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fixed compilation for old compute capabilities

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This commit is contained in:
Vladislav Vinogradov 2012-02-15 19:25:29 +00:00
parent 65bef258cb
commit ada6ab3778
3 changed files with 102 additions and 55 deletions
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61
modules/gpu/src/cuda/column_filter.cu
View File

@ -61,12 +61,20 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaMemcpyToSymbol(c_kernel, kernel, ksize * sizeof(float)) );
}
template <int BLOCK_DIM_X, int BLOCK_DIM_Y, int PATCH_PER_BLOCK, int HALO_SIZE, int KSIZE, typename T, typename D, typename B>
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearColumnFilter(const DevMem2D_<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
Static<KSIZE <= MAX_KERNEL_SIZE>::check();
Static<HALO_SIZE * BLOCK_DIM_Y >= KSIZE>::check();
Static<VecTraits<T>::cn == VecTraits<D>::cn>::check();
#if __CUDA_ARCH__ >= 200
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 16;
const int PATCH_PER_BLOCK = 4;
const int HALO_SIZE = KSIZE <= 16 ? 1 : 2;
#else
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 8;
const int PATCH_PER_BLOCK = 2;
const int HALO_SIZE = 2;
#endif
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_t;
@ -103,9 +111,8 @@ namespace cv { namespace gpu { namespace device
{
const int y = yStart + j * BLOCK_DIM_Y;
if (y >= src.rows)
return;
if (y < src.rows)
{
sum_t sum = VecTraits<sum_t>::all(0);
#pragma unroll
@ -115,20 +122,34 @@ namespace cv { namespace gpu { namespace device
dst(y, x) = saturate_cast<D>(sum);
}
}
}
template <int KSIZE, typename T, typename D, template<typename> class B>
void linearColumnFilter_caller(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream)
void linearColumnFilter_caller(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, int cc, cudaStream_t stream)
{
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 16;
const int PATCH_PER_BLOCK = 4;
int BLOCK_DIM_X;
int BLOCK_DIM_Y;
int PATCH_PER_BLOCK;
if (cc >= 20)
{
BLOCK_DIM_X = 16;
BLOCK_DIM_Y = 16;
PATCH_PER_BLOCK = 4;
}
else
{
BLOCK_DIM_X = 16;
BLOCK_DIM_Y = 8;
PATCH_PER_BLOCK = 2;
}
const dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y);
const dim3 grid(divUp(src.cols, BLOCK_DIM_X), divUp(src.rows, BLOCK_DIM_Y * PATCH_PER_BLOCK));
B<T> brd(src.rows);
linearColumnFilter<BLOCK_DIM_X, BLOCK_DIM_Y, PATCH_PER_BLOCK, KSIZE <= 16 ? 1 : 2, KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
linearColumnFilter<KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
cudaSafeCall( cudaGetLastError() );
@ -137,9 +158,9 @@ namespace cv { namespace gpu { namespace device
}
template <typename T, typename D>
void linearColumnFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream)
void linearColumnFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream)
{
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream);
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, int cc, cudaStream_t stream);
static const caller_t callers[5][33] =
{
@ -322,13 +343,13 @@ namespace cv { namespace gpu { namespace device
loadKernel(kernel, ksize);
callers[brd_type][ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor, stream);
callers[brd_type][ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor, cc, stream);
}
template void linearColumnFilter_gpu<float , uchar >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float4, uchar4>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float3, short3>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , int >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , uchar >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearColumnFilter_gpu<float4, uchar4>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearColumnFilter_gpu<float3, short3>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearColumnFilter_gpu<float , int >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearColumnFilter_gpu<float , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
} // namespace column_filter
}}} // namespace cv { namespace gpu { namespace device

