline MatPL::MatPL(const MatPL& m) : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(0), dataend(0)
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 MatPL::MatPL(const Mat& m, int type_alloc) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
inline 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() , type_alloc);
create( m.size(), m.type(), _alloc_type);
Mat tmp = createMatHeader();
m.copyTo(tmp);
}
inline MatPL::~MatPL()
inline CudaMem::~CudaMem()
{
release();
}
inline MatPL& MatPL::operator = (const MatPL& m)
inline CudaMem& CudaMem::operator = (const CudaMem& m)
{
if( this != &m )
{
@ -393,31 +393,31 @@ inline MatPL& MatPL::operator = (const MatPL& m)
return *this;
}
inline MatPL MatPL::clone() const
inline CudaMem CudaMem::clone() const
{
MatPL m(size(), type());
CudaMem m(size(), type(), alloc_type);
Mat to = m;
Mat from = *this;
from.copyTo(to);
return m;
}
inline void MatPL::create(Size _size, int _type, int type_alloc) { create(_size.height, _size.width, _type, type_alloc); }
//CCP void MatPL::create(int _rows, int _cols, int _type);
//CPP void MatPL::release();
inline Mat MatPL::createMatHeader() const { return Mat(size(), type(), data); }
inline MatPL::operator Mat() const { return createMatHeader(); }
inline bool MatPL::isContinuous() const { return (flags & Mat::CONTINUOUS_FLAG) != 0; }
inline size_t MatPL::elemSize() const { return CV_ELEM_SIZE(flags); }
inline size_t MatPL::elemSize1() const { return CV_ELEM_SIZE1(flags); }
inline int MatPL::type() const { return CV_MAT_TYPE(flags); }
inline int MatPL::depth() const { return CV_MAT_DEPTH(flags); }
inline int MatPL::channels() const { return CV_MAT_CN(flags); }
inline size_t MatPL::step1() const { return step/elemSize1(); }
inline Size MatPL::size() const { return Size(cols, rows); }
inline bool MatPL::empty() const { return data == 0; }
inline void CudaMem::create(Size _size, int _type, int _alloc_type) { create(_size.height, _size.width, _type, _alloc_type); }
//CCP void CudaMem::create(int _rows, int _cols, int _type, int _alloc_type);
//CPP void CudaMem::release();
inline Mat CudaMem::createMatHeader() const { return Mat(size(), type(), data); }
inline CudaMem::operator Mat() const { return createMatHeader(); }
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; }
} /* end of namespace gpu */

2
modules/gpu/src/beliefpropagation_gpu.cpp
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@ -234,7 +234,7 @@ namespace
if (disp.empty())
disp.create(rows, cols, CV_16S);
out = ((disp.type() == CV_16S) ? disp : GpuMat(rows, cols, CV_16S));
out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
out = zero;
bp::output(rthis.msg_type, u, d, l, r, datas.front(), disp, stream);

2
modules/gpu/src/constantspacebp_gpu.cpp
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@ -251,7 +251,7 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
if (disp.empty())
disp.create(rows, cols, CV_16S);
out = ((disp.type() == CV_16S) ? disp : GpuMat(rows, cols, CV_16S));
out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
out = zero;
csbp::compute_disp(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),

8
modules/gpu/src/cudastream.cpp
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@ -57,8 +57,8 @@ Stream& cv::gpu::Stream::operator=(const Stream& /*stream*/) { throw_nogpu(); re
bool cv::gpu::Stream::queryIfComplete() { throw_nogpu(); return true; }
void cv::gpu::Stream::waitForCompletion() { throw_nogpu(); }
void cv::gpu::Stream::enqueueDownload(const GpuMat& /*src*/, Mat& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueDownload(const GpuMat& /*src*/, MatPL& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueUpload(const MatPL& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueDownload(const GpuMat& /*src*/, CudaMem& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueUpload(const CudaMem& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueUpload(const Mat& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueCopy(const GpuMat& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
void cv::gpu::Stream::enqueueMemSet(const GpuMat& /*src*/, Scalar /*val*/) { throw_nogpu(); }
@ -150,9 +150,9 @@ void cv::gpu::Stream::enqueueDownload(const GpuMat& src, Mat& dst)
CV_Assert(src.cols == dst.cols && src.rows == dst.rows && src.type() == dst.type() )
devcopy(src, dst, impl->stream, cudaMemcpyDeviceToHost);
}
void cv::gpu::Stream::enqueueDownload(const GpuMat& src, MatPL& dst) { devcopy(src, dst, impl->stream, cudaMemcpyDeviceToHost); }
void cv::gpu::Stream::enqueueDownload(const GpuMat& src, CudaMem& dst) { devcopy(src, dst, impl->stream, cudaMemcpyDeviceToHost); }
void cv::gpu::Stream::enqueueUpload(const MatPL& src, GpuMat& dst){ devcopy(src, dst, impl->stream, cudaMemcpyHostToDevice); }
void cv::gpu::Stream::enqueueUpload(const CudaMem& src, GpuMat& dst){ devcopy(src, dst, impl->stream, cudaMemcpyHostToDevice); }
void cv::gpu::Stream::enqueueUpload(const Mat& src, GpuMat& dst) { devcopy(src, dst, impl->stream, cudaMemcpyHostToDevice); }
void cv::gpu::Stream::enqueueCopy(const GpuMat& src, GpuMat& dst) { devcopy(src, dst, impl->stream, cudaMemcpyDeviceToDevice); }