61
modules/gpu/src/cuda/row_filter.cu
View File

@ -61,12 +61,20 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaMemcpyToSymbol(c_kernel, kernel, ksize * sizeof(float)) );
}
template <int BLOCK_DIM_X, int BLOCK_DIM_Y, int PATCH_PER_BLOCK, int HALO_SIZE, int KSIZE, typename T, typename D, typename B>
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearRowFilter(const DevMem2D_<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
Static<KSIZE <= MAX_KERNEL_SIZE>::check();
Static<HALO_SIZE * BLOCK_DIM_X >= KSIZE>::check();
Static<VecTraits<T>::cn == VecTraits<D>::cn>::check();
#if __CUDA_ARCH__ >= 200
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 8;
const int PATCH_PER_BLOCK = 4;
const int HALO_SIZE = 1;
#else
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 4;
const int PATCH_PER_BLOCK = 4;
const int HALO_SIZE = 1;
#endif
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_t;
@ -103,9 +111,8 @@ namespace cv { namespace gpu { namespace device
{
const int x = xStart + j * BLOCK_DIM_X;
if (x >= src.cols)
return;
if (x < src.cols)
{
sum_t sum = VecTraits<sum_t>::all(0);
#pragma unroll
@ -115,20 +122,34 @@ namespace cv { namespace gpu { namespace device
dst(y, x) = saturate_cast<D>(sum);
}
}
}
template <int KSIZE, typename T, typename D, template<typename> class B>
void linearRowFilter_caller(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream)
void linearRowFilter_caller(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, int cc, cudaStream_t stream)
{
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 8;
const int PATCH_PER_BLOCK = 4;
int BLOCK_DIM_X;
int BLOCK_DIM_Y;
int PATCH_PER_BLOCK;
if (cc >= 20)
{
BLOCK_DIM_X = 32;
BLOCK_DIM_Y = 8;
PATCH_PER_BLOCK = 4;
}
else
{
BLOCK_DIM_X = 32;
BLOCK_DIM_Y = 4;
PATCH_PER_BLOCK = 4;
}
const dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y);
const dim3 grid(divUp(src.cols, BLOCK_DIM_X * PATCH_PER_BLOCK), divUp(src.rows, BLOCK_DIM_Y));
B<T> brd(src.cols);
linearRowFilter<BLOCK_DIM_X, BLOCK_DIM_Y, PATCH_PER_BLOCK, 1, KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
linearRowFilter<KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
@ -136,9 +157,9 @@ namespace cv { namespace gpu { namespace device
}
template <typename T, typename D>
void linearRowFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream)
void linearRowFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream)
{
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream);
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, int cc, cudaStream_t stream);
static const caller_t callers[5][33] =
{
@ -321,13 +342,13 @@ namespace cv { namespace gpu { namespace device
loadKernel(kernel, ksize);
callers[brd_type][ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor, stream);
callers[brd_type][ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor, cc, stream);
}
template void linearRowFilter_gpu<uchar , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<uchar4, float4>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<short3, float3>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<int , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<float , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<uchar , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearRowFilter_gpu<uchar4, float4>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearRowFilter_gpu<short3, float3>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearRowFilter_gpu<int , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
template void linearRowFilter_gpu<float , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
} // namespace row_filter
}}} // namespace cv { namespace gpu { namespace device

15
modules/gpu/src/filtering.cpp
View File

@ -740,13 +740,13 @@ namespace cv { namespace gpu { namespace device
namespace row_filter
{
template <typename T, typename D>
void linearRowFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
void linearRowFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
}
namespace column_filter
{
template <typename T, typename D>
void linearColumnFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
void linearColumnFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
}
}}}
@ -755,7 +755,7 @@ namespace
typedef NppStatus (*nppFilter1D_t)(const Npp8u * pSrc, Npp32s nSrcStep, Npp8u * pDst, Npp32s nDstStep, NppiSize oROI,
const Npp32s * pKernel, Npp32s nMaskSize, Npp32s nAnchor, Npp32s nDivisor);
typedef void (*gpuFilter1D_t)(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
typedef void (*gpuFilter1D_t)(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, int cc, cudaStream_t stream);
struct NppLinearRowFilter : public BaseRowFilter_GPU
{
@ -791,7 +791,9 @@ namespace
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& s = Stream::Null())
{
func(src, dst, kernel.ptr<float>(), ksize, anchor, brd_type, StreamAccessor::getStream(s));
DeviceInfo devInfo;
int cc = devInfo.majorVersion() * 10 + devInfo.minorVersion();
func(src, dst, kernel.ptr<float>(), ksize, anchor, brd_type, cc, StreamAccessor::getStream(s));
}
Mat kernel;
@ -899,7 +901,10 @@ namespace
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& s = Stream::Null())
{
func(src, dst, kernel.ptr<float>(), ksize, anchor, brd_type, StreamAccessor::getStream(s));
DeviceInfo devInfo;
int cc = devInfo.majorVersion() * 10 + devInfo.minorVersion();
CV_Assert(cc >= 20 || ksize <= 16);
func(src, dst, kernel.ptr<float>(), ksize, anchor, brd_type, cc, StreamAccessor::getStream(s));
}
Mat kernel;