73
modules/gpu/src/matrix_operations.cpp
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@ -67,9 +67,9 @@ namespace cv
void GpuMat::create(int /*_rows*/, int /*_cols*/, int /*_type*/) { throw_nogpu(); }
void GpuMat::release() { throw_nogpu(); }
void MatPL::create(int /*_rows*/, int /*_cols*/, int /*_type*/, int /*type_alloc*/) { throw_nogpu(); }
bool MatPL::can_device_map_to_host() { throw_nogpu(); return false; }
void MatPL::release() { throw_nogpu(); }
void CudaMem::create(int /*_rows*/, int /*_cols*/, int /*_type*/, int /*type_alloc*/) { throw_nogpu(); }
bool CudaMem::can_device_map_to_host() { throw_nogpu(); return false; }
void CudaMem::release() { throw_nogpu(); }
}
}
@ -83,7 +83,7 @@ void cv::gpu::GpuMat::upload(const Mat& m)
cudaSafeCall( cudaMemcpy2D(data, step, m.data, m.step, cols * elemSize(), rows, cudaMemcpyHostToDevice) );
}
void cv::gpu::GpuMat::upload(const MatPL& m, Stream& stream)
void cv::gpu::GpuMat::upload(const CudaMem& m, Stream& stream)
{
CV_DbgAssert(!m.empty());
stream.enqueueUpload(m, *this);
@ -96,7 +96,7 @@ void cv::gpu::GpuMat::download(cv::Mat& m) const
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) );
}
void cv::gpu::GpuMat::download(MatPL& m, Stream& stream) const
void cv::gpu::GpuMat::download(CudaMem& m, Stream& stream) const
{
CV_DbgAssert(!m.empty());
stream.enqueueDownload(*this, m);
@ -210,15 +210,6 @@ GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const
return hdr;
}
bool cv::gpu::MatPL::can_device_map_to_host()
{
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
return (prop.canMapHostMemory != 0) ? true : false;
}
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
{
_type &= TYPE_MASK;
@ -266,12 +257,21 @@ void cv::gpu::GpuMat::release()
///////////////////////////////////////////////////////////////////////
//////////////////////////////// MatPL ////////////////////////////////
//////////////////////////////// CudaMem //////////////////////////////
///////////////////////////////////////////////////////////////////////
void cv::gpu::MatPL::create(int _rows, int _cols, int _type, int type_alloc)
bool cv::gpu::CudaMem::can_device_map_to_host()
{
alloc_type = type_alloc;
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
return (prop.canMapHostMemory != 0) ? true : false;
}
void cv::gpu::CudaMem::create(int _rows, int _cols, int _type, int _alloc_type)
{
if (_alloc_type == ALLOC_ZEROCOPY && !can_device_map_to_host())
cv::gpu::error("ZeroCopy is not supported by current device", __FILE__, __LINE__);
_type &= TYPE_MASK;
if( rows == _rows && cols == _cols && type() == _type && data )
return;
@ -279,7 +279,7 @@ void cv::gpu::MatPL::create(int _rows, int _cols, int _type, int type_alloc)
release();
CV_DbgAssert( _rows >= 0 && _cols >= 0 );
if( _rows > 0 && _cols > 0 )
{
{
flags = Mat::MAGIC_VAL + Mat::CONTINUOUS_FLAG + _type;
rows = _rows;
cols = _cols;
@ -291,24 +291,15 @@ void cv::gpu::MatPL::create(int _rows, int _cols, int _type, int type_alloc)
size_t datasize = alignSize(nettosize, (int)sizeof(*refcount));
//datastart = data = (uchar*)fastMalloc(datasize + sizeof(*refcount));
alloc_type = _alloc_type;
void *ptr;
switch (type_alloc)
switch (alloc_type)
{
case ALLOC_PAGE_LOCKED: cudaSafeCall( cudaHostAlloc( &ptr, datasize, cudaHostAllocDefault) ); break;
case ALLOC_ZEROCOPY:
if (can_device_map_to_host() == true)
{
cudaSafeCall( cudaHostAlloc( &ptr, datasize, cudaHostAllocMapped) );
}
else
cv::gpu::error("ZeroCopy is not supported by current device", __FILE__, __LINE__);
break;
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::gpu::error("Invalid alloc type", __FILE__, __LINE__);
default: cv::gpu::error("Invalid alloc type", __FILE__, __LINE__);
}
datastart = data = (uchar*)ptr;
@ -319,20 +310,22 @@ void cv::gpu::MatPL::create(int _rows, int _cols, int _type, int type_alloc)
}
}
inline MatPL::operator GpuMat() const
inline CudaMem::operator GpuMat() const
{
GpuMat res;
if (alloc_type == ALLOC_ZEROCOPY)
{
void ** pdev;
cudaHostGetDevicePointer( pdev, this->data, 0 );
GpuMat m(this->rows, this->cols, this->type(), *pdev, this->step);
return m;
void *pdev;
cudaSafeCall( cudaHostGetDevicePointer( &pdev, data, 0 ) );
res = GpuMat(rows, cols, type(), pdev, step);
}
else
cv::gpu::error("", __FILE__, __LINE__);
cv::gpu::error("Zero-copy is not supported or memory was allocated without zero-copy flag", __FILE__, __LINE__);
return res;
}
void cv::gpu::MatPL::release()
void cv::gpu::CudaMem::release()
{
if( refcount && CV_XADD(refcount, -1) == 1 )
{

